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Leong U, Chakraborty B. Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review. JMIR Mhealth Uhealth 2023; 11:e44685. [PMID: 37213178 DOI: 10.2196/44685] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/20/2023] [Accepted: 03/31/2023] [Indexed: 05/23/2023] Open
Abstract
BACKGROUND Microrandomized trials (MRTs) have emerged as the gold standard for the development and evaluation of multicomponent, adaptive mobile health (mHealth) interventions. However, not much is known about the state of participant engagement measurement in MRTs of mHealth interventions. OBJECTIVE In this scoping review, we aimed to quantify the proportion of existing or planned MRTs of mHealth interventions to date that have assessed (or have planned to assess) engagement. In addition, for the trials that have explicitly assessed (or have planned to assess) engagement, we aimed to investigate how engagement has been operationalized and to identify the factors that have been studied as determinants of engagement in MRTs of mHealth interventions. METHODS We conducted a broad search for MRTs of mHealth interventions in 5 databases and manually searched preprint servers and trial registries. Study characteristics of each included evidence source were extracted. We coded and categorized these data to identify how engagement has been operationalized and which determinants, moderators, and covariates have been assessed in existing MRTs. RESULTS Our database and manual search yielded 22 eligible evidence sources. Most of these studies (14/22, 64%) were designed to evaluate the effects of intervention components. The median sample size of the included MRTs was 110.5. At least 1 explicit measure of engagement was included in 91% (20/22) of the included MRTs. We found that objective measures such as system usage data (16/20, 80%) and sensor data (7/20, 35%) are the most common methods of measuring engagement. All studies included at least 1 measure of the physical facet of engagement, but the affective and cognitive facets of engagement have largely been neglected (only measured by 1 study each). Most studies measured engagement with the mHealth intervention (Little e) and not with the health behavior of interest (Big E). Only 6 (30%) of the 20 studies that measured engagement assessed the determinants of engagement in MRTs of mHealth interventions; notification-related variables were the most common determinants of engagement assessed (4/6, 67% studies). Of the 6 studies, 3 (50%) examined the moderators of participant engagement-2 studies investigated time-related moderators exclusively, and 1 study planned to investigate a comprehensive set of physiological and psychosocial moderators in addition to time-related moderators. CONCLUSIONS Although the measurement of participant engagement in MRTs of mHealth interventions is prevalent, there is a need for future trials to diversify the measurement of engagement. There is also a need for researchers to address the lack of attention to how engagement is determined and moderated. We hope that by mapping the state of engagement measurement in existing MRTs of mHealth interventions, this review will encourage researchers to pay more attention to these issues when planning for engagement measurement in future trials.
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Affiliation(s)
- Utek Leong
- Department of Psychology, National University of Singapore, Singapore, Singapore
| | - Bibhas Chakraborty
- Centre for Quantitative Medicine and Program in Health Services and Systems Research, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States
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Aguilera A, Hernandez-Ramos R, Haro-Ramos AY, Boone CE, Luo TC, Xu J, Chakraborty B, Karr C, Darrow S, Figueroa CA. A Text Messaging Intervention (StayWell at Home) to Counteract Depression and Anxiety During COVID-19 Social Distancing: Pre-Post Study. JMIR Ment Health 2021; 8:e25298. [PMID: 34543230 PMCID: PMC8562416 DOI: 10.2196/25298] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/07/2021] [Accepted: 09/13/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Social distancing and stay-at-home orders are critical interventions to slow down person-to-person transmission of COVID-19. While these societal changes help contain the pandemic, they also have unintended negative consequences, including anxiety and depression. We developed StayWell, a daily skills-based SMS text messaging program, to mitigate COVID-19-related depression and anxiety symptoms among people who speak English and Spanish in the United States. OBJECTIVE This paper describes the changes in StayWell participants' anxiety and depression levels after 60 days of exposure to skills-based SMS text messages. METHODS We used self-administered, empirically supported web-based questionnaires to assess the demographic and clinical characteristics of StayWell participants. Anxiety and depression were measured using the 2-item Generalized Anxiety Disorder (GAD-2) scale and the 8-item Patient Health Questionnaire-8 (PHQ-8) scale at baseline and 60-day timepoints. We used 2-tailed paired t tests to detect changes in PHQ-8 and GAD-2 scores from baseline to follow-up measured 60 days later. RESULTS The analytic sample includes 193 participants who completed both the baseline and 60-day exit questionnaires. At the 60-day time point, there were significant reductions in both PHQ-8 and GAD-2 scores from baseline. We found an average reduction of -1.72 (95% CI -2.35 to -1.09) in PHQ-8 scores and -0.48 (95% CI -0.71 to -0.25) in GAD-2 scores. These improvements translated to an 18.5% and 17.2% reduction in mean PHQ-8 and GAD-2 scores, respectively. CONCLUSIONS StayWell is an accessible, low-intensity population-level mental health intervention. Participation in StayWell focused on COVID-19 mental health coping skills and was related to improved depression and anxiety symptoms. In addition to improvements in outcomes, we found high levels of engagement during the 60-day intervention period. Text messaging interventions could serve as an important public health tool for disseminating strategies to manage mental health. TRIAL REGISTRATION ClinicalTrials.gov NCT04473599; https://clinicaltrials.gov/ct2/show/NCT04473599. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/23592.
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Affiliation(s)
- Adrian Aguilera
- School of Social Welfare, University of California, Berkeley, Berkeley, CA, United States.,Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, United States
| | - Rosa Hernandez-Ramos
- School of Social Welfare, University of California, Berkeley, Berkeley, CA, United States
| | - Alein Y Haro-Ramos
- School of Public Health, University of California, Berkeley, Berkeley, CA, United States
| | - Claire Elizabeth Boone
- School of Public Health, University of California, Berkeley, Berkeley, CA, United States
| | - Tiffany Christina Luo
- School of Social Welfare, University of California, Berkeley, Berkeley, CA, United States
| | - Jing Xu
- Data Science Programme, Division of Science and Technology, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China.,Center for Quantitative Medicine, Duke National University of Singapore, Singapore, Singapore
| | - Bibhas Chakraborty
- Center for Quantitative Medicine, Duke National University of Singapore, Singapore, Singapore.,Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States.,Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Chris Karr
- Audacious Software, Chicago, IL, United States
| | - Sabrina Darrow
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, United States
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