building health behavior models to guide the...

Building health behavior models to guide the development of just-in-time adaptive

interventions: A pragmatic framework

Inbal Nahum-ShaniThe University of Michigan

Eric B. HeklerArizona State UniversityDonna Spruijt-Metz

The University of Southern California

Technical Report Number 15-131

Copyright 2015, The Pennsylvania State University

ALL RIGHTS RESERVED

Please send questions and comments to Inbal Nahum-Shani, [email protected] .

The suggested citation for this technical report is

Nahum-Shani, I., Hekler, E. B., & Spruijt-Metz, D. (2015). Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework (Technical Report No. 15-131). University Park, PA: The Methodology Center, Penn State.

This work was supported by Awards P50DA010075, R01 AA-014851, and U54 EB020404 the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

mailto:[email protected]

The Methodology Center Tech Report No. 15-131 Pragmatic Framework 2

Abstract

Advances in wireless devices and mobile technology offer many opportunities for delivering

just-in-time adaptive interventions (JITAIs)—suites of interventions that adapt over time to an

individual’s changing status and circumstances with the goal to address the individual’s need for

support, whenever this need arises. A major challenge confronting behavioral scientists aiming to

develop a JITAI concerns the selection and integration of existing empirical, theoretical and

practical evidence into a scientific model that can inform the construction of a JITAI and help

identify scientific gaps. The purpose of this paper is to establish a pragmatic framework that can

be used to organize existing empirical and theoretical evidence into a useful model for JITAI

construction. This framework involves clarifying the conceptual purpose of a JITAI, namely the

provision of just-in-time (JIT) support via adaptation, as well as describing the components of a

JITAI and articulating a list of concrete questions to guide the establishment of a useful model

for JITAI construction. The proposed framework includes an organizing scheme for translating

the relatively static scientific models underlying many health behavior interventions into a more

dynamic model that better incorporates the element of time. This framework will help to guide

the next generation of empirical work to support the creation of effective JITAIs.


Introduction

Advances in wireless devices and mobile technology offer many opportunities for

delivering interventions at any time, and in a way that accommodates an individual’s immediate

needs (Riley et al., 2011). The term “Just-In-Time Adaptive Interventions” (JITAIs) (Spruijt-

Metz & Nilsen, 2014) is used to describe a suite of interventions that adapt over time to an

individual’s changing status and circumstances, with the goal to address the individual’s need for

support, whenever this need arises. Recent advances in mobile technology and wearable sensors

make these interventions increasingly more feasible and acceptable. For example, mHealth

interventions attempting to provide timely support are being developed and evaluated for a wide

range of health issues and behaviors, such as physical activity (King et al. 2013; Consolvo et al.

2008), drug abuse (Dennis, Scott, Funk, & Nicholson, 2014), alcohol use (Witkiewitz et al.,

2014; Gustafson et al., 2014), smoking cessation (Riley, Obermayer, & Jean-Mary, 2008),

obesity/weight management (Patrick et al., 2009), and mental illnesses (Ben-Zeev et al., 2014).

Despite the increased use and appealing nature of JITAIs, a major gap exists between the

technological capacity to deliver JITAIs and existing health behavior models. Establishing a

scientific model is an important step in constructing behavioral interventions (Collins, Murphy,

& Bierman, 2004). Most behavioral interventions are developed based on scientific models that

articulate key risk and protective factors that are associated with the targeted health outcome.

These models are often used to construct interventions to address these key risk and protective

factors. However, most existing models largely emphasize and articulate static relationships,

focusing on risk and protective factors that change relatively slowly over time, such as

demographic factors, psychiatric diagnoses, and past high-risk behaviors (Spruijt-Metz, Nilsen,

& Pavel, 2014). JITAIs, on the other hand, provide support whenever such support is needed,


seeking to address risk and protective factors that are dynamic and likely to change (often

rapidly) over time, such as mood, location, social interactions and immediate crises in everyday

life (Csikszentmihalyi & Rathunde, 1993).

A major challenge confronting behavioral scientists aiming to develop a JITAI concerns

the selection and integration of existing evidence into a scientific model that can inform the

construction of a JITAI. While the need to develop more dynamic health behavior models has

been well-established (Riley et al., 2011), the current literature provides little guidance on the

structure and predictions needed from these models to scientifically inform the development of

efficacious JITAIs. The choice of scientific models to inform the development of a JITAI should

be guided partially by the requirements of the JITAI itself, such as providing insights not only on

how to intervene, but also when and when not to intervene.

The aim of this paper is to establish a pragmatic framework that can be used to organize

existing and new empirical and theoretical evidence into a useful model for JITAI construction.

The foundation for this framework is established by clarifying the conceptual purpose of a

JITAI, namely the provision of just-in-time (JIT) support via adaptation. After briefly reviewing

the key elements in operationalizing JITAIs (Nahum-Shani et al. 2014), we offer a list of

concrete questions to guide the process of establishing a useful model for JITAI construction.

We conclude by discussing opportunities for future research that can advance the science of

JITAIs. The hope is that the proposed framework will help guide the next generation of empirical

work to support the creation of effective JITAIs. Table 1 summarizes key terms and definitions.

What is a JITAI?

To clarify the conceptual purpose of JITAIs, we elaborate on the two key concepts that

distinguish these interventions from standard intervention designs: just-in-time and adaptive.


