Assessing Real-Time Moderation for Developing Adaptive Mobile Health Interventions for Medical Interns: Micro-Randomized Trial
| Autor: | Elena Frank, Maureen A. Walton, Yu Fang, Zhenke Wu, Ambuj Tewari, Timothy NeCamp, Srijan Sen, Edward L. Ionides |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
020205 medical informatics mood Population Psychological intervention digital health physical activity Health Informatics 02 engineering and technology lcsh:Computer applications to medicine. Medical informatics smartphone law.invention 03 medical and health sciences 0302 clinical medicine wearable devices Randomized controlled trial law 0202 electrical engineering electronic engineering information engineering Medicine Humans 030212 general & internal medicine sleep education mHealth mobile health moderator variables education.field_of_study Original Paper mobile phone business.industry lcsh:Public aspects of medicine ecological momentary assessment Internship and Residency lcsh:RA1-1270 Moderation Digital health Mental health Telemedicine Mood depression lcsh:R858-859.7 Female business Clinical psychology |
| Zdroj: | Journal of Medical Internet Research Journal of Medical Internet Research, Vol 22, Iss 3, p e15033 (2020) |
| ISSN: | 1438-8871 1439-4456 |
| Popis: | 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 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; http://clinicaltrials.gov/ct2/show/NCT03972293 |
| Databáze: | OpenAIRE |
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