The Society for Clinical Child and Adolescent Psychology (SCCAP):

Initiative for Dissemination of Evidence-based Treatments for Childhood and Adolescent

Mental Health Problems

With additional support from Florida International University and The Children’s Trust.

Keynote Evidence Based Interventions for Pediatric Medical Treatment Adherence

Michael A. Rapoff, Ph.D. Ralph L. Smith Professor of Pediatrics University of Kansas Medical Center

Disclosures

Dr. Rapoff is on the scientific advisory board of Adheris,

a company that works with pharmacy chains to promote

adherence to medications.

“Physicians like to succeed in their

treatment, and an essential ingredient for

that success is a patient’s cooperation”

Groopman, J. (2007). How doctors think (p. 45). Boston: Houghton Mifflin

Learning Objectives

1. Define adherence and types of nonadherence

2. Describe methods for assessing adherence

3. Document the incidence and consequences of

nonadherence to pediatric medical regimens

4. Describe measures of barriers to adherence and their

use in interventions

5. Describe adherence enhancement strategies

6. Review meta-analyses of interventions to improve

adherence to pediatric medical regimens

Adherence Definitions

“The extent to which a person’s behavior (in terms of taking medications, following diets, or executing lifestyle changes) coincides with medical or health advice.”

Haynes et al. (1979). Compliance in health care. Baltimore: The Johns Hopkins University Press.

“The extent to which a person’s behaviour – taking medication, following a diet, and/or executing lifestyle changes, corresponds with agreed recommendations from a health care provider.”

World Health Organization (2003). Adherence to long-term therapies: Evidence for action. Geneva, Switzerland.

Types of Medication Nonadherence

Types of Medication Nonadherence

• Not filling prescription

• Not (or delays in) refilling prescription

• Omitting doses

• Drug holidays (no doses for several concurrent days)

• “Toothbrush Effect” or “White-coat” Adherence (increased adherence around clinic visits)

• Overdosing or taking extra “make-up” doses

Adherence Measures

• Assays

• Observation

• Automated Devices

• Pill Counts

• Treatment Outcome

• Provider Estimates

• Patient Report

• Rapoff, 2010

Assays

• Assets

– Adjust Drug Levels

– Objective and

Quantifiable

• Liabilities

– Pharmacokinetics may

affect absorption and

excretion rates

– Short-term & Invasive

Observation

• Assets

– Direct measure of non-

medication regimen

adherence

– Can measure

adherence on

repeated occasions

• Liabilities

– Obtrusive and reactive

– Difficult to obtain

representative

samples

Automated Measures

• Assets

– Precise dosing and

dosing interval data

obtained

– Continuous and long-

term measurement

possible

• Liabilities

– Does not measure

consumption

– Mechanical failures

Medication Event Monitoring System

(MEMS)

MEMS

• Records date and time cap removed

• 18-month battery life

• Stores up to 3800 events

• Software to download data

• Cost is ~ $185 per device

• http://www.aardexgroup.com

SmartTrack Compatible Medications:

GSK Advair/Seretide Dose Counter

GSK Flovent Dose Counter

GSK Flixotide No Dose Counter

GSK Serevent

Sepracor Xopenex HFA

Smartinhalers

• Record inhaler installed and removed and canister

actuation, date and time stamped

• 2-3 month between charges

• Reusable

• Have devices for disk and turbo inhalers

• Cost is $195 per device

• Software available to download data

• Nexus6: http://www.smartinhaler.com/

Pill Counts/Canister Weights

• Assets

– Inexpensive &

Feasible

– Superior to patient or

physician estimates

• Liabilities

– Overestimates

adherence

– Does not guarantee

medication taken

Treatment Outcome

• Assets

– Evaluate regimen

efficacy

– Clinically feasible

• Liabilities

– Inexact or unknown

relationship to

adherence

– Factors other than

patient adherence can

affect outcome

Physician Estimates

• Assets

– Clinically feasible

– Generally more

accurate than global

patient estimates

• Liabilities

– Overestimates

adherence

– Physician experience

or familiarity with

patient unrelated to

accuracy

Patient Report

• Assets

– Clinically feasible

– Generally accurate for

nonadherence

• Liabilities

– Overestimates

adherence

– Subject to reporting

bias – “faking good”

Improving Self-Reported Adherence

• Directly evaluate adherence behaviors in an information-

intensive approach (“Which medications are you taking?