Just-In-Time (JIT). The concept JIT has long traditions in various fields. For example,

in industrial management and operation research JIT is a philosophy of manufacturing that seeks

to “produce the right item, at the right time, in the right quantities” (Canel, Rosen & Anderson,

2000, pp.52). It is based on a management plan that emphasizes continuous improvement and

identifies and then eliminates all “waste”— defined as anything that does not add value to the

product. In the field of education, the term JIT is rooted in instructional approaches that focus on

real-life tasks as the driving force for learning. Because these tasks and the real-life context in

which they are performed involve high cognitive load, these approaches emphasize the need to

take the limited human-processing capacity into account. Hence, strategies for scaffolding

include JIT support, meaning providing the type of support needed, precisely when needed, and

only when needed during task performance (see van Merrienboer, Kirschner, & Kester, 2003 for

review). Overall, the traditions above, as well as others (e.g., Drews et al., 2007; Frazier,

Spekman, & O’Neal, 1988; Karolak & Karolak, 1995), conceptualize JIT as the effective

provision of timely support, operationalized by offering the type of support needed, precisely

when needed, in a way that minimizes waste (i.e., defined as anything that does not benefit the

person) and accommodates the real-life setting in which support is needed.

We build on the traditions above to suggest that in the context of health behavior

interventions, the JIT approach is primarily motivated by the need to effectively assist people

whenever they are vulnerable and/or whenever opportunities for positive changes arise (Ben-

Zeev et al., 2014; King et al. 2013). Given that vulnerability and opportunity can occur anytime

in everyday life (Fletcher, Tobias, & Wisher, 2007; Witkiewitz & Marlatt, 2004), JIT support in

this setting can be operationalized by (a) identifying states of vulnerability or opportunity for

progress and providing the type of support needed in such states, precisely and only when


needed; as well as by (b) ensuring that the person is in a state of receptivity; that is, in a state

where s/he can receive, process and use the type of support needed.

State of vulnerability/opportunity. Stress-vulnerability and coping theories (Zubin &

Spring, 1977, Lazarus, 1993, Witkiewitz & Marlatt, 2004) conceptualize a vulnerable state as the

person’s transient tendency to experience adverse health outcomes or to engage in maladaptive

behaviors. A vulnerable state is a function of the interplay between relatively stable factors (e.g.,

personality traits, socio-econmic status, air polution) and more dynamic situational factors

ranging from relatively rare life events (e.g., unemployement), to more transient experiences

(e.g., conflict with a coworkers). Here, JIT support can be operationalized by identifying times in

which the person is vulnerable and providing the type of support needed, only when needed, in

order to break the link between the vulnerable state and adverse health outcomes.

With regard to opportunities for positive change, various learning and motivational

theories highlight the importance of concepts such as shaping (i.e., training by reinforcing

successively improving approximations of a desired behavior: Bouton, 2007; Ferster & Skinner,

1957) and teachable moments (i.e., a time when a person is more likely to internalize information

and take action; Fisher, Piazza, & Roane, 2011; Murimi et al., 2014; Leist & Kristofco, 1990).

The underlying assumption is that in order to facilitate improvement in some behavioral or

cognitive domain, it is important to identify transient oportunities for learning and improvement

and provide the type of support needed, only when needed in order to gradually move the

person’s actions/cognitions in the desired direction. Here, JIT support can be operationalized by

identifying real-life oppotunitites for change and immediately providing the type of support

needed to capitalize on these oportunities, only when such oportunities arise.

State of receptivity. To further minimize waste and accommodate the real-life setting in


which support is often needed, it is critical to ensure that the person is in a state where s/he is

receptive to the support needed. Integrating research in the area of supportive communication

and ubiquitous computing (e.g., Ford & Ellis, 1998; McIntosh, Seaton, & Jeffrey, 2007;

Resnicow, Baranowski, Ahluwalia, & Braithwaite, 1998; Sarker et al., 2014), we define a state

of receptivity as a restricted time interval in which the person can receive, process, and use the

type of support needed. A variety of facets can impact receptivity, and existing work can help

guide thinking about this concept.

The dual process theory of supportive communication outcomes (Burleson, 2009),

provides a logical foundation for understanding receptivity, suggesting that this construct is a

function of the interplay between two key elements. The first element-- the nature of support--

includes features such as the type of supportive content (which might be more or less demanding

in terms of reflective processing, depending on facets such as structure, length and complexity),

and the presence of cues (i.e., paraverbal and/or nonverbal aspects of support, that trigger

heuristics, associations, or sensations relevant to the situation, and hence are less cognitively

demanding: see also Evans, 2008; Castelo et al., 2012). The second element -- the recipient’s

ability and/or motivation to process the support provided -- can be influenced by relatively stable

characteristics (e.g., attachment style, locus of control, age, cognitive complexity, and working

memory capacity) and more dynamic/situational factors (e.g., the severity of problem, timing,

emotions, location and presence/absence of attention distracters) (Burleson, 2009).

To improve a person’s motivation and ability to process and use the type of support

needed, when needed, research in human-computer interaction articulates ways to improve the

overall usability and enjoyment of using mHealth and ubiquitous computing interventions. For

example, Consolvo, McDonald, and Landay (2009) generated eight design guidelines (e.g.,


abstract and reflective, unobtrusive, possible to be used in public) that provide heuristics for

improving receptivity to mHealth interventions by ensuring that support is designed to be

conducive in the moment it is needed. Other research has focused on how balance between

perceived usefulness/value and perceived burden influences one’s motivation to use the support

provided (e.g., Or & Karsh, 2009; Polonsky, Fisher, Hessler, & Edelman, 2014), as well as on

heuristics for simplifying supportive content in order to reduce burden (Fogg, 2009).

