Skip to main content
SpringerLink
Log in

Integrating Intensive Longitudinal Data (ILD) to Inform the Development of Dynamic Theories of Behavior Change and Intervention Design: a Case Study of Scientific and Practical Considerations

  • Published:
Prevention Science Aims and scope Submit manuscript

Abstract

The increasing sophistication of mobile and sensing technology has enabled the collection of intensive longitudinal data (ILD) concerning dynamic changes in an individual’s state and context. ILD can be used to develop dynamic theories of behavior change which, in turn, can be used to provide a conceptual framework for the development of just-in-time adaptive interventions (JITAIs) that leverage advances in mobile and sensing technology to determine when and how to intervene. As such, JITAIs hold tremendous potential in addressing major public health concerns such as cigarette smoking, which can recur and arise unexpectedly. In tandem, a growing number of studies have utilized multiple methods to collect data on a particular dynamic construct of interest from the same individual. This approach holds promise in providing investigators with a significantly more detailed view of how a behavior change processes unfold within the same individual than ever before. However, nuanced challenges relating to coarse data, noisy data, and incoherence among data sources are introduced. In this manuscript, we use a mobile health (mHealth) study on smokers motivated to quit (Break Free; R01MD010362) to illustrate these challenges. Practical approaches to integrate multiple data sources are discussed within the greater scientific context of developing dynamic theories of behavior change and JITAIs.

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

Similar content being viewed by others

Data Availability

Data available upon reasonable request.

Notes

  1. In practice, investigators might consider what thresholds to designate the earliest possible cigarette time when “more than 12 h” is selected by the participant (e.g., a longer interval such as “12–24 h,” a shorter interval such as “12–14 h,” or an interval bounded by the most recent EMA prior to the current EMA).

References

  • Ahluwalia, I. B., Smith, T., Arrazola, R. A., Palipudi, K. M., de Quevedo, I. G., Prasad, V. M., ... & Armour, B. S. (2018). Current tobacco smoking, quit attempts, and knowledge about smoking risks among persons aged > = 15 years - global adult tobacco survey, 28 Countries, 2008–2016. Morbidity and Mortality Weekly Report, 67(38), 1072–1076. https://doi.org/10.15585/mmwr.mm6738a7

  • Babb, S., Malarcher, A., Schauer, G., Asman, K., & Jamal, A. (2017). Quitting smoking among adults - United States, 2000–2015. Mmwr-Morbidity and Mortality Weekly Report, 65(52), 1457–1464.

    Article  PubMed  Google Scholar 

  • Bolger, N., & Laurenceau, J. P. (2013). Intensive longitudinal methods. The Guilford Press.

    Google Scholar 

  • Collins, L. M. (2006). Analysis of longitudinal data: The integration of theoretical model, temporal design, and statistical model. Annual Review of Psychology, 57, 505–528. https://doi.org/10.1146/annurev.psych.57.102904.190146

    Article  PubMed  Google Scholar 

  • Collins, L. M., & Graham, J. W. (2002). The effect of the timing and spacing of observations in longitudinal studies of tobacco and other drug use: Temporal design considerations. Drug and Alcohol Dependence, 68(Suppl 1), S85-96. https://doi.org/10.1016/s0376-8716(02)00217-x

    Article  PubMed  Google Scholar 

  • Creamer, M. R., Wang, T. W., Babb, S., Cullen, K. A., Day, H., Willis, G., ... & Neff, L. (2019). Tobacco product use and cessation indicators among adults - United States, 2018. Mmwr-Morbidity and Mortality Weekly Report, 68(45), 1013–1019. https://doi.org/10.15585/mmwr.mm6845a2

  • Drope, J., Liber, A. C., Cahn, Z., Stoklosa, M., Kennedy, R., Douglas, C. E., & Henson, R. (2018). Who’s still smoking? Disparities in adult cigarette smoking prevalence in the United States [Article]. CA-A Cancer Journal for Clinicians, 68(2), 106–115. https://doi.org/10.3322/caac.21444

    Article  PubMed  Google Scholar 

  • Dube, S. R., Asman, K., Malarcher, A., & Carabollo, R. (2009). Cigarette smoking among adults and trends in smoking cessation-United States, 2008 (Reprinted from MMWR, vol 58, pg 1227–1232, 2009). Jama-Journal of the American Medical Association, 302(24), 2651–2654.

    CAS  Google Scholar 

  • Ertin, E., Stohs, N., Kumar, S., Raij, A., Al'Absi, M., & Shah, S. (2011). AutoSense: Unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field. ACM Conference on Embedded Networked Sensor Systems.

