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Positive Interventions 1
Running head: WHAT ARE POSITIVE INTERVENTIONS
What are Positive Interventions and How Effective Are They? A Conceptual Discussion
of the “Positive” and a Meta-Analysis of the Effectiveness of Positive Interventions
Stephen M. Schueller
Major Area Qualifying Exam
Paper 1
Committee:
Martin E. P. Seligman, Ph.D. (Advisor)
Michael J. Kahana, Ph.D. (Chair)
Robert J. DeRubeis, Ph.D.
Positive Interventions 2
Abstract
One focus of positive psychology is to promote the good life. Several researchers have
developed interventions aimed at promoting well-being. This paper offers a definition of
a positive intervention consistent with subjective well-being (SWB) approaches to
defining happiness. From this perspective, a positive intervention is a cognitive or
behavioral strategy that promotes happiness, positive affect, or satisfaction with life, all
key components to subjective well-being. A meta-analysis computed the average effect
size of studies that met this definition. 58 research studies with a total of 4,502
participants were included in this analysis. This study found that positive interventions
lead to small-to-moderate boosts in subjective well-being with an average effect size of
.44 and moderate effects for reduction of depressive symptoms.
Positive Interventions 3
What are Positive Interventions and How Effective Are They? A Conceptual Discussion
of the “Positive” and a Meta-Analysis of the Effectiveness of Positive Interventions
Ten years ago, Martin Seligman established the field of positive psychology to
foster research on “positive” aspects of living and to create a psychology of human
strengths and flourishing. Seligman noted that since World War II, psychological
research has focused on pathology, learning much about the predisposing factors for
pathology, yet neglecting the study of factors associated with well-being. However, with
the advent of the positive psychology enterprise, research on these topics has flourished.
The measurement of once fuzzy constructs, such as happiness, has advanced to the point
that we now have well-validated and widely used measures (see Diener & Suh, 1997).
This advancement in measurement has promoted research and expanded knowledge on
well-being and happiness. A search of PsycINFO finds 1233 articles with “happiness” as
a descriptor published since Seligman’s 1998 American Psychological Association
Presidential Address, compared to only 921 articles published prior to it. This increase in
research has prompted for the creation of new journals, such as the Journal of Positive
Psychology and the Journal of Happiness Studies, to serve as an outlet for these studies.
Research has advanced our understanding of happiness and the factors that
promote it (see Diener, Suh, Lucas, & Smith, 1999; Lyubomirsky, Schkade, & Sheldon,
2005). Researchers have used this knowledge to develop interventions based on positive
psychology principles. This trend in empirical research has been mirrored in public
interest. Several self-help books dot the shelves of bookstores, only a few of which have
any empirical grounding (see Lyubomirsky, 2007; Seligman, 1991, 2002). Others offer
advice with little scientific backing yet still draw immense popular attention, such as the
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New York Times bestseller, The Secret. In order to separate the science of happiness
from self-proclaimed self-help gurus whose advice is not empirically grounded, positive
psychologists must continue to tighten definitions of, and better operationalize key
constructs, which would aid theoretical and empirical progress.
As positive psychology advances into its second decade, an important indicator of
its usefulness to psychology will be its staying power. Is positive psychology merely a
fad or does it still have something to offer? In order to better promote research,
investigators interested in positive psychology must better operationalize the terms used
by the field.
Despite many attempts, a satisfactory definition of what exactly is “positive”
about positive psychology is still lacking. The dominant paradigm in the treatment of
mental disorders follows a medical model that focuses on correcting illness, but not on
improving individuals once they become illness-free. Positive psychology offers an
alternative to the medical model by adopting a strengths-perspective that builds on what
individuals do well. Furthermore, positive psychology emphasizes building positive
emotions and positive character traits that promote flourishing and optimal human
functioning. In the terminology of positive psychology, interventions using this
promotion-focused view are called “positive interventions.” A wide variety of
interventions could be “positive” depending on the definition adopted. This term faces
the same hurdle facing positive psychology. What exactly is “positive” about a positive
intervention?
The aim of this paper is two-fold. First, I will attempt to reach a theoretical
definition of what is meant by “positive,” especially with regards to positive
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interventions. I will focus mostly on how “positive” applies to intervention studies - that
is, studies that impart some change in the individual to increase well-being. I will
consider 6 different definitions of what constitutes a positive intervention. Second, I will
analyze the effectiveness of these interventions through meta-analytic techniques.
Defining a “Positive” Intervention
My definition of a positive intervention is as follows: a positive intervention is a
cognitive or behavioral strategy, or a collection of cognitive and behavioral strategies that
attempts to promote well-being – building happiness, satisfaction with life, or positive
affect through processes we have learned that lead to well-being such as engagement,
meaning, and pleasure. This definition is a better descriptor of the science of positive
psychology than 5 alternative definitions that will be considered. These alternative
definitions fall into different categories. The first three are definitions that agree that
positive interventions increase well-being, but that differ on the definition of well-being.
Several definitions for well-being exist, including what Dolan and colleagues (2006,
2007) characterize as wanting, needing, and liking theories. Therefore, any definition of a
positive intervention has to address which of these perspectives best captures the meaning
of well-being. I adopt a “liking” theory of well-being, an approach built on a subjective
well-being perspective. Alternative definitions of positive interventions can be built on
each of these different perspectives and a brief review of each perspective and the
implications for definitions of positive interventions will be considered.
Besides disagreeing about the nature of well-being, the fourth definition of a
positive intervention, offered by Pawelski (2007), states that positive interventions either
focus on fostering what is good or advances someone who is already well further along
Positive Interventions 6
the continuum. For example, many interventions attempt to address a deficit and move a
person back to a zero point of a scale. Positive psychology, following from this second
aspect of the definition, attempts to move individuals to higher positive values on the
continuum.
The term positive generally refers to adding something as opposed to subtracting
something, or fixing something that is broken. The fifth definition of a positive
intervention is an enhancement view of positive psychology. Following from this view,
positive psychology is a commission approach, an active approach to creating something
new as opposed to an omission approach, which addresses something that is not working.
The definition helps clarify the difference between a “negative” intervention and a
“positive” intervention.
One last definition of a positive intervention is that a positive intervention is one
that has a good effect. It improves a person’s life in some way. As this definition is the
most encompassing of all the proposed definitions, it will be considered first.
The broadest definition of a positive intervention is that it leads to improvement
in people’s lives. Although this is the purpose of positive interventions, this is the general
aim of interventions as a whole, namely to improve functioning and decrease distress. As
psychologists, our efforts are motivated by the same mindset of the physicians who
follow the Hippocratic Oath, primum non nocere, “first, do no harm.” Indeed, the Ethics
Code of the American Psychological Association instructs psychologists to “take
reasonable steps to avoid harming their clients/patients.” (APA, 2002, p. 1065).
Following from this, all of the interventions proposed by psychologists are intended to
benefit those who receive the intervention. The general goal of therapy is not to remove
Positive Interventions 7
disorder, but to increase the well-being of the patients involved (Kazdin, 1992; Strupp,
1996). Therefore, this definition is too broad and fails to exclude any interventions.
Positive psychology cannot lay claim to all interventions that aim to improve individuals.
Instead, there must be something distinctive about positive interventions.
What is “Positive” About Positive Interventions?
Positive psychology emphasizes building strengths and positive emotions as
opposed to relieving negative states. This simple statement, however, is both theoretically
rich and empirically complicated. What is the difference between building the positive
and alleviating the negative and does this difference matter? In order to examine this
difference, I will first offer different definitions of what is “positive” and consider the
implications on defining a “positive intervention.” I will then conceptually discuss the
benefits of building the positive as opposed to addressing the negative.
What is positive?
The word “positive” has several definitions. The definition most commonly
associated with positive psychology is that “positive” refers to something that has a good
effect. Following from this definition, positive psychology is the scientific study of
processes that have a good effect in our lives. This is consistent with Gable and Haidt’s
(2005) definition that positive psychology “is the study of the conditions and processes
that contribute to the flourishing and optimal functioning of people” (pg. 104). This
definition, however, leads to another conceptual problem: If “positive” means that an
intervention will have a good effect and a good effect is defined as flourishing and
optimal functioning of people, then what exactly does it mean to flourish or function
optimally? As mentioned previously, merely stating that positive interventions do “good”
Positive Interventions 8
fails to exclude any definitions. Furthermore, defining positive psychology using terms
such as flourishing and well-being without offering a clear definition of these concepts is
hardly a definition either. The concept of well-being is a difficult one to unpack and a
complete analysis is beyond the scope of this paper; however, given that positive
psychology is interested in studying the processes that contribute to flourishing and
optimal functioning, and positive interventions are interventions that augment well-being
through promoting these processes, some discussion of theories of well-being is relevant.
As mentioned previously, definitions of well-being and by extension, positive
interventions, can be divided into categories: liking, wanting, and needing theories.
Liking (or Subjective Well-Being Theories)
Liking theories of well-being are those most often adopted by psychology because
it focuses on the mental-state of the individual. These accounts view well-being as
characterized by the presence of pleasure and the absence of pain. Pleasure, however,
does not merely include the experience of pleasant affect or the fulfillment of drives.
Instead, pleasure encompasses subjective evaluations about one’s life and one’s
conditions as well. This approach has the considerable advantage of being face valid
(Dolan & White, 2007). An individual is considered happy if he reports himself as such
and each individual is considered the expert on his or her own happiness (Kesebir &
Diener, 2008; Myers & Diener, 1995).
Well-being, however, is comprised of more than just happiness1. Well-being includes
two components – a subjective cognitive evaluation (such as global happiness or life
1 Some authors use the terms happiness and well-being interchangeably (Lyubomirsky,
2001; Seligman, 2003). In my usage, however, happiness refers to subjective happiness,
an individual’s evaluation of how happy one is (Lyubomirsky & Lepper, 1999). Well-
Positive Interventions 9
satisfaction) along with an affective component, namely the presence of positive emotions in
the absence of negative emotions (Diener, 1984; 1994). Therefore a person high in subjective
well-being both evaluates her life to be good as well as experiences more positive emotions
compared to negative emotions.
Although some would argue that subjective well-being is not sufficient for defining a
good life, adopting the SWB approach confers several advantages (Jayawickreme, 2008). First,
as mentioned previously, it has face validity and makes the measurement of subjective well-
being straight forward. Researchers use several measures of subjective well-being depending
on the context and specific component that interests researchers. Measures can assess
differences in the presence of positive and negative moods using measures such as the Positive
and Negative Affect Scale (PANAS; Watson, Clark, & Tellegen, 1998) or the Profile of Mood
States (POMS; McNair, Lorr, & Droppleman, 1971, 1992). These measures assess the affective
component of subjective well-being. Other measures tap the cognitive or evaluative component
of well-being. These measures use a variety of approaches to assess how people feel and make
judgments about their satisfaction including generalized feelings (e.g., Satisfaction with Life;
Diener, Emmons, Larsen, & Griffin, 1985) versus domain satisfaction (Quality of Life
Inventory; Frisch, 1992), social comparisons ( e.g. “Compared to most my peers, I consider
myself: less happy to more happy;” Subjective Happiness Scale, Lyubomirsky & Lepper,
1999) versus temporal evaluations, or even single item measures such as Bradburn’s (1969)
global happiness item “Taking all things together, how would you say things are these days?”
or Andrews & Withey’s (1976) Delighted-Terrible Scale (e.g., “How do you feel about your
being is broader and akin to Diener’s (1984) definition of subjective well-being that
includes both cognitive and affective components – high life satisfaction, high positive
affect, and low negative affect.
Positive Interventions 10
life right now?”). Despite focusing on the individual as the expert of his happiness, self-ratings
of happiness converge with ratings made by significant others (Lepper, 1998; Sandvik, Diener,
& Seidlitz, 1993) and even observers with minimal contact (Redelmeier & Kahneman, 1996).
Therefore, although self-evaluations are thought of as the most important indicator of well-
being, these ratings conform to what others observe. This supports the validity of the self-
report, suggesting that those individuals whom we see as happy tend to in fact be happy.
Liking accounts have often been accused of reducing well-being to hedonism - that
well-being is achieved when pleasure is maximized. Researchers, however, use subjective
well-being as a proxy for pleasure. This avoids the criticism that happiness from a subjective
well-being account is hedonism because subjective well-being involves a cognitive evaluation
of how one’s life is living up to one’s expectations in addition to just maximizing pleasure and
minimizing pain. Inclusion of a reflective and evaluative component is important because well-
being is achieved and maintained through a variety of cognitive processes. These cognitive
processes help individuals maintain levels of well-being in the face of a variety of threats to
well-being. This is a “top-down” approach that emphasizes that events and experiences
influence our well-being only after they pass through our cognitive evaluations (Lyubomirsky,
2001). Therefore, the intensity and valence of emotional experience is important and correlated
with our evaluations in that it is more likely we will have a positive evaluation of a positive
experience, and individuals who are happy will tend to see things in a positive light (Taylor &
Brown, 1988).
These approaches differ from “bottom-up” approaches that posit that well-being is the
sum of moment-to-moment positive emotions. From this perspective, well-being is calculated
by integrating the area under the curve of a plot of well-being measures assessed frequently
Positive Interventions 11
over a given time period. One method of obtaining this data is the day reconstruction method
(DRM) that requires participants to systematically reconstruct a day to reduce biases inherent
in recall (Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004). Kahneman’s (1999) view of
well-being focuses on adding up these momentary experiences to achieve a measure of the
“experiencing self” as an indicator of well-being. One problem with this approach is that
examining momentary positive and negative affect can produce different conclusions than
evaluate approaches on how much well-being an individual derives from certain experiences
and may produce prescriptions that could ultimately decrease well-being when using amore
evaluative measure. For example, using the DRM, Kahneman and colleagues (2004) found that
individuals reported more negative affect caring for children than commuting or doing
housework, with caring for children producing more momentary negative affect than any other
activity assessed besides working. Furthermore, interactions with children produced less
positive affect than any other close relationships (spouses, friends, and family) and were only
rated slightly higher than clients or customers. Following from Kahneman’s approach to taking
momentary ratings of affect to form the assessment of well-being, children would pose a
serious detriment to well-being, although, most people consider their children as an important
source of well-being. Subjective well-being approaches that include an evaluative component
of life satisfaction avoids reducing the construct to mere hedonism, focusing on increasing
pleasure in the moment, and instead allows a chance to evaluate how much we enjoy the
circumstances we experience. Therefore, even if children do correspond to a high degree of
negative affect and low positive affect in the moment, our evaluations of how much we like
having children allow our children to contribute to our well-being. Evaluative approaches to
well-being allow for the individual’s preferences, goals, desires, expectations, and mental
Positive Interventions 12
states to drive the final assessment of well-being. These approaches, therefore, allow the
individual the final say in whether or not her life is “happy”.
Despite being subjective, these measures of well-being are correlated with a host of
other benefits. Recent evidence suggests that subjective well-being may cause benefits in work,
relationships, and health (see Lyubomirsky, Diener, & King, 2005; Pressman & Cohen, 2005
for a review). Subjective well-being has been linked to better health, longer life spans (Danner,
Snowden, & Friesen, 2001); and better work performance, better social relationships, and more
ethical behavior (Diener, 2007). Furthermore, individuals rate happier lives as more desirable
and believe that happier individuals are more likely to go to Heaven (King & Napa, 1998).
Individuals throughout the world rate happiness and well-being as more valuable than income
(Diener, 2000). Therefore, individuals put great value on subjective well-being and seek out
experiences that maximize their satisfaction.
Needing (or Objective List Theories) and “Hybrid” Theories
Needing accounts of well-being theories focus on the content of one’s life as an
important determinant of well-being. These theories list objective circumstances required for a
good life. Needing approaches have been referred to elsewhere as objective list theories of
well-being (Nussbaum, 1992; Sen, 1985, 1999). In these approaches, well-being is achieved
through satisfaction of these needs which can be listed a priori. Different theorists propose
different elements that should be included on these objective lists. For example, Maslow’s
(1954/1970) hierarchy of needs is an objective list that argues for the fulfillment of basic needs
before people can meet their full potential. The capabilities approach argues similarly for
essential needs including food, shelter, health, security, and freedom as a condition for
individuals to have the ability to create their own well-being. Objective lists highlight the
Positive Interventions 13
importance of certain things that are truly valuable to well-being, irrespective our value
assigned to it. A list could include several elements, such as education, relationships, career
success, democracy, beauty, and material comforts (Seligman & Royzman, 2003).
A definition of a positive intervention from an objective list theory would include
interventions that increase or provide elements included in these objective lists. For example, if
an objective list included education, sending one’s child to college would be a positive
intervention regardless if the child enjoyed attending college or not. Objective lists approaches
have two advantages. First, like Maslow’s hierarchy of needs, it highlights the need for
addressing primary needs before indulging in higher pursuits. This emphasizes the importance
of providing basic resources for individuals - food, shelter, safety - to promote well-being as
opposed to focusing on higher level processes such as promoting emotions such as elevation
and awe. In certain circumstances, addressing these basic needs may be the most beneficial
approach as opposed to focusing on higher psychological processes. In the aftermath of
Hurricane Katrina, Edna Foa, an expert on the treatment of post-traumatic stress disorder,
emphasized this point, suggesting initial efforts should address basic needs as opposed to
providing psychological services to the survivors (Medscape, 2005). The second advantage is
that these lists do provide objective indicators of well-being. That is, one cannot be considered
“happy” unless he or she is objectively well-off in some sense. Seligman and Royzman (2003)
provide an example of orphan children living on the streets who might be subjectively “happy”
by engaging in activities that provide momentary pleasure with little concern for the future.
