Assessing Real-Time Moderation for Developing Adaptive Mobile Health Interventions for Medical Interns: Micro-Randomized Trial

Timothy NeCamp, Srijan Sen, Elena Frank, Maureen A Walton, Edward L Ionides, Yu Fang, Ambuj Tewari, Zhenke Wu, Timothy NeCamp, Srijan Sen, Elena Frank, Maureen A Walton, Edward L Ionides, Yu Fang, Ambuj Tewari, Zhenke Wu

Abstract

Background: Individuals in stressful work environments often experience mental health issues, such as depression. Reducing depression rates is difficult because of persistently stressful work environments and inadequate time or resources to access traditional mental health care services. Mobile health (mHealth) interventions provide an opportunity to deliver real-time interventions in the real world. In addition, the delivery times of interventions can be based on real-time data collected with a mobile device. To date, data and analyses informing the timing of delivery of mHealth interventions are generally lacking.

Objective: This study aimed to investigate when to provide mHealth interventions to individuals in stressful work environments to improve their behavior and mental health. The mHealth interventions targeted 3 categories of behavior: mood, activity, and sleep. The interventions aimed to improve 3 different outcomes: weekly mood (assessed through a daily survey), weekly step count, and weekly sleep time. We explored when these interventions were most effective, based on previous mood, step, and sleep scores.

Methods: We conducted a 6-month micro-randomized trial on 1565 medical interns. Medical internship, during the first year of physician residency training, is highly stressful, resulting in depression rates several folds higher than those of the general population. Every week, interns were randomly assigned to receive push notifications related to a particular category (mood, activity, sleep, or no notifications). Every day, we collected interns' daily mood valence, sleep, and step data. We assessed the causal effect moderation by the previous week's mood, steps, and sleep. Specifically, we examined changes in the effect of notifications containing mood, activity, and sleep messages based on the previous week's mood, step, and sleep scores. Moderation was assessed with a weighted and centered least-squares estimator.

Results: We found that the previous week's mood negatively moderated the effect of notifications on the current week's mood with an estimated moderation of -0.052 (P=.001). That is, notifications had a better impact on mood when the studied interns had a low mood in the previous week. Similarly, we found that the previous week's step count negatively moderated the effect of activity notifications on the current week's step count, with an estimated moderation of -0.039 (P=.01) and that the previous week's sleep negatively moderated the effect of sleep notifications on the current week's sleep with an estimated moderation of -0.075 (P<.001). For all three of these moderators, we estimated that the treatment effect was positive (beneficial) when the moderator was low, and negative (harmful) when the moderator was high.

Conclusions: These findings suggest that an individual's current state meaningfully influences their receptivity to mHealth interventions for mental health. Timing interventions to match an individual's state may be critical to maximizing the efficacy of interventions.

Trial registration: ClinicalTrials.gov NCT03972293; https://ichgcp.net/clinical-trials-registry/NCT03972293.

Keywords: depression; digital health; ecological momentary assessment; mobile health; mobile phone; moderator variables; mood; physical activity; sleep; smartphone; wearable devices.

Conflict of interest statement

Conflicts of Interest: None declared.

©Timothy NeCamp, Srijan Sen, Elena Frank, Maureen A Walton, Edward L Ionides, Yu Fang, Ambuj Tewari, Zhenke Wu. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 31.03.2020.

Figures

Figure 1
Figure 1
Screenshots of the app dashboard, mood ecological momentary assessment, and lock screen notifications.
Figure 2
Figure 2
Randomization scheme of the Intern Health Study micro-randomized trial.
Figure 3
Figure 3
Percentage of interns with at least one nonmissing sleep, step, or mood observation for each week in the study.
Figure 4
Figure 4
Estimated treatment effects (compared with no notifications) of notifications on average daily mood, at various values of previous week’s mood. The x-axis also contains a scaled histogram of previous week’s average mood.
Figure 5
Figure 5
Estimated treatment effects (compared with no notifications) of different notification categories on average daily mood, at various values of previous week’s mood. The x-axis also contains a scaled histogram of previous week’s average mood.
Figure 6
Figure 6
Estimated treatment effects (compared with no notifications) of activity notifications on average daily steps, at various values of previous week’s step counts. The x-axis also contains a scaled histogram of previous week’s average daily step count.
Figure 7
Figure 7
Estimated treatment effects (compared with no notifications) of sleep notifications on average daily sleep minutes, at various values of previous week’s hourly sleep. The x-axis also contains a scaled histogram of previous week’s average daily sleep count.