What dose? How often? Have you had any side-

effects?”).

• Probe for nonadherence in a non-judgmental and non-

threatening manner (“Many people have trouble

remembering to take their medication. Do you ever

forget to take yours? Do you ever stop taking your

medication on purpose?”)

Improving Self-Reported Adherence

• Time frame for questioning about adherence

should be limited to the previous 7 to 10 days.

• Ask families about barriers to adherence

(personal, financial, social & cultural).

Rand (2000)

RX-Adherence Assessment

“AA” Rating = Automated plus Assay measure

Adherence to Inhaled Steroids in the

treatment of Asthma

• M = 69% (65% classified as nonadherent, <80%) by

electronic monitoring (Berg et al., 2007)

• M = 44% by canister weight (Celano et al., 1998)

• Underuse recorded on M = 55% of days by electronic

monitoring (Coutts et al., 1992)

• M = 48% by electronic monitoring (McQuaid et al., 2003)

• M = 51% by electronic monitoring (McQuaid et al., 2005)

• M = 46% by electronic monitoring (Walders et al., 2005)

• M = 77% @ 3-month f/u to M = 49% @ 27-month f/u by

dose counting (Jónasson et al., 2000)

Adherence to Prednisone in the treatment of

Cancer

• 52% had subtherapeutic levels by serum assay (Festa et

al., 1992)

• 42% had subtherapeutic levels by urine assay (Lansky et

al., 1983)

• 33% had subtherapeutic levels by urine assay (Smith et al.,

1979)

• 19% nonadherent (any missed dose in preceding month)

@ 2-weeks, 40% @ 20-weeks, & 35% @ 50-weeks by

patient and parent report (corroborated by serum assay) (Tebbi et al., 1986)

Adherence to Gluten-free Diet for Celiac

Disease

• 28% classified as nonadherent by pediatric

gastroenterologist (Anson et al., 1990)

• 46% classified as “occasional nonadherence” and 15%

as “frequent nonadherence” by dietician interview (Bazzigaluppi et al., 2006)

• 40% had “poor adherence” by serum anti-bodies and

clinical exam (Demir et al., 2005)

• 17% nonadherent by serum nitric oxide levels @ 1-yr f/u (Ertekin et al., 2005)

• 54% had “occasional lapses” by “clinical evaluation”

(patient and parent interview plus serum anti-bodies)

(Hartman et al., 2004)

• 29% nonadherent by serum anti-bodies (Kolaček et al., 2004)

Adherence to Antiretroviral Medications in

the treatment of HIV/AIDS

• 44% of caregiver-youth dyads reported missing doses in

the past week (Dolezal et al., 2003)

• M = 80.9% adherence rate during 1st 3 months & M =

78.5% during last 3 months by electronic monitoring (Martin et al., 2007)

• 40% of caregivers & 56% of patients reported missed

doses in the past month (Mellins et al., 2004)

• 43% of caregivers reported a missed dose in the

previous week (Reddington et al., 2000)

• 30% of caregivers or patients reported missing some or

all doses in the past 3 days (Van Dyke et al., 2002)

• 16% of caregivers or patients reported missing some

doses in the past 3 days (Williams et al., 2006)

Adherence to NSAIDS in the treatment of

JRA

• Baseline M = 86%; 3-mos f/u M = 92%; 6-mos f/u M =

90%; 9-mos f/u M = 92%; 12-mos f/u M = 89% by parent

report over past 3 months (Feldman et al., 2007)

• 3% nonadherent (<60% of doses) by pill counts (Giannini et

al., 1990)

• M = 95% by pill counts (Kvien & Reimers, 1983)

• 45% nonadherent by serum salicylate assay (Litt & Cuskey,

1981)