The appeal of an objective list approach is that even if these individuals report being happy
they would fall short of being considered as such based on a lack of objective criteria.
Positive Interventions 14
Despite this appeal of objective lists, there are limitations as well. First, there is no
general consensus on which exact items should be included on objective lists. Although some
needs may be universal, other needs are likely to differ across cultures or across individuals
who have different values. Second, objective lists accounts of well-being are often linked to
objective outcomes to determine well-being. Therefore measuring well-being requires the
assessment of outcomes such as literacy rates or crime rates. Again, the relative importance of
these outcomes may differ by individual values, which introduces a subjective element into this
assessment. Lastly, objective lists ignore any subjective evaluation of well-being, which in the
end is a too strict criteria for developing a theory of well-being.
This final critique, that objective lists are too strict, is addressed by theories that
combine subjective appraisals with need approaches. These theories expand upon the
constructs of subjective well-being and happiness that leave out several important components
of the good life and consider the content of one’s life as well. These theories can be thought of
as “hybrid” theories because they combine liking and needing approaches. I propose that
combining an objective list approach with a liking approach encompasses more aspects than
what are included in subjective well-being accounts and makes up a construct that I call
“wellness.”
Wellness incorporates additional hallmarks of the good life including positive mental
health, flourishing, positive physical health, adaptive functioning, quality of life, and
psychological well-being as well as happiness and subjective well-being. Wellness, therefore,
is a multifaceted concept that integrates signs of well-being from several objective conditions
of one’s life in addition to subjective evaluations. A complete discussion of wellness is beyond
the scope of this paper; however, because my definition of a positive intervention is an
Positive Interventions 15
intervention that builds well-being through promoting and enhancing the factors that lead to it,
it is important to differentiate well-being from wellness. Interventions could promote an aspect
of wellness without enhancing well-being and would not be considered a positive intervention.
Therefore, a brief discussion of the concept of wellness will follow.
In her seminal work on positive mental health, Jahoda (1958) identified 6 factors that
contribute to the mental health of an individual. These factors include attitudes towards the
self, self-actualization, integration of self, autonomy, perception of reality, and environmental
mastery. Ryff (1989) found that many of these concepts are absent in definitions of happiness
or well-being. Drawing on past theoretical concepts and her own empirical investigations, Ryff
concluded that there should be at least six dimensions of well-being: self-acceptance, positive
relations, environmental mastery, purpose in life, and personal growth. Furthermore, research
supports that models that only address satisfaction with life and affect may be insufficient at
capturing the multifaceted nature of positive functioning (Ryff & Keyes, 1995).
This conception of mental health is similar to the term “competency” used within
community psychology. Competency refers to individuals or communities that have a
repertoire of resources and the knowledge and desire to utilize these resources effectively
(Iscoe, 1974). Being a competent individual and having a range of available resources is an
important contributor to the functioning and wellness of an individual.
Furthermore, the ability to adapt and respond to the challenges of life is one important
component of wellness. Often individuals are thought to be well if they are able to withstand
the several stressors of life and demonstrate resilience. Lorion (2000) describes wellness using
of a river. Those who are “well” are able to respond the ebbs and flow of the river. According
to this conceptualization of wellness, preferred psychological functioning is showing resilience
Positive Interventions 16
in the face of life stresses. This is an important component as life stresses may be a crucible
that promotes further positive developments.
Another perspective on wellness is that wellness is a state characterized by strengths
and virtues. Seligman and Peterson (2004) identified and classified strengths that are pervasive
across cultures. A taxonomy of character strengths was developed as a parallel to the
Diagnostic and Statistical Manual of Mental Disorders (DSM), which focuses on
psychopathology. These strengths are characteristics definitive of high functioning and
flourishing individuals. Seligman and Peterson identified 24 different virtues or strengths of
character that are thought to transcend cultures. In addition to identifying the virtues, they
developed a questionnaire that assesses these strengths of character. Exhibiting a large number
of these character strengths is another way to conceptualize wellness.
Cowen (1994, 1999, 2000) has differentiated wellness enhancement from the efforts of
primary prevention of psychopathology and maladaptation. In order to better understand what a
positive intervention is, it is useful to distinguish it from wellness enhancement. Wellness
enhancement assumes that by increasing competencies and functioning, psychological well-
being will increase, which in turn will buffer the individual against pathology. This buffering is
accomplished by helping the individual withstand or better deal with life stressors or other
aspects that might contribute to pathology. Cowen (1991) defines wellness based on two
clusters of indicators. The first cluster include an assessment of how well the individual is
functioning – being able to satisfy basic needs such as eating and sleeping as well as
performing life’s tasks well, a notion Cowen ties to Freud’s conception of “Leben und
Arbeiten” or “to love and to work.” The second set of indicators includes evaluative measures,
such as life satisfaction. Life satisfaction is often included as an important factor in definitions
Positive Interventions 17
of subjective well-being (Diener, 1984). Cowen’s definition incorporates the cognitive
component of subjective well-being, but leaves out the affective component.
Positive emotions, one of the three pillars of study in positive psychology, are a crucial
component to think of when considering wellness. Research supports the notion that those who
frequently experience positive emotions consider themselves happy (Diener et al., 1999;
Diener, Larsen, Levine, & Emmons, 1985; Larsen & Ketelaar, 1991). Indeed, positive affect
may actually be one of the most important contributors to a person’s overall wellness.
Increasing positive affect may not only be beneficial for increasing emotions that people desire
on an abstract, common-sense level (i.e., individuals report liking to experience positive
emotions such emotions as joy, awe, love) but also act as markers of flourishing and success
(Cantor et al., 1991; Carver & Scheier, 1998; Clore, Wyer, Dienes, Gasper, & Isbell, 2001).
Positive emotions may undo the effects of negative emotions and may broaden-and-build
resources (Fredrickson, 1998, 2001). Inducing positive emotions in the short-term leads
increased to creativity and cognitive flexibility (see Fredrickson, 1998, for a review). Recent
evidence, however, suggests that positive emotions may not only be markers of success but
that positive emotions may actually lead to success, better job performance, social relationships
(Lyubomirsky, King, & Diener, 2005) and better health (Pressman & Cohen, 2005).
Therefore, positive emotions are important to optimal psychological functioning and well-
being and increasing positive emotions is an important goal of positive interventions.
Consistent with the notion that mental health is not merely the opposite of mental
illness, Keyes’ (2005, 2006a, 2006b, 2007) proposes a complete state model that includes
a conception of flourishing in addition to a continuum of pathology. These dimensions
are correlated, yet are separate dimensions. Keyes modeled his definition of flourishing
Positive Interventions 18
on the DSM definition of depression: To be diagnosed with flourishing, a person must
have at least one symptom of hedonia as defined by high positive affect and happiness or
life satisfaction and six or more symptoms of positive functioning that include self-
acceptance, social acceptance, personal growth, social actualization, purpose in life,
social contribution, environmental mastery, social coherence, autonomy, positive
relations with others, and social integration (Keyes, 2005). This concept emphasizes
positive emotions as one of the hallmark signs of flourishing, much as sadness is a
hallmark symptom of depression. This collection of flourishing “symptoms” represents a
synthesis of different conceptions of mental health and encompasses all the aspects of
wellness discussed in this paper. A person is considered to be languishing if he or she
lacks the criteria for flourishing. Therefore, flourishing and languishing are a bipolar
continuum that— although related to presence and absence of mental health— is a
separate identifiable factor. A confirmatory factor analysis of different theories testing
the relationship between mental health (as defined by flourishing) and DSM disorders
found that a two axis, oblique solution was the only model with good fit compared to
models of independence, single axis and orthogonal, two axes solutions (Keyes, 2005).
Flourishing has also been shown to be related to positive outcomes such as fewer days off
work, increasing intimacy, and better goal pursuit; whereas languishing and mental
illness combined are a much better predictor of days of work missed and helplessness
than mental illness alone. Furthermore, Keyes (2007) found that in the absence of mental
illness, those who were identified as languishing functioned worse than those with mental
illness who demonstrated a moderate level of mental health. Keyes’ research emphasizes
the importance of distinguishing the positive from the negative and also highlights the
Positive Interventions 19
importance of subjective well-being – which includes an affective component - as a core
feature of definitions of wellness.
This distinction between subjective well-being and wellness is similar to that in the
literature between hedonic and eudamonic conceptions of well-being (see Ryan & Deci, 2001).
Hedonic views follow liking theories, whereas eudamonic conceptions involve an objective
component and are either needing or hybrid theories. Hedonic well-being is often reduced to
equating well-being with pleasure; however, most research within hedonic conceptions of well-
being use subjective well-being as a proxy for pleasure. Therefore, in a hedonic view, well-
being is a summation of positive cognitive evaluations of one’s life (in the form of subjective
happiness or life satisfaction), and the presence of positive affect and the absence of negative
affect. Eudamonic views of well-being define well-being as living a good and virtuous life.
Eudamonic well-being is often paralleled with Ryff’s conception of psychological well-being.
Although, eudamonic conceptions of well-being are important to consider and are important
components of positive mental health, eudamonic well-being is a pathway to subjective well-
being. That is to say, aspects of eudamonic well-being foster subjective well-being. Indeed, one
analysis found that subjective well-being fully mediated the relationship between
psychological well-being and measures of quality of life (Ring, Höfer, McGee, Hickey, &
O’Boyle, 2007). Therefore, eudemonic well-being contributes to quality of life only insofar as
these activities contribute to subjective well-being, such as using one’s signature strengths and
pursuing meaning. Thus, paths to eudamonic well-being are important to consider, but the
ultimate goal of interventions is to increase subjective well-being.
Positive interventions can increase several aspects of wellness. However, positive
interventions are ultimately defined by the fact that they promote this core component of
Positive Interventions 20
flourishing, namely subjective well-being. This definition allows us to distinguish positive
interventions from interventions based on objective list accounts. I see subjective well-being as
the central feature of building the positive as presented by positive psychology. Because
subjective well-being is the building block of the good life, it is the most important feature to
increase. Increases in eudamonic conceptions of well-being may even lead to moment-to-
moment and long-term increases in hedonic well-being. This is similar to Kahneman’s (1999)
notion of well-being as the sum of moment-to-moment positive experiences. Therefore, a
positive intervention must build well-being through the promotion of happiness and positive
emotions, which in turn involves increasing factors that support them.
Wanting (or Desire-Fulfillment Theories)
Wanting or desire-fulfillment accounts define well-being as the ability to fulfill
one’s desires. These approaches dominate economics, where the perspective on well-
being is that it comes from satisfying one’s preferences. Economists use money as a
proxy for well-being because money helps individuals satisfy their preferences. Desire
theories overlap with subjective well-being theories when we desire happiness or
subjective well-being. Desire-fulfillment accounts, however, depart from subjective well-
being accounts in many instances. Liking accounts contend that maximizing subjective
well-being is beneficial even if this is not what we desire, whereas desire-fulfillment
accounts contend that getting what we want contributes to well-being even if it does not
increase pleasure. Desire-fulfillment accounts explain how one can consider his life
happy, even if it is filled with displeasure. If one is reaching one’s goals and obtaining
one’s preferences, then her life will be considered a happy one. The desire-fulfillment
Positive Interventions 21
account is appealing because assessment involves examining a person’s choices directly
(Dolan & White, 2007).
A positive intervention defined according to a desire-fulfillment account would be
an intervention that fulfills people’s desires. This could be thought of as an intervention
that increases utility for a person. One potential problem with this definition is that we
believe certain things will increase our well-being, but in actuality these things do not. In
this case, our desires and expectations do not match with the eventual experience. This is
due to several biases that distort our expectations about what emotions will be triggered
by various outcomes and how long these emotions will last (Wilson & Gilbert, 2003).
Two experimental examples help illustrate that when individuals are given
freedom to make choices, they act in ways that do fail to promote well-being (as defined
by other theories). In one study, participants planned a menu of snacks they would
receive when they returned to the laboratory over the following three weeks (Read &
Loewenstein, 1995). Participants tended to pick a variety of snacks as opposed to picking
their favorite snack for all three weeks. If participants were picking snacks to eat all at
once, this would be a good strategy as snacks have diminishing marginal returns, since
the third Snickers bar hardly satisfies as much as the first. Over a longer time period,
however, snacks do not have this same effect and participants are disappointed when they
return to the laboratory and receive something other than their favorite snack. This study
demonstrates that our long-term choices are often overly influenced by our evaluations of
what we need in the moment as opposed to being able to properly predict what our
experience will be like when it occurs. In this example, participants made an error in
predicting what they would want and make a choice that in the end left them
Positive Interventions 22
disappointed. This illustrates what Gilbert and Wilson (2007) term miswanting or a
disparity between what we say want (by the choices we make) and what will make us
happy.
Another problem in determining well-being using desire-fulfillment accounts is
that individuals focus on means as opposed to ends, despite the fact that, in most cases,
ends ultimately contribute to our well-being. To illustrate this, participants with a
preference for vanilla over pistachio ice cream were randomly assigned to two conditions
(Hsee, Yu, Zhang, & Zhang, 2003). In both conditions participants had to choose
between a low-effort and a high-effort task. Participants were rewarded for completing
these tasks in different ways. In the no-medium conditions, participants received
pistachio ice cream for completing the low-effort task and vanilla ice cream for the high-
effort task. In the medium condition, participants received points for completing these
tasks: 60 points for completing the low-effort task and 100 points for completing the high
effort task. These points could be used to buy ice cream. The favored vanilla ice cream
cost 60 points whereas the pistachio ice cream cost 100 points. In the no-medium
condition, most participants chose the low-effort task in order to receive their preferred
reward, vanilla ice cream. In the medium condition, however, participants were more
likely to complete the high-effort task, even though the extra points would not help
receive their stated preference. In this example, even though participants know what they
want, they focus on the means rather than the ends of utility, a phenomenon Hsee and
colleagues call “medium maximization.” This experiment offers an interesting
comparison to how money might effect decision making. Although money does allow for
fulfillment of desires, the focus on money as the means as opposed to our desires as the
Positive Interventions 23
ends might lead individuals to allocate their time and resources in ways that do not
ultimately maximize well-being.
One approach to define a positive intervention based on desire-fulfillment
accounts would be to say that a positive intervention increases the number of available
choices available to individuals. Therefore, it is not increasing desires per se, but giving
the individual the opportunity to have the most available options to choose from. Desire-
fulfillment accounts believe that increasing the number of available choices should
increase well-being. Schwartz (2004) argues that increasing the number of options,
however, actually leads to decreases in well-being because it is harder to choose from
multiple options. This paradox of choice leads to procrastination and post-choice regret
which results in less satisfaction with the eventual choice and lower levels of well-being
(Iyengar & Lepper, 2000; Schwartz, Ward, Lyubomirsky, Monterosso, White, &
Lehman, 2002).
These studies highlight the drawbacks of desire-fulfillment accounts. When given
the opportunity to make choices and fulfill desires, individuals do not act in ways that
increase well-being. Furthermore, increasing the number of available choices, an
intervention that would be beneficial according to desire-fulfillment accounts leads to
depressed levels of well-being.
The specific benefits of adopting a subjective well-being approach will be
discussed at more length later as this forms the core of my definition of a positive
intervention. I will consider two similar definitions. These definitions do not attempt to
define well-being.
Pawelski’s Definition
Positive Interventions 24
Pawelski’s (2007) is the only theorist to directly tackle the question of what is
positive psychology and to offer a definition of a positive intervention. This approach is
similar to mine in that it focuses on interventions that increase well-being (although does
not specify which type of well-being) but uses two distinct characteristics of the
intervention to determine whether or not an intervention is “positive.” Pawelski states
that positive psychologists use the term “positive” in reference to two properties. The first
is the point of application. Positive psychology focuses on people who are well but want
to become better. Interventions can be aimed towards individuals with identifiable
deficits or individuals who merely want to better themselves. Pawelski describes the
latter as “normal weather” interventions because they are used when things are going
well. The second is what Pawelski refers to as “green-cape” approaches, or approaches
that make things better by focusing on what’s there and helping developing it – building
strengths as opposed to alleviating pathology. These two definitions highlight different
aspects of an intervention. Therefore an intervention could either meet the requirements
of one of these definitions, both, or neither. Positive interventions are those interventions
that are either “green-cape” approaches or “normal weather” interventions or they are
both. I disagree with this definition as interventions that are “positive” in application but
not “positive” in focus are nothing more than standard interventions applied to non-
clinical populations. Therefore, Learned Optimism based programs, which teach
cognitive behavioral therapy (CBT) skills to individuals who are not depressed should not
be considered a positive intervention. I will consider each of the two dimensions included
in this definition in turn. “Positive” in application refers to interventions that help people
who are already doing well do better by further reducing the negative in their lives
Positive Interventions 25
(Pawelski, 2007). Interventions that are “positive” in methods refers to the content of the
intervention.