References

    1. World Health Organization. 2017. [2020-01-31]. Depression and Other Common Mental Disorders: Global Health Estimates .
    1. Greenberg PE, Fournier A, Sisitsky T, Pike CT, Kessler RC. The economic burden of adults with major depressive disorder in the United States (2005 and 2010) J Clin Psychiatry. 2015 Mar;76(2):155–62. doi: 10.4088/JCP.14m09298.
    1. Hawkins JD, Jenson JM, Catalano R, Fraser MW, Botvin GJ, Shapiro V, Brown CH, Beardslee W, Brent D, Leslie LK, Rotheram-Borus MJ, Shea P, Shih A, Anthony E, Haggerty KP, Bender K, Gorman-Smith D, Casey E, Stone S. Unleashing the power of prevention. NAM Perspectives. 2015;5(6):1343–4. doi: 10.31478/201506c.
    1. Tennant C. Work-related stress and depressive disorders. J Psychosom Res. 2001 Dec;51(5):697–704. doi: 10.1016/s0022-3999(01)00255-0.
    1. Baucom KJ, Queen TL, Wiebe DJ, Turner SL, Wolfe KL, Godbey EI, Fortenberry KT, Mansfield JH, Berg CA. Depressive symptoms, daily stress, and adherence in late adolescents with type 1 diabetes. Health Psychol. 2015 May;34(5):522–30. doi: 10.1037/hea0000219.
    1. Everett KD, Brantley PJ, Sletten C, Jones GN, McKnight GT. The relation of stress and depression to interdialytic weight gain in hemodialysis patients. Behav Med. 1995;21(1):25–30. doi: 10.1080/08964289.1995.9933739.
    1. Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. 2018 May 18;52(6):446–62. doi: 10.1007/s12160-016-9830-8.
    1. Zhang S, Elhadad N. Factors contributing to dropping-out in an online health community: static and longitudinal analyses. AMIA Annu Symp Proc. 2016;2016:2090–9.
    1. Heckman BW, Mathew AR, Carpenter MJ. Treatment burden and treatment fatigue as barriers to health. Curr Opin Psychol. 2015 Oct 1;5:31–6. doi: 10.1016/j.copsyc.2015.03.004.
    1. Riley WT, Rivera DE, Atienza AA, Nilsen W, Allison SM, Mermelstein R. Health behavior models in the age of mobile interventions: are our theories up to the task? Transl Behav Med. 2011 Mar;1(1):53–71. doi: 10.1007/s13142-011-0021-7.
    1. Spruijt-Metz D, Nilsen W. Dynamic models of behavior for just-in-time adaptive interventions. IEEE Pervasive Comput. 2014;13(3):13–7. doi: 10.1109/MPRV.2014.46.
    1. Riley WT. Theoretical models to inform technology-based health behavior interventions. In: Marsch LA, Lord SE, Dallery J, editors. Behavioral Healthcare and Technology: Using Science-Based Innovations to Transform Practice. Oxford, United Kingdom: Oxford University Press; 2014. pp. 13–24.
    1. Wang L, Miller LC. Just-in-the-moment adaptive interventions (JITAI): a meta-analytical review. Health Commun. 2019 Oct 5;:1–14. doi: 10.1080/10410236.2019.1652388.
    1. Miner A, Kuhn E, Hoffman JE, Owen JE, Ruzek JI, Taylor CB. Feasibility, acceptability, and potential efficacy of the PTSD Coach app: a pilot randomized controlled trial with community trauma survivors. Psychol Trauma. 2016 May;8(3):384–92. doi: 10.1037/tra0000092.
    1. Ahmedani BK, Crotty N, Abdulhak MM, Ondersma SJ. Pilot feasibility study of a brief, tailored mobile health intervention for depression among patients with chronic pain. Behav Med. 2015;41(1):25–32. doi: 10.1080/08964289.2013.867827.
    1. Possemato K, Kuhn E, Johnson E, Hoffman JE, Owen JE, Kanuri N, de Stefano L, Brooks E. Using PTSD Coach in primary care with and without clinician support: a pilot randomized controlled trial. Gen Hosp Psychiatry. 2016;38:94–8. doi: 10.1016/j.genhosppsych.2015.09.005.
    1. Rizvi SL, Dimeff LA, Skutch J, Carroll D, Linehan MM. A pilot study of the DBT coach: an interactive mobile phone application for individuals with borderline personality disorder and substance use disorder. Behav Ther. 2011 Dec;42(4):589–600. doi: 10.1016/j.beth.2011.01.003.