An important facet to consider with regard to receptivity is the ethics of intervening in a

real-life setting (Capron & Spruijt-Metz, 2014). Ethical considerations such as privacy,

confidentiality, safety and the general welfare of the recipient might lead to the decision not to

provide support even though it is needed (Kjeldskov, Skov, Als, & Høegh, 2004). For example,

when the person is driving a car, it might not be safe to deliver support; when s/he is in a

meeting, support can be disruptive; and when s/he is around other people, providing certain types

of support (e.g., feedback) might jeopardize the person’s privacy (De Costa et al., 2010).

Beyond this, a variety of other theories and empirical evidence can help explain

receptivity. Although a full discussion of these is beyond the scope of the current manuscript (see

King, Currie, & Petersen, 2014; Yatchmenoff, 2005; Staudt, 2007; Naughton, Jamison, & Sutton,

2013), this line of research builds the foundation for future research aiming to identify times at

which a person might be more or less receptive to specific types of support.

Adaptation. The discussion above suggests that in the context of health behavior

interventions, JIT support can be operationalized by offering the right type of support only when

the person is (a) vulnerable or open to positive changes, and (b) receptive to the support needed.

This requires a strategy for adapting the type (or dose/modality) and timing of support.

The distinctions between targeted, tailored, personalized, and adaptive interventions are


important ones, yet the terms are often used interchangeably in research literature. Moreover, the

same terminology often captures different meanings in different fields1. To standardize the

terminology, we use the term individualization to capture the use of information from the

individual to make decisions about when, where and how to intervene. Additionally, we

distinguish between individualization that is static, where relatively stable information from the

person (e.g., gender, baseline severity of symptoms) is used to make intervention-related

decisions (e.g., to offer intervention package A or B); and dynamic, where time-varying

information from the person (e.g., changes in psychological distress, response to an intervention,

intervention adherence) is used to make intervention decisions repeatedly in the course of the

intervention (e.g., changing the type, dosage, or timing of intervention delivery). The term

adaptive is used to describe this dynamic form of individualization (Collins et al., 2004).

Building on the above terminology, we conceptualize the JITAI as an intervention design

that uses a dynamic form of individualization to operationalize the provision of JIT support.

Specifically, JITAIs operationalize the individualization of the selection and delivery of

intervention options based on ongoing assessments of the individual’s state and ecological

context, with the goal to offer the right type of support precisely when, and only when, the

person is in a state of vulnerability/opportunity and receptivity.

JITAIs become increasingly possible with the growing availability of technology such as

wearable and ubiquitous computing sensors (e.g., wearable activity monitors, smartwatches,

home automation and tracking systems such as a smart thermometers), mobile-phone-based

sensing (e.g., accelerometry, GPS, light sensors, microphones), digital footprints (e.g., social

1 For example, the term “personalized” had different meanings in medical research (i.e., the use of diagnostic tools to identify specific biological markers to help determine which medical treatments will be best for each patient; PMC: Personalized Medicine Coalition) compared to health communication research, (i.e., describing the use of a person's name to draw attention to an otherwise generic message; Kreuter, Strecher & Glassman, 1999).


media interactions, email, digital calendars), and low-effort self-reporting (e.g., ecological

momentary assessment [EMA] and more advanced low-burden opportunities available via

technologies like smartwatches). The portability and pervasive nature of these devices make it

possible to monitor the individual anytime and to identify states of vulnerability/opportunity and

receptivity at any given moment (Hekler, Klasnja, Traver, & Hendriks, 2013).

Elements of a JITAI

With the conceptual purpose of a JITAI established, we now turn to describing the

elements of a JITAI, to help ground our pragmatic framework. A JITAI includes 6 key elements:

a distal outcome, proximal outcomes, decision points, intervention options, tailoring variables,

and decision rules (Nahum-Shani et al., 2014). The distal outcome is the ultimate goal the JITAI

is intended to achieve. The proximal outcomes are the short-term goals the intervention is

intended to achieve, and are often mediators of the distal outcome. Decision points are times at

which an intervention option is selected based on currently available information (e.g., at 2pm,

every 3 minutes). Intervention options are the array of possible type/dose/timing of support that

might be employed at any given decision point. Tailoring variables are baseline and time-varying

information that informs which intervention option to offer at each decision point (e.g., levels of

urge, location, daily drinking). Finally, decision rules are used to operationalize the

individualization by specifying which intervention option to offer to whom and when.

---------------Figure 1

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For example, consider JITAI #1 in Figure 1, where the decision rule is designed to reduce

prolonged sitting (distal outcome) among office workers, by encouraging them to take active

breaks (proximal outcome). There is a decision point every 5 minutes; the tailoring variable is

the current bout of accumulated uninterrupted computer activity; the intervention options are


either to recommend movement, or to provide nothing; and the decision rule links information

from the individual (tailoring variable) to specific intervention options, by specifying 30 minutes

as the cut-point of the tailoring variable that determines whether the individual should be offered

either a recommendation or nothing.

To summarize, a JITAI is an intervention design that employs dynamic individualization

(i.e., adaptation) to facilitate the provision of JIT support. To construct JITAIs, it is important to

clearly understand and articulate the key elements, namely the distal outcome, the proximal

outcome(s), decision point(s), tailoring variable(s), intervention options, and decision rule(s).

However, such understanding is well-beyond current behavioral theories and empirical evidence.

