Skip to main content

Advertisement

SpringerLink
Log in

Prospective Measurement of Daily Health Behaviors: Modeling Temporal Patterns in Missing Data, Sexual Behavior, and Substance Use in an Online Daily Diary Study of Gay and Bisexual Men

  • Original Paper
  • Published:
AIDS and Behavior Aims and scope Submit manuscript

Abstract

Daily diary and other intensive longitudinal methods are increasingly being used to investigate fluctuations in psychological and behavioral processes. To inform the development of this methodology, we sought to explore predictors of and patterns in diary compliance and behavioral reports. We used multilevel modeling to analyze data from an online daily diary study of 371 gay and bisexual men focused on sexual behavior and substance use. We found that greater education and older age as well as lower frequency of substance use were associated with higher compliance. Using polynomial and trigonometric functions, we found evidence for circaseptan patterns in compliance, sexual behavior, and substance use, as well as linear declines in compliance and behavior over time. The results suggest potential sources of non-random patterns of missing data and suggest that trigonometric terms provide a similar but more parsimonious investigation of circaseptan rhythms than do third-order polynomial terms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Bolger N, Davis A, Rafaeli E. Diary methods: capturing life as it is lived. Annu Rev Psychol. 2003;54:579–616.

    Article  PubMed  Google Scholar 

  2. Wheeler L, Reis HT. Self-recording of everyday life events: origins, types, and uses. J Pers. 1991;59:339–54.

    Article  Google Scholar 

  3. Mehl MR, Conner TS. Handbook of research methods for studying daily life. New York: The Guilford Press; 2012.

    Google Scholar 

  4. Abu-Hasaballah K, James A, Aseltine RH Jr. Lessons and pitfalls of interactive voice response in medical research. Contemp Clin Trials. 2007;28(5):593–602.

    Article  PubMed  Google Scholar 

  5. Cranford JA, Tennen HA, Zucker RA. Feasibility of using interactive voice response to monitor daily drinking, moods, and relationship processes on a daily bais on alcoholic couples. Alcohol Clin Exp Res. 2010;34(3):499–508.

    Article  PubMed  Google Scholar 

  6. Haberer JE, Kiwanuka J, Nansera D, Wilson IB, Bangsberg DR. Challenges in using mobile phones for collection of antiretroviral therapy adherence data in a resource-limited setting. AIDS Behav. 2010;14:1294–301.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kobak KA, Greist JH, Jefferson JW, Mundt JC, Katzelnick DJ. Computerized assessment of depression and anxiety over the telephone using interactive voice response. MD Comput. 1999;16(3):64–8.

    CAS  PubMed  Google Scholar 

  8. Lieberman G, Naylor MR. Interactive voice response technology for symptom monitoring and as an adjunct to the treatment of chronic pain. Transl Behav Med. 2012;2(1):93–101.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Newes-Adeyi G, Greece J, Bozeman S, Walker DK, Lewis F, Gidudu J. Active surveillance for influenza vaccine adverse events: the integrated vaccine surveillance system. Vaccine. 2012;30(6):1050–5.

    Article  PubMed  Google Scholar 

  10. Piette JD. Interactive voice response systems in the diagnosis and management of chronic disease. Am J Manag Care. 2000;6(7):817–27.

    CAS  PubMed  Google Scholar 

  11. Schroder KE, Johnson CJ. Interactive voice response technology to measure HIV-related behavior. Curr HIV/AIDS Rep. 2009;6(4):210–6.

    Article  PubMed  Google Scholar 

  12. Toll BA, Conney NL, McKee SA, O’Malley SS. Do daily interactive voice response reports of smoking behavior correspond with retrospective reports? Psychol Addict Behav. 2005;19(3):291–5.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Tucker JA, Foushee HR, Black BC, Roth DL. Agreement between prospective IVR self-monitoring and structured retrospective reports of drinking and contextual variables during natural resolution attempts. J Stud Alcohol Drugs. 2007;68:538–42.