“Positive” in Application
“Positive” in application refers to the context in which interventions are applied.
Positive psychology is an alternative to clinical psychology that focuses on
psychopathology and mental illness. Instead, positive psychology focuses on learning
more about what normal people do well and how normal people flourish (Seligman &
Csikszentmihalyi, 2000). Following from this, one aspect of the Pawelski definition is
that positive interventions are aimed at people without any deficits or psychopathology,
that is, normal people who want to better themselves. For example, people who are not
depressed, but could be happier or individuals who are good at math, but wish they could
be better. I argue that although these motivations to build an already existing strength are
admirable, it is an inaccurate definition of positive interventions. This definition is
inaccurate as it focuses on characteristics of the population as opposed to an aspect of the
intervention. Using only this definition, an intervention that aims to increase well-being
in depressed populations would not be considered a positive intervention.. Furthermore,
according to such a definition, whether or not an exercise is considered a positive
intervention (e.g., increasing gratitude) would fully depend on the population in which it
is practiced. One reason to include these two dimensions in the definition of what
constitutes a positive intervention is to be able to include standard “negative”
interventions used in non-pathological or well-functioning populations. I believe positive
psychology cannot lay claim to these practices.
Positive Interventions 26
To further illustrate the weaknesses of this definition, consider again CBT for
depression. As mentioned previously, CBT is a “negative” intervention because it
attempts to correct maladaptive cognitions in order to alleviate depression. If we consider
the characteristics of the population receiving the intervention as defining if the
intervention is positive or not, then once an individual is no longer depressed, it would
become a positive intervention. There is overwhelming support that using techniques
from CBT prior to a depressive episode can prevent subsequent depressive episodes in
both children and adults (see the Penn Resiliency Program, Jaycox, Reivich, Gillham, &
Seligman, 1994; APEX program, Seligman, Schulman, DeRubeis, & Hollon, 1999;
Seligman, Schulman, & Tyron, 2007). Therefore, it is not to say that applying cognitive
therapy to individuals who are not depressed will have no beneficial effect. The
classification of the intervention as positive or not, however, should not change based on
who is receiving it.
Therefore, applying CBT techniques to individuals before they are depressed
should not be considered a positive intervention, since such efforts simply apply the same
“negative” intervention of learning skills to dispute thoughts and fix maladaptive
cognitions before the occurrence of a full-blown depressive disorder. In order to be
considered a positive intervention, the intervention must be positive in more than just the
point of application, it must be positive in its methods and mechanism of action.
“Positive” in Methods
What does it mean for an intervention to be “positive” in methods? Interventions
that are positive in methods are interventions that attempt to promote happiness and well-
being through increasing the factors that contribute to them such as positive emotions and
Positive Interventions 27
character strengths. In his definition, Pawelski posits that these are interventions that
“help good things to grow” (pg. 7). Positive psychology from this perspective looks for
what an individual does well and strengthens those existing resources.
One particularly important and oft overlooked approach to promote happiness is
changing cognitive and motivational processes. As previously discussed, happiness is a
subjective judgment; each individual is considered an expert of his own happiness (Myers
& Diener, 1995). Several cognitive processes influence happiness such as social
comparisons, postdecisional rationalization, event construal, and self-reflection (see
Lyubomirsky, 2001, for a review). In much the same way that the cognitive theory of
depression conceptualizes depression as a result of negative views about the self, world,
and future (Beck, 1974), a construal approach to happiness posits that happiness is the
result of self-enhancing cognitions that moderate the objective impact of events on our
subjective judgments. Cognitive therapy attempts to correct cognitive distortions that lead
to depression. Positive interventions, instead, consider the causes of well-being and
attempt to increase these.
Fordyce (1977, 1983) took this approach in constructing one of the first
empirically tested positive interventions. Fordyce created a program deemed the 14
Fundamentals by observing the behaviors of happy people and assigning individuals to an
intervention where they engaged in these behaviors. These recommendations included:
socialize, strengthen close relationships, be outgoing, be a better friend, develop a healthy
personality, lower expectations, be optimistic, make happiness a goal, be active, create
meaning, get organized and make plans, develop a present orientation, reduce negative
feelings, and stop worrying. Included in these recommendations are both positive and
Positive Interventions 28
negative intervention suggestions. Some suggestions, such as create meaning and develop
a present orientation, involve creating something new and developing new approaches
that promote happiness and well-being; whereas others, such as stop worrying and reduce
negative feelings, are clearly measures aimed at reducing the negative and addressing
deficits. In Fordyce’s study, although there was a great variety in which strategies each
person preferred and received the most benefit from, positive strategies such as being
optimistic, trying new activities and being more active, being more social, and
developing a more outgoing and extraverted personality were the most often reported
beneficial approaches. Fordyce’s program is a positive intervention because the majority
of recommendations conform to the definition of a positive intervention. Just as
“negative” interventions at times may engage in an exercise that is positive in focus, there
is a place in positive psychology for reducing the negative as well.
Consider Seligman and colleagues (2006) recent attempt to create a positive
psychotherapy. In positive psychotherapy, individuals discuss troubles and therapist and
client engage in problem-solving to help address these concerns; however, the major
focus of therapy was on promoting the positive. Therefore, although some of the
individual therapeutic interventions applied can be considered either positive or negative,
the overall focus of the therapy is on the positive. These approaches are all considered
positive interventions.
Enhancement Definitions
A second definition of “positive” however should also be considered. According
to Merriam-Webster, positive also means “indicating, relating to, or characterized by
affirmation, addition, inclusion, or presence rather than negation, withholding, or
Positive Interventions 29
absence.” In this way, “positive” means building something new as opposed to
correcting something that is existing but maladaptive. This definition is an enhancement
perspective of positive psychology and positive interventions. Following from this
definition, a positive intervention builds something new or enhances something good as
opposed to fixing a deficit.
Incorporating this definition can help distinguish the difference between a
“negative” and “positive” intervention. Consider a sports analogy to the game of baseball.
If a pitcher wants to improve his game, he could accomplish this by focusing on different
aspects of his pitching repertoire. This pitcher could attempt to fix the mechanics of his
curveball, learn how to throw a proper knuckleball, or develop his already effective
change up to increase the speed between his slowball and his fastball. A “negative”
intervention would be noticing the mechanics on his curveball are poor and working to
use the proper release to apply the proper spin to the ball. A “positive” intervention could
either develop something new, adding a new pitching to the repertoire such as learning
how to throw a knuckleball or developing something that is already good, such as
increasing the speed between one’s slowball and fastball to develop a more effective
change up. All of these interventions would have the net effect of improving one’s
overall pitching; however, the focus of the interventions are different. This mirrors the
focus of therapy in having a net effect of improving one’s life (Kazdin, 1992; Strupp,
1996).
Traditional therapy techniques focus on correcting maladaptive patterns of
thoughts and behaviors. CBT, for example, is based on the notion that maladaptive
thinking patters are the root of depressive symptoms and that fixing those patterns will
Positive Interventions 30
lead to relief (Beck, 1967). Positive psychology, however, believes that psychology
should go beyond the relief of a negative state and instead focus on promoting positive
states. Part of promoting positive states involves the creation of something new.
DeRubeis (2000) echoes this distinction proposing that “a focus on the positive very
often involves a focus on learning to do things, or learning to do things well” (pg. 266).
This aspect of the definition is important, yet in my view incomplete. A proper definition
of what is “positive” about positive psychology needs to combine both meanings of the
word “positive.” That is to say, simply focusing on learning to do new things or
becoming better at what one already does well is not sufficient to be considered a positive
intervention. One could become better at doing things which do not contribute to one’s
sense of happiness or wellness. Instead, a positive intervention must teach an individual a
new skill that contributes to their happiness through increasing pleasure, engagement, or
meaning.
What is a Positive Intervention?
A positive intervention is a cognitive or behavioral strategy, or a collection of
cognitive and behavioral strategies that attempts to promote well-being – building
happiness, satisfaction with life, or positive affect through processes we have learned that
lead to well-being such as engagement, meaning, and pleasure. Furthermore, a positive
intervention attempts to build these factors for the long-term and looks to increase tonic
as opposed to phasic levels of happiness and well-being. Mood induction procedures are
widely used in psychological research; however, they provide only short-term boosts in
positive emotions. Watching a funny movie clip is not a positive intervention because this
boost in mood is short lived. However, a positive intervention could be designed out of a
Positive Interventions 31
short-term intervention. For example, a person could find a show that consistently makes
him or her laugh and make sure to schedule watching this show each day. Over time, this
extra few minutes of positive emotions could help promote happiness in the long-term
and could then be considered a positive intervention.
This definition expands upon the definition Duckworth and colleagues (2005) presented
when coining the term “positive interventions. They specified that a “positive intervention” is a
technique that helps build the pleasant life, engaged life, and meaningful life. A positive
intervention is an intervention that aims to build well-being through the promotion of pleasure,
engagement, and meaning.
Furthermore, positive interventions should add something new, either a new skill
or a new way to foster these aspects of flourishing or increase expertise or strengths in an
area to better promote well-being. The core of this definition, however, is in increasing
subjective well-being.
Although many existing therapies may indeed increase positive emotions, these
therapies are focused on combating disorder and are not included as positive
interventions. Therapies that focus on building well-being and positive emotions such as
well-being therapy (Fava, 1999) and positive psychotherapy (Seligman, Rashid, & Parks,
2006) are included under the definition of positive interventions. This is a similar
distinction to that made by Durlak and Wells (1997) in distinguishing between preventive
programs that prevent behavioral and social problems by either reducing deficits or by
promoting positive behaviors that confer a protective benefit to the individual. Therefore,
the overall end state of an individual after undergoing an intervention is not enough to
distinguish it into either a positive intervention or otherwise. Psychotherapy can promote
Positive Interventions 32
well-being and positive interventions can prevent disorder. But the focus of the
intervention on promoting the building blocks of positive mental health, subjective well-
being, is the critical factor for defining an intervention as “positive.”
What is Included in this Definition?
Now I will turn to a clarification of what is included in this definition of a positive
intervention. As mentioned previously, the building block of positive psychology is
subjective well-being with increasing happiness, life satisfaction, and perhaps most
importantly, positive emotions as the thrust of positive interventions. This is consistent
with Keyes’ conception of flourishing, which highlights hedonia as the hallmark feature
of flourishing. An individual must possess either high positive affect or high happiness or
life satisfaction to be considered flourishing. Building this hedonic conception of well-
being is the most important aspect of a positive intervention.
In order to discuss what is included in this definition and why, it is important to
consider what the field knows about increasing well-being. One finding from the research
is that affect is increased through “doing, not thinking” (Watson, 2002). This corresponds
to Seligman’s (2002) pleasure route to happiness, which has the goal of introducing
something new to the client that will get them activated (e.g. behavioral activation).
Theoretically, this arousal is related to increases in positive affect. The second principle
is that mood is a result of the complex interactions based on goal pursuits. We strive to
achieve things we desire (a goal) or to avoid things that are undesirable (an anti-goal).
Working towards our goals and thinking we are doing well will lead to elation and joy
whereas failing to progress towards our goals will lead to depression. Furthermore,
avoiding our anti-goals and preventing what we fear will lead to relief, whereas believing
Positive Interventions 33
we are approaching these undesired events will cause anxiety. Setting goals can help
provide meaning in our lives and pursuing these goals can help produce engagement.
From this view, setting goals and engaging in activity to pursue these goals are important
aspects of positive interventions.
Positive Interventions as Throughputs
I argue that subjective well-being is the core component of the good life. As
mentioned previously, positive emotions do not only correlate with several indicators of
life success but experimental and longitudinal research suggests that positive emotions
cause success. Subjective well-being, therefore, is the ultimate outcome of positive
interventions but what role do interventions play in theories of well-being?
In an attempt to integrate several theories of well-being, Jayawickreme (2008)
proposes a model that divides well-being into the different processes that contribute to
achieving well-being. These processes include inputs, throughputs, and outputs.
Throughputs are reactions to and choices an individual makes in response to his or her
environment. Therefore, throughputs represent the processes that help foster subjective
well-being.
Why Don’t We Only Focus on Studies that Increase Happiness?
Focusing only on studies that increase happiness would confound the definition
of positive intervention with which variables the researchers choose to measure. In the
case of prevention programs based on CBT, the main outcome variables include
optimism as well as depression. In research studies on cognitive therapy, researchers
measure changes in depressive symptoms. A research team could just as easily measure
changes in optimism while conducting an outcome study of CBT. Indeed, research
Positive Interventions 34
supports that patients experience changes in measures of optimism over the course of
cognitive therapy that correspond to improvements in depressive symptoms (Seligman et
al., 1988).
It is important, however, to define a positive intervention on more than just the
outcome measured in a study. A positive intervention cannot be only thought of as an
intervention that increases well-being – positive emotions, life satisfaction, happiness, or
other components. Many interventions may increase many aspects of well-being yet the
studies may not assess these as outcomes. Continuing CBT even after the depression
subsides could continue to benefit life satisfaction. When studies are conducted,
researchers decide on which outcomes to measure. In any instance, researchers could just
as easily measure a different outcome, but this does not change the nature of the
intervention. Therefore, just as defining a positive intervention by point of application
could lead an intervention to be considered positive in some cases and negative in others,
this same problem could occur by defining a positive intervention by outcome.
Is Increasing the Positive Different (and Better) than Decreasing the Negative?
Positive psychology, which focuses on strengths and what people do well, is often
presented as an alternative to the medical model that focuses on deficits and where people
need improvement. Both of these approaches have merit and the goal of positive
psychology is not to replace the medical model but to offer a complimentary approach.
Approaching deficits and focusing on reducing the negative has helped create
interventions to treat a host of psychological disorders. Existing interventions can remedy
at least 14 psychiatric disorders (Seligman, 1994) and guidelines for developing
empirically-supported treatments will likely lead to additional treatments for disorders
Positive Interventions 35
that still are in need of long-lasting treatments (Chambless et al., 1998). Positive
psychology and positive interventions, however, offer another approach to combat the
burden of psychological distress. Increasing positive emotions complements decreasing
negative emotions as an important strategy for preventing and treating problems
(Fredrickson, 2000). Therefore, positive interventions represent another tool in the
toolbox of psychologists to both promote well-being in addition to combating mental
disorders.
It should be noted that the difference between positive and negative may be
purely semantic. From this view the main benefit of positive psychology would be to
provide a different language for health care professionals to present interventions to a
client. For example, an intervention might “promote optimistic thinking” as opposed to
“reducing negative cognitions” or working towards “happiness” and “meaning” rather
than attempting to address a “depression.” Even if the only difference in a positive
psychology approach is the language presented to clients, this does not necessarily
undermine the possible importance of this shift in terminology. It is consistent with
cognitive models that the language we use makes a significant impact on our
interpretations of events and subsequent emotional reactions (Beck, 1995). Socializing
clients with positive terms rather than deficits language may make clients more amenable
to the process. Further research is needed on the use the importance of framing
interventions differently and assessing impact of treatment outcome.
Another important consideration is whether there is any inherent benefit to
building the positive rather than addressing the negative. In some circumstances, negative
and positive thoughts and emotions may be a zero-sum game and increasing the positive
Positive Interventions 36
may be the most efficient way to increase overall well-being (Harris & Thoresen, 2006).
Positive states undo negative states in addition to the benefits that come from the positive
states themselves. This is consistent with the undoing aspect of positive emotions
proposed by the broaden-and-build theory of positive emotions (Fredrickson, 2001,
2002). Positive emotions serve to undo the deleterious effects of negative emotions.
Negative emotions tend to lead to specific responses whereas positive emotions broaden
the available mental options in a situation. Furthermore, positive emotions reduce
accelerated heart rate that accompanies negative emotions. Evidence suggests that
although positive affect and negative affect are orthogonal in the long-term, momentary
experience of positive affect is negatively correlated with negative affect (Larsen, Diener,
Emmons, 1986; Watson, Clark, & Tellegen, 1984; Watson & Tellegen, 1985).
Furthermore, knowledge of increasing positive emotions may be important for
disorders in which deficits in positive emotions provide specificity from other disorders.
For example, low positive affect, rather than high negative affect, is a specific feature of
some disorders, such as depression. Low positive affectivity separates individuals with
depression from individuals with other disorders (Clark, Watson, & Mineka, 1994;
Watson, Clark, & Carey, 1988). Individuals with anxiety disorders, for example, have
high levels of negative affect but do not experience reduced positive affect. Increasing the
positive, therefore, might be an important tool that is not currently employed or studied
often enough in the context of depression.