  • Garvey, A. J., Bliss, R. E., Hitchcock, J. L., Heinold, J. W., & Rosner, B. (1992). Predictors of smoking relapse among self-quitters: A report from the normative aging study. Addictive Behaviors, 17(4), 367–377. https://doi.org/10.1016/0306-4603(92)90042-t

    Article  CAS  PubMed  Google Scholar 

  • He, Y., Landrum, M. B., & Zaslavsky, A. M. (2014). Combining information from two data sources with misreporting and incompleteness to assess hospice-use among cancer patients: A multiple imputation approach. Statistics in Medicine, 33(21), 3710–3724. https://doi.org/10.1002/sim.6173

    Article  PubMed  PubMed Central  Google Scholar 

  • Heitjan, D. F., & Rubin, D. B. (1991). Ignorability and coarse data. Annals of Statistics, 19(4), 2244–2253. https://doi.org/10.1214/aos/1176348396

    Article  Google Scholar 

  • Hughes, J. R., Keely, J., & Naud, S. (2004). Shape of the relapse curve and long-term abstinence among untreated smokers [Review]. Addiction, 99(1), 29–38. https://doi.org/10.1111/j.1360-0443.2004.00540.x

    Article  PubMed  Google Scholar 

  • Ji, L., Chow, S. M., Schermerhorn, A. C., Jacobson, N. C., & Cummings, E. M. (2018). Handling missing data in the modeling of intensive longitudinal data. Structural Equation Modeling: A Multidisciplinary Journal, 25(5), 715–736. https://doi.org/10.1080/10705511.2017.1417046

    Article  PubMed  Google Scholar 

  • Kenford, S. L., Fiore, M. C., Jorenby, D. E., Smith, S. S., Wetter, D., & Baker, T. B. (1994). Predicting smoking cessation. Who will quit with and without the nicotine patch. Jama, 271(8), 589–594. https://doi.org/10.1001/jama.271.8.589

  • Klasnja, P., Hekler, E. B., Shiffman, S., Boruvka, A., Almirall, D., Tewari, A., & Murphy, S. A. (2016). Micro-randomized trials: An experimental design for developing Just-in-Time Adaptive Interventions. Health Psychology, 34, 1220–1228.

    Article  Google Scholar 

  • Klasnja, P., Smith, S., Seewald, N. J., Lee, A., Hall, K., Luers, B., ... & Murphy, S. A. (2019). Efficacy of contextually tailored suggestions for physical activity: A micro-randomized optimization trial of heartsteps. Annals of Behavioral Medicine, 53(6), 573–582. https://doi.org/10.1093/abm/kay067

  • Kleiman, E., Millner, A. J., Joyce, V. W., Nash, C. C., Buonopane, R. J., & Nock, M. K. (2019). Using wearable physiological monitors with suicidal adolescent inpatients: Feasibility and acceptability study. JMIR mHealth and uHealth. https://doi.org/10.2196/13725

    Article  PubMed  PubMed Central  Google Scholar 

  • Kulak, J. A., Cornelius, M. E., Fong, G. T., & Giovino, G. A. (2016). Differences in quit attempts and cigarette smoking abstinence between whites and african americans in the United States: Literature Review and results from the international tobacco control US survey. Nicotine and Tobacco Research, 18 Suppl 1(Suppl 1), S79–87. https://doi.org/10.1093/ntr/ntv228

  • Lohr, S. L., & Raghunathan, T. E. (2017). Combining survey data with other data sources. Statistical Science, 32(2), 293–312. https://doi.org/10.1214/16-sts584

    Article  Google Scholar 

  • Mallinckrodt, C., Roger, J., Chuang-Stein, C., Molenberghs, G., Lane, P. W., O'kelly, M., ... & Thijs, H. (2013). Missing data: Turning guidance into action. Statistics in Biopharmaceutical Research, 5(4).

  • McBain, H., Shipley, M., & Newman, S. (2015). The impact of self-monitoring in chronic illness on healthcare utilisation: A systematic review of reviews. BMC Health Services Research, 15, 565. https://doi.org/10.1186/s12913-015-1221-5

    Article  PubMed  PubMed Central  Google Scholar 

  • Mokdad, A. H., Marks, J. S., Stroup, D. F., & Gerberding, J. L. (2004). Actual causes of death in the United States, 2000 [Article]. Jama-Journal of the American Medical Association, 291(10), 1238–1245. https://doi.org/10.1001/jama.291.10.1238

