Accepted for/Published in: JMIR Mental Health
Date Submitted: May 9, 2020
Date Accepted: Nov 15, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Characterizing Emotional State Transitions During Prolonged Use of a Mindfulness and Meditation App: An Observational Study
ABSTRACT
Background:
With the rising need for mental health care, shortages in mental healthcare providers, and unequal access to care, digital device apps that can be used to adjust mood show promise for helping meet mental health care demands. The therapeutic content delivered through a digital therapeutic to combat aspects of mental health disorder can also be ‘personalized,’ and hence potentially provide greater benefit.
Objective:
We analyzed data collected during the use of the Stop, Breathe and Think (SBT) mindfulness app. The SBT app prompts users to input their emotional state prior to and immediately after engaging with MMAs recommended by the app. Data were collected on more than 650 thousand SBT users involving over nearly 5 million MMAs. We limited the scope of our analysis to users with 10 or more MMA sessions and data on at least 6 basal emotional state evaluations. Using clustering techniques, we empirically grouped emotions into more coherent groups and then applied longitudinal mixed effect models to determine the effects that individual recommended MMAs had on emotional state transitions.
Methods:
The SBT app asks users to input their emotional state prior to and immediately after engaging with their MMAs providing immediate intervention response data. Data was collected on more than 650 thousand users, and over nearly 5 million MMAs. We limited the scope of our analysis to users on the same platform with 10 sessions and at least 6 emotional check-ins. Using clustering techniques, we grouped emotions into empirically formed clusters. We implemented mixed effect models and examined the interaction a MMA had on transitioning a user to another cluster given their initial emotional state.
Results:
We found that basal emotional states have a strong effect on transitions to a different emotional state after MMA engagements and that different MMA impact these transitions. We found that MMAs were effective in eliciting a healthy transition but only under certain conditions, and also observed gender and age effects on these transitions.
Conclusions:
We find that SBT MMA app users' initial emotional state has an impact on which MMAs will have a favorable effect on their transition to another emotional state. Our results have implications for the design and use of guided recommendations for digital therapeutics.
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