Another important contribution of positive psychology is that building and
working with an individual’s strengths may be more beneficial than working on
weaknesses. A commonly used positive intervention is using a signature strength (a
Positive Interventions 37
signature strength is a strength in the individual’s top 5 strengths as assessed by the
Values in Action (VIA) Strengths Questionnaire; Peterson & Seligman, 2004) in a new
way. Research suggests that this intervention leads to increases in happiness and
decreases in depressive symptoms (Seligman et al., 2005; 2006). One study compared the
benefits of building on strengths versus working on a weakness (Haidt, 2002). Students
were randomly assigned to either use a signature strength or work on a weakness (a
strength in the bottom 5 on the VIA). Although there were no significant differences in
changes in well-being between individuals who worked on a strength or weakness, those
who worked on a strength reported significantly greater enjoyment than those who
worked on a weakness. Enjoyment may be an important long-term predictor of benefit
from interventions as it leads participants to be more motivated, which other studies have
found is an important mediator of intervention effectiveness (Dickerhoof, Lyubomirsky,
Sheldon, 2007). In a follow-up study, individuals who were assigned to engage in an
activity that matched to a signature strength reported more intense and longer lasting
pleasure from the activity (Haidt, 2004). Matching to strengths, therefore, contributes to
positive affect and engagement while doing an activity and can form the basis of a
positive interventions.
Results from the National Institute of Mental Health Treatment of Depression
Collaborative Research Program suggest that matching to strengths may be more
effective than addressing a deficit. Analysis from this study found that individuals with
the lowest levels of cognitive dysfunction responded better to CBT whereas interpersonal
therapy (IPT) was a better treatment modality for individuals with high social skills. In
both of these instances, these baseline predictors are the actual skills that CBT and IPT
Positive Interventions 38
aim to increase. This supports that capitalizing on strengths and doing what one does well
is an important basis and tool for interventions.
Should Positive Interventions Work?
In addition to debating the definition of well-being, researchers disagree over
whether well-being is stable over time or amenable to change. This debate is important to
consider before evaluating the effectiveness of positive interventions because if well-
being is resistant to long-term changes then it would be futile to develop interventions to
enhance well-being. Researchers who conclude that well-being is immutable use three
main arguments: (1) well-being is due largely to genetic factors, (2) well-being is
strongly related to personality, (3) life circumstances may have immediate impacts, but
individuals adapt to any changes and return to previous levels of well-being.
The first argument is that an individual’s level of subjective well-being is due to a
genetically-determined set point (or set range). Events push individuals higher or lower
within their set ranges but ultimately individuals return to their predetermined level of
well-being. Lykken and Tellegen (1996) present evidence from twin and adoption studies
that suggests the genetic contributions to well-being corresponds to a heritability estimate
as high as 80% (although several other studies find the estimate is more likely to be 50%;
see Braungart, Plomin, DeFries, & Fulker, 1992; Tellegen et al., 1988). Both of these
estimates, however, suggest that a large portion of the variance in individual levels of
well-being is due to genetic factors. This suggests that levels of well-being are likely to
be stable across time. Indeed, data from a four-wave panel study found that individuals
tend to return to their baseline level of well-being after the initial psychological impact of
the events dissipates (Headey & Wearing, 1989). Lykken (2000) compares this process to
Positive Interventions 39
waves passing through water, events may cause the water to swell but eventually the
water will settle to its initial levels.
The stability of well-being is also due to the strong relationship between well-
being and personality traits. Traits are stable patterns of thinking, feeling, and behaving.
Recent research suggests that as much as two-thirds of the variance in subjective well-
being is accounted for by personality (Steel, Schmidt, Shultz, 2008; cf. DeNeve &
Cooper, 1998; Ozer & Benet-Martínez, 2006). Costa and McCrae (1990) have presented
evidence that supports the long-term stability of personality traits, especially for
neuroticism and extraversion. These two traits have the most overlap with subjective
well-being as they are strongly related to levels of positive and negative affect (Lucas &
Fujita, 2000; Watson & Clark, 1992; Tellegen & Waller, 1992; Yik & Russell, 2001).
Furthermore, personality tends to shape the type of events people experience. Headey and
Wearing (1989) found that the same life events tend to happen repeatedly to the same
people. This commonality of circumstances helps contribute to the stability of well-being.
Barring the occurrence of any unusual events, situations repeat themselves and well-
being remains constant.
The last evidence supporting long-term stability of well-being is the notion of the
hedonic treadmill (Brickman & Campbell, 1971). The hedonic treadmill refers to the
tendency for individuals to adapt to hedonically relevant stimuli over time, returning to
initial levels of well-being. One of the most famous studies supporting this conclusion
involved lottery winners and accident victims (Brickman, Coates, & Janoff-Bulman,
1978). Results of this study are often cited to show that after the initial psychological
impact of the event wore off, the lottery winners and the accident victims who became
Positive Interventions 40
quadriplegic were no different in terms of happiness. In fact, the accident victims were
significantly lower on well-being than the lottery winners. The researchers concluded,
however, that the accident victims were not as unhappy as one would expect. The group
mean of the accident victims was still above the midpoint of the scale. This finding,
however, is not surprising given that most individuals consider themselves happy and rate
themselves well above the midpoint of any happiness scale (Diener & Diener, 1996).
Therefore, this study is often miscited and used as strong support for the hedonic
treadmill when in actuality this study supports that individuals do not adapt completely to
becoming a quadriplegic.
Despite the fact that the results of this study are often misreported, considerable
evidence supports the notion of the hedonic treadmill. As mentioned previously, life
events have little long-term impact on well-being (Headey & Wearing, 1989; Suh,
Diener, & Fujita, 1996). A number of studies examining reactions to the death of a
spouse show that most individuals demonstrate considerable psychological resilience and
that their initial emotional reactions fade over time (Bonanno et al., 2002; Bonanno,
Wortman, & Nesse, 2004; Lucas, Clark, Georgellis, & Diener, 2003). Thus, despite some
evidence to the contrary, the hedonic treadmill has received considerable support (see
Fredrick & Loewenstein, 1999, for a review).
Support in Opposition of Set-Point Theory
Recently, the notion that well-being is unalterable has been challenged. Evidence
suggests that well-being increases across the lifespan (Fujita & Diener, 2005). Several
life events do lead to lasting changes in life satisfaction including marriage (Lucas et al.,
2003), divorce (Lucas, 2005), and unemployment (Lucas, Clark, Georgellis, & Diener,
Positive Interventions 41
2004). Furthermore, some countries show considerable variability in nation-wide levels
of well-being across time. Objective measures of the success of nations predict national
levels of well-being, including wealth, democracy, and productivity (Diener, Diener, &
Diener, 1995). Nations even show considerable changes in well-being that correspond to
historical events that improve or decrease the standard of living in those countries, such
as the fall of communism in Russia and Eastern Europe.
Lyubomirsky and colleagues (2005) present a framework of well-being that
suggests that although 50% of the variance in subjective well-being is due to genetic
factors, a further 10% of the variance is due to life circumstances, and 40% of the
variance is due to intentional activity. One important implication of this theory is that
individuals can perform cognitive and behavioral strategies to boost their levels of well-
being above their set points. Research can help identify which strategies contribute to
well-being and help design interventions to lead to long-term changes in well-being.
How Effective Are Positive Interventions?
Thus far, the aim of this paper has been to describe what a positive intervention is
and what it is not. In summary my view of a positive intervention is as follows:
(1) A positive intervention aims to increase well-being
(2) Well-being is defined based on a liking approach that emphasizes subjective
well-being as the core feature of well-being
(3) A positive intervention is a cognitive or behavioral strategy that looks to
increase subjective well-being
(4) Positive interventions add something new or build on a strength through
increasing and promoting these factors that lead to subjective well-being
Positive Interventions 42
This definition expands upon previous definitions of positive interventions because it
gives a clear objective for what positive interventions aim to increase, namely subjective
well-being. Prior definitions have often either been too broad, such as the improvement
definition that states that positive interventions aim to improve an individual’s life and
therefore fail to exclude anything, or are not operationally defined, such as Duckworth
and colleagues (2005) definition that positive interventions are those that lead to pleasure,
meaning, or engagement. The definition presented in this paper clearly outlines the focus
of positive interventions as subjective well-being. Furthermore, this definition leaves out
a group of interventions that many consider positive interventions or instances of positive
psychology that I believe are misclassified. These are interventions that adopt the same
strategies and exercises as typically used in clinical populations but apply these
techniques to a novel, non-suffering population. Examples of these interventions include
the Penn Resiliency Program and the APEX program that teach cognitive-behavioral
skills to individuals before a depressive episode. As mentioned previously, these
approaches are not positive interventions because defining them as such involves a
classification of the context of the intervention as opposed to the content. A definition of
positive interventions should revolve around the content of the intervention. Lastly,
although my definition does adopt an enhancement approach, it restricts enhancement
definitions to only those that target subjective well-being. Table 1 presents a summary of
all of the definitions of positive interventions considered thus far in this paper.
One distinction between my definitions and other possible definitions of positive
interventions is that I believe positive interventions refer to interventions that aim to
increase well-being. The Pawelski approach and Enhancement definitions both describe a
Positive Interventions 43
feature that some theorists posit is the real distinction of positive interventions - that it is
an intervention that aims to increase or better something. The differences between these
definitions and the definition adopted by this paper are based on a theoretical rationale as
to what is the goal of positive psychology. I see positive psychology as focusing on
finding ways to enhance and promote well-being, others view positive psychology as a
framework for improving things in general. Once adopting an approach that focuses on
building well-being then empirical research can bear on understanding what the best
definition of well-being is. Nevertheless, I can find no empirical way to ground the notion
that positive psychology should build well-being or should find how to better things in
general. This is a theoretical question and open for further debate. However, the evidence
presented in this paper suggests that well-being is something that individuals care very
deeply about and that research in the domain of positive psychology supports an be
increased. Furthermore, many researchers within the field of positive psychology have
developed interventions that focus on this very component, as opposed to the
enhancement view; therefore if the types of studies researchers conduct can be used as
evidence of their views, then the subjective well-being approach appears to be well
supported. This definition is adopted for the rest of the paper that will focus on
summarizing and describing the existing literature through a meta-analysis of the
effectiveness of these interventions.
Meta-Analysis of Positive Interventions
Selection of Studies
The primary purpose of a meta-analysis is to summarize the average relationship
in a given research domain. In order to accurately estimate the average effect, researchers
Positive Interventions 44
must make an attempt to compile the existing studies that bear on this relationship. In
order to do so I employed several different search techniques. First, I conducted a
PsycINFO search using the following search terms: happiness, well-being, subjective
well-being, quality of life, positive intervention, positive emotions, life satisfaction,
intervention, and positive psychology. The PsycINFO search was conducted using various
search features until the search produced 300 or fewer hits. Each term was entered into
PsycINFO first searching for the term anywhere. If this search produced more than 300
hits I used advanced features to limit the search to first only empirical articles, then using
the term as a descriptor, and lastly using the term as a descriptor selecting only empirical
articles. Once a search produced 300 or fewer hits I scanned the titles and abstracts for
acceptability. In cases where the next level search was not completely nested in the
previous search (for example if the first search that produced 300 hits was all empirical
articles using that term) I then preceded to the next level to search for additional articles.
After each using each term individually, I created 45 two-term pairs by producing all
combinations of the 9 search terms. These pair of terms were searched using a similar
procedure in which each pair was examined first in all instances, then all empirical
instances, then using the first term as a descriptor, then all empirical instances of the first
term as a descriptor, then using the second term as a descriptor, then all empirical
instances of the second term as a descriptor, then both terms as a descriptor, and lastly all
empirical instances using both terms as a descriptor. Again, searches were reduced until it
produced 300 hits or fewer at which point the titles and abstracts of all studies retrieved
were scanned for acceptability. Table 2 presents the number of articles produced for each
aspect of this search. Summing across the highest level of all searches produced 471,436
Positive Interventions 45
hits. Counting only the searches that produced 300 or fewer hits, 9,404 titles and abstracts
were scanned for acceptability. Of these studies, I deemed 140 studies acceptable for
further analysis and obtained the full article to determine if it met the inclusion/exclusion
criteria.
In addition to a PsycINFO search I obtained articles from additional sources. I
conducted a hand search of all issues of two prominent journals in positive psychology
(The Journal of Positive Psychology and The Journal of Happiness Studies). I also
reviewed all articles from a database that a student created of positive psychology articles
(positivepsycharticles.com) for fulfillment of the program requirements for the Masters
of Applied Positive Psychology Program at the University of Pennsylvania. Furthermore,
I searched all of the Masters projects that had been completed in this program’s two-year
history. I conducted a search of titles (and abstracts when available) of three conferences:
The Positive Psychology Summit (past 9 years), and the past five years for the American
Psychological Association and the Association for Psychological Science. Additionally, I
searched the references from a recently published book on positive psychology
interventions titled The How of Happiness: A Scientific Approach to Getting the Life you
Want (Lyubomirsky, 2007). I also completed a reference search of all articles that were
found in order to find additional articles. I also requested unpublished data from
prominent positive psychology laboratories and contacted experts for other suggestions
for sources of positive interventions.
All of these search procedures together produced 236 studies that I examined to
determine if they were acceptable to enter the meta-analyses based on the inclusion and
exclusion criteria.
Positive Interventions 46
Inclusion/Exclusion Criteria
From this pool of 236 studies I examined each study to determine if it met
eligibility for inclusion in the meta-analysis. For inclusion in the study each study had to
meet the following criteria:
1. The study must have included an intervention that is either a cognitive or
behavioral strategy that attempts to promote well-being – building happiness, satisfaction
with life, or positive affect through processes we have learned that lead to well being
such as engagement, meaning, and pleasure as well as cognitive and behavioral strategies
we have linked to increased well-being.
2. The study must have been an experiment and included a suitable comparison
control group, such as a neutral control activity or a no-treatment control group.2
3. The study must have a measurement of subjective well-being, i.e., happiness,
positive affect, or life satisfaction OR depression. Many of these studies contained
measures of depression. Very few studies included other measures of psychopathology.
One study reported results on changes in anxiety symptoms and 1 study reported changes
in alcohol consumption. Because depression was by far the most widely assessed
measure of psychopathology it was included in the analysis.
4. The intervention must attempt to build these factors in the long-term; that is,
increase tonic as opposed to phasic levels of happiness and well-being. Mood induction
procedures are widely used in psychological research; however, such inductions provide
only short-term boosts in positive emotions.
2 This exclusion criterion was not applied in the search phase of the meta-analysis.
Therefore, the initial results include studies with active control groups (For example,
CBT and treatment as usual). This will be discussed further in the results section.
Positive Interventions 47
5. The study must contain statistical information sufficient to compute an effect
size.
6. Although studies on exercise and behavioral activation fit the definition
proposed in the introduction of a positive intervention, these studies were excluded from
the meta-analysis for the following reasons.
(1) Many of these studies have only measures of depression and not of well-being.
(2) The link between positive emotions and activity is best understood at a
biological level as opposed to a psychological level.
(3) Given the large amount of studies of this nature, the average effect size would
be strongly weighted by these studies.
(4) Several recent meta-analyses of the effectiveness of exercise and behavioral
activation have already been conducted (see Cuijpers, van Straten, &
Warmerdam, 2007; Puetz, O’Connor, & Dishman, 2006; Stathopoulou, Powers,
Berry, Smits, & Otto, 2006), therefore the average effect size for positive
interventions can be compared to the average effect size of exercise without
overly weighting the effect size computed from this study to reflect the effects of
exercise and behavioral activation.
After applying these criteria to each study 58 research studies were selected for
data extraction.
Data Extracted
I extracted several aspects of each study that met the inclusion criteria. Each study
received a rating of study quality based on a 7-point scale. Aspects of the study
considered for study quality included: whether or not the study used random assignment
Positive Interventions 48
to condition, the quality of the statistical analysis, internal validity, external validity,
strength of the comparison condition, adequacy of measures used, and the mode of
administration of the intervention. One point was awarded for each aspect of the study
allowing the scores to range from 0 to 7. The quality scores for the studies included in
this analysis ranged from 3 to 7. The intervention of each study was classified into one of
14 different groups based on the construct that the intervention targeted. Furthermore,
each intervention was classified was either a behavioral, cognitive, or cognitive-
behavioral intervention. Other variables included length of the intervention and the type
of sample used (either patient sample versus non-patient sample). Lastly for each study a
measure of effect size was extracted. The calculation of effect sizes followed principles
set forth by Hedges and Olkin (1985) and Rosenthal (1991).
Effect Size Calculation
For each study an effect size was calculated based on the post-intervention
difference between the treatment and control group using the following methods. For
studies that reported the post-treatment means and standard deviations, effect sizes were
calculated by subtracting the mean of the control group ( CX ) from mean of the treatment
group ( TX ) and dividing by the pooled standard deviation ( pooledσ ). This produces a
Cohen’s d estimation of effect size for each study.
pooled
CT XXd
σ
−=
For some studies, this information was not provided and the effect sizes were estimated
using the following equations that allow conversion from standard statistical tests
(Rosenthal, 1991):
Positive Interventions 49
df
td
2=
df
Fd
x),1(2 ⋅=
If the authors provided neither the means and standard deviations nor a test statistic and
corresponding degrees of freedom, I estimated the effect size by converting the p-value to
a relevant effect size using the following equations (Rosenthal, 1991):
N
Zr =
21
2
r
rd
−
=
If the results were reported as significant the p-value was considered to be equal to the
alpha value reported. In studies that reported the results as non-significant then the effect
size for the study was assumed to be zero. This is a rather conservative estimate for the
effect sizes therefore reducing the value of the overall effect size estimated as opposed to
biasing the estimation in favoring of finding an effect.