    Article  Google Scholar 

  • Nahum-Shani, I., Rabbi, M., Yap, J., Philyaw-Kotov, M. L., Klasnja, P., Bonar, E. E., ... & Walton, M. A. (2021). Translating strategies for promoting engagement in mobile health: A proof-of-concept microrandomized trial. Health Psychology. https://doi.org/10.1037/hea0001101

  • Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018). Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support [Article]. Annals of Behavioral Medicine, 52(6), 446–462. https://doi.org/10.1007/s12160-016-9830-8

    Article  PubMed  Google Scholar 

  • Nahum-Shani, I., Wetter, D. W., & Murphy, S. A. (in press). Adapting just-in-time interventions to vulnerability and receptivity: Conceptual and methodological considerations. In L. M. N. Jacobson, & T. Kowatsch (Ed.), Digital Therapeutics for Mental Health and Addiction: State of the Science and Vision for the Future. Elsevier.

  • Nakajima, M., Lemieux, A. M., Fiecas, M., Chatterjee, S., Sarker, H., Saleheen, N., ... & al 'Absi, M. (2020). Using novel mobile sensors to assess stress and smoking lapse. International Journal of Psychophysiology, 158, 411–418. https://doi.org/10.1016/j.ijpsycho.2020.11.005

  • Nelson, B. W., Low, C. A., Jacobson, N., Areán, P., Torous, J., & Allen, N. B. (2020). Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research. NPJ Digital Medicine, 3, 90. https://doi.org/10.1038/s41746-020-0297-4

    Article  PubMed  PubMed Central  Google Scholar 

  • Pacek, L. R., McClernon, F. J., & Bosworth, H. B. (2018). Adherence to pharmacological smoking cessation interventions: A literature review and synthesis of correlates and barriers. Nicotine & Tobacco Research, 20(10), 1163–1172. https://doi.org/10.1093/ntr/ntx210

    Article  Google Scholar 

  • Piper, M. E., Bullen, C., Krishnan-Sarin, S., Rigotti, N. A., Steinberg, M. L., Streck, J. M., & Joseph, A. M. (2019). Defining and measuring abstinence in clinical trials of smoking cessation interventions: An updated review. Nicotine & Tobacco Research, 1–9.

  • Rabbi, M., Philyaw Kotov, M., Cunningham, R., Bonar, E. E., Nahum-Shani, I., Klasnja, P., ... & Murphy, S. (2018). Toward increasing engagement in substance use data collection: Development of the substance abuse research assistant app and protocol for a microrandomized trial using adolescents and emerging adults. JMIR Research Protocols, 7(7), e166. https://doi.org/10.2196/resprot.9850

  • Rabbi, M., Philyaw-Kotov, M., Lee, J., Mansour, A., Dent, L., Wang, X., ... & Murphy, S. (2017). SARA: A mobile app to engage users in health data collection. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017, 781–789. https://doi.org/10.1145/3123024.3125611

  • Rhee, S. Y., Dickerson, J., & Xu, D. (2006). Bioinformatics and its applications in plant biology. Annual Review of Plant Biology, 57, 335–360. https://doi.org/10.1146/annurev.arplant.56.032604.144103

    Article  CAS  PubMed  Google Scholar 

  • Riley, W. T., Serrano, K. J., Nilsen, W., & Atienza, A. A. (2015). Mobile and wireless technologies in health behavior and the potential for intensively adaptive interventions. Current Opinion in Psychology, 5, 67–71. https://doi.org/10.1016/j.copsyc.2015.03.024

    Article  PubMed  PubMed Central  Google Scholar 

  • Saleheen, N., Ali, A. A., Hossain, S. M., Sarker, H., Chatterjee, S., Marlin, B., ... & Kumar, S. (2015). puffMarker: A multi-sensor approach for pinpointing the timing of first lapse in smoking cessation. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 999–1010.

  • Samet, J. M. (2013). Tobacco smoking: the leading cause of preventable disease worldwide. Thoracic Surgery Clinics, 23(2), 103-112.. https://doi.org/10.1016/j.thorsurg.2013.01.009

  • Schenker, N., & Raghunathan, T. E. (2007). Combining information from multiple surveys to enhance estimation of measures of health. Statistics in Medicine, 26(8), 1802–1811. https://doi.org/10.1002/sim.2801

    Article  PubMed  Google Scholar 

  • Schenker, N., Raghunathan, T. E., & Bondarenko, I. (2010). Improving on analyses of self-reported data in a large-scale health survey by using information from an examination-based survey. Statistics in Medicine, 29(5), 533–545. https://doi.org/10.1002/sim.3809