    1. Kristjánsdóttir OB, Fors EA, Eide E, Finset A, Stensrud TL, van Dulmen S, Wigers SH, Eide H. A smartphone-based intervention with diaries and therapist-feedback to reduce catastrophizing and increase functioning in women with chronic widespread pain: randomized controlled trial. J Med Internet Res. 2013 Jan 7;15(1):e5. doi: 10.2196/jmir.2249.
    1. Proudfoot J, Clarke J, Birch M, Whitton AE, Parker G, Manicavasagar V, Harrison V, Christensen H, Hadzi-Pavlovic D. Impact of a mobile phone and web program on symptom and functional outcomes for people with mild-to-moderate depression, anxiety and stress: a randomised controlled trial. BMC Psychiatry. 2013 Dec 18;13:312. doi: 10.1186/1471-244X-13-312.
    1. Newman MG, Przeworski A, Consoli AJ, Taylor CB. A randomized controlled trial of ecological momentary intervention plus brief group therapy for generalized anxiety disorder. Psychotherapy (Chic) 2014 Jul;51(2):198–206. doi: 10.1037/a0032519.
    1. Burns MN, Begale M, Duffecy J, Gergle D, Karr CJ, Giangrande E, Mohr DC. Harnessing context sensing to develop a mobile intervention for depression. J Med Internet Res. 2011 Aug 12;13(3):e55. doi: 10.2196/jmir.1838.
    1. Smyth JM, Heron KE. Is Providing Mobile Interventions 'Just-in-time' Helpful? An Experimental Proof of Concept Study of Just-in-time Intervention for Stress Management. Proceedings of the 2016 IEEE Wireless Health; WH'16; October 25-27, 2016; Bethesda, MD, USA. 2016. pp. 1–7.
    1. Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, Murphy SA. Microrandomized trials: an experimental design for developing just-in-time adaptive interventions. Health Psychol. 2015 Dec;34S:1220–8. doi: 10.1037/hea0000305.
    1. Kalmbach DA, Fang Y, Arnedt JT, Cochran AL, Deldin PJ, Kaplin AI, Sen S. Effects of sleep, physical activity, and shift work on daily mood: a prospective mobile monitoring study of medical interns. J Gen Intern Med. 2018 Jun;33(6):914–20. doi: 10.1007/s11606-018-4373-2.
    1. Akerstedt T. Psychosocial stress and impaired sleep. Scand J Work Environ Health. 2006 Dec;32(6):493–501.
    1. Lallukka T, Sarlio-Lähteenkorva S, Roos E, Laaksonen M, Rahkonen O, Lahelma E. Working conditions and health behaviours among employed women and men: the Helsinki Health Study. Prev Med. 2004 Jan;38(1):48–56. doi: 10.1016/j.ypmed.2003.09.027.
    1. Baglioni C, Battagliese G, Feige B, Spiegelhalder K, Nissen C, Voderholzer U, Lombardo C, Riemann D. Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies. J Affect Disord. 2011 Dec;135(1-3):10–9. doi: 10.1016/j.jad.2011.01.011.
    1. Ströhle A. Physical activity, exercise, depression and anxiety disorders. J Neural Transm (Vienna) 2009 Jul;116(6):777–84. doi: 10.1007/s00702-008-0092-x.
    1. Mata DA, Ramos MA, Bansal N, Khan R, Guille C, di Angelantonio E, Sen S. Prevalence of depression and depressive symptoms among resident physicians: a systematic review and meta-analysis. J Am Med Assoc. 2015 Dec 8;314(22):2373–83. doi: 10.1001/jama.2015.15845.
    1. The Sen Lab. [2020-01-31]. Intern Health Study .
    1. Kraemer HC, Wilson GT, Fairburn CG, Agras WS. Mediators and moderators of treatment effects in randomized clinical trials. Arch Gen Psychiatry. 2002 Oct;59(10):877–83. doi: 10.1001/archpsyc.59.10.877.
    1. Fitbit. [2020-01-31]. Fitbit Charge 2 User Manual .
    1. Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1–32. doi: 10.1146/annurev.clinpsy.3.022806.091415.