    Article  PubMed  Google Scholar 

  14. Tucker JA, Simpson CA, Huan J, Roth DL, Stewart KE. Utility of an interactive voice response system to assess antiretroviral pharmacotherapy adherence among substance users living with HIV/AIDS in the rural south. AIDS Patient Care STDs. 2013;27(5):280–6.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Shiffman S, Hufford M, Hickcox M, Paty JA, Gnys M, Kassel JD. Remember that? A comparison of real-time versus retrospective recall of smoking lapses. J Consult Clin Psychol. 1997;65(2):292.

    Article  CAS  PubMed  Google Scholar 

  16. Fortenberry JD, Temkit MH, Tu W, Graham CA, Katz BP, Orr DP. Daily mood, partner support, sexual interest, and sexual activity among adolescent women. Health Psychol. 2005;24(3):252.

    Article  PubMed  Google Scholar 

  17. Mustanski B. Are sexual partners met online associated with HIV/STI risk behaviours? Retrospective and daily diary data in conflict. AIDS Care. 2007;19(6):822–7.

    Article  CAS  PubMed  Google Scholar 

  18. Newcomb ME. Moderating effect of age on the association between alcohol use and sexual risk in MSM: evidence for elevated risk among younger MSM. AIDS Behav. 2013;17(5):1746–54.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Newcomb ME, Mustanski B. Diaries for observation or intervention of health behaviors: factors that predict reactivity in a sexual diary study of men who have sex with men. Ann Behav Med. 2014;47(3):325–34.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Parsons JT, Rendina HJ, Grov C, Ventuneac A, Mustanski B. Accuracy of highly sexually active gay and bisexual men’s predictions of their daily likelihood of anal sex and its relevance for intermittent event-driven HIV pre-exposure prophylaxis. J Acquir Immune Defic Syndr. 2015;68(4):449–55.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Rendina HJ, Moody RL, Ventuneac A, Grov C, Parsons JT. Aggregate and event-level associations between substance use and sexual behavior among gay and bisexual men: comparing retrospective and prospective data. Drug Alcohol Depend. 2015;154:199–207.

    Article  PubMed  Google Scholar 

  22. Schroder KE, Johnson CJ, Wiebe JS. Interactive voice response technology applied to sexual behavior self-reports: a comparison of three methods. AIDS Behav. 2007;11:313–23.

    Article  PubMed  Google Scholar 

  23. Glick SN, Winer RL, Golden MR. Web-based sex diaries and young adult men who have sex with men: assessing feasibility, reactivity, and data agreement. Arch Sex Behav. 2013;42(7):1327–35.

    Article  PubMed  Google Scholar 

  24. Green AS, Rafaeli E, Bolger N, Shrout PE, Reis HT. Paper or plastic? Data equivalence in paper and electronic diaries. Psychol Methods. 2006;11(1):87–105.

    Article  PubMed  Google Scholar 

  25. Grov C, Golub SA, Mustanski B, Parsons JT. Sexual compulsivity, state affect, and sexual risk behavior in a daily diary study of gay and bisexual men. Psychol Addict Behav. 2010;24(3):487.

    Article  PubMed  Google Scholar 

  26. Horvath KJ, Beadnell B, Bowen AM. A daily web diary of the sexual experiences of men who have sex with men: comparisons with a retrospective recall survey. AIDS Behav. 2007;11(4):537–48.

    Article  PubMed  Google Scholar 

  27. Mundt JC, Perrine MW, Searles JS, Walter D. An application of interactive voice response (IVR) technology to longitudinal studies of daily behavior. Behav Res Methods Instrum Comput. 1995;27(3):351–7.

    Article  Google Scholar 

  28. Gunthert KC, Wenze SJ. Daily diary methods. In: Mehl MR, Conner TS, editors. Handbook of research methods for studying daily life. New York: The Guilford Press; 2012.

