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

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; http://clinicaltrials.gov/ct2/show/NCT03972293.

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

Figure 1

Screenshots of the app dashboard, mood ecological momentary assessment, and lock screen notifications.

Figure 2

Randomization scheme of the Intern Health Study micro-randomized trial.

Figure 3

Percentage of interns with at least one nonmissing sleep, step, or mood observation for each week in the study.

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

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

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

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.