The technology-science gap

Current health behavior theories and related empirical evidence paint a largely static

picture of human behavior, cognition and emotions; they fail to capture the dynamic processes

underlying the emergence of a vulnerable state or the adoption and maintenance of healthy

behaviors (Spruijt-Metz et al., under review). Even dynamic models that acknowledge the role of

episodic factors in health behavior processes (e.g., the dynamic model of relapse; Witkiewitz &

Marlatt, 2004) do not specify the temporal nature of each factor in a way that informs when and

how to intervene. Although many health behavioral models acknowledge individual differences

in response to treatment, in most cases these models can only inform the most basic form of

individualization (i.e., they use single time point factors like age, gender, or baseline symptom

severity to make intervention decisions) rather than the dynamic individualization required to

operationalize JIT support. Finally, existing intervention models often adopt a one-size-fits-all

approach to intervention provision (Drotar & Lemanek, 2001; Marcus et al., 2000; Sorensen,

Emmons, Hunt, & Johnston, 1998), failing to provide actionable insights with regard to the


various elements of individualization articulated above. A major step in building the theoretical

and empirical foundation for creating effective JITAIs is to articulate a pragmatic framework

guided by specific questions that must be answered to create a JITAI. These specific questions

can help develop and refine existing theories and to inform prioritization of future studies aiming

to construct JITAIs. Below, we offer a set of pragmatic questions to guide the process of

establishing a scientific model that can inform the construction of JITAIs.

New pragmatic framework

To build high quality JITAIs, theoretical and empirical evidence should be organized into

a scientific model that can help investigators identify and devise a plan to address states of

vulnerability/ opportunity and receptivity. The following questions can be used to facilitate the

development of such a model. We recommend that investigators first address these questions in

the suggested order, and then refine the model by addressing them again in reverse order.

Implied here is the acknowledgement that the development of a scientific model is an iterative

process that will likely benefit from examining the problem from multiple perspectives. For

illustration, we will use a hypothetical example in which an investigator wishes to build an

intervention for full-time employees between the ages of 30-65 who exhibit hazardous drinking2.

(1) Who are you trying to help? A logical foundation for constructing scientific models is to

identify a target population and, if possible, articulate key attributes of the target population that

should be considered in the context of support provision (e.g., employed individuals might be

unable to engage in interventions that require more than a few minutes during the work day).

This step can be aided by using formative work, such as clinical experience with the target

population and evidence based on user-centered design methods (e.g., interviews, observation,

2 Hazardous (or “at-risk”) drinking is defined as drinking above the National Institute on Alcohol Abuse and Alcoholism low risk guidelines: ≤14 drinks per week and ≤ 4 drinks per occasion for men, and ≤ 7 drinks per week and ≤ 3 drinks per occasion for women.


prototyping to elicit opinions; Buxton, 2010; Rogers, Sharp, & Preece, 2012).

(2) What is the distal outcome of the JITAI? This step involves articulating a clinically

meaningful goal. In our hypothetical example, a distal outcome might be to transition employees

who engage in hazardous drinking to non-hazardous drinking patterns in the course of a year.

(3) What factors impact the distal outcome? This step involves using available theories and

empirical evidence to identify key factors (e.g., direct predictors, mediators, and moderators) that

likely influence the distal outcome and to articulate their general structure. The goal is to

establish an initial, relatively static model that can be further developed in more dynamic

theorizing. Returning to our illustrative example, the generic model in Figure 2 builds on

evidence in the area of employee drinking (e.g., Bamberger & Bacharach, 2014; Bacharach,

Bamberger, & Doveh, 2008; Wang, Liu, Zhan, & Shi, 2010) to suggest a process wherein work-

related stressors (stress exposure) increase hazardous drinking by generating psychological

distress (i.e., discomforting mental state experienced in response to stress exposure; Kessler,

1979). This process is expected to attenuate to the extent that the employee is characterized by

high coping capacity (i.e., an overarching construct capturing one’s ability to effectively invest

cognitive/behavioral efforts to manage external and/or internal demands; Lazarus, 1993).

---------------Figure 2

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(4) What is the temporal progression of the key factors towards the distal outcome? This

involves translating the relatively static model established above into a more dynamic model that

better incorporates the element of time into the theorizing. Because this is a unique and essential

step in the process of establishing a useful model for constructing a JITAI, we describe this step

in great detail, providing an organizing scheme.

Our organizing scheme requires scientists to specify the temporal progression of key


variables towards the distal outcome. The term temporal progression refers to the way in which

the process leading to the distal outcome unfolds over time and what role each key factor plays

in this process. Note that progression is not always linear or straightforward3. The task of

describing the temporal progression can be simplified by using time scales to organize the key

factors and their effects (Spruijt-Metz et al., under review).

Building on theories in organizational research (Zaheer, Albert & Zaheer, 1999), we

define a time scale as the size of the temporal intervals used to build or test a theory about a

process, pattern, phenomenon, or event. Time scales partition the temporal continuum into units,

and can be conceptualized as levels in a hierarchy, with lower levels representing shorter time

scales, and higher levels representing longer time scales. For example, hours can be

conceptualized as lower level units nested within a day, and as a higher level unit within which

minutes are nested. The same phenomenon can be associated with several types of time scales.

Moreover, the meaning of the phenomenon and the relationships between factors might vary

depending on the time scale (Zaheer et al., 1999).

In Figure 3 we offer an approach for using time scales to organize existing evidence in a

way that can describe the progression towards the distal outcome and enable investigators to

identify open scientific questions that require further investigation. This approach involves (a)

identifying different time scales, within which the process leading to the distal outcome might

unfold; and (b) specifying what aspects of the process are likely to occur within each time scale,

using key factors and effects at a lower level (i.e., shorter) time scale. For example, for a monthly

time scale, investigators can specify what aspects of the process leading to the distal outcome are

3 For example, chronic disorders like depression and substance use follow a waxing and waning course, involving periods of remission followed by symptom recurrence and exacerbation or relapse (and vice versa). Similarly, learning a new skill is not a linear process. “The learning brain naturally assimilates concepts in a spiraling, progressive manner” (Hamid, 2001: 315), and the pace of learning is parabolic in nature, with relatively slow early progress followed by more rapid learning (Case & Gunstone, 2002).


likely to occur in the course of a month, using factors and effects that occur weekly or biweekly.