• 45% nonadherent by serum salicylate assay (Litt et al., 1982)

• Median levels showed partial or no adherence on 21% of

28 days; 48% nonadherent(<80% of doses) by electronic

monitoring (Rapoff et al., 2005)

Adherence to Immunosuppresive

Medications Post-renal Transplantation

• 43% nonadherent by pill counts (Beck et al., 1980)

• 21% nonadherent (<80% doses) by electronic monitoring (Blowey et al., 1997)

• 50% nonadherent by patient report plus serum assay (Ettenger et al., 1991)

• 16% nonadherent by patient report plus serum assay (Feinstein et al., 2005)

• M = 80% by electronic monitoring (Gerson et al., 2004)

• 14% nonadherent (missing medication ≥ 3 times a

month) by patient report (Penkower et al., 2003)

Consequences of Nonadherence

• Physicians unaware of nonadherence may order more invasive, risky, and costly procedures and may prescribe more potent meds with greater side-effects (Rapoff, 2010).

• More days with functional limitations and school absences; increased ER visits and hospitalizations; & increase in asthma-related deaths (Rapoff, 2010).

• 71% of nonadherent patients experienced rejection & had partial or

total loss of allograft function (Ettenger et al., 1991).

• Nonadherence associated with higher viral loads in HIV/AIDS (Martin et al., 2007; Reddington et al., 2000).

• Cost of nonadherence in U.S. estimated at $100 billion per year (Berg et al., 1993).

Barriers to Adherence

• Barriers defined: “the person’s perception of

impediments to adhere to treatments, including a cost-

benefit analysis where the person weighs the pros and

cons of taking action” (Rapoff, 2010).

• Most predictive variable from the Health Belief Model.

• Match unique barriers identified by patients and families

to specific protocols to address each barrier.

Measures of Barriers to Adherence

• Parents Barriers Questionnaire-Juvenile Arthritis

(JA) (Tsai, Matson, Rapoff, & Lindsley)

• Define barriers as “obstacles or things that get in

the way of you helping your child be consistent

in following medical treatments for arthritis.”

• Parents check “yes” for each barrier

experienced in the past week or “no” if it not

experienced.

Parents Barriers Questionnaire –

Juvenile Arthritis (sample items)

• “I just forget when to give my child medications.”

• “It is too hard to give my child medications when

we are not at home.”

• “The pills are too hard for my child to swallow.”

• “My child simply refuses to take the

medications.”

• “I am not sure that my child needs medication.”

• “I did not fill or refill my child’s prescription

because I could not afford them.”

Top Five Barriers identified by parents of

children with JA

• “My child says that the medication tastes bad.”

• “I just forget to give my child the medications.”

• “The pills are too hard for my child to swallow.”

• “I am not always there to remind my child to take

medications.”

• “My child feels physically worse when he/she

takes the pills."

Top Five Barriers identified by parents of

children with a chronic disease (Michele Tsai, in progress)

• Patient or parent forgets

• Patient dislikes medication taste

• Oppositional behavior

• Treatment interferes with daily activities

• Difficulty incorporating treatment regimen into

daily life

Burgess et al., (2008); De Civita et al., (2005); Greenley et al., (2010); Hommel &

Baldassano (2010); Ingerski et al., (2010); Modi & Quittner (2006); Modi et al., (2009);

Modi et al., (2010); Simons & Blount (2007)

Top Five Barriers identified by young people

with a chronic disease (Michele Tsai, in progress)

• Patient or parent forgets (overlap with parents)

• Treatment interferes with daily activities (overlap with

parents)

• Psychosocial adjustment difficulties

• Disagreement or communication problems with

health care provider

• Regimen too complex

April et al., (2006); Dziuban et al., (2010); Greenley et al., (2010); Logan et al., (2003);

Modi & Quittner (2006); Rhee et al., (2009); Modi et al., (2009); Simons & Blount (2007);

Zelikovsky et al., (2008).

