Digital Phenotyping Self-Monitoring Behaviors for Individuals With Type 2 Diabetes Mellitus: Observational Study Using Latent Class Growth Analysis

Qing Yang, Daniel Hatch, Matthew J Crowley, Allison A Lewinski, Jacqueline Vaughn, Dori Steinberg, Allison Vorderstrasse, Meilin Jiang, Ryan J Shaw, Qing Yang, Daniel Hatch, Matthew J Crowley, Allison A Lewinski, Jacqueline Vaughn, Dori Steinberg, Allison Vorderstrasse, Meilin Jiang, Ryan J Shaw

Abstract

Background: Sustained self-monitoring and self-management behaviors are crucial to maintain optimal health for individuals with type 2 diabetes mellitus (T2DM). As smartphones and mobile health (mHealth) devices become widely available, self-monitoring using mHealth devices is an appealing strategy in support of successful self-management of T2DM. However, research indicates that engagement with mHealth devices decreases over time. Thus, it is important to understand engagement trajectories to provide varying levels of support that can improve self-monitoring and self-management behaviors.

Objective: The aims of this study were to develop (1) digital phenotypes of the self-monitoring behaviors of patients with T2DM based on their engagement trajectory of using multiple mHealth devices, and (2) assess the association of individual digital phenotypes of self-monitoring behaviors with baseline demographic and clinical characteristics.

Methods: This longitudinal observational feasibility study included 60 participants with T2DM who were instructed to monitor their weight, blood glucose, and physical activity using a wireless weight scale, phone-tethered glucometer, and accelerometer, respectively, over 6 months. We used latent class growth analysis (LCGA) with multitrajectory modeling to associate the digital phenotypes of participants' self-monitoring behaviors based on their engagement trajectories with multiple mHealth devices. Associations between individual characteristics and digital phenotypes on participants' self-monitoring behavior were assessed by analysis of variance or the Chi square test.

Results: The engagement with accelerometers to monitor daily physical activities was consistently high for all participants over time. Three distinct digital phenotypes were identified based on participants' engagement with the wireless weight scale and glucometer: (1) low and waning engagement group (24/60, 40%), (2) medium engagement group (20/60, 33%), and (3) consistently high engagement group (16/60, 27%). Participants that were younger, female, nonwhite, had a low income, and with a higher baseline hemoglobin A1c level were more likely to be in the low and waning engagement group.

Conclusions: We demonstrated how to digitally phenotype individuals' self-monitoring behavior based on their engagement trajectory with multiple mHealth devices. Distinct self-monitoring behavior groups were identified. Individual demographic and clinical characteristics were associated with different self-monitoring behavior groups. Future research should identify methods to provide tailored support for people with T2DM to help them better monitor and manage their condition.

International registered report identifier (irrid): RR2-10.2196/13517.

Keywords: Mobile Health; digital phenotype; latent class growth analysis; self-management; self-monitoring; type 2 diabetes.

Conflict of interest statement

Conflicts of Interest: DS is a consultant with Omada Health. The other authors declare no conflict of interest.

©Qing Yang, Daniel Hatch, Matthew J Crowley, Allison A Lewinski, Jacqueline Vaughn, Dori Steinberg, Allison Vorderstrasse, Meilin Jiang, Ryan J Shaw. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 11.06.2020.

Figures

Figure 1
Figure 1
Empirical plots (mean, SEM) for biweekly engagement trajectories for each mobile health device over all 6 months.
Figure 2
Figure 2
Empirical summary plot for biweekly engagement trajectories with the (A) glucometer and (B) wireless weight scale by different engagement groups.