Within each selected time scale, the focus should be on describing how key factors and

effects are ordered and related over time, namely the dynamics expected within each time scale.

To facilitate this, we organize the time scale hierarchy as a pyramid. This structure can aid in

describing (a) how factors and effects are ordered and related within each time scale; (b) how

factors and effects accumulate over time from lower level (e.g., minutes) to higher level units

(e.g., hours); and (c) how factors and effects that occur at a higher level (e.g., a year) can affect

variables and effects that occur later, at a lower level (e.g., a month).

Returning to our illustrative example, the hypothetical model in Figure 3 builds on

theoretical and empirical evidence on stress, coping, and employee drinking to specify the

expected dynamics within an hour, day, week, month and a year4. As very limited empirical

evidence exists concerning the ordering of stressors, psychological distress, and drinking

behaviors (Armeli, Todd, & Mohr, 2005), this model relies heavily on existing theories and

logic. Still, this exercise, by design, is meant to facilitate better understanding of existing gaps in

the literature. For simplicity, the process in Figure 3 begins with hours as the higher level units,

seeking to describe aspects of the process that unfold between minutes (or several minutes)5. We

then move up the time scale from hours to days, weeks, months and years (where a year is the

unit of the distal outcome), seeking to specify the expected dynamics within each time scale.

---------------Figure 3

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For example, sub-model #1 in Figure 3 seeks to describe the expected dynamics within

an hour and between minutes (or several minutes). Here we build on research suggesting that

4 For illustrative purposes we opted to specify a relatively simple dynamic model; however more complex models can be specified. 5 Neurobiological stress reactivity can occur within faster time scales, such as seconds (Obradović et al., 2010).


work-related hassles (e.g., losing things, traffic jams, arguments, disappointments, and workload;

see Kanner, Coyne, Schaefer, & Lazarus, 1981) can occur at any given minute and generate

rather immediate psychological distress (de Andrade, Viana, Abrão, Bittencourt, & Céspedes,

2014; Sinha et al., 2003). This immediate stress reaction can be attenuated to the extent that the

person’s state coping capacity is high (i.e., the extent to which, at a given time point, the person

is able to employ adaptive strategies to manage psychological stress; Gil., 2005; Gil & Caspi,

2006; Lazarus, 1993). State coping capacity is a function of situational variables (which are

important but for simplicity are not captured in this model), but also a function of the trait coping

capacity of the person (Roesch et al., 2010)--defined as a variety of more stable attributes (e.g.,

hardiness, self-esteem, dispositional optimism) that contribute to the management of stress

(Hobfoll, 1989; Kessler et al., 1985).

Second, sub-model #2 seeks to describe aspects of the process leading to employee

drinking that unfold over a day, using hours as the unit for variables and effects. Here, a cyclical

process is expected, whereby hassles that occur or accumulate over an hour lead to increased

psychological distress in the following hour or few hours (e.g., via perseverative cognition;

Brosschot, Gerin, & Thayer, 2006; Brosschot, VanDijk, & Thayer, 2007). This in turn reduces

the person’s hourly (state) capacity to cope with future stressors (Bar-Tal, Cohen-Mansfield, &

Golander, 1998), which leads to increased subsequent stress exposure (i.e., stress generation

hypothesis; Liu & Alloy, 2010) and stress-reactivity. This model also predicts that psychological

distress accumulating during work hours (i.e., over a work day) might lead to drinking behaviors

at the end of a work day to alleviate distress (Conger, 1956; see also Mohr et al., 2001; Tennen,

Affleck, Armeli, & Carney, 2000), with this process likely to attenuate to the extent that the

employee is characterized by high (Hedeker, Flay, & Petraitis, 1996) trait coping capacity


(Armelli et al., 2000; Armeli, Todd, & Mohr, 2005).

In a similar manner, sub-model #3 seeks to describe aspects of the process leading to

employee drinking that unfold in the course of a week, using days as the unit for variables and

effects; sub-model #4 seeks to describe aspects of the process that unfold over a month, using

weeks as the unit for variables and effects; and sub-model #5 seeks to describe aspects of the

process that unfold over a year, using months as the unit for variables and effects. Note that in

the latter, it is expected that monthly distress, or distress accumulated over several months can

attenuate the trait coping capacity of the person. This is based on research suggesting that

sustained or repetitive exposure to stressors (i.e., chronic stress) may trigger maladaptive

changes in some individuals, producing a vulnerable phenotype that facilitates increased risk of

illness (Blugeot et al., 2011; de Kloet, Joels, & Holsboer, 2005). This illustrates how dynamics

that accumulate over a long period of time (e.g., months) can facilitate momentary vulnerability

(e.g., poor state coping capacity) by affecting more stable aspects of vulnerability (e.g., the more

generalized coping capacity of the person).

The pyramid structure and the depth of the boxes describing the factors in Figure 3 are

used to capture, in a simple manner, the general accumulation of variables and processes over

time. For example, minute-level psychological distress (in sub-model #1) can accumulate over an

hour, contributing to hourly psychological distress (in sub-model #2); and the cyclical stress-

reactivity process described in sub-model #2 (using hours as the unit) can accumulate over a

work day to contribute to daily psychological distress (in sub-model #3).

In sum, this illustrative example articulates how the use of time scales can help shift the

relatively static model structures currently guiding intervention research into more dynamic

model structures that better incorporate the role of time. The questions below clarify how this


dynamic model structure can be used to inform the development of a JITAI.