After calculating the Cohen’s d for each outcome the effect size was adjusted for
small sample sizes using a correction suggested by Hedges and Olin (1985). This
correction produces a Hedge’s g from a Cohen’s d with the following equation:
−+−=
9)(4
31
21 nndg
In addition to applying this correction, each effect size requires an estimation of the
variance component corresponding to that effect size. These variance components were
estimated using the following equations:
)1()1( 221112
21
ppnppn
nnw
−+−=
2'1 d
Nw
−=
Positive Interventions 50
Lastly the overall average effect size was calculated by weighting each individual effect
size by its variance.
j
jj
w
gwg
Σ
Σ=
This overall effect size corrects for differences in sample sizes and variance, giving more
weight to effect sizes from large studies that are estimated more accurately. Table 3
displays the information from each study extracted from the original article including the
values of the effect size computed and any moderator variables used in subsequent
analyses. I will now discuss the results of computing these average effect sizes.
Results
There were 58 research studies combined in this meta-analysis that included a
total of 4,502 participants. All studies met my criteria for a “positive intervention”;
however, the type of intervention varied greatly from study to study. This feature of the
data suggests the analyses should use random effects as opposed to fixed effects. Fixed
effects models assume that there is one true effect size for all studies included in the
analysis. This might be the case for several replications of the same intervention,
however, with several distinct interventions it is unlikely that one true effect size exists
for all the studies. It has been suggested that if there is likely to be variability amongst the
interventions, a random effects model is more appropriate (Hedges & Olkin, 1985). A
random effects model produces a summary of the average effect sizes of the studies as
opposed to estimating the true effect size. One way to support this empirically is to
examine the amount of dispersion in effect sizes that is between studies. This is
accomplished by analyzing for significant homogeneity between the studies using
Positive Interventions 51
Cochran’s Q statistic. The Q statistic is a measure of the total amount of variance
amongst the effect sizes. A non-significant Q statistics suggests that the effect sizes are
similar enough to compute a true population effect sizes (Cochran, 1954). In the case
where a Q statistic is non-significant and there is theoretical justification to do so an
estimate of the true effect size in the population is computed using a fixed effects model.
If the Q statistic is significant, a random effects model that calculates the average effect
size of a distribution of effect sizes is more appropriate.
Calculating the overall effect size of positive interventions on well-being using a
random effects model produced an effect size of .54 (95% confidence interval .40, .68),
which is a moderate sized effect (Cohen, 1977). This effect size was based on 51 studies
that reported measures of well-being. In addition to determining the average effect size
the distribution of effect sizes between studies is another consideration. Figure 1 displays
a Forest plot of the effect sizes for the individual studies. There was significant
heterogeneity in this distribution (Q(50) = 201.53, p < .001). In order to quantify the
amount of heterogeneity the I2 value was computed. The I
2 is a ratio of the amount of
variance that is between effect sizes compared to the total variance (Higgins, Thompson,
Deeks, & Altman, 2003). Cut-offs proposed for evaluating the magnitude of an I2 are I
2 =
25 is low, I2 = 50 is moderate, and I
2 = 75 is high. For the overall estimation of the well-
being effect size, I2 = 75.19 suggesting that a large amount of the total variation in the
effect sizes is between-studies. Given that over three-fourths of the total variance is
represented by between-study variation, moderation analysis should consider aspects of
the studies that can explain this between-study difference. Moderator analyses will be
examined in turn.
Positive Interventions 52
One criticism often raised against meta-analyses is that many studies on the topic
are conducted yet only those that produce statistically significant findings are published.
This is referred to as the file-drawer problem and is an issue because this would bias the
effect sizes because often only the published (and significant) findings are used to form
the overall effect size (Rosenthal, 1991). The most extreme form of this criticism has
claimed that for every published study, an unpublished study exists with an effect size of
the same magnitude in favor of control (Rosenthal & Rubin, 1978).
This study attempted to address this criticism in several ways. One is through
extensive literature searching and inclusion of unpublished data. This includes poster
presentations, doctoral dissertations, and master’s theses. Of the 58 studies included, 4
were unpublished doctoral dissertations, 1 was a poster presentation, 1 was an
unpublished master’s thesis, and 2 were manuscripts in preparation based on unpublished
data. Although these studies have not undergone the peer review process, their inclusion
helps address the concern that the effect size obtained is based on the few significant
findings that were published.
Furthermore, fail-safe values were computed to describe the number of studies
that would have to exist (yet not included) with null findings to reduce the given effect
size to zero (Rosenthal, 1991). The classic fail-safe N for the effect size on well-being
was 2617, meaning there would have to be 2617 studies not included with null findings to
reduce the effect size to zero. Given the effort to include unpublished data and the
number of published studies found, this number of unpublished studies is unlikely. The
classic fail-safe N, however, assumes that the unpublished studies have a d = .00 and
examines the number needed to reduce to effect size to zero. This may not be of interest
Positive Interventions 53
to researchers because effect sizes larger than zero may still be considered uninteresting
and unpublished studies may have non-zero effect sizes that would still reduce the value
of the overall effect size considerable. To address these concerns, another approach,
Orwin’s (1983) fail-safe N, determines the number of studies with a specified effect size
that would be needed to reduce the effect size to a specified value that is considered to be
uninteresting to the researcher. I calculated two values of this fail-safe N, one examined
the number of studies with a d = .00 that would have to exist to reduce the average effect
size to .199 and the other the number of studies with a d = .10 that would have to exist to
reduce the average effect size to .199. These fail-safe N’s produced values of 49 studies
and 99 studies respectively. Again, given the number of studies found and the number of
unpublished studies included it is unlikely that this number of unpublished studies with
such small effect sizes exist yet were not obtained for this analysis.
Other than computing fail-safe values, additional methods of assessing
publication bias compare the obtained effect size to the variance of each study. These
methods assume that if the obtained effect sizes were from the sample of studies that
happened to be published only because they obtained significant results due to chance,
then studies with high standard errors (or small studies) would be associated with the
largest effect sizes. The first method is to assess this graphical by plotting the obtained
effect sizes versus the standard errors. This is referred to as a funnel plot due to the shape
it produces (Light, Singer, & Willett, 1994). Figure 2 displays the funnel plot of the effect
sizes for measures of well-being. If this sample included studies from the complete
population of studies then the effect sizes should cluster symmetrically around the line
indicating the combined effect size. If this sample was biased by only including published
Positive Interventions 54
and significant studies then there would be asymmetry in the plot. In this case the effect
sizes appear to be symmetrically distributed around the line, which supports the notion
that this analysis is not subject to publication bias. Although funnel plots provide a
graphical display of the data it is left to the researcher to interpret whether or not the
funnel plot indicates a significant bias or not. Quantitative assessments of the relationship
between the effect sizes and the standard errors also aid in the interpretation. Begg and
Mazumdar (1994) suggest calculating a correlation between the standardized effect size
and the variances of these effects. The value of Begg and Mazumdar’s rank correlation
test is positive if large effect sizes correspond to studies with small variances and
negative if large effect sizes come from studies with large variances. For the effect sizes
of well-being, τ = .30, p = .002, suggesting that the largest effect sizes come from the
studies with smaller variances. The results of these statistics support that the overall
effect size was unlikely to be biased by only sampling the significant and published
studies.
This conclusion is not surprising because this analysis included 8 unpublished
studies (or roughly 16% of the included studies). In order to further address whether that
average effect size could be biased due to the existence of unpublished studies
moderation analysis compared the effect sizes for published studies to unpublished
studies for this sample. The average effect size for the 8 included unpublished studies
was .26 (95% confidence interval .06, .46), whereas the average effect size for published
studies was .49 (95% confidence interval .37, .62). The average effect sizes for published
versus unpublished were not statistically different at the standard α = .05 level, Q(1) =
3.71, p = .054. However, there was a trend suggesting that unpublished studies have
Positive Interventions 55
lower effect sizes than published studies. This finding suggests that if anything the
average effect size computed for this meta-analysis is a conservative estimate given that a
large effort was made to include unpublished studies. Furthermore, the average effect size
for unpublished studies included in this study was still in the small-to-moderate range and
was significantly different from zero.
In addition to analyzing the effects of positive interventions on well-being, I
calculated an average effect size for differences in depressive symptoms between
treatment and control post-intervention. Twenty-five studies reported measures of
depressive symptoms. Again, a random effects model estimated the overall average effect
size for depressive symptoms, which was equal to .64 (95% confidence interval .40, .88)
this is a moderate sized effect. There was significant heterogeneity in the distribution of
effect sizes between studies as well, Q(24) = 77.70, p < .001, I2 = 69.11.
An analysis of the indicators of publication bias raised greater concern, however,
for measures of depressive symptoms than for the well-being effect. The classic fail-safe
N had a value of 435 studies. Orwin’s fail-safe N was 20 studies to reduce the average
effect size to .199 if the unretrieved studies had an effect size of .00 and 39 studies if the
unretrieved studies had an effect size of .10. Given the number of studies that measured
depressive symptoms in the sample it is unlikely that this number of unretrieved studies
exist, however, these values are much lower than those found for the measures of well-
being.
Figure 4 displays the funnel plot for effect sizes of measures of depressive
symptoms. This funnel plot raises the concern that there could be additional studies not
included in the analysis that would lead to a reduction in the overall effect size. A
Positive Interventions 56
majority of the effect sizes are clustered around the right side of the line with only three
effect sizes below the average effect size. Begg and Mazumdar’s rank correlation test is τ
= .40, p = .005 which suggests that the studies with the largest effect sizes had the smaller
variances. Therefore, with regards to depressive symptoms, there is some concern that
these studies are biased. One possibility is that because most of these studies use well-
being as the primary outcome measure the measures of depressive symptoms are reported
only when the effects are significant. Again, while the funnel plot and Begg and
Mazumdar’s rank correlation reach different conclusions, the fact that several sources of
data suggests some bias merits concern.
Again, I compared the average effect sizes on depressive symptoms for published
versus unpublished studies. Only 2 of the 8 unpublished studies reported measures of
depressive symptoms for the sample. The average effect size for changes in depressive
symptoms from published studies was .79 (95% confidence interval .52, 1.07). For
unpublished studies the average effect size was .12 (95% confidence interval -.52, .76).
The average effect size for unpublished studies was not statistically significantly different
from zero. Although, the average effect sizes were not significantly different from each
other, there was a trend suggesting that unpublished studies have smaller effect sizes on
depressive symptoms, Q(1) = 3.53, p = .06. Given concerns raised from other measures
of publication bias, this suggests the average effect size for depressive symptoms should
be interpreted cautiously; however, the inclusion of unpublished studies in this analyses
supports the view that the estimate presented is conservative.
Given that there was significant heterogeneity in effect sizes for both measures of
well-being and depressive symptoms and that a large proportion of the variance was
Positive Interventions 57
between effect sizes, the next set of analyses analyzed moderator variables. Before I
conducted this moderation analysis, some studies included in the initial analyses were
excluded. First, 6 studies were excluded from this analyses because the control
comparisons used were active comparison conditions (either CBT, treatment as usual, or
a partial intervention control group). This violates one of the exclusion criteria however I
decided to run an initial run of data analyses including these studies. Furthermore,
extreme values of effect sizes were assessed using the sample-adjusted meta-analytic
deviancy (SAMD) statistic (Huffcutt & Arthur, 1995). The SAMD statistic is a ratio of
the raw deviancy of a given study divided by the sampling error of that difference. It is
based on a similar logic to the difference-in-fit standardized (DFFITS) statistic used to
assess outliers in regression analysis. The value of the SAMD statistic fits a t distribution
and therefore values of greater than 2 should be considered as outliers. For meta-
analysis, however, there is a trade-off in excluding outliers because one goal of meta-
analysis is to search for meaningful moderator variables to characterize the variability of
effect sizes. Excluding too many studies can eliminate possibly interesting variation in
effect sizes, however, very extreme values could be due to a variety of errors (such as a
value being entered incorrectly) that could bias the overall effect size. An analysis of a
scree plot is required in order to determine if a value is too extreme. Figure 5 displays the
screen plot of SAMD statistics based on well-being effect sizes. Figure 6 displays the
same plot for effect sizes of depressive symptoms. Two studies with well-being measures
and 1 study with depression measures were excluded on the basis of being extreme
outliers. The studies for well-being had effect sizes of d = 5.00 and d = 2.98. The study
with a measure of depressive symptoms had an effect size of d = -.12. This reduces the
Positive Interventions 58
number of studies in the moderation analysis to 49. Table 4 displays the overall
estimations of average effect sizes after excluding these studies.
Five variables were coded from the studies to be considered as moderators of the
effectiveness of positive interventions – construct measured, study population, target of
the intervention, type of intervention, length of the intervention and quality of the study.
Given that subjective well-being is a multi-faceted construct I also computed
individual effect sizes for different components of subjective well-being. Table 5 displays
the results for average effect size on each component that contributes to subjective well-
being, happiness, life satisfaction, positive affect, well-being, and composites of the
previous measures. Well-being measures are those measures that assessed a construct that
represents some combination of both affective and cognitive measures. Therefore, these
effect sizes are based on a theoretical a priori combination of these components in a
single measure and cannot be parsed out. Composite measures come from studies that
combined different measures of well-being into a single value to calculate an effect size.
Construct was a significant moderator of the average effect sizes, Q(4) = 15.62, p = .004.
By far the largest average effect size was on measures of happiness (.71). Life
satisfaction had the lowest overall effect size (.44). One noteworthy finding was that
studies that combined measures into composite values (.21) had lower effect sizes than
any of the individual measures of subjective well-being.
Another potential moderator was the study population. Studies were divided into
those studies that use a patient population versus a normal sample. Table 6 displays the
data relevant to the average effect size of well-being measures in both patient populations
and normal populations. Type of sample was not a significant moderator of differences in
Positive Interventions 59
effect size for well-being, Q(1) = 1.82, p = .18. The estimated average effect size in the
normal populations was .46; whereas the estimated average effect size in patient
populations was .29. The difference in effect sizes for depressive symptoms was quite
similar, although both effect sizes had greater magnitudes. Table 7 displays the values for
measures of depressive symptoms. The patient population had an average effect size of
.89, whereas the non-patient sample had an effect size of .71. These effect sizes were not
significantly different, Q(1) = .41, p = .52. Although these effects did not reach
significance dividing the sample into patient versus non-patient populations did decrease
the I2 values in each of the subgroups. For well-being measures, studies using patient
samples had a homogenous distribution of effect sizes, Q(6) = 3.74, p = .71.
The interventions were classified into 14 different categories based on the target
of the intervention. Table 8 displays the results for average effect sizes for interventions
aimed at targeting different constructs. Reminiscence interventions comprised the largest
group of studied interventions. For well-being measures, target of intervention was not a
significant moderator, Q(13) = 13.40, p = .42. Table 9 displays the results of the
moderation analysis for effect sizes of depressive symptoms. Studies that assessed
depressive symptoms used only 9 different types of positive interventions. Again, target
of intervention was not a significant moderator of the effectiveness of positive
interventions with regards to depressive symptoms, Q(8) = 13.92, p = .08.
Each intervention also was classified into either a behavioral, cognitive, or
cognitive-behavioral intervention. Table 10 displays the average effect size of well-being
measures broken up by type of intervention. Type of intervention was a significant
moderator of the effectiveness of positive interventions using well-being measures as an
Positive Interventions 60
outcome, Q(2) = 15.1, p = .001. Behavioral interventions had the lowest average effect
size of .15 whereas cognitive interventions had an average effect size of .47 and
cognitive-behavioral interventions had an average effect size of .56. This same pattern
did not hold for measures of depressive symptoms. Table 11 displays the results for the
effect sizes of depressive symptoms. Type of intervention was not a significant
moderator, Q(2) = .77, p = .68. Although behavioral interventions still had the smallest
average effect size, all effect sizes for depressive symptoms were in the moderate to large
range.
Length of study was coded in terms of number of days the intervention lasted.
Given that length of study is a continuous variable, meta-regression was used to
determine if there was a relationship between effect size and length of study. Figure 7
displays the values of effect size for well-being plotted against the length of the study.
The value of the slope for the regression was .00043, and was not significant, p = .65.
This suggests that a linear trend between effect size and length of intervention was not
present for well-being measures. Figure 8 displays the effect sizes of depressive
symptoms plotted against length of the study. The estimate for the slope of the line of
best fit was .00038, and this value was not significant, p = .33.