References

    1. Powers MA, Bardsley J, Cypress M, Duker P, Funnell MM, Fischl AH, Maryniuk MD, Siminerio L, Vivian E. Diabetes Self-Management Education and Support in Type 2 Diabetes: A Joint Position Statement of the American Diabetes Association, the American Association of Diabetes Educators, and the Academy of Nutrition and Dietetics. J Acad Nutr Diet. 2015 Aug;115(8):1323–1334. doi: 10.1016/j.jand.2015.05.012.
    1. Shaw RJ, Bonnet JP, Modarai F, George A, Shahsahebi M. Mobile health technology for personalized primary care medicine. Am J Med. 2015 Jun;128(6):555–557. doi: 10.1016/j.amjmed.2015.01.005.
    1. Greenwood DA, Gee PM, Fatkin KJ, Peeples M. A Systematic Review of Reviews Evaluating Technology-Enabled Diabetes Self-Management Education and Support. J Diabetes Sci Technol. 2017 Sep;11(5):1015–1027. doi: 10.1177/1932296817713506.
    1. Wu X, Guo X, Zhang Z. The Efficacy of Mobile Phone Apps for Lifestyle Modification in Diabetes: Systematic Review and Meta-Analysis. JMIR Mhealth Uhealth. 2019 Jan 15;7(1):e12297. doi: 10.2196/12297.
    1. Shaw RJ, Barnes A, Steinberg D, Vaughn J, Diane A, Levine E, Vorderstrasse A, Crowley MJ, Wood E, Hatch D, Lewinski A, Jiang M, Stevenson J, Yang Q. Enhancing Diabetes Self-Management Through Collection and Visualization of Data From Multiple Mobile Health Technologies: Protocol for a Development and Feasibility Trial. JMIR Res Protoc. 2019 Jun 03;8(6):e13517. doi: 10.2196/13517.
    1. Milward J, Deluca P, Drummond C, Kimergård A. Developing Typologies of User Engagement With the BRANCH Alcohol-Harm Reduction Smartphone App: Qualitative Study. JMIR Mhealth Uhealth. 2018 Dec 13;6(12):e11692. doi: 10.2196/11692.
    1. Goh G, Tan NC, Malhotra R, Padmanabhan U, Barbier S, Allen JC, Østbye T. Short-term trajectories of use of a caloric-monitoring mobile phone app among patients with type 2 diabetes mellitus in a primary care setting. J Med Internet Res. 2015;17(2):e33. doi: 10.2196/jmir.3938.
    1. Pham Q, Graham G, Carrion C, Morita PP, Seto E, Stinson JN, Cafazzo JA. A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review. JMIR Mhealth Uhealth. 2019 Jan 18;7(1):e11941. doi: 10.2196/11941.
    1. Koot D, Goh PSC, Lim RSM, Tian Y, Yau TY, Tan NC, Finkelstein EA. A Mobile Lifestyle Management Program (GlycoLeap) for People With Type 2 Diabetes: Single-Arm Feasibility Study. JMIR Mhealth Uhealth. 2019 May 24;7(5):e12965. doi: 10.2196/12965.
    1. Shaw RJ, Yang Q, Barnes A, Hatch D, Crowley MJ, Vorderstrasse A, Vaughn J, Diane A, Lewinski AA, Jiang M, Stevenson J, Steinberg D. Self-monitoring diabetes with multiple mobile health devices. J Am Med Inform Assoc. 2020 May 01;27(5):667–676. doi: 10.1093/jamia/ocaa007.
    1. Jain SH, Powers BW, Hawkins JB, Brownstein JS. The digital phenotype. Nat Biotechnol. 2015 May;33(5):462–463. doi: 10.1038/nbt.3223.
    1. Torous J, Kiang MV, Lorme J, Onnela J. New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research. JMIR Ment Health. 2016 May 05;3(2):e16. doi: 10.2196/mental.5165.
    1. Ienca M, Vayena E, Blasimme A. Big Data and Dementia: Charting the Route Ahead for Research, Ethics, and Policy. Front Med (Lausanne) 2018;5:13. doi: 10.3389/fmed.2018.00013.