(5) What are contender proximal outcomes? Here, the goal is to select factors that can capture

intermediate progress towards the distal outcome (i.e., proximal outcomes). This is an important

step because the proximal outcomes and their time scale guide the selection of intervention

options and their adaptation. Moreover, for study purposes, the proximal outcomes provide

intermediate metrics of response or success in achieving progression from the current state (e.g.,

hazardous drinking) to the desired distal outcome (e.g., non-hazardous drinking patterns).

To select the proximal outcomes, investigators can build on the dynamic model structure

established in the previous step, beginning with the lowest level (shortest) time scale as a starting

point for this iterative process. Implicit to this suggestion is the acknowledgement that targeting

proximal outcomes at the shortest possible timescale might (a) facilitate early prevention of

“snowballing effects,” namely preventing a short-term, single occurrence (e.g., lapse) or

experience (e.g., negative mood) from escalating into an adverse health outcome (e.g., total

relapse; Bukowski, Laursen, & Hoza, 2010; Marlatt & George, 1984; Sapienza & Masten, 2011),

and/or (b) lead to improved scaffolding strategies that target short-term progress and goal

attainment (Drews et al., 2007; Roscoe & Chi, 2007).

In the context of our illustrative example, based on the model above, investigators might

begin selecting proximal outcomes by attending to possible outcomes at the minute level (e.g.,

minute-level distress), then possible outcomes at the hour level (e.g., hour-level coping capacity

in the form of hourly self-regulation of attention; Sonnentag, Binnewies & Ohly, 2012;

Cuningham, Brandon, & Frydenberg, 2002; or self-regulatory fatigue; Liu, Lanza, Vasilenko, &

Piper, 2013), then possible outcomes at the daily level (e.g., drinking behaviors at the end of the

work day) and so on. The ultimate goal is to select those proximal outcomes that in aggregate are


expected to have the most meaningful impact on the distal outcome, and that can be influenced

in a meaningful manner by providing support. A JITAI can target multiple proximal outcomes.

(6) What factors mark a state of vulnerability/opportunity with respect to each of the

candidate proximal outcomes? Here, the goal is to identify when and under what conditions the

person is likely to be more vulnerable to experiencing adverse outcomes, or is more open to

positive changes. Adverse outcomes and positive changes in this context are defined with respect

to each of the candidate proximal outcomes. Identifying factors that mark (i.e., capture) a state of

vulnerability/opportunity given the selected proximal outcomes has implications on the selection

of decision points in a JITAI (as well as on individualization, as we describe below).

In the context of our illustrative example, the model summarized in Figure 3 suggests that

exposure to hassles at any given minute might lead to immediate reactivity in the form of

psychological distress (a candidate proximal outcome). Hence, minute-level exposure can mark a

state of vulnerability to immediate psychological distress. In this setting, selecting minute-level

distress as a proximal outcome implies that there might be a decision point at every given

minute, because the person might be in a state of vulnerability at any given minute. Additionally,

hourly psychological distress is expected to negatively impact the person’s hourly coping

capacity (a candidate proximal outcome). In other words, high levels of psychological distress in

a given hour can mark a state of vulnerability for reduced coping capacity in the subsequent

hour. In this setting, selecting hour-level coping capacity as a proximal outcome implies that

there might be a decision point at least at every given hour.

(7) What possible intervention options can affect the candidate proximal outcomes? Here,

the focus is on identifying intervention options that (a) can affect the candidate proximal

outcomes and (b) can be delivered in a JIT manner (i.e., precisely when the person is an a state of


opportunity/vulnerability vis-à-vis the proximal outcome).

An important part of this process is to explore whether and what intervention options

might be feasible and effective given the timescale of each candidate proximal outcome. To

explain this idea, consider our illustrative example. With respect to the lowest level proximal

outcome (i.e., distress at the minute level), given the immediacy and automaticity of

physiological reactivity to stress (Allen, Blascovich, Tomaka, & Kelsey, 1991; Smeets et al.,

2012; Sinha et al., 2003), it might not be feasible to intervene in a JIT manner (i.e., precisely

when the person is vulnerable) to attenuate such relatively immediate response to distress.

However, in such a setting, other forms of support (e.g., training sessions designed to help the

person learn adaptive coping skills), might be more suitable for improving this proximal outcome

although such support is not offered precisely, and only upon exposure to hassles (see Lindquist

& Cooper, 1999; Cunningham, Brandon, & Frydenberg, 2002).

With respect to the hour-level candidate proximal outcome (i.e., state coping capacity),

empirical evidence suggests that brief interventions in the form of recommendations/ suggestions

that integrate elements of cognitive behavioral therapy (CBT) and/or acceptance and

commitment therapy (ACT) can be delivered in a timely manner to help individuals regulate

hourly or daily negative emotions (e.g., Witkiewitz et al., 2014; Norberg et al., 2013; Proudfoot

et al., 2013; Morris et al., 2010) and break the link between hour-level distress and reduced

coping capacity. In the absence of empirically based intervention options that can be delivered

JIT (i.e., precisely when, and only when the person is in a state of vulnerability/ opportunity) to

influence a proximal outcome, investigators can either direct research efforts towards building

and evaluating new intervention options or attend to other candidate proximal outcomes (e.g.,

that change on a longer time scale, such as drinking behaviors at the end of a work day).


Finally, given that in a JIT setting, support should be provided only when the person is

(a) in a state of vulnerability/opportunity; and (b) receptive, it is important to include a “provide

nothing” intervention option, to minimize waste, intrusiveness, and burden (Nahum-Shani et al.,

2014). In the current example, assume that two intervention options are considered: (1)

recommendation to engage in emotion-regulation techniques building on principals of CBT

and/or ACT (Yovel, Mor, & Shakarov, 2014); and (2) provide nothing.