Adherence Enhancement Strategies

• Educational (about disease, treatments, and importance

of adherence)

• Organizational (delivering health care in a way that

facilitates adherence)

• Behavioral (cognitive and behavior change strategies to

enhance adherence)

The What of Education (Content)

• The Disease (causes, course & prognosis)

• Treatments (what to do and why)

• Negative Side Effects (how to minimize)

• Adherence (importance and improvement strategies)

The How of Education (Strategies)

• As an ongoing process

• Effective verbal communication (avoid jargon, stress

instructions, repeat info., encourage questions)

• Written & other media

• Modeling and behavioral rehearsal

Organizational Strategies

• Increase access to health care

• Consumer-friendly clinical settings

• Increase provider supervision

• Simplify regimens

• Minimize negative side effects

Behavioral Strategies

• Increased parental monitoring & supervision

• Prompting adherence

• Adherence incentives

• Discipline strategies

• Contracting

• Self-management strategies (goal setting, self-

monitoring, self-administered consequences, problem-

solving, & cognitive reframing)

• More complex family behavior therapy interventions

Meta-Analyses of Adherence Interventions

• Quantitative syntheses of studies reporting on

interventions to improve adherence to regimens

for chronic pediatric diseases.

• Report effect sizes (ES), the magnitude of

treatment effects as measured by:

d, the difference between the means (M1 – M2) divided by the pooled

standard deviation. Let M1 = experimental group mean and M2 = the

control group mean, so that the difference is positive if it is in the

direction of improved adherence (predicted direction). (Cohen, 1988)

d, for single subject designs uses baseline and treatment mean scores and they are subtracted and divided by the pooled within-phase standard deviations

Effect Size Interpretations

If d = 0.0, the distribution of scores for the experimental

group overlaps completely with the distribution of scores

for the control group. Cohen (1988) classified d as:

“Small”, d = 0.2 (14.7% nonoverlap)

“Medium”, d = 0.5 (33% nonoverlap)

“Large”, d = 0.8 (47.4% nonoverlap)

http://web.uccs.edu/lbecker/Psy590/es.htm (information about effect size and effect-size

calculators-University of Colorado at Colorado Springs)

Effects Size Examples: r = .10 [small]; .30

[medium]; .50 [large]

• Low dose aspirin & ↓ risk of heart attack

= .02 (N = 22,071; 44% reduction in risk)

• Antihypertensives & ↓ risk of stroke

= .03

• Calcium intake & bone mass in premenopausal women

= .08

• Ever smoking and subsequent incidence of lung cancer within 25 yrs.

= .08

• Prominent movie critic reviews & box office success

= .17

• Psychotherapy & well being

= .32

• Viagra & improved male sexual functioning

= .38 Meyer et al., 2001

Meta-Analysis I (Kahana, Drotar, & Frazier,

2008)

• N = 70 adherence-promotion studies identified by literature

search (using multiple search terms, such as intervention,

treatment, adherence, compliance and various chronic

conditions)

• # studies by condition: 32 (45.7%)-asthma; 16 (22.9%)-diabetes;

10 (14.3%)-CF; 2 each with JRA & obesity (2.9%, respectively);

& one each for hemodialysis, hemophilia, HIV, IBD, PKU, seizure

disorders, sickle cell disease, and TB (1.4% each)

• Of the 70 studies, 29 (41.4%) were identified as RCT; 42 (60%)

reported effect size based on an experimental vs. control group

design, while 19 (27.1%) reported effect size based on pre-post

differences and another 9 (12.9%) reported both.