    1. Apple Developer. [2020-01-31]. ResearchKit
    1. Löwe B, Kroenke K, Gräfe K. Detecting and monitoring depression with a two-item questionnaire (PHQ-2) J Psychosom Res. 2005 Mar;58(2):163–71. doi: 10.1016/j.jpsychores.2004.09.006.
    1. Foreman AC, Hall C, Bone K, Cheng J, Kaplin A. Just text me: using SMS technology for collaborative patient mood charting. J Particip Med. 2011;3:e45.
    1. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001 Oct;16(9):606–13. doi: 10.1046/j.1525-1497.2001.016009606.x.
    1. Fitbit. [2020-01-31]. Welcome. It's an API .
    1. Korotitsch WJ, Nelson-Gray RO. An overview of self-monitoring research in assessment and treatment. Psychol Assess. 1999;11(4):415–25. doi: 10.1037/1040-3590.11.4.415.
    1. Fogg BJ. A Behavior Model for Persuasive Design. Proceedings of the 4th International Conference on Persuasive Technology; Persuasive'09; April 26 - 29, 2009; Claremont, California, USA. 2009. pp. 1–7.
    1. de Leon E, Fuentes LW, Cohen JE. Characterizing periodic messaging interventions across health behaviors and media: systematic review. J Med Internet Res. 2014 Mar 25;16(3):e93. doi: 10.2196/jmir.2837.
    1. Morrison LG, Hargood C, Pejovic V, Geraghty AW, Lloyd S, Goodman N, Michaelides DT, Weston A, Musolesi M, Weal MJ, Yardley L. The effect of timing and frequency of push notifications on usage of a smartphone-based stress management intervention: an exploratory trial. PLoS One. 2017;12(1):e0169162. doi: 10.1371/journal.pone.0169162.
    1. Ben-Zeev D, Schueller SM, Begale M, Duffecy J, Kane JM, Mohr DC. Strategies for mHealth research: lessons from 3 mobile intervention studies. Adm Policy Ment Health. 2015 Mar;42(2):157–67. doi: 10.1007/s10488-014-0556-2.
    1. Danaher BG, Brendryen H, Seeley JR, Tyler MS, Woolley T. From black box to toolbox: outlining device functionality, engagement activities, and the pervasive information architecture of mHealth interventions. Internet Interv. 2015 Mar 1;2(1):91–101. doi: 10.1016/j.invent.2015.01.002.
    1. Suggs S, Blake H, Bardus M, Lloyd S. Effects of text messaging in addition to emails on physical activity among university and college employees in the UK. J Health Serv Res Policy. 2013 May;18(1 Suppl):56–64. doi: 10.1177/1355819613478001.
    1. Miller WR, Rollnick S. Motivational Interviewing: Preparing People for Change. Second Edition. New York, US: The Guilford Press; 2002.
    1. Miller WR, Rose GS. Toward a theory of motivational interviewing. Am Psychol. 2009 Oct;64(6):527–37. doi: 10.1037/a0016830.
    1. Resnicow K, McMaster F. Motivational Interviewing: moving from why to how with autonomy support. Int J Behav Nutr Phys Act. 2012 Mar 2;9:19. doi: 10.1186/1479-5868-9-19.
    1. Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Upper Saddle River, New Jersey: Prentice Hall; 1986.
    1. DiClemente CC, Marinilli AS, Singh M, Bellino LE. The role of feedback in the process of health behavior change. Am J Health Behav. 2001;25(3):217–27. doi: 10.5993/ajhb.25.3.8.
    1. Harkin B, Webb TL, Chang BP, Prestwich A, Conner M, Kellar I, Benn Y, Sheeran P. Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence. Psychol Bull. 2016 Mar;142(2):198–229. doi: 10.1037/bul0000025.
    1. Hockey R. The Psychology of Fatigue: Work, Effort and Control. Cambridge, UK: Cambridge University Press; 2013.
    1. Firebase - Google. [2020-01-31]. Firebase Cloud Messaging .
    1. Qualtrics XM // The Leading Experience Management Software. [2020-01-31]. .
    1. Boruvka A, Almirall D, Witkiewitz K, Murphy SA. Assessing time-varying causal effect moderation in mobile health. J Am Stat Assoc. 2018;113(523):1112–21. doi: 10.1080/01621459.2017.1305274.