(8) What tailoring variables can be used to decide what intervention option to offer? Here

the goal is to identify the type of information from the individual that can be used to decide

whether at a given time point providing one of the selected intervention options over another will

lead to better proximal outcome(s). The selected tailoring variables should include factors that

mark a state of vulnerability/opportunity with respect to the selected proximal outcome, as well

as factors that mark a state of receptivity to the selected intervention options. Throughout,

practical implications related to the feasibility of properly measuring the tailoring variables at the

selected time scale should be taken into account (see Kumar at el., 2013 for more details).

In the context of our example, assume that the investigator selected hour-level coping

capacity as the proximal outcome. The dynamic model in Figure 3 suggests that hourly

psychological distress can mark a state of vulnerability for reduced hour-level coping capacity.

This implies that information about the level of psychological distress might be useful in

identifying times at which the employee would benefit from a recommendation to engage in

emotion-regulation techniques, compared to providing nothing. In other words, information

about hourly psychological distress might be used to identify time points at which offering a

recommendation would lead to a better proximal outcome than offering nothing.

Additionally, factors that mark a state of receptivity to the selected intervention options


should be considered as tailoring variables. In our example, certain factors might be obvious. For

example, consider a situation where wearable sensors indicate that a person experienced high

psychological distress in the past hour, but s/he is in a meeting and hence cannot process the

recommendation. In such cases, the “provide nothing” intervention option should be used given

that offering a recommendation might be disruptive and hence negatively affect the proximal

outcome. Other candidate tailoring variables might require further investigation. For example,

the number of recommendations the person received previously might lead to burden, and

extreme levels of negative affect might hinder the ability to use emotion-regulation techniques.

Additional research attention should be given to identifying tailoring variables that mark a state

of receptivity to immediate recommendations and feedback.

(9) For each possible level of the tailoring variables, which intervention option is likely to

have the desired effect on the proximal outcome? Here, the goal is to think through the

implications (benefits as well as costs) of delivering each of the selected intervention options at

various levels of the tailoring variables. In the context of our illustrative example, it is expected

that individuals who experienced high psychological distress in the past hour are likely to benefit

from a recommendation compared to providing nothing. However, there is insufficient empirical

evidence to identify the cut-point of psychological distress that differentiates when such

recommendation is indeed beneficial versus when this recommendation might not be necessary

(i.e., providing a recommendation will lead to similar or worse proximal outcome compared to

providing nothing). Further research is required to identify cut-points in various situations.

(10) What plausible decision rules can be generated to operationalize effective adaptation?

This final step involves the creation of appropriate decision rules that link all the information

above in a systematic manner. The decision rule in Figure 1 (JITAI #2) builds on some of the


ideas established above regarding our illustrative example to operationalize a JITAI aiming to

transition employees from hazardous to non-hazardous drinking patterns over one year.

Discussion

This manuscript provides a pragmatic framework for organizing empirical and theoretical

evidence into a scientific model that can guide JITAI construction. This framework builds on the

idea that in order to inform the construction of a JITAI, existing evidence should be organized in

a way that helps investigators identify and devise a plan to address states of vulnerability/

opportunity and receptivity to support. A key element in this process is using time scales to

describe the progression of key factors towards the distal outcome. However, extant empirical

evidence often does not provide insight into how key factors are ordered and related over time.

For example, the dynamic model in Figure 3, aimed to describe the expected progression

of stress, distress, and coping towards hazardous drinking over a year (the distal outcome), builds

primarily on existing theoretical approaches and logic. This is because, as noted by Tennen and

colleagues (Tennen et al., 2000: 626), “The gap between theory and research in the study of

stress, coping, and psychological adaptation has become an abyss. Whereas theoreticians develop

increasingly elegant formulations of temporally unfolding adaptational processes …

investigators continue to rely primarily on cross-sectional assessments.” This gap between

existing theories and empirical evidence makes identifying time scales and describing the

dynamics within each time scale rather challenging. Research attention should be given to

implementing study designs and analytic methods that directly inform how and why distal

outcomes unfold over time.

Studies that involve the collection of intensive longitudinal data can be highly useful in

this setting (Bolger & Laurenceau, 2013). For example, study designs involving EMAs offer


many opportunities for investigating dynamics within different time scales. These studies use

immediate reporting of experiences in the everyday life of individuals, thereby achieving a high

level of data accuracy, ecological validity and representative design (Stone et al. 2007; Kaplan &

Stone, 2013). Recent advances in mobile and wearable sensors provides many opportunities for

monitoring momentary experiences with higher precision, improved sampling frequency, fewer

missing data, and reduced burden on the participant (Kumar et al., 2013).

Perhaps the biggest challenge relating to such study designs is the volume of information

they produce. As noted by Kaplan and Stone (2013), “Most psychologists were trained to use

statistical inference techniques designed for the study of agronomy in the 1930s. Although these

methods have become much more sophisticated, inferential statistics can be meaningless for data

sets composed of hundreds of millions of data points.” Contemporary approaches involving data

mining, machine learning, control systems engineering techniques, and other modern analytic

methods are needed to allow investigators capitalize on intensive longitudinal data to identify

factors (or combinations of factors) that mark a state of vulnerability/opportunity and receptivity

(Plarre et al., 2011). Novel approaches to determining reliability and validity in the context of

intensive longitudinal data are also warranted (see detailed discussion in Kumar et al., 2013).

Further, even if empirical evidence suggests that a given factor (e.g., psychological

distress) marks state of vulnerability to a specific proximal outcome (e.g., it is highly predictive

of poor state coping capacity), there is often insufficient empirical evidence concerning the cut-

point of this factor that can inform the selection of one intervention option over another.

