Testing the Feasibility of Using Ecological Momentary Assessment to Collect Real-Time Behavior and Mood to Predict Technology-Measured HIV Medication Adherence

  • Enbal ShachamEmail author
  • Daphne Lew
  • Ting Xiao
  • Julia López
  • Timothy Trull
  • Mario Schootman
  • Rachel Presti
Original Paper


Identifying distinct patterns of behavior and mood in natural environments that interrupt medication adherence among individuals with HIV will be useful in informing intervention development. This pilot study assessed the initial efficacy of using ecologic momentary assessment to define patterns of alcohol use, mood, and medication adherence. Participants reported intraday alcohol use and mood using app-enabled smartphones and MEMSCap pill bottles to measure medication adherence. There were 34 enrolled participants, 29 of whom completed the 28-day study. Participants drank a mean of 7.75 days of the study period. The positive and negative affect scores ranged from 10 to 50, with a mean of 25.7 and 11.4 for each, respectively. The average medication adherence for the sample was 94.1%. These findings suggest these types of data collection methods are increasingly acceptable in measuring real-time mood and behavior, which may better inform interventions addressed at increasing HIV adherence practices.


HIV/AIDS Alcohol Ecological momentary assessment MEMSCap Electronic medication monitoring Medication adherence 


Identificación de patrones distintos de comportamiento y estado de ánimo en los ambientes naturales que interrumpen el cumplimiento con la medicación entre los individuos con VIH será útil para informar el desarrollo de la intervención. Este estudio piloto evaluó la eficacia inicial de utilizar evaluación ecológica momentánea (EMA) para definir patrones de adherencia de uso de alcohol, estado de ánimo y medicación. Los participantes informaron uso de alcohol intradía y estado de ánimo usando MEMSCap píldora botellas y teléfonos inteligentes basados en la aplicación se utilizaron para medir el cumplimiento con la medicación. Hubo 34 participantes inscritos, 29 de los cuales completaron el estudio de 28 días. Los participantes tomaban un promedio de 7,75 días del período de estudio. Las puntuaciones de afecto positivo y negativo variaron de 10 a 50, con una media de 25,7 y de 11,4 para cada uno, respectivamente. El cumplimiento con la medicación media para la muestra fue de 94,1%. Estos resultados sugieren que estos tipos de métodos de recolección de datos son cada vez más aceptables en la medición en tiempo real estado de ánimo y comportamiento, y que puede informar mejor a las intervenciones dirigidas a aumentar prácticas de adherencia VIH.



The study team appreciates Dr. Yi Shang and the student team at University of Missouri- Columbia, Stephen Scroggins, Jake Gilliland, Walter Orr, Lisa Kessels, Michael Royal, Pharmacist, Sara Hubert, and the research participants for their time and commitment.


This grant was funded by NIAAA (1R21AA022064).