Lastly, the average effect sizes for both well-being and depression overall were
recalculated using the quality ratings of the study as weights. For the well-being effect
sizes, the correlation between effect size and quality ratings was not significant, r (43) = -
.07, p = .66. Adjusting for quality, however, increased the overall effect size for well-
being = .51, and there was no longer significant heterogeneity between the effect sizes,
Q(42) = 38.49, p = .67, I2 = 0.00. This suggests that some of the heterogeneity in effect
Positive Interventions 61
sizes for well-being measures was the varying quality of the studies combined to produce
an average effect size. For depressive symptoms, the correlation between quality ratings
and effect sizes was r(17) = .22, p = .39. The average effect size was .93 although there
was still considerable heterogeneity between these effect sizes even after accounting for
quality of the studies, Q(16) = 35.71, p < .001, I2 = 55.19.
Discussion
The results of this meta-analysis support the view that positive interventions lead
to significant increases in well-being and decreases in depressive symptoms. This
synthesis of the existing literature on positive interventions highlights the variety of
exercises used to increase subjective well-being. It is not surprising, therefore, that there
was considerable heterogeneity amongst study effect sizes. Furthermore, analysis of
moderators showed that differences in aspects of the interventions could explain some of
the differences of the effect sizes. Specifically, this study found that exercises that
included a cognitive component (either by itself or in combination with a behavioral
component) corresponded to larger effect sizes than behavioral interventions.
After accounting for significant outliers the overall average effect size on
measures of well-being was .44 (.51 correcting for quality of study). This suggests that on
average, positive interventions lead to small-to-moderate boosts in subjective well-being
compared to inactive or no-treatment control conditions. These effect sizes are smaller
than those reported by Okun and colleagues (1990) who meta-analyzed interventions
designed to increase subjective well-being among the elderly and found an average effect
size of .67. These effect sizes, however, compare favorably to prevention programs
including prevention programs focused on wellness (average effect size of .41; MacLeod
Positive Interventions 62
& Nelson, 2000), programs for children (average effect size of .34; Durlak & Wells,
1997), and depression prevention programs (average effect size of .16; Horowitz &
Garber, 2006). Furthermore, moderate effect sizes, such as those reported for measures of
depressive symptoms, are often found in meta-analyses of psychotherapy (average effect
size of .68; Smith & Glass, 1977). The results of this meta-analysis support that positive
interventions are as effective as many other forms of interventions and the size of the
effects are comparable to many prevention programs.
It is not surprising that we did not observe large effects of positive interventions
given the populations used most often in these studies. A majority of studies investigate
interventions applied to samples of college students. College students on average are
neither depressed, nor particularly unhappy, and although many Americans do report the
desire to be happier (see Lyubomirsky et al., 2005), a vast majority of these participants
did not seek out these interventions. Therefore, the effectiveness of these interventions
may be smaller than what would be expected in an applied setting where individuals are
more devoted to the goal of becoming happier and motivated to complete the
intervention.
Furthermore, these interventions lead to a moderate decrease in depressive
symptoms that corresponds to an average effect size of .77 (.93 correcting for quality of
study). This is larger than the effect size found for measures of well-being. There is some
concern, however, that this effect size may be inflated due to publication bias based on
the results of the fail-safe calculations as well as the funnel plot. One possibility is that
researchers only report the results for depression if they are significant.
Positive Interventions 63
Another important comparison are those interventions which fit the definition
proposed for a “positive” intervention but were excluded due to the fact that recent meta-
analyses were already available on these specific interventions. This includes exercise
and behavioral activation. A recent meta-analysis of behavioral activation found that
activity scheduling compared to control conditions corresponded to an effect size of .87
(Cuijpers et al., 2007). This effect size is comparable to the effect size found for measures
of depressive symptoms in this study and is almost identical to the effect size of .89
reported for reduction of depressive symptoms in patient populations. Therefore, even
though behavioral activation studies were excluded from the analysis, their effectiveness
was on par with the positive interventions synthesized in this study. Meta-analyses of
exercise have found similar ranges of effect with small to moderate effects of increases in
affect and decreases in depressive symptoms in non-depressed populations (North,
McCullagh, & Tran, 1990; Puetz et al., 2006) and large combined effects for reduction of
depressive symptoms in clinical samples (Stathopoulou et al., 2006). The results of this
study found a large effect size corresponding to reduction of depressive symptoms in
patient populations for positive interventions. Note, however, that this effect size was still
smaller than the effects of exercise on depression.
The moderation analyses provided some insight into which aspects of the
interventions affect how efficacious the interventions are and which measures of well-
being positive psychology exercises have the largest effects on. The length of the
intervention was not related to the size of the effect. This is consistent with the notion
that positive psychology exercises are brief interventions that can lead to significant
boosts in well-being and decreases in depressive symptoms (Schueller & Seligman, in
Positive Interventions 64
press; Seligman et al., 2005, 2006). Given the low cost of applying these interventions,
which are easily disseminated through modalities with no therapist or human contact,
small-to-moderate effects are impressive. Prentice and Miller (1992) suggest that even
small effects are quite important when they involve any small manipulation of the
independent variable leading to any explainable variance. Although, the average length of
time for interventions included in this analysis was one month, the duration of over half
of the interventions was shorter than 3 weeks. This supports the notion that the
interventions included in this analyses were brief and involved little manipulation of the
independent variable yet still produced significant effects in the outcomes of interest.
Although it failed to reach significance, the effect sizes for measures of well-
being was lower for those interventions applied to patient populations compared to non-
patient populations. This could be because the most effective intervention for patient
populations is to focus on their given disorder first. That is, for individuals with
depression it may be more effective to first target the depression and then apply a positive
intervention to help prevent relapse and promote recovery. Recovery from a disorder
includes more than just a reduction of symptoms (Fava, Ruini, & Belaise; 2007; Fava,
Tomba, & Grandi, 2007). “Recovery” often refers to when an individual no longer meets
full criteria for a disorder, but this concept has come to include definitions of
psychological well-being as well (Fava, 1996). Therefore, to truly achieve a state of
recovery, existing interventions may be enhanced by combining positive interventions to
both decrease disorder and increase well-being. Well-being therapy, an intervention
based on Ryff’s model of psychological well-being, espouses this very philosophy, as it
designed to be used in the treatment of affective disorders during the residual phase of
Positive Interventions 65
therapy (Fava, 1999; Fava & Ruini, 2003). The results of this meta-analysis support that
positive interventions might be best applied during this period because they have larger
effect sizes in non-patient populations. Further investigation of the use of positive
interventions in patient populations following treatment offers rich questions for future
research. For example, are positive interventions an effective form of prevention of new
episodes following a proper trial of cognitive-behavioral therapy? Would positive
interventions lead to increases in well-being to supplement the reduction of depressive
symptoms? These are exciting, yet untested, empirical questions.
Exercises that included a cognitive component had significantly larger effect sizes
for well-being than behavioral only interventions. These findings are inconsistent with
the argument that well-being may be increased more by doing than thinking (Watson,
2002). For increasing happiness, changing an individual’s pattern of thinking may be
more effective at leading to short term boosts in well-being than a behavioral
intervention. This is consistent with cognitive theories of happiness that implicate
cognitive processing patterns as an important determinant of an individual’s level of
happiness (see Lyubomirsky, 2001; Veenhoven, 2006). An important caveat to this
conclusion is that all measures of well-being for this meta-analysis compared treatment to
control post-intervention. Behavioral interventions may have a stronger influence on
affective components of well-being that may be slower acting, but ultimately more
important to overall well-being. Veenhoven (2006) argues that affective theories of
happiness not only account for a larger portion of the variance in measures of life
satisfaction, but are a more important foundation for intervention; if one could change
happiness by changing thinking than happiness is divorced from the actual conditions of
Positive Interventions 66
one’s life. This leads to the rather dangerous philosophical position that one could be
happy in Hell if one just changed their thoughts about living in Hell. Although, this
argument is an extreme one, it is important to remember that interventions should not just
be increasing subjective judgments in isolation of concern for other aspects of an
individual’s life.
Of the studies examined the only measure assessed that could address the other
theories of well-being discussed was health and only 4 studies reported these outcomes.
Therefore, it is difficult to directly compare theories of well-being empirically to
determine the effectiveness of interventions that met the definition of positive
interventions according to other definitions proposed for well-being. This speaks to the
the types of researchers typically conducting studies on interventions. The subjective
well-being approach is heavily favored in the psychological literature because it focuses
on subjective mental states assessed with self-report measures. Other conceptions of well-
being would include changes in health, literacy, education, beauty, etc. as a result of
positive interventions. Furthermore, desire-fulfillment accounts would require some
assessment of what an individual wants and if the intervention is helping achieve those
goals. In order to determine the effects of positive interventions on other conceptions of
well-being researchers need to include more diverse measures in studies. A more
complete conception of well-being including objective measures could provide support
for the important of objective lists or underscore the value of adopting a subjective well-
being perspective. Future research should include more objective measures in order to
answer this question empirically.
Positive Interventions 67
By contrast, there were no significant differences between behavioral, cognitive,
and cognitive behavioral approaches for measures of depression symptoms, which
suggests that behavioral approaches are appropriate for decreasing depression. In this
way, positive psychology interventions may be acting through a similar mechanism as
behavioral activation. These findings support the view that all of these approaches are
reasonable to combat depression and positive interventions may be yet another approach.
Positive interventions contribute most strongly to measures of happiness and least
strongly to measures of life satisfaction. This smaller effect on life satisfaction is not
surprising given that life satisfaction is a global and stable cognitive appraisal of how
good one’s life is (Diener et al., 1985). Furthermore, the fact that life satisfaction
measures were the least malleable component of subjective well-being provides support
that these interventions do not simply make an individual feel better about his or her life
conditions, but do lead to increases in positive mood and happiness. Such an intervention
effect is preferable to finding increases in life satisfaction with no changes in mood
because such gains may be more likely to fade over time (R. Veenhoven, personal
communication, March 29, 2008). Additionally, the fact that interventions have larger
effects on happiness and mood compared to life satisfaction contradicts the criticism that
positive interventions are simply making individuals feel better about living in Hell.
The fact that composite measures of happiness corresponded to the smallest effect
sizes is difficult to interpret. Multiple indicators of subjective well-being should lead to a
more accurate estimation of the true well-being construct. One problem with this
approach as it is often implemented in the literature is that researchers typically use an
equal weighting of the different components to compute a well-being composite. The use
Positive Interventions 68
of a well-being composite should be constructed through either a theoretical or empirical
weighting of the different aspects of the constructs. Most often, when multiple indicators
are combined to form a latent construct, a measurement model helps guide the researcher
to the nature of the relationship between the variables and suggests values for weighting.
Thus the equal weighted composite used in studies included in this analysis is not
necessarily more reliable than the individual measures. More caution should be used to
have a clear theoretical or empirical rational before constructing a composite variable and
the effects on different aspects of well-being should be reported. In this meta-analysis, if
the studies reported the individual components of well-being, those values were entered
into the analyses instead of the composite.
Directions for Future Research
This meta-analysis provides support for the effectiveness of positive psychology
interventions. Furthermore, it suggests that cognitive (and cognitive-behavioral)
interventions are more effective than behavioral interventions. Future research should
attempt to understand why these interventions are effective. What are the processes of
change involved in positive psychology interventions and how are these changes leading
to improvements in well-being? Furthermore, although relatively long interventions were
included (length of intervention ranged from 1 day to 180 days with an average length of
approximately 1 month), there was no moderating effect of length of intervention,
suggesting that positive interventions may be a cost- and time-effective way of increasing
well-being.
As mentioned previously, to directly compare conceptions of well-being, more
studies need to include outcomes that address both needing and wanting theories of well-
Positive Interventions 69
being. Psychological accounts of well-being focus almost exclusively on well-being and
thus researchers are biased in not considering other accounts. Although I do agree that a
subjective well-being approach is the best conception of well-being and most relevant to
positive interventions, including variables that tap other accounts of well-being could aid
empirical support for this position. Furthermore, even if positive interventions are defined
using a subjective well-being approach, understanding the connections between positive
interventions and objective criteria for well-being may help positive interventions shape
public policy.
Limitations
One limitation of this study is that it included only post-intervention outcomes.
Whether or not changes in happiness can be maintained is an important research
questions. Very few studies assess follow-up boosts on well-being, although some more
recent studies are beginning to follow participants for longer periods after the
intervention. Although several researchers have reached the pessimistic conclusion that
happiness is the product of genes and personality and that individuals quickly adapt to
any boosts more recent evidence supports that long-term changes in happiness are
achievable and that adaptation is not as ubiquitous or inevitable as once thought (see
Diener, Lucas, & Scollon, 2006; Lyubomirsky et al., 2005). Once more positive
interventions have assessed long-term changes, however, this can be further assessed
with future meta-analyses.
Another limitation of this study is that the findings regarding publication bias on
measures of depressive symptoms were inconclusive. The possibility remains that
researchers do not report measures of depressive symptoms if the findings are not
Positive Interventions 70
significant. Although this cannot be determined based on this data, the effect size of
measures of depressive symptoms should be interpreted with caution. Inclusion of
unpublished studies in this analysis partially accounts for addressing this concern.
Overall, approximately 1 out of 8 studies included in this analysis were unpublished
studies. Furthermore, although there was a trend of lower effect sizes in unpublished
studies as compared to published studies, the effect sizes obtained from both sources
were not significantly different. Lastly, inclusion of unpublished studies supports that the
effect sizes obtained are conservative in regards to addressing publication bias.
Lastly, several studies were excluded from this analysis because they did not have
adequate assessment of outcomes. This is because positive psychology is a relatively
young field and many of the efforts in intervention research are still pilot studies. Future
intervention studies should include proper measures to complete a quantitative analysis of
the effectiveness of the intervention. This will help positive psychology grow through
empirical investigation of its tenets and practices. Some of the omissions are studies that
positive psychologists often have an interest in determining if data can support the
effectiveness of these interventions. These include self-help groups such as large group
awareness training (such as Erhard Seminars Training and Forum). In cases where there
were empirical studies on these interventions they lacked the outcome measures to be
included in this analyses. In most instances, these interventions are investigating through
testimonials or case studies rather than rigorous empirical testing (see Finkelstein,
Wenegrat, & Yalom, 1982; Fisher et al., 1989). In order to compare these interventions to
positive psychology interventions research studies must include adequate assessments of
outcome.
Positive Interventions 71
Conclusions
Overall, this study found support for the effectiveness of positive interventions
with effect sizes on par to average effects found in other forms of prevention and
treatment. Furthermore, cognitive and cognitive-behavioral forms of positive
interventions were significantly more effective at increasing measures of subjective well-
being than behavioral interventions. The accumulation of research on positive
intervention supports the view that fostering the positive aspects can lead to increases in
subjective well-being and decreases in depressive symptoms. Although more studies can
help provide support for the usefulness of these interventions in addition to other forms of
treatment, the existing evidence points to these forms of intervention as a useful tool for
psychologists in attempting to promote well-being.
Positive Interventions 72
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Figure 1. Standardized effect sizes of positive interventions on well-being compared to control conditions at
post-test.