Meta-Analysis I: Demographics

• Mean age ranged from 2 to 15 yrs. (M = 10.2, SD = 3.2)

• Gender prevalence (based on 53 studies): 53.3% males

vs. 47.4% females

• Ethnicity prevalence (based on 26 studies): 82%

Caucasians

• Only 15 studies reported on SES; data could not be

aggregated because they were based on very different

indices of SES

Meta-Analysis I: Intervention Type and

Format

• 34 (48.6%) - multicomponent

• 18 (25.7%) - educational

• 7 (10%) - behavioral

• 7 (10%) - technology based

• 4 (5.7%) - psychosocial

• 63 studies reported format

– 52.4% were groups

– 39.7% were individual based

– the remainder included both group & individual components

Meta-Analysis I: Adherence Measures

• 67 studies reported information on ratings of adherence: – Parents (n = 26; 38.8%)

– Children/Adolescents (n = 17; 25.4%)

– Both parents and youth (n = 11; 16.4%)

– Ratings by psychologists or medical personnel (n = 4; 6%)

– Electronic monitor (n = 2; 3%)

– Blood or urine assay (n = 3; 4.5%)

– Teacher or “outsider” ratings (n = 2; 3%)

– Pharmacy records and parent (n = 1; 1.5%)

– Electronic monitor and child report (n = 1; 1.5%)

Parent or patient reports or combination of the two (n = 54; 81%)

Meta-Analysis I: Results

Weighted (by sample size) mean d across all adherence

outcomes was in the “small” range:

d = .34 (95% confidence interval (CI) = 0.30 – 0.38)

However, there was significant heterogeneity across

adherence outcomes. Therefore, the authors examined

potential moderators of d.

Meta-Analysis I: Moderators of Adherence

Outcome d

Types of Interventions – Behavioral: d = .54 (medium), 95% CI = 0.34-0.73

– Multicomponent: d = .51 (medium), 95% CI = 0.45-0.57

– Psychosocial: d = .44 (small to medium), 95% CI = 0.23-0.65

– Educational: d = .16 (small), 95% CI = 0.10-0.22

– Technology based: d = .08 (NS), 95% CI = 0.09-0.25

Meta-Analysis I: Moderators of Adherence

Outcome d

• Type of Adherence Outcome: self-management, self-care behaviors,

dietary change, and exercise-environmental changes yielded

medium ds, while medication adherence yielded small ds

• Type of Disorder: CF (medium to large), miscellaneous disorders

(medium), diabetes (small to medium), & asthma (small)

• Type of Design: combined pre-post & experimental vs. control group

(medium to large), pre-post only (small to medium), & experimental

vs. control only (small)

• Follow-up: d diminished over time (0-6 mos. f/u, d = .63, 95% CI =

0.46-0.80; 7-12 mos. f/u, d = .24, 95% CI = 0.06-0.42; >12 mos. f/u,

d = .50, 95% CI = -1.15-0.15)

Meta-Analysis II (Graves et al., 2010)

• N = 71 studies identified from literature search with terms

adherence or compliance and paired with treatment,

strategies, improve, interventions, education, medication,

child, adolescent, and pediatric.

• Did not include obesity or lifestyle changes, just chronic

illnesses.

• 34 (48.6%) used a comparison group design

(experimental vs. control), 17 (24.3%) used a within

subject design (pre-post), and 19 (28.2%) used a single-

subject design.

Meta-Analysis II: Chronic Diseases

• Of group designs (N 51) with N =3027 patients (M=35.6):

• 16 (31.4%) asthma

• 15 (29.4%) type 1 diabetes

• 5 (9.8%) CF

• 3 each (5.9%) with HIV/AIDS or post-transplant

• 2 each (3.9%) with hyperlipidemia, JIA, & sickle cell

• 1 each (2%) with epilepsy, hemophilia, & PKU

Meta-Analysis II: Chronic Diseases

• Single subject design studies (N=20), with N=50

patients (M=2.6)

• 7 (36.8%) type 1 diabetes

• 3 (15.8%) each JIA & CF

• 2 (10.5%) asthma

• 1 (5.3%) each epilepsy, lung disease, rheumatic

diseases, & sickle cell

Meta-Analysis II: Adherence Measures

• Group design studies: – Child report (N=14)

– Parent report (N=9

– Diary (N=9)

– 24-h recall (N=8)

– Electronic monitoring (N=10)

– Pill count (N=7)

– Blood or urine assay (N=6)

• Single subject design studies:

– Diary (N=23, 71.9%)

– Electronic monitoring (N=4, 12.5%)