Intensive longitudinal research designs that allow direct comparison of selected intervention

options under different levels of candidate tailoring variables are needed. For example, to

identify the levels of hourly psychological distress at which a recommendation will be more


beneficial than the alternative, an ideal study design would allow investigators to assess the

causal effect of providing a recommendation compared to the alternative on the proximal

outcome under different levels of hourly psychological distress.

Concerning receptivity, although it is clear that this multi-faceted feature plays an

important role in intervention effectiveness, very limited behavioral research has been devoted to

identifying markers of receptivity, and we have no robust models that can clarify how receptivity

can shift as a function of continued exposure to specific types of support. While insights can

likely be gleaned from the field of human-computer interaction, which focuses on ensuring

systems are useful, usable, and enjoyable (and thus provides insights on ensuring the messaging

is delivered in a way that facilitates receptivity), much more work is needed in this domain.

Future studies should be designed to allow the simultaneous investigation of various factors that

can potentially mark a state of receptivity (e.g., location, emotions, and presence of other people)

to a given type of support and investigate ways to adapt the type of support so as to foster

sustained use to counteract the law of attrition (Eysenbach, 2005). To do this, investigators

should consider collecting information following support provision, such as whether support was

received, whether it was used, attention to and attitudes about the support provided. Throughout,

ethical considerations should be reviewed and addressed, in light of the intervention options

under consideration and the population targeted.

In sum, the pragmatic framework proposed in this manuscript builds the foundation for

collecting the empirical evidence necessary for constructing high quality JITAIs. While it offers

ways to integrate and build on existing theories and traditions to inform the development of

JITAIs, it also clearly highlights the scientific questions and methodological gaps that must be

addressed in order to move the science of JITAIs forward.


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Figure 1. Examples of Decision Rules

JITAI #1: based on SitCoach; Dantzig, Geleijnse, & Halteren (2013)

Distal Outcome: Prolonged Sitting

Proximal Outcome: Active Breaks

Decision Point: Every 5 minutes

Decision Rule: If current bout of accumulated uninterrupted computer activity > 30 minutes

Then, Intervention option = {recommend movement}

Else if current accumulated uninterrupted computer activity ≤ 30 minutes

Then, Intervention option = {provide nothing}

JITAI #2: based on our illustrative example

Distal Outcome: transitioning from hazardous to non-hazardous drinking over 12 months.

Proximal Outcome: Hourly coping capacity

Timescale of the Decision Point: Every hour

Decision Rule: If psychological distress >=X; and

{Driving = NO, meeting = NO, around other people=NO,

and # of recommendations received since morning < Z}

Then, intervention option= recommend emotion-regulation

Else if psychological distress < X; or

{Driving=YES, or Meeting=YES, or around other people=YES, or # of

recommendations received since morning >= Z}

Then, intervention option= “provide nothing”

Tailoring Variable Intervention

Options

Cut-point of tailoring variable


Figure 2. Hypothetical Generic Model Describing Key Factors Leading to Drinking

Psychological distress

Hazardous Drinking

Coping capacity

Stressors

+

+


Figure 3. Hypothetical Dynamic Model of Employee Stress Reactivity, Coping and Drinking

Time scale

Sub-Model #5: What aspects of the process unfold over a year, between months?

Sub-Model #4: What aspects of the process unfold over a month, between weeks?

Sub-Model #3: What aspects of the process unfold over a week, between days?

Sub-Model #2: What aspects of the process unfold over a day, between hours?

Sub-Model #1: What aspects of the process unfold over an hour, between minutes?

Note. Dashed lines indicate moderating effects.

Coping capacity trait

Lower level (shorter time scales)

Higher Level (longer time scales)

(Relatively) stable aspects Time-varying aspects


Table 1. Key terms and definitions

Key term DefinitionJust-in-time A concept that captures the effective provision of timely support,

operationalized by offering the type of support needed, precisely when needed, in a way that seeks to minimize waste and accommodate the real-life setting in which support is often needed.

Individualization The use of information from the individual to select when, where and how to intervene.

Adaptation A dynamic form of individualization, whereby time-varying information from the person is used to select intervention options repeatedly over time.

Just-in-time adaptive interventions (JITAIs)

Intervention design aiming to address an individual’s need for support whenever such need arises, by adapting over time to the person’s changing status and circumstances. JITAIs operationalize the individualization of the selection and delivery of intervention options based on ongoing assessments of the individual’s state and ecological context. A JITAI includes 6 key elements: a distal outcome, proximal outcomes, decision points, intervention options, tailoring variables, and decision rules.

Distal outcome Ultimate goal the JITAI is intended to achieve.Proximal outcomes

Mechanisms whereby an intervention can affect the distal outcome. These are the short-term goals the intervention is intended to achieve, and in many cases are mediators of the distal outcome.

Decision points Points in time at which an intervention option is selected based on currently available information.

Tailoring variables

Baseline and time-varying information from the individual that is useful in selecting the type/dose/timing of support at each decision point.

Intervention options

Array of possible type/dose/timing of support that might be employed at any given decision point.

Decision rules A way to operationalize the individualization by specifying which intervention option to offer to whom and when (based on tailoring variables).

Temporal progression

A process capturing the way by which a distal outcome evolves or unfolds over time and the dynamic role key factors play in this process.

Time scale Size of the temporal intervals used to build or test a theory about a process, pattern, phenomenon, or event.

State of vulnerability

The person’s transient tendency to experience adverse health outcomes or to engage in maladaptive behaviors .

State of opportunity

Transient oportunities for learning and improvement

State of receptivity

A restricted time interval in which the person can receive, process, and use the type of support needed

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