Study name Outcome Statistics for each study Std diff in means and 95% CI
Std diff Standard Lower Upper in means error Variance limit limit Z-Value p-Value
Algoe, 2005 Happiness 0.051 0.152 0.023 -0.246 0.348 0.336 0.737
Aylett, 2004 Life Satisfaction 0.344 0.217 0.047 -0.082 0.770 1.583 0.113
Bedard et al. 2003 Well-Being 0.341 0.662 0.438 -0.956 1.638 0.515 0.606
Bennett & Maas, 1988 Life Satisfaction 2.979 0.570 0.325 1.862 4.096 5.229 0.000
Bryant et al., 2005 Happiness 0.650 0.282 0.080 0.097 1.203 2.304 0.021
Burton & King, 2004 Well-Being 1.300 0.232 0.054 0.844 1.756 5.593 0.000
Cook, 1998 Life Satisfaction 0.830 0.367 0.135 0.111 1.549 2.263 0.024
Davis, 2004 Life Satisfaction 0.870 0.559 0.313 -0.226 1.966 1.556 0.120
Dickerhoof et al., 2007 Well-Being 0.140 0.117 0.014 -0.089 0.369 1.199 0.230
Eells, 2006 Life Satisfaction 0.793 0.186 0.034 0.429 1.157 4.271 0.000
Elizabeth, 2006 Well-Being 5.000 0.524 0.275 3.972 6.028 9.535 0.000
Emmons & McCullough, 2003 Study 2 Positive Affect 0.588 0.200 0.040 0.195 0.981 2.935 0.003
Emmons & McCullough, Study 3 Combined 0.632 0.255 0.065 0.133 1.131 2.483 0.013
Fallot, 1979-1980 Happiness 0.484 0.370 0.137 -0.242 1.210 1.306 0.191
Fava et al., 1998 Well-Being 0.000 0.447 0.200 -0.877 0.877 0.000 1.000
Fava et al., 2007 Combined 1.000 0.587 0.345 -0.151 2.151 1.702 0.089
Fordyce, 1977 Study 1 Happiness 0.572 0.168 0.028 0.242 0.902 3.396 0.001
Fordyce, 1977 Study 2 Happiness 0.741 0.253 0.064 0.245 1.237 2.925 0.003
Fordyce, 1983 Study 4 Combined 0.424 0.214 0.046 0.004 0.844 1.978 0.048
Fordyce, 1983 Study 5 Combined 0.389 0.262 0.069 -0.125 0.903 1.484 0.138
Frieswijk et al., 2006 Well-Being 0.265 0.145 0.021 -0.018 0.548 1.833 0.067
Goldwurm et al., 2003 Combined 0.450 0.211 0.045 0.036 0.864 2.128 0.033
Green et al., 2006 Combined 0.808 0.295 0.087 0.230 1.385 2.741 0.006
Grossman et al., 2007 Positive Affect 0.680 0.327 0.107 0.039 1.321 2.079 0.038
Guse et al., 2006 Combined 0.495 0.299 0.090 -0.092 1.082 1.653 0.098
Haight, 1984 Life Satisfaction 2.051 0.713 0.509 0.653 3.449 2.876 0.004
Harris et al., 2006 Positive Affect 0.299 0.125 0.016 0.054 0.544 2.391 0.017
King & Miner, 2000 Positive Affect 0.470 0.187 0.035 0.104 0.836 2.517 0.012
King, 2001 Combined 0.780 0.235 0.055 0.319 1.241 3.319 0.001
Kremers et al., 2006 Well-Being 0.262 0.170 0.029 -0.070 0.594 1.545 0.122
Lichter et al., 1980 Study 1 Combined 0.684 0.434 0.188 -0.166 1.534 1.577 0.115
Lichter et al., 1980 Study 2 Combined 0.496 0.294 0.086 -0.079 1.071 1.690 0.091
Low et al., 2006 Positive Affect 0.185 0.313 0.098 -0.429 0.799 0.591 0.555
Lyubomirsky et al., 2004 Well-Being 0.360 0.209 0.044 -0.050 0.770 1.722 0.085
Lyubomirsky et al., 2006 Combined 0.016 0.265 0.070 -0.503 0.535 0.060 0.952
MacDonald & Settin, 1978 Life Satisfaction 0.660 0.459 0.211 -0.240 1.560 1.437 0.151
Mitchell et al., 2007 Well-Being 0.450 0.396 0.157 -0.327 1.227 1.135 0.256
Otake et al., 2006 Happiness 0.407 0.189 0.036 0.037 0.777 2.157 0.031
Ruini et al., 2006 Positive Affect 0.180 0.190 0.036 -0.193 0.553 0.946 0.344
Savelkoul et al., 2001 Life Satisfaction 0.130 0.164 0.027 -0.191 0.451 0.794 0.427
Scates et al., 1986 Life Satisfaction 0.000 0.343 0.118 -0.672 0.672 0.000 1.000
Schwartz & Sendor, 1999 Life Satisfaction 0.250 0.464 0.215 -0.660 1.160 0.539 0.590
Seligman et al., 2006 Study 1 Life Satisfaction 0.420 0.320 0.102 -0.207 1.047 1.312 0.189
Seligman et al., 2006 Study 2 Combined 0.855 0.399 0.160 0.072 1.638 2.140 0.032
Sheldon & Lyubomirsky, 2006 Positive Affect 0.340 0.259 0.067 -0.168 0.848 1.313 0.189
Smith et al., 1995 Combined 2.092 0.402 0.161 1.305 2.878 5.209 0.000
Spence & Grant, 2007 Combined 0.610 0.277 0.077 0.068 1.152 2.205 0.027
Tkach, 2005 Well-Being 0.004 0.126 0.016 -0.243 0.251 0.032 0.975
Updegraff & Suh, 2007 Life Satisfaction 0.290 0.205 0.042 -0.112 0.692 1.413 0.158
Wing et al., 2006 Life Satisfaction -0.190 0.163 0.027 -0.510 0.130 -1.165 0.244
Campbell & Donovan, 2007 Combined 0.310 0.712 0.507 -1.086 1.706 0.435 0.663
0.540 0.070 0.005 0.403 0.677 7.729 0.000
-1.00 -0.50 0.00 0.50 1.00
Positive Interventions 95
Figure 2. Funnel plot of well-being effect sizes.
-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
Sta
nd
ard
Err
or
Std diff in means
Funnel Plot of Standard Error by Std diff in means
Positive Interventions 96
Figure 3. Standardized effect sizes of positive interventions on depressive symptoms compared to control
conditions at post-test.
Study name Outcome Statistics for each study Std diff in means and 95% CI
Std diff Standard Lower Upper in means error Variance limit limit Z-Value p-Value
Bedard et al. 2003 Depression 0.312 0.661 0.437 -0.984 1.608 0.472 0.637
Cheavens et al., 2006 Depression 0.730 0.365 0.133 0.014 1.446 1.999 0.046
Davis, 2004 Depression 2.750 0.746 0.556 1.289 4.211 3.689 0.000
Dickerhoof et al., 2007 Depression -0.120 0.117 0.014 -0.349 0.109 -1.028 0.304
Fallot, 1979-1980 Depression 0.918 0.384 0.147 0.166 1.670 2.391 0.017
Fava et al., 1998 Combined 0.668 0.469 0.220 -0.252 1.587 1.424 0.155
Fava et al., 2007 Combined 0.820 0.521 0.271 -0.200 1.840 1.575 0.115
Fordyce, 1983 Study 4 Depression 0.309 0.213 0.046 -0.109 0.727 1.448 0.148
Fordyce, 1983 Study 5 Depression 0.558 0.264 0.070 0.040 1.076 2.112 0.035
Freedman & Enright, 1996 Depression 1.204 0.627 0.394 -0.026 2.434 1.919 0.055
Grossman et al., 2007 Depression 0.560 0.325 0.106 -0.077 1.197 1.723 0.085
Grosssman et al., 2007 Depression2 0.630 0.326 0.106 -0.009 1.269 1.932 0.053
Guse et al., 2006 Depression 0.670 0.303 0.092 0.076 1.264 2.211 0.027
Hebl & Enright, 1993 Depression 0.704 0.422 0.178 -0.123 1.531 1.668 0.095
Hedgepeth & Hale, 1983 Depression 0.128 0.258 0.067 -0.379 0.635 0.495 0.620
Lichter et al., 1980 Study 2Depression 0.407 0.292 0.085 -0.165 0.979 1.394 0.163
Lin et al., 2004 Depression 1.751 0.629 0.395 0.519 2.983 2.785 0.005
Mitchell et al., 2007 Depression 0.570 0.399 0.159 -0.213 1.353 1.428 0.153
Ruini et al., 2006 Depression -0.290 0.191 0.036 -0.664 0.084 -1.519 0.129
Schwartz & Sendor, 1999 Depression 0.730 0.468 0.219 -0.186 1.646 1.561 0.118
Seligman et al., 2006 Study 1Depression 0.530 0.322 0.104 -0.101 1.161 1.645 0.100
Seligman et al., 2006 Study 2Combined 1.265 0.415 0.173 0.451 2.079 3.045 0.002
Smith et al., 1995 Depression 1.998 0.395 0.156 1.224 2.772 5.056 0.000
Stevens-Ratchford, 1993 Depression 0.605 0.417 0.174 -0.213 1.423 1.449 0.147
Surway et al., 2005 Depression 0.575 0.496 0.246 -0.397 1.547 1.160 0.246
0.641 0.121 0.015 0.403 0.879 5.280 0.000
-1.00 -0.50 0.00 0.50 1.00
Fav ours A Fav ours B
Positive Interventions 97
Figure 4. Funnel plot of depression effect sizes.
-3 -2 -1 0 1 2 3
0.0
0.2
0.4
0.6
0.8
Sta
nd
ard
Err
or
Std diff in means
Funnel Plot of Standard Error by Std diff in means
Positive Interventions 98
SA
MD
Figure 5. Scree plot analysis for sample-adjusted meta-analytic deviancy (SAMD) statistics for
well-being measures.
0
2
4
6
8
10
12
14
16
18
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45
Rank-order Position
Positive Interventions 99
SA
MD
Figure 6. Scree plot analysis for sample-adjusted meta-analytic deviancy (SAMD) statistics for
depression measures.
0
1
2
3
4
5
6
7
8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Rank-order Position
Positive Interventions 100
Figure 7. Meta-regression Assessing Length of Intervention as a Moderator, Measures of Well-Being
Regression of Length - Days on Std diff in means
Length - Days
Std
dif
f in
me
an
s
-16.90 4.58 26.06 47.54 69.02 90.50 111.98 133.46 154.94 176.42 197.90
3.00
2.68
2.36
2.04
1.72
1.40
1.08
0.76
0.44
0.12
-0.20
Positive Interventions 101
Figure 8. Meta-regression Assessing Length of Intervention as a Moderator, Measures of Depression
Regression of Length - Days on Std diff in means
Length - Days
Std
dif
f in
me
an
s
-6.70 4.94 16.58 28.22 39.86 51.50 63.14 74.78 86.42 98.06 109.70
3.00
2.70
2.40
2.10
1.80
1.50
1.20
0.90
0.60
0.30
0.00
Positive Interventions 102
Table 1. A summary of the definitions of positive interventions considered
Definition Description Strengths Weaknesses
Improvement A positive intervention leads
to improvement in an
individual’s life
Describes everything that
individuals would intuitively
want positive psychology to
include
Does not leave out any
interventions. All
interventions presumably
look to improve an
individual’s life
Well-Being:
Liking
A positive intervention
improves well-being which
is defined as subjective well-
being
Gives a clear definition of
what the goal of a positive
intervention is as well as a
way to measure well-being
Relies on self-report, which
is subjective. One is
considered happy if he or she
reports being happy
Well-Being:
Needing
A positive intervention
improves well-being which
is defined using an objective
list
Provides objective criteria to
base judgments of well-being
on
Still have to decide which
objective list to use and
which to measure to
determine effectiveness of
intervention
Well-Being:
Wanting
A positive intervention
improves well-being which
is defined as fulfilling one’s
desires
Allows well-being to be
based on criteria besides
subjective well-being that
individuals desire. Can
account for a “happy” yet
miserable life if individual
accomplishes one’s goals
and meets one’s desires
People are bad at knowing
what consequences events
will have on their well-being.
People often make decisions
that do not promote well-
being
Pawelski’s A positive intervention is an
intervention that either (1)
build or improves upon what
is good (2) is applied to an
individual that lacks deficits
in that area
Accounts for both positive
psychology definitions build
on well-being approaches as
well as those that believe
positive interventions build
or promote something good
(besides well-being)
No definition of what the
good is. Allows for
interventions to be positive
in some instances but not
others depending on the
context in which it is applied
Enhancement A positive intervention
builds something new or
enhances something that is
present as opposed to fixing
a deficit
Captures definitions of
positive interventions that do
not include well-being as a
component.
No definition of what the
good is. Hard to determine
what the difference is
between something that is
broken and something that is
not present in many
situations
Positive Interventions 103
Table 2. Search terms and results from PsycINFO search
Term 1 Term 2 All Empirical Descriptor 1 Empirical Descriptor 2 Empirical Descriptor - Both Empirical Articles Found
happiness 13604 7514 2162 1186 -
well-being 64443 41941 11050 7615 -
subjective well-being 7551 5200 0 0 -
quality of life 43143 28203 12896 8948 -
positive intervention 59 - - - 5
positive emotions 1023 700 0 0 -
emotions 58661 26972 17585 6692 -
life satisfaction 12184 9014 4007 3411 -
intervention 152153 79377 20779 8790 -
positive psychology 3802 1475 669 104 9
happiness well-being 5551 3320 786 418 1695 1084 365 157 5
happiness subjective well-being 2630 1641 438 252 0 0 0 0 10
happiness quality of life 1966 1143 307 129 593 341 133 - 6
happiness positive intervention 8 - - - - - - - 0
happiness positive emotions 648 321 115 - 0 0 0 0 2
happiness emotions 3855 2181 530 327 909 522 134 - 1
happiness life satisfaction 2168 1457 431 231 795 613 247 - 6
happiness intervention 1791 807 114 - 141 - - - 22
happiness positive psychology 1287 491 217 - 277 - - - 8
well-being subjective well-being 7551 5200 2120 1587 0 0 0 0 -
well-being quality of life 11891 8138 2383 1662 3897 2837 947 581 -
well-being positive intervention 29 - - - - - - - 1
well-being positive emotions 1035 537 246 - 0 0 - - 3
well-being emotions 7145 3840 1035 612 1167 621 198 - 2
well-being life satisfaction 6230 4573 1811 1377 1769 1481 683 522 -
well-being intervention 14756 8223 1895 1181 1573 688 227 - 23
well-being positive psychology 2137 911 683 280 373 59 203 - 12
subjective well-beingquality of life 2301 1560 0 0 - - - - -
subjective well-beingpositive intervention 6 - 0 0 - - - - 1
subjective well-beingpositive emotions 419 210 0 0 0 0 - 6
subjective well-beingemotions 1704 943 0 0 238 - - - 1
subjective well-beinglife satisfaction 2544 1843 0 0 882 748 - - -
subjective well-beingintervention 1411 777 0 0 94 - - - 7
subjective well-beingpositive psychology 896 434 0 0 138 - - - 2
quality of life positive intervention 8 - - - - - - - 1
quality of life positive emotions 261 - - - - - - - 6
quality of life emotions 2093 1129 341 224 265 - 53 - 9
quality of life life satisfaction 3791 2796 1594 1204 1117 871 611 432 -
quality of life intervention 11002 6413 2187 1476 1241 653 225 - 14
quality of life positive psychology 707 292 182 - 139 - - - 2
positive interventionpositive emotions 7 - - - - - - - 0
positive interventionemotions 18 - - - - - - - 0
positive interventionlife satisfaction 4 - - - - - - - 0
positive interventionintervention 103 - - - - - - - 5
positive interventionpositive psychology 6 - - - - - - - 0
positive emotionsemotions 2405 1407 0 0 499 265 0 0 3
positive emotionslife satisfaction 251 - - - - - - - 6
positive emotionsintervention 457 169 0 0 43 - 0 0 12
positive emotionspositive psychology 721 296 0 0 117 - 0 0 10
emotions life satisfaction 1224 698 149 - 248 - - - 4
emotions intervention 6895 3031 957 478 598 249 84 - 14
emotions positive psychology 1318 493 211 - 184 - - - 5
life satisfactionintervention 1869 1160 298 - 130 - - - 34
life satisfactionpositive psychology 610 297 224 - 122 - - - 5
intervention positive psychology 1104 350 161 - 170 - - - 16
Total 471436 278
Excluding Repeats 140
Positive Interventions 104
Table 3. Summary of studies included in the meta-analysis
Study N N Well-being Depression Quality Participants Length Description Target Type
Treatment Control d p d p (Patient / Non-patient) (Days)
Algoe, 2005 174 87 87 .051 .737 - - 5 Undergraduates
(Non-patient)
1 Roommates recruited and
randomly assigned to write
gratitude letter to roommate
or emotionally neutral topic
Gratitude B
Aylett, 2004 86 43 43 .344 .113 - - 3 Undergraduates
(Non-patient)
1 Undergraduates given
intervention focused on
identifying strengths and
goals and examining effects
on well-being
Hope and
Goals
C
Bédard et al.,
2003
13 10 3 .341 .606 .312 .637 4 Community sample -
individuals with mild
to moderate brain
injuries (Patient)
84 Individuals who
experienced traumatic brain
injuries were exposed to
meditation
Mindfulness C-B
Bennett &
Maas, 1988
26 13 13 2.979 .000 - - 4 Elderly Women,
Nursing Home
Residents
(Non-patient)
42 Elderly women residing in
nursing homes or hostels
randomly assigned to verbal
review or music life review
Reminiscence C
Bryant et al.,
2005
55 33 22 .650 .021 - - 6 Undergraduates
(Non-patient)
7 Students randomly assigned
to reminiscence sessions
twice a week over 1 week
period
Reminiscence C
Burton &
King, 2004
90 48 42 1.300 .000 - - 7 Undergraduates
(Non-patient)
3 Students wrote about
intensely positive
experiences for 3 days
Reminiscence C
Campbell &
Donovan,
2007
9 6 3 .310 .663 - - 3 Couples
(Non-patient)
14 Randomly assigned to
blessings exercise or waitlist
control. Participants in
intervention group shared
three good things with their
partner at end of each day
Gratitude C
Positive Interventions 105
Cheavens et
al., 2006
32 16 16 - - .730 .046 6 Community Sample
(Non-patient)