– Pill Count (N=3, 9.4%)

– 24-hr recall (N=2, 6.3%)

Meta-Analysis II: Demographics

• Group design studies:

– Age 2 to 15 yrs. (M = 9.9)

– % males = 24% to 91% (M = 51.7%)

– Minorities = 0% to 100% (M = 39.1%)

• Single subject design studies:

– Age 2 to 17 yrs. (M = 11)

– % males = 0% to 100% (M = 47.1%)

– Minorities = 0% in 2 studies & 100% in 2 studies

Meta-Analysis II: Intervention Types

• For Group Design Studies:

– Combined educational and behavioral (n=24, 47%)

– Organizational (n=6, 11.8%)

– Behavioral (n=5, 9.8%)

– Educational (n=2, 3.9%)

– Variety of combinations (n=13, 25.4%)

• For Single Subject Design Studies:

– Educational and behavioral (n=9, 47.4%)

– Behavioral (n=9, 47.4%)

– Behavioral and organizational (n=1, 5.3%)

Meta-Analysis II: Adherence Outcomes

• Mean effect size (weighted by sample size) for group

designs: d = 0.58 (“medium” range), 95% CI = 0.51-0.65

• Moderators of effect size: Higher effect size for studies

using a wait-list control design (mean d = 1.09) vs. an

alternative treatment design (mean d = 0.43)

• Mean effect size (weighted by sample size) for single-

subject designs: d = 1.53 (“large” range), 95% CI – 1.07-

1.98). No moderators of effect size as homogeneous.

Meta-Analysis II: Health Outcomes

• 31 studies reported health outcomes: direct (e.g., A1C)

indirect (disease activity), healthcare utilization, or

subjective (quality of life = QOL).

• Mean d = .40 (small to medium),

95% CI = 0.31 – 0.50

• d higher for A1C, PFT, disease activity, & healthcare

utilization vs. BMI & QOL

Research Implications from Meta-Analyses

1. Less reliance on indirect measures of adherence

(parent & patient reports)

2. Need for larger, RCTs with attention-placebo and long-

term follow-up

3. Include health outcomes (direct, indirect, health care

utilization & costs, and QOL)

4. Explore moderators of effect sizes

5. Dismantling studies of multicomponent interventions

6. Recruit more ethnically diverse samples

7. Assess treatment fidelity and integrity (i.e., did

patients/families receive intervention as intended &

use the skills/knowledge imparted?)

8. Develop & test technology-based interventions

Clinical Implications for Enhancing

Adherence

1. Educate and re-educate about disease, purpose of

regimen, and need for consistent adherence

2. Secure patient/family agreement to follow regimen

3. Parent involvement key component (monitoring,

supervising, & positive reinforcement)

4. Provide incentives to patients

5. Self-management skills for adolescents

6. One-shot bolus of an adherence intervention will not

have lasting effects: interventions need to be part of

ongoing clinical management of pediatric chronic

diseases

When Is Nonadherence Medical Neglect?

• N = 6 patients perinatally HIV-infected children whose

therapy was failing based on HIV RNA levels

• 3-Step approach taken:

1. Home health care nurse visits 2 times per week for at least 2 wks

2. Directly observed therapy (DOT) while patient was hospitalized

for 4 days

3. Physician-initiated medical neglect report to the Arkansas

Department of Human Services

• Results: for 2 of 6 patients, a medical neglect report was

necessary and resulted in foster care placement with

improvements in viral load

Roberts et al. (2004)

For more information, please go to the main website and browse for workshops

on this topic or check out our additional resources.

Additional Resources

Online resources: 1. Society of Clinical Child & Adolescent Psychology: https://clinicalchildpsychology.org 2. Society of Pediatric Psychology Adherence to Pediatric Medical Regimens Fact Sheet: http://www.apadivisions.org/division-54/evidence-based/medical-regimens.aspx

Books: 1. Rapoff, M.A. (2010). Adherence to pediatric medical regimens, (2nd ed.). New York: Springer.

Peer-reviewed Journal Articles: 1. Berg, J. S., Dischler, J., Wagner, D. J., Raia, J., & Palmer-Shevlin, N. (1993). Medication compliance: A health care problem. The Annals of Pharmacotherapy, 27 (suppl.), 2-21. 2. Berg, C.J., Rapoff, M.A., Snyder, C.R., & Belmont, J.M. (2007). The relationship of children’s hope to pediatric asthma treatment adherence. The Journal of Positive Psychology, 2, 176-184. 3. Burgess, S. W., Sly, P. D., Morawska, A., & Devadason, S. G. (2008). Assessing adherence and factors associated with adherence in young children with asthma. Respirology, 13, 559-563. 4. McQuaid, E.L., Walders, N., Kopel, S.J., Fritz, G.K., & Klinnert, M.D. (2005). Pediatric asthma management in the family context: The family asthma management system scale. Journal of Pediatric Psychology, 30, 492-502. 5. Modi, A. C., & Quittner, A. L. (2006). Barriers to treatment adherence for children with cystic fibrosis and asthma: What gets in the way? Journal of Pediatric Psychology, 31(8), 846-858 6. Rapoff, M.A., Belmont, J.M., Lindsley, C.B., & Olson, N.Y. (2005). Electronically monitored adherence to medications by newly diagnosed patients with juvenile rheumatoid arthritis. Arthritis Care & Research, 53, 905-910. 7. World Health Organization (2003). Adherence to long-term therapies: Evidence for action. Geneva, Switzerland.

Full Reference List

Keynote: Evidence Based Interventions for Pediatric Medical Treatment Adherence by Michael Rapoff, Ph.D.

Websites: 1. Society of Clinical Child and Adolescent Psychology website: http://effectivechildtherapy.com 2. Society of Pediatric Psychology Adherence to Pediatric Medical Regimens Fact Sheet: http://www.apadivisions.org/division-54/evidence-based/medical-regimens.aspx Books: Groopman, J. (2007). How doctors think. New York: Houghton Mifflin. Haynes, R. B. (1979). Introduction. In D. W. Taylor & D. C. Sackett (Eds.), Compliance in health care (pp.

1-7). Baltimore: The Johns Hopkins University Press. Rand, C.S. (2000). “I took the medicine like you told me, doctor”: Self-report of adherence with medical

regimens. In A.A. Stone, J.S. Turkkan, C.A. Bachrach, J.B. Jobe, H.S. Kurtzman & Cain, V.S. (Eds.), The science of self-report: Implications for research and practice (pp. 257-276). Mahwah, NJ: Lawrence Erlbaum.

Rapoff, M.A. (2010). Adherence to pediatric medical regimens, (2nd ed.). New York: Springer. Peer Reviewed Journal Articles: Anson, O., Weizman, Z., & Zeevi, N. (1990). Celiac disease: Parental knowledge and attitudes of dietary

compliance. Pediatrics, 85, 98-103. April, K. T., Feldman, D. E., Platt, R. W., & Duffy, C. M. (2006). Comparison between children with

juvenile idiopathic arthritis and their parents concerning perceived treatment adherence. Arthritis and Rheumatism, 55(4), 558-563. doi: 10.1002/art.22104

Bazzigaluppi, E., Roggero, P., Parma, B., Brambillasca, M.F., Meroni, F., Mora, S., Bosi, E. & Barera, G. (2006). Antibodies to recombinant human tissue-transglutaminase in celiac disease: Diagnostic effectiveness and decline pattern after gluten-free diet. Digestive and Liver Disease, 38, 98-102.

Beck, D. E., Fennell, R. S., Yost, R. L., Robinson, J. D., Geary, D., & Richards, G. A. (1980). Evaluation of an educational program on compliance with medication regimens in pediatric patients with renal transplants. The Journal of Pediatrics, 96, 1094-1097.

Berg, J. S., Dischler, J., Wagner, D. J., Raia, J., & Palmer-Shevlin, N. (1993). Medication compliance: A health care problem. The Annals of Pharmacotherapy, 27 (suppl.), 2-21.

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