    Google Scholar 

  29. Gatehouse CS, Tennen HA, Feinn RS, Abu-Hasaballah KS, Lalla RV. Association between interactive voice response adherence and subject retention in a randomized controlled trial. Contemp Clin Trials. 2012;33(4):589–92.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Courvoisier DS, Eid M, Lischetzke T. Compliance to a cell phone-based ecological momentary assessment study: the effect of time and personality characteristics. Psychol Assess. 2012;24(3):713.

    Article  PubMed  Google Scholar 

  31. Stone AA, Broderick JE, Schwartz JE, Shiffman S, Litcher-Kelly L, Calvanese P. Intensive momentary reporting of pain with an electronic diary: reactivity, compliance, and patient satisfaction. Pain. 2003;104(1):343–51.

    Article  PubMed  Google Scholar 

  32. Black AC, Harel O, Matthews G. Techniques for analyzing intensive longitudinal data with missing values. In: Mehl MR, Conner TS, editors. Handbook of research methods for studying daily life. New York: Guilford; 2012. p. 339–56.

    Google Scholar 

  33. Broderick JE, Schwartz JE, Shiffman S, Hufford MR, Stone AA. Signaling does not adequately improve diary compliance. Ann Behav Med. 2003;26(2):139–48.

    Article  PubMed  Google Scholar 

  34. Stone AA, Shiffman S. Capturing momentary, self-report data: a proposal for reporting guidelines. Ann Behav Med. 2002;24(3):236–43.

    Article  PubMed  Google Scholar 

  35. Stone AA, Shiffman S, Schwartz JE, Broderick JE, Hufford MR. Patient non-compliance with paper diaries. Br Med J. 2002;324:1193–4.

    Article  Google Scholar 

  36. Stone AA, Shiffman S, Schwartz JE, Broderick JE, Hufford MR. Patient compliance with paper and electronic diaries. Control Clin Trials. 2003;24(2):182–99.

    Article  PubMed  Google Scholar 

  37. Heyer GL, Rose SC. Which factors affect daily compliance with an internet headache diary among youth with migraine? Clin J Pain. 2015;31:1075–9.

    Article  PubMed  Google Scholar 

  38. Lewandowski AS, Palermo TM, Kirchner HL, Drotar D. Comparing diary and retrospective reports of pain and activity restriction in children and adolescents with chronic pain conditions. Clin J Pain. 2009;25(4):299.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kiene SM, Tennen H, Armeli S. Today I’ll use a condom, but who knows about tomorrow: a daily process study of variability in predictors of condom use. Health Psychol. 2008;27(4):463.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Edwards S, Evans P, Hucklebridge F, Clow A. Association between time of awakening and diurnal cortisol secretory activity. Psychoneuroendocrinology. 2001;26(6):613–22.

    Article  CAS  PubMed  Google Scholar 

  41. Nater UM, Hoppmann CA, Scott SB. Diurnal profiles of salivary cortisol and alpha-amylase change across the adult lifespan: evidence from repeated daily life assessments. Psychoneuroendocrinology. 2013;38(12):3167–71.

    Article  CAS  PubMed  Google Scholar 

  42. Jobin J, Wrosch C, Scheier MF. Associations between dispositional optimism and diurnal cortisol in a community sample: when stress is perceived as higher than normal. Health Psychol. 2014;33(4):382.

    Article  PubMed  Google Scholar 

  43. DeSantis AS, Adam EK, Hawkley LC, Kudielka BM, Cacioppo JT. Racial and ethnic differences in diurnal cortisol rhythms: are they consistent over time? Psychosom Med. 2015;77(1):6–15.

    Article  PubMed  Google Scholar 

  44. Doane LD, Mineka S, Zinbarg RE, Craske M, Griffith JW, Adam EK. Are flatter diurnal cortisol rhythms associated with major depression and anxiety disorders in late adolescence? The role of life stress and daily negative emotion. Dev Psychopathol. 2013;25(03):629–42.

    Article  PubMed  Google Scholar 

  45. Dawkins L, Powell JH, Pickering A, Powell J, West R. Patterns of change in withdrawal symptoms, desire to smoke, reward motivation and response inhibition across 3 months of smoking abstinence. Addiction. 2009;104(5):850–8.