    1. Huber PJ. The behavior of maximum likelihood estimates under nonstandard conditions. Berkeley Symp Math Stat Probab. 1967;1:221–33.
    1. Halekoh U, Højsgaard S, Yan J. The R package GEEPACK for generalized estimating equations. J Stat Soft. 2006;15(2):1–11. doi: 10.18637/jss.v015.i02.
    1. Little RJ, Rubin DB. Statistical Analysis with Missing Data. Second Edition. Hoboken, New Jersey: Wiley-interscience; 2002.
    1. Bell ML, Fairclough DL. Practical and statistical issues in missing data for longitudinal patient-reported outcomes. Stat Methods Med Res. 2014 Oct;23(5):440–59. doi: 10.1177/0962280213476378.
    1. Shortreed SM, Laber E, Scott Stroup T, Pineau J, Murphy SA. A multiple imputation strategy for sequential multiple assignment randomized trials. Stat Med. 2014 Oct 30;33(24):4202–14. doi: 10.1002/sim.6223.
    1. Grund S, Robitzsch A, Luedtke O. mitml: Tools for Multiple Imputation in Multilevel Modeling.
    1. Pielot M, Cardoso B, Katevas K, Serrà J, Matic A, Oliver N. Beyond interruptibility: predicting opportune moments to engage mobile phone users. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2017;1(3):1–25. doi: 10.1145/3130956.
    1. Sarker H, Sharmin M, Ali AA, Rahman MM, Bari R, Hossain SM, Kumar S. Assessing the availability of users to engage in just-in-time intervention in the natural environment. Proc ACM Int Conf Ubiquitous Comput. 2014;2014:909–20. doi: 10.1145/2632048.2636082.
    1. Bidargaddi N, Almirall D, Murphy S, Nahum-Shani I, Kovalcik M, Pituch T, Maaieh H, Strecher V. To prompt or not to prompt? A microrandomized trial of time-varying push notifications to increase proximal engagement with a mobile health app. JMIR Mhealth Uhealth. 2018 Dec 29;6(11):e10123. doi: 10.2196/10123.
    1. Cohen J. Statistical Power Analysis for the Behavioral Sciences. Second Edition. Mahwah, New Jersey, United States: Lawrence Erlbaum Associates; 1988.
    1. Luers B, Klasnja P, Murphy S. Standardized effect sizes for preventive mobile health interventions in micro-randomized trials. Prev Sci. 2019 Jan;20(1):100–9. doi: 10.1007/s11121-017-0862-5.
    1. Klasnja P, Smith S, Seewald NJ, Lee A, Hall K, Luers B, Hekler EB, Murphy SA. Efficacy of contextually tailored suggestions for physical activity: a micro-randomized optimization trial of HeartSteps. Ann Behav Med. 2019 May 3;53(6):573–82. doi: 10.1093/abm/kay067.
    1. Collins LM, Murphy SA, Strecher V. The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. Am J Prev Med. 2007 May;32(5 Suppl):S112–8. doi: 10.1016/j.amepre.2007.01.022.
    1. The Methodology Center. [2020-01-31]. Overview of MOST
    1. Prince SA, Adamo KB, Hamel ME, Hardt J, Gorber SC, Tremblay M. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Act. 2008 Dec 6;5:56. doi: 10.1186/1479-5868-5-56.
    1. Frost MH, Reeve BB, Liepa AM, Stauffer JW, Hays RD, Mayo/FDA Patient-Reported Outcomes Consensus Meeting Group What is sufficient evidence for the reliability and validity of patient-reported outcome measures? Value Health. 2007;10(Suppl 2):S94–105. doi: 10.1111/j.1524-4733.2007.00272.x.
    1. Krebs P, Prochaska JO, Rossi JS. A meta-analysis of computer-tailored interventions for health behavior change. Prev Med. 2010;51(3-4):214–21. doi: 10.1016/j.ypmed.2010.06.004.
    1. Hawkins RP, Kreuter M, Resnicow K, Fishbein M, Dijkstra A. Understanding tailoring in communicating about health. Health Educ Res. 2008 Jul;23(3):454–66. doi: 10.1093/her/cyn004.

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