8 Individuals randomly
assigned to receive hope-
based, group therapy
protocol (eight-sessions
emphasizing building goal
pursuit skills)
Hope and
Goals
C-B
Cook, 1998 36 12 24 .830 .024 - - 4 Elderly Women,
Nursing Home
Residents over Age of
65 (Non-patient)
112 Elderly woman assigned to
a reminiscence condition of
positive and pleasant
experiences
Reminiscence C
Davis, 2004 14 7 7 .870 .120 2.750 .000 5 Individuals with
cerebral vascular
accidents
(Patient)
3 Individuals with right
hemisphere cerebral
vascular accidents were
administered life review
therapy
Reminiscence C
Dickerhoof et
al., 2007
332 222 110 .140 .230 -.120 .304 5 Undergraduates
(Non-patient)
56 Undergraduates allowed to
either sign up for a
happiness increasing study
or recruited from the subject
pool and randomly assigned
to either control or a
gratitude or optimism
intervention
Gratitude and
Optimism
B
Eells, 2006 130 52 78 .793 .000 - - 5 Undergraduates
(Non-patient)
1 Undergraduate couples
recruited and randomly
assigned to 1 of 3
expressive writing
conditions: trauma, falling
in love, or control
Reminiscence C
Elizabeth,
2006
60 30 30 5.000 .000 - - 4 Undergraduate
females
(Non-patient)
60 Individuals randomly
assigned to spiritual well-
being intervention based on
loving-kindness meditation
or control program
Mindfulness C-B
Positive Interventions 106
Emmons &
McCullough,
2003
Study 2
104 52 52 .588 .003 - - 6 Undergraduates
(Non-patient)
70 Undergraduates randomly
assigned to gratitude,
hassles, or social
comparison condition
Gratitude C
Emmons &
McCullough,
2003
Study 3
65 33 32 .632 .013 - - 4 Community sample
(Patient)
21 Participants from the UC
Davis Medical Center
mailing list mailed
questionnaires containing
either gratitude intervention
or control condition
Gratitude C
Fallot, 1979-
1980
30 15 15 .484 .191 .918 .017 4 Community
participants
(Non-patient)
3 Verbal reminiscing
compared with talking about
the present or future in
female participants
Reminiscence C
Fava et al.,
1998
20 10 10 .000 1.000 .668 .155 6 Remitted patients with
affective disorders
(Patient)
112 Effects of well-being
therapy
Therapy C-B
Fava et al.,
2005
16 8 8 1.000 .089 .820 .115 7 Outpatients with GAD
(Patient)
112 Randomly assigned to either
a protocol of CBT or CBT +
WBT
Therapy C-B
Fordyce,
1977
Study 1
154 94 60 .572 .001 - - 5 Undergraduates
(Non-patient)
14 Randomly assigned to 1 of 4
groups: education about
happiness, fundamentals
program, activities program
(engage in self-selected
happiness activities), or
control
14
Fundamentals
C-B
Fordyce,
1977
Study 2
68 39 29 .741 .003 - - 5 Undergraduates
(Non-patient)
42 Participants were assigned
by class to the 14
fundamentals program,
expanded form the "nifty
nine" of study 1, or
expectancy control
14
Fundamentals
C-B
Positive Interventions 107
Fordyce,
1983
Study 4
98 64 34 .424 .048 .309 .148 4 Undergraduates
(Non-patient)
77 Participants randomly
assigned by class to either
the 14 fundamentals
(consists of introductory
training and detailed
elaboration 14
fundamentals) or
information only control
14
Fundamentals
C-B
Fordyce,
1983
Study 5
71 50 21 .389 .138 .558 .035 4 Undergraduates
(Non-patient)
17 Randomly assigned to full
14 fundamentals program or
a control with instruction in
some fundamentals but not
the full program
14
Fundamentals
C-B
Freedman &
Enright, 1996
12 6 6 - - 1.204 .055 7 Community sample -
survivors of incest
(Patient)
100 Survivors of incest
randomly assigned to
forgiveness intervention
Forgiveness C-B
Frieswijk et
al., 2006
193 97 96 .265 .067 - - 5 Community-dwelling
elderly population
(Non-patient)
70 The effects of bibliotherapy
intervention designed
around self-management
ability of well-being
Cognitive-
Intervention
C
Goldwurm et
al., 2003
92 45 47 .450 .033 - - 3 Students of
psychotherapy -
psychologists and
medical doctors
(Non-patient)
180 Randomly assigned to either
Fordyce style subjective
well-being program or
general information control
14
Fundamentals
C-B
Green et al.,
2006
50 25 25 .808 .006 - - 6 Community sample
volunteers responding
to an ad for the "Coach
Yourself" program
(Non-patient)
70 Volunteers randomly
assigned to life coaching or
waitlist control
14
Fundamentals
C-B
Grossman et
al., 2007
52 39 13 .680 .038 .595 .068 5 Patients with
fibromyalgia
(Patient)
56 Randomly assigned to either
mindfulness-based stress
reduction or social support
Mindfulness C-B
Positive Interventions 108
control
Guse et al.,
2006
46 23 23 .495 .098 .670 .027 4 Expectant mothers
(Non-patient)
6 Pregnant women selected
into semi-randomized test of
a hypnotherapeutic program
aimed to increase strengths
Strengths B
Haight &
Bahr, 1984
12 6 6 2.051 .004 - - 4 Elderly Community
Members
(Non-patient)
30 Elderly participants
randomly assigned to either
life review therapy or
visitation and test on
measures of life satisfaction
Reminiscence C
Harp Scates
et al., 1985-
1986
34 17 17 .000 1.000 - - 5 Elderly Community
Members
(Non-patient)
21 Senior citizens randomly
assigned to cognitive-
behavioral group,
reminiscence treatment
group, or activity control
group
Reminiscence C
Harris et al.,
2006
259 134 125 .299 .017 - - 5 Community Sample
(Non-patient)
42 Adults who experienced a
hurtful interpersonal
transgression from were
randomized to forgiveness-
training program or no-
treatment control group
Forgiveness C-B
Hebl &
Enright, 2003
24 13 11 - - .704 .095 6 Community sample -
church members
(Non-patient)
56 Randomly assigned to group
forgiveness intervention or
control group
Forgiveness C-B
Hedgepeth &
Hale, 1983
60 30 30 - - .128 .620 3 Elderly Women from
community settings
(Non-patient)
3 Elderly females randomly
assigned to 1 of 3 groups:
positive reminiscing,
present experiences, or
control group.
Reminiscence C
King, 2001 79 41 38 .780 .001 - - 7 Undergraduates
(Non-patient)
4 Students randomly assigned
to write about their best
possible self or non-
Optimism C
Positive Interventions 109
emotional control topic
King &
Miner, 2000
118 57 61 .470 .012 - - 7 Undergraduates
(Non-patient)
3 Students randomly assigned
to write about perceived
benefits of events
Optimism C
Kremers et
al., 2006
142 63 79 .262 .122 - - 4 Single community-
dwelling women, age
55 or older
(Non-patient)
6 Single women randomly
assigned to either self-
management of well-being
intervention or control
group
Cognitive-
Intervention
C
Lichter et al.,
1980
Study 1
23 10 13 .684 .115 - - 5 Community
participants
(Non-patient)
28 Examining and
implementing pro-happy
beliefs
Cognitive-
Intervention
C
Lichter et al.,
1980
Study 2
48 25 23 .496 .091 .407 .163 5 Community
participants
(Non-patient)
14 Rehearsal of Positive
Feeling Statements
Cognitive-
Intervention
C
Lin et al.,
2004
14 7 7 - - 1.751 .005 7 Residential treatment
facility individuals
with substance abuse
(Patient)
42 Randomly assigned to either
forgiveness therapy or
alternative treatment
Forgiveness C-B
Low et al.,
2006
41 21 20 .185 .555 - - 7 Breast cancer patients
(Patient)
21 Effects of writing about
benefit finding in cancer
Optimism C
Lyubomirsky
et al., 2004
104 35 69 .360 .085 - - 5 Undergraduates
(Non-patient)
42 Effects of thinking, writing,
or talking about a positive
experience compared to
neutral condition
Kindness B
Lyubomirsky
et al., 2006
58 26 32 .016 .952 - - 5 Undergraduates
(Non-patient)
3 Randomly assigned to 6-
week acts of kindness
intervention
Reminiscence C
MacDonald
& Settin,
1978
20 10 10 .660 .151 - - 4 Community sample,
nursing home
residents
(Non-patient)
35 Effects of two psychosocial
treatments compared to a
control. One was a sheltered
workshop where individuals
Kindness B
Positive Interventions 110
created goods for others.
Mitchell et
al., 2007
27 11 16 .450 .256 .570 .153 4 Visitors to online
website
(Non-patient)
35 A website was designed to
deliver positive psychology
exercises online for
participants
Strengths B
Otake et al.,
2006
119 71 48 .407 .031 - - 4 Japanese community
sample, all female
(Non-patient)
7 Japanese women assigned to
either count acts of kindness
or control group; effects on
well-being were examined
Kindness C
Ruini et al.,
2006
111 57 54 .180 .344 -.290 .129 6 Middle school
students
(Non-patient)
4 One middle-school had 6
classes volunteer to
participate in a RCT of
well-being therapy versus
CBT principles in the
classroom
Therapy C-B
Savelkoul et
al., 2001
168 56 112 .130 .427 - - 5 Patients with chronic
rheumatic diseases
(Patient)
10 Randomly assigned to cope
actively with their problems
Coping C-B
Schwartz &
Sendor, 1999
72 5 67 .250 .590 .730 .118 3 Individuals with
disability
(Patient)
4 Participants recruited to
help individuals with the
same chronic disease they
have
Kindness B
Seligman et
al., 2006
Study 1
40 19 21 .420 .189 .530 .100 5 Undergraduates with
mild-to-moderate
depressive symptoms
(Patient)
42 Individuals with mild-to-
moderate depressive
symptoms randomly
assigned to a group PPT
versus no-treatment control
Therapy C-B
Seligman et
al., 2006
Study 2
28 13 15 .855 .032 1.265 .002 6 Clients from
University Counseling
Center (Patient)
84 Randomly assigned to
individual PPT, treatment as
usual, or TAU + medication
Therapy C-B
Sheldon &
Lyubomirsky,
2006
67 44 23 .340 .189 - - 7 Undergraduates
(Non-patient)
4 Students randomly assigned
to express gratitude or
visualize their best possible
Optimism C-B
Positive Interventions 111
self
Smith et al.,
1995
46 34 12 2.092 .000 1.998 .000 3 Undergraduates
(Non-patient)
42 Individuals assigned to
control group, personal
happiness enhancement
program, or PHEP plus
meditation
14
Fundamentals
C-B
Spence &
Grant, 2007
63 43 20 .610 .027 - - 6 Community
participants
(Non-patient)
70 Randomly assigned to
undergo either professional
coaching or peer coaching
based on the GROW Model
Coaching C-B
Stevens-
Ratchford,
1987
24 12 12 - - .605 .147 5 Elderly Adults from a
Retirement
Community
(Non-patient)
21 Older adults randomly
assigned to a reminiscence
intervention and control
Reminiscence C
Surway et al.,
2005
17 9 8 - - .575 .246 4 Patients with chronic
fatigue syndrome
(Patient)
56 Patients with Chronic
Fatigue Syndrome on a
waiting list for CBT were
randomly assigned into a
mindfulness based stress
reduction intervention
Reminiscence C
Tkach, 2006 285 191 94 .004 .975 - - 4 Undergraduates
(Non-patient)
63 Randomly assigned to
perform acts of kindness at
different variety and
frequency
Kindness B
Updegraff &
Suh, 2007
96 48 48 .290 .158 - - 6 Undergraduates
(Non-patient)
1 Instructed to take a concrete
or an abstract self-focused
mindset
Cognitive-
Intervention
C
Wing et al.,
2006
175 120 55 -.190 .244 - - 5 Undergraduates and
community members
(Non-patient)
3 Randomly assigned to write
about positive emotional
experiences or control
groups
Reminiscence C
Note: B = Behavioral, C = Cognitive, C-B = Cognitive Behavioral
Positive Interventions 112
Table 4. Average effect sizes with excluded Studies
Analyses
Random Effects
Variance Standard
Error
95% Confidence
Interval
Q-
value
df p I2
Combined
Effect
Size Lower Upper
Well-Being (43) .44 .001 .06 .33 .55 98.59 42 .000 57.40
Depression (17) .77 .02 .13 .51 1.03 28.99 16 .024 44.81
Note: The number of studies included for each variable is included in parentheses.
Table 5. Average effect sizes of positive interventions – by components of well-being
95% Confidence
Interval
Analyses
Random Effects
Combined
Effect
Size
Variance Standard
Error
Lower Upper
Q-
value
df p I2
Happiness (11) .71 .025 .158 .40 1.02 42.83 10 .000 76.65
Life Satisfaction (20) .44 .008 .089 .27 .62 35.42 19 .012 46.36
Positive Affect (16) .56 .009 .097 .37 .75 29.70 15 .013 49.49
Well-Being (7) .48 .012 .110 .26 .69 8.23 6 .222 27.12
Composite (6) .21 .004 .066 .08 .34 5.53 5 .354 9.65
Note: The number of studies included for each variable is included in parentheses.
Table 6. Moderation Analysis of Type of Sample, Well-Being Measures, N = 43
95% Confidence
Interval
Analyses
Random Effects
Combined
Effect
Size
Variance Standard
Error
Lower Upper
Q-
value
df p I2
Non-Patient Sample (36) .46 .004 .062 .34 .58 94.33 35 .000 62.90
Patient Sample (7) .29 .013 .114 .06 .51 3.74 6 .712 0.00
Note: The number of studies included for each variable is included in parentheses.
Table 7. Moderation Analysis of Type of Sample, Depression Measures, N = 17
95% Confidence
Interval
Analyses
Random Effects
Combined
Effect
Size
Variance Standard
Error
Lower Upper
Q-
value
df p I2
Non-Patient Sample (9) .71 .029 .171 .37 1.04 17.02 8 .030 52.99
Patient Sample (8) .89 .050 .223 .45 1.32 11.42 7 .121 38.69
Note: The number of studies included for each variable is included in parentheses.
Positive Interventions 113
Table 8. Moderation Analysis of Target of Intervention, Well-Being Measures, N = 43
95% Confidence
Interval
Analyses
Random Effects
Combined
Effect
Size
Variance Standard
Error
Lower Upper
Q-value df p I2
14 Fundamentals (5) .84 .044 .210 .43 1.25 14.14 4 .007 71.72
Coaching (1) .61 .077 .277 .07 1.15
Cognitive
Intervention (5)
.31 .008 .090 .13 .48 1.33 4 .856 0.00
Coping (1) .13 .027 .164 -.19 .45 - - - -
Forgiveness (1) .30 .016 .125 .05 .54 - - - -
Gratitude (5) .29 .016 .125 .05 .54 7.68 4 .104 47.95
Hope and Goals (1) .34 .047 .217 -.08 .77 - - - -
Kindness (5) .24 .013 .114 .01 .46 5.22 4 .26 23.41
Mindfulness (2) .61 .086 .293 .04 1.19 .21 1 .646 0.00
Optimism (3) .51 .022 .149 .22 .81 2.44 2 .295 18.02
Optimism and
Gratitude (1)
.34 .067 .259 -.17 .85 - - - -
Reminiscence (10) .59 .041 .203 .19 .99 43.75 9 .000 79.43
Strengths (2) .48 .057 .239 .01 .95 .01 1 .93 0.00
Positive Therapy (1) .42 .102 .320 -.21 1.05 - - - -
Note: The number of studies included for each variable is included in parentheses.
Table 9. Moderation Analysis of Target of Intervention, Depression Measures, N = 17
95% Confidence
Interval
Analyses
Random Effects
Combined
Effect
Size
Variance Standard
Error
Lower Upper
Q-
value
df p I2
14 Fundamentals (1) 2.00 .156 .395 1.22 2.77 - - - -
Cognitive
Intervention (1)
.41 .085 .292 -.16 .98 - - - -
Forgiveness (3) 1.07 .094 .306 .47 1.67 1.97 2 .373 0.00
Hope and Goals (1) .73 .133 .365 .01 1.45 - - - -
Kindness (1) .73 .219 .468 -.19 1.65 - - - -
Mindfulness (3) .55 .063 .252 .06 1.04 .15 2 .927 0.00
Reminiscence (4) .90 .173 .416 .08 1.71 12.30 3 .006 75.62
Strengths (2) .63 .058 .241 .16 1.11 .040 1 .842 0.00
Positive Therapy (1) .53 .104 .322 -.10 1.16 - - - -
Note: The number of studies included for each variable is included in parentheses.
Positive Interventions 114
Table 10. Moderation Analysis of Type of Intervention, Well-Being Measures, N = 43
95% Confidence
Interval
Analyses Combined
Effect
Size
Variance Standard
Error
Lower Upper
Q-value df p I2
Behavioral (8) .15 .004 .066 .02 .28 5.96 7 .544 0.000
Cognitive (23) .47 .007 .081 .31 .63 51.36 22 .000 57.16
Cognitive-
Behavioral (12)
.56 .012 .109 .34 .77 26.30 11 .006 58.17
Note: The number of studies included for each variable is included in parentheses.
Table 11. Moderation Analysis of Type of Intervention, Depression Measures, N = 17
95% Confidence
Interval
Analyses Combined
Effect
Size
Variance Standard
Error
Lower Upper
Q-value df p I2
Behavioral (3) .65 .046 .214 .23 1.07 .073 2 .964 0.00
Cognitive (5) .73 .088 .297 .14 1.31 12.51 4 .014 68.04
Cognitive-
Behavioral (9)
.90 .037 .191 .52 1.27 13.59 8 .093 41.14
Note: The number of studies included for each variable is included in parentheses.