    Article  PubMed  Google Scholar 

  • Schifeling, T., Reiter, J. P., & Deyoreo, M. (2019). Data fusion for correcting measurement errors. Journal of Survey Statistics and Methodology, 7(2), 175–200. https://doi.org/10.1093/jssam/smy010

    Article  Google Scholar 

  • Shiffman, S., Hufford, M., Hickcox, M., Paty, J. A., Gnys, M., & Kassel, J. D. (1997). Remember that? A comparison of real-time versus retrospective recall of smoking lapses. Journal of Consulting and Clinical Psychology, 65(2), 292–300. https://doi.org/10.1037/0022-006x.65.2.292.a

    Article  CAS  PubMed  Google Scholar 

  • Snyder, M. (1979). Self-monitoring processes. In Advances in experimental and social psychology (Vol. 12, pp. 85–128). Academic Press.

  • Stahre, M., Okuyemi, K. S., Joseph, A. M., & Fu, S. S. (2010). Racial/ethnic differences in menthol cigarette smoking, population quit ratios and utilization of evidence-based tobacco cessation treatments. Addiction, 105(Suppl 1), 75–83. https://doi.org/10.1111/j.1360-0443.2010.03200.x

    Article  PubMed  Google Scholar 

  • Toscos, T., Drouin, M., Pater, J. A., Flanagan, M., Wagner, S., Coupe, A., ... &  Mirro, M. J. (2020). Medication adherence for atrial fibrillation patients: triangulating measures from a smart pill bottle, e-prescribing software, and patient communication through the electronic health record. JAMIA Open, 3(2), 233–242. https://doi.org/10.1093/jamiaopen/ooaa007

  • Vardigan, M., Heus, P., & Thomas, W. (2008). Data documentation initiative: Toward a standard for the social sciences. International Journal of Digital Curation, 3(1).

  • Watkins, K. L., Regan, S. D., Nguyen, N., Businelle, M. S., Kendzor, D. E., Lam, C., ... & Reitzel, L. R. (2014). Advancing cessation research by integrating EMA and geospatial methodologies: associations between tobacco retail outlets and real-time smoking urges during a quit attempt [Article]. Nicotine & Tobacco Research, 16, S93-S101. https://doi.org/10.1093/ntr/ntt135

  • Yap, J. (2019). jamieyap/SARA v1.0.0.

  • Zhou, X. L., Nonnemaker, J., Sherrill, B., Gilsenan, A. W., Coste, F., & West, R. (2009). Attempts to quit smoking and relapse: Factors associated with success or failure from the ATTEMPT cohort study. Addictive Behaviors, 34(4), 365–373. https://doi.org/10.1016/j.addbeh.2008.11.013

    Article  PubMed  Google Scholar 

Download references

Funding

The research reported in this publication was supported by awards from the National Institute on Drug Abuse (P50DA054039; R01DA039901); National Institute on Minority Health and Health Disparities (R01MD010362), National Cancer Institute (P30CA042014; K99CA252604-01A1; U01CA220437), National Center for Advancing Translational Sciences of the National Institutes of Health (UL1TR002538; 5TL1TR002540), and the Huntsman Cancer Foundation.

Author information

Author notes

  1. Lindsey N. Potter and Jamie Yap share first-authorship.

Authors and Affiliations

  1. Center for Health Outcomes and Population Equity (Center for HOPE), Huntsman Cancer Institute, Salt Lake City, UT, USA

    Lindsey N. Potter & David W. Wetter

  2. Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA

    Lindsey N. Potter & David W. Wetter

  3. Institute for Social Research, University of Michigan, Ann Arbor, MI, USA

    Jamie Yap, Walter Dempsey & Inbal Nahum-Shani

  4. Center for Methodologies for Adapting and Personalizing Prevention, Treatment, and Recovery Services for SUD and HIV (MAPS Center), University of Michigan, Ann Arbor, MI, USA

    Walter Dempsey

  5. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

    Walter Dempsey

Authors
  1. Lindsey N. Potter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jamie Yap
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Walter Dempsey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David W. Wetter
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Inbal Nahum-Shani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lindsey N. Potter.

Ethics declarations

Ethics Approval

All Break Free study procedures were approved by the appropriate Institutional Review Board.

Consent to Participate

Not applicable.

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Potter, L.N., Yap, J., Dempsey, W. et al. Integrating Intensive Longitudinal Data (ILD) to Inform the Development of Dynamic Theories of Behavior Change and Intervention Design: a Case Study of Scientific and Practical Considerations. Prev Sci 24, 1659–1671 (2023). https://doi.org/10.1007/s11121-023-01495-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11121-023-01495-4

Keywords

Search

Navigation