Compliance with Ethical Standards

Conflict of Interest

None of the authors have a conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    CDC. Trends in U.S. HIV diagnoses, 2005–2014. 2016; Accessed November 10, 2016, 2016.
  2. 2.
    Bangsberg DR, Hecht FM, Clague H, et al. Provider assessment of adherence to HIV antiretroviral therapy. J Acquir Immune Defic Syndr. 2001;26(5):435–42.PubMedCrossRefGoogle Scholar
  3. 3.
    Bangsberg DR, Moss AR, Deeks SG. Paradoxes of adherence and drug resistance to HIV antiretroviral therapy. J Antimicrob Chemother. 2004;53(5):696–9.PubMedCrossRefGoogle Scholar
  4. 4.
    Ebner-Priemer UW, Trull TJ. Ecological momentary assessment of mood disorders and mood dysregulation. Psychol Assess. 2009;21(4):463–75.PubMedCrossRefGoogle Scholar
  5. 5.
    Jahng S, Solhan MB, Tomko RL, Wood PK, Piasecki TM, Trull TJ. Affect and alcohol use: an ecological momentary assessment study of outpatients with borderline personality disorder. J Abnorm Psychol. 2011;120(3):572–84.PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Trull TJ, Ebner-Priemer UW. Using experience sampling methods/ecological momentary assessment (ESM/EMA) in clinical assessment and clinical research: Introduction to the special section. Psychol Assess. 2009;21(4):457–62.PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Lopez JD, Shacham E, Brown T. The impact of the Ryan White HIV/AIDS Medical Case Management Program on HIV clinical outcomes: a longitudinal study. AIDS Behav. 2018;22(9):3091–9.PubMedCrossRefGoogle Scholar
  8. 8.
    Wiggers LC, de Wit JB, Gras MJ, Coutinho RA, van den Hoek A. Risk behavior and social-cognitive determinants of condom use among ethnic minority communities in Amsterdam. AIDS Educ Prev. 2003;15(5):430–47.PubMedCrossRefGoogle Scholar
  9. 9.
    Solhan MB, Trull TJ, Jahng S, Wood PK. Clinical assessment of affective instability: comparing EMA indices, questionnaire reports, and retrospective recall. Psychol Assess. 2009;21(3):425–36.PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Shacham E, Cottler LB. Sexual behaviors among club drug users: prevalence and reliability. Arch Sex Behav. 2010;39(6):1331–41.PubMedCrossRefGoogle Scholar
  11. 11.
    Shiffman S. Ecological momentary assessment (EMA) in studies of substance use. Psychol Assess. 2009;21(4):486–97.PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    Mustanski B. The influence of state and trait affect on HIV risk behaviors: a daily diary study of MSM. Health Psychol. 2007;26(5):618–26.PubMedCrossRefGoogle Scholar
  13. 13.
    Cook PF, McElwain CJ, Bradley-Springer LA. Brief report on ecological momentary assessment: everyday states predict HIV prevention behaviors. BMC Res Notes. 2016;9:9.PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Piasecki TM, Hufford MR, Solhan M, Trull TJ. Assessing clients in their natural environments with electronic diaries: rationale, benefits, limitations, and barriers. Psychol Assess. 2007;19(1):25–43.PubMedCrossRefGoogle Scholar
  15. 15.
    Buysse DJ, Thompson W, Scott J, et al. Daytime symptoms in primary insomnia: a prospective analysis using ecological momentary assessment. Sleep Med. 2007;8(3):198–208.PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Collins RL, Kashdan TB, Gollnisch G. The feasibility of using cellular phones to collect ecological momentary assessment data: Application to alcohol consumption. Exp Clin Psychopharmacol. 2003;11(1):73–8.PubMedCrossRefGoogle Scholar
  17. 17.
    Heron KE, Smyth JM. Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol. 2010;15(1):1–39.PubMedCrossRefGoogle Scholar
  18. 18.
    Wray TB, Kahler CW, Monti PM. Using ecological momentary assessment (EMA) to study sex events among very high-risk men who have sex with men (MSM). AIDS Behav. 2016;20(10):2231–42.PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Yang C, Linas B, Kirk G, et al. Feasibility and acceptability of smartphone-based ecological momentary assessment of alcohol use among African American men who have sex with men in Baltimore. JMIR Mhealth Uhealth. 2015;3(2):e67.PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Rowe C, Hern J, DeMartini A, et al. Concordance of text message ecological momentary assessment and retrospective survey data among substance-using men who have sex with men: a secondary analysis of a randomized controlled trial. JMIR Mhealth Uhealth. 2016;4(2):e44.PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Paolillo WE, Tang B, Depp AC, et al. Temporal associations between social activity and mood, fatigue, and pain in older adults with HIV: an ecological momentary assessment study. JMIR Ment Health. 2018;5(2):e38.PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Robinson SM, Sobell LC, Sobell MB, Leo GI. Reliability of the timeline followback for cocaine, cannabis, and cigarette use. Psychol Addict Behav. 2014;28(1):154–62.PubMedCrossRefGoogle Scholar
  23. 23.
    Compton WM, Cottler LB, Dorsey KB, Spitznagel EL, Mager DE (1996) Comparing assessments of DSM-IV substance dependence disorders using CIDI-SAM and SCAN. Drug Alcohol Depend 41(3):179–87. Erratum appears in Drug Alcohol Depend 1996 Nov; 42(3):217–9.Google Scholar
  24. 24.
    Cottler LB. The CIDI and CIDI-substance abuse module (SAM): cross-cultural instruments for assessing DSM-III, DSM-III-R and ICD-10 criteria. NIDA Res Monogr. 1991;105:220–6.Google Scholar
  25. 25.
    Crawford J, Henry J. The positive and negative affect schedule (PANAS): construct validity, measurement properties and normative data in a large non-clinical sample. Br J Clin Psychol. 2004;43:245–65.PubMedCrossRefGoogle Scholar
  26. 26.
    Greenfield TK, Kerr WC. Alcohol measurement methodology in epidemiology: recent advances and opportunities. Addiction. 2008;103(7):1082–99.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Karim J, Weisz R, Rehman SU. International positive and negative affect schedule short-form (I-PANAS-SF): testing for factorial invariance across cultures. Proc Soc Behav Sci. 2011;15:2016–22.CrossRefGoogle Scholar
  28. 28.
    Galloway GP, Coyle JR, Guillén JE, Flower K, Mendelson JE. A simple, novel method for assessing medication adherence: capsule photographs taken with cellular telephones. J Addict Med. 2011;5(3):170–4. Scholar
  29. 29.
    Howard AA, Arnsten JH, Lo Y, et al. A prospective study of adherence and viral load in a large multi-center cohort of HIV-infected women. AIDS. 2002;16(16):2175–82.PubMedCrossRefGoogle Scholar
  30. 30.
    Pearson C, Simoni J, Hoff P, Kurth A, Martin D. Assessing antiretroviral adherence via electronic drug monitoring and self-report: an examination of key methodological issues. AIDS Behav. 2007;11(2):161–73.PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Piasecki TM, Jahng S, Wood PK, et al. The subjective effects of alcohol–tobacco co-use: an ecological momentary assessment investigation. J Abnorm Psychol. 2011;120(3):557–71.PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Moore RC, Kaufmann CN, Rooney AS, et al. Feasibility and acceptability of ecological momentary assessment of daily functioning among older adults with HIV. Am J Geriatr Psychiatry. 2017;25(8):829–40.PubMedCrossRefGoogle Scholar
  33. 33.
    McCarney R, Warner J, Iliffe S, van Haselen R, Griffin M, Fisher P. The Hawthorne effect: a randomised, controlled trial. BMC Med Res Methodol. 2007;7(1):30.PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Griffith SD, Shiffman S, Heitjan DF. A method comparison study of timeline followback and ecological momentary assessment of daily cigarette consumption. Nicotine Tob Res. 2009;11(11):1368–73.PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Gupta R, Rao UP. An exploration to location based service and its privacy preserving techniques: a survey. Wirel Pers Commun. 2017;96(2):1973–2007.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College for Public Health and Social JusticeSaint Louis UniversitySt. LouisUSA
  2. 2.Washington University School of MedicineSt. LouisUSA
  3. 3.University of MissouriColumbiaUSA

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