    1. Jaimini U, Thirunarayan K, Kalra M, Venkataraman R, Kadariya D, Sheth A. "How Is My Child's Asthma?" Digital Phenotype and Actionable Insights for Pediatric Asthma. JMIR Pediatr Parent. 2018;1(2):e11988. doi: 10.2196/11988.
    1. Steins D, Dawes H, Esser P, Collett J. Wearable accelerometry-based technology capable of assessing functional activities in neurological populations in community settings: a systematic review. J Neuroeng Rehabil. 2014 Mar 13;11:36. doi: 10.1186/1743-0003-11-36.
    1. Jung T, Wickrama KAS. An Introduction to Latent Class Growth Analysis and Growth Mixture Modeling. Social Pers Psych Compass. 2008 Jan;2(1):302–317. doi: 10.1111/j.1751-9004.2007.00054.x.
    1. Allen NB, Siddique J, Wilkins JT, Shay C, Lewis CE, Goff DC, Jacobs DR, Liu K, Lloyd-Jones D. Blood pressure trajectories in early adulthood and subclinical atherosclerosis in middle age. JAMA. 2014 Feb 05;311(5):490–497. doi: 10.1001/jama.2013.285122.
    1. Hockenberry MJ, Hooke MC, Rodgers C, Taylor O, Koerner KM, Mitby P, Moore I, Scheurer ME, Pan W. Symptom Trajectories in Children Receiving Treatment for Leukemia: A Latent Class Growth Analysis With Multitrajectory Modeling. J Pain Symptom Manage. 2017 Jul;54(1):1–8. doi: 10.1016/j.jpainsymman.2017.03.002.
    1. Jones BL, Nagin DS. Advances in Group-Based Trajectory Modeling and an SAS Procedure for Estimating Them. Sociol Methods Res. 2016 Jun 30;35(4):542–571. doi: 10.1177/0049124106292364.
    1. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, McLeod L, Delacqua G, Delacqua F, Kirby J, Duda SN, REDCap Consortium The REDCap consortium: Building an international community of software platform partners. J Biomed Inform. 2019 Jul;95:103208. doi: 10.1016/j.jbi.2019.103208.
    1. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009 Apr;42(2):377–381. doi: 10.1016/j.jbi.2008.08.010.
    1. Jones BL, Nagin DS. A Note on a Stata Plugin for Estimating Group-based Trajectory Models. Sociol Methods Res. 2013 Sep 30;42(4):608–613. doi: 10.1177/0049124113503141.
    1. Hartz J, Yingling L, Powell-Wiley TM. Use of Mobile Health Technology in the Prevention and Management of Diabetes Mellitus. Curr Cardiol Rep. 2016 Dec;18(12):130. doi: 10.1007/s11886-016-0796-8.
    1. Steinsbekk A, Rygg L, Lisulo M, Rise MB, Fretheim A. Group based diabetes self-management education compared to routine treatment for people with type 2 diabetes mellitus. A systematic review with meta-analysis. BMC Health Serv Res. 2012 Jul 23;12:213. doi: 10.1186/1472-6963-12-213.
    1. Janevic MR, Shute V, Murphy SL, Piette JD. Acceptability and Effects of Commercially Available Activity Trackers for Chronic Pain Management Among Older African American Adults. Pain Med. 2020 Feb 01;21(2):e68–e78. doi: 10.1093/pm/pnz215.
    1. Mendoza JA, Baker KS, Moreno MA, Whitlock K, Abbey-Lambertz M, Waite A, Colburn T, Chow EJ. A Fitbit and Facebook mHealth intervention for promoting physical activity among adolescent and young adult childhood cancer survivors: A pilot study. Pediatr Blood Cancer. 2017 Dec;64(12) doi: 10.1002/pbc.26660.
    1. Glazier RH, Bajcar J, Kennie NR, Willson K. A systematic review of interventions to improve diabetes care in socially disadvantaged populations. Diabetes Care. 2006 Jul;29(7):1675–1688. doi: 10.2337/dc05-1942.

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe