Factors predicting compliance to ecological momentary assessment among adolescent smokers.

INTRODUCTION Ecological momentary assessments (EMAs) are increasingly used in smoking research to understand contextual and individual differences related to smoking and changes in smoking. To date, there has been little detailed research into the predictors of EMA compliance. However, patterns or predictors of compliance may affect key relationships under investigation and introduce sources of bias in results. The purpose of this study was to investigate predictors of compliance to random prompts among a sample of adolescents who had ever smoked. METHODS Data for this study were drawn from a sample of 461 adolescents (9th and 10th graders at baseline) participating in a longitudinal study of smoking escalation. We examined 2 outcomes: subject-level EMA compliance (overall rate of compliance over a week-long EMA wave), and in-the-moment prompt-level compliance to the most proximal random prompt. We investigated several covariates including gender, race, smoking rate, alcohol use, psychological symptomatology, home composition, mood, social context, time in study, inter-prompt interval, and location. RESULTS At the overall subject level, higher mean negative affect, smoking rate, alcohol use, and male gender predicted lower compliance with random EMA prompts. At the prompt level, after controlling for significant subject-level predictors of compliance, increased positive affect, being outside of the home, and longer inter-prompt interval predicted lower momentary compliance. CONCLUSIONS This study identifies several factors associated with overall and momentary EMA compliance among a sample of adolescents participating in a longitudinal study of smoking. We also propose a conceptual framework for investigating the contextual and momentary predictors of compliance within EMA studies.

[1]  Étude des problèmes comportementaux et émotionnels chez l’adolescent : faisabilité et validité de l’approche ESM , 2009 .

[2]  J Peter Rosenfeld,et al.  A novel countermeasure against the reaction time index of countermeasure use in the P300-based complex trial protocol for detection of concealed information. , 2011, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[3]  S. Shiffman,et al.  Ecological momentary assessment. , 2008, Annual review of clinical psychology.

[4]  S. Shiffman,et al.  Assessment Methods for Patient-Reported Outcomes , 2003 .

[5]  B. Gerber,et al.  ACE inhibitor and ARB medication use among Medicaid enrollees with diabetes. , 2013, Ethnicity & disease.

[6]  E. Keogh,et al.  Testing the discriminant and convergent validity of the mood and anxiety symptoms questionnaire using a British sample , 1997 .

[7]  C. Depp,et al.  Ecological momentary assessment in aging research: a critical review. , 2009, Journal of psychiatric research.

[8]  M. Windle A longitudinal study of stress buffering for adolescent problem behaviors. , 1992 .

[9]  Jean-Marc Alexandre,et al.  Ecological momentary assessment in alcohol, tobacco, cannabis and opiate dependence: a comparison of feasibility and validity. , 2012, Drug and alcohol dependence.

[10]  L. Radloff The CES-D Scale , 1977 .

[11]  Judi Scheffer,et al.  Dealing with Missing Data , 2020, The Big R‐Book.

[12]  H. Fitzgerald,et al.  Cognitive and Motoric Functioning of Sons of Alcoholic Fathers and Controls: The Early Childhood Years , 1992 .

[13]  S. Shiffman,et al.  Patient non-compliance with paper diaries , 2002, BMJ : British Medical Journal.

[14]  Peter J Norton,et al.  The Mood and Anxiety Symptom Questionnaire across four ethnoracial groups in an undergraduate sample. , 2015, The American journal of orthopsychiatry.

[15]  R A McCormick,et al.  Testing a tripartite model: I. Evaluating the convergent and discriminant validity of anxiety and depression symptom scales. , 1995, Journal of abnormal psychology.

[16]  L. Radloff The use of the Center for Epidemiologic Studies Depression Scale in adolescents and young adults , 1991, Journal of youth and adolescence.

[17]  S. Shiffman,et al.  Capturing momentary, self-report data: A proposal for reporting guidelines , 2002, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[18]  Paul P Stork,et al.  A randomized trial of electronic versus paper pain diaries in children: impact on compliance, accuracy, and acceptability , 2004, Pain.

[19]  Michael R. Winograd,et al.  P900: A Putative Novel ERP Component that Indexes Countermeasure Use in the P300-Based Concealed Information Test , 2013, Applied psychophysiology and biofeedback.

[20]  Joseph E. Schwartz,et al.  Intensive momentary reporting of pain with an electronic diary: reactivity, compliance, and patient satisfaction , 2003, Pain.

[21]  E. Hacker,et al.  Ecological momentary assessment of fatigue in patients receiving intensive cancer therapy. , 2007, Journal of pain and symptom management.

[22]  Robert A. Cohen,et al.  Introducing the GLMSELECT PROCEDURE for Model Selection , 2006 .

[23]  M. Litt,et al.  Ecological momentary assessment (EMA) with treated alcoholics: methodological problems and potential solutions. , 1998, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[24]  R. Jamison,et al.  Electronic diaries for monitoring chronic pain: 1-year validation study , 2001, Pain.

[25]  Hsin-Chieh Yeh,et al.  Effect of the 2011 vs 2003 duty hour regulation-compliant models on sleep duration, trainee education, and continuity of patient care among internal medicine house staff: a randomized trial. , 2013, JAMA internal medicine.

[26]  B. Kudielka,et al.  Compliance With Saliva Sampling Protocols: Electronic Monitoring Reveals Invalid Cortisol Daytime Profiles in Noncompliant Subjects , 2003, Psychosomatic medicine.

[27]  Michael Eid,et al.  Compliance to a cell phone-based ecological momentary assessment study: the effect of time and personality characteristics. , 2012, Psychological assessment.

[28]  Donald Hedeker,et al.  Analysis of binary outcomes with missing data: missing = smoking, last observation carried forward, and a little multiple imputation. , 2007, Addiction.

[29]  Donald Hedeker,et al.  Longitudinal Data Analysis , 2006 .

[30]  J. Schafer,et al.  Missing data: our view of the state of the art. , 2002, Psychological methods.