    Article  PubMed  PubMed Central  Google Scholar 

  46. McCarthy DE, Piasecki TM, Fiore MC, Baker TB. Life before and after quitting smoking: an electronic diary study. J Abnorm Psychol. 2006;115(3):454.

    Article  PubMed  Google Scholar 

  47. Stolwijk A, Straatman H, Zielhuis G. Studying seasonality by using sine and cosine functions in regression analysis. J Epidemiol Commun Health. 1999;53(4):235–8.

    Article  CAS  Google Scholar 

  48. Steinberg DM, Levine EL, Lane I, et al. Adherence to self-monitoring via interactive voice response technology in an eHealth intervention targeting weight gain prevention among Black women: randomized controlled trial. J Med Intern Res. 2014;16(4):e114.

    Google Scholar 

  49. Pachankis JE, Rendina HJ, Ventuneac A, Grov C, Parsons JT. The role of maladaptive cognitions in hypersexuality among highly sexually active gay and bisexual men. Arch Sex Behav. 2014;43(4):669–83.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Parsons JT, Rendina HJ, Ventuneac A, Cook KF, Grov C, Mustanski B. A psychometric investigation of the hypersexual disorder screening inventory among highly sexually active gay and bisexual men: an item response theory analysis. J Sex Med. 2013;10(12):3088–101.

    Article  PubMed  Google Scholar 

  51. Ventuneac A, Rendina HJ, Grov C, Mustanski B, Parsons JT. An item response theory analysis of the sexual compulsivity scale and its correspondence with the hypersexual disorder screening inventory among a sample of highly sexually active gay and bisexual men. J Sex Med. 2015;12(2):481–93.

    Article  PubMed  Google Scholar 

  52. 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.

    Article  PubMed  Google Scholar 

  53. Shiffman S. Ecological momentary assessment (EMA) in studies of substance use. Psychol Assess. 2009;21(4):486.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This project was supported by a research grant from the National Institute of Mental Health (R01-MH087714; Jeffrey T. Parsons, Principal Investigator). H. Jonathon Rendina was supported by a Career Development Award from the National Institute on Drug Abuse (K01-DA039030; H. Jonathon Rendina, Principal Investigator). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors would like to acknowledge the contributions of the Pillow Talk Research Team: Ruben Jimenez, Demetria Cain, Sitaji Gurung, and John Pachankis. We would also like to thank the CHEST staff, particularly those who played important roles in the implementation of the project: Chris Hietikko, Chloe Mirzayi, and Thomas Whitfield, as well as our team of recruiters and interns. Finally, we thank Chris Ryan, Daniel Nardicio and the participants who volunteered their time for this study.

Author information

Authors and Affiliations

  1. The Center for HIV/AIDS Educational Studies & Training (CHEST), New York, NY, USA

    H. Jonathon Rendina, Ana Ventuneac, Christian Grov & Jeffrey T. Parsons

  2. Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Brian Mustanski

  3. CUNY Graduate School of Public Health and Health Policy, New York, NY, USA

    Christian Grov

  4. Department of Psychology, Hunter College of the City University of New York (CUNY), 695 Park Ave., New York, NY, USA

    Jeffrey T. Parsons

  5. Health Psychology and Clinical Sciences Doctoral Program, The Graduate Center of the City University of New York (CUNY), New York, NY, USA

    Jeffrey T. Parsons

Authors
  1. H. Jonathon Rendina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ana Ventuneac
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brian Mustanski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christian Grov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jeffrey T. Parsons
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeffrey T. Parsons.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rendina, H.J., Ventuneac, A., Mustanski, B. et al. Prospective Measurement of Daily Health Behaviors: Modeling Temporal Patterns in Missing Data, Sexual Behavior, and Substance Use in an Online Daily Diary Study of Gay and Bisexual Men. AIDS Behav 20, 1730–1743 (2016). https://doi.org/10.1007/s10461-016-1359-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10461-016-1359-0

Keywords

Search

Navigation