User Behavior of a Publicly Available, Free-to-Use, Self-guided mHealth App for Depression: Observational Study in a Global Sample
Bibliographic record
Abstract
BACKGROUND: Reducing the burden of depression is a global health concern. Self-guided mobile health (mHealth) apps are one approach to address this problem. However, there is little research on self-guided mHealth apps in a global sample or on how they are used in the real world. These gaps in our knowledge must be addressed to bring the promise of mHealth apps for reducing the global burden of depression closer to reality. OBJECTIVE: The purpose of this study is to examine the naturalistic user behavior of MoodTools, a publicly available, free-to-use, self-guided mHealth app designed to improve symptoms of depression, in a global community sample. METHODS: Mobile analytics data were collected from all unique downloads of the Android version of MoodTools between March 1, 2016, and February 28, 2018. Due to the deidentification and data aggregation process, no demographic or personal identifying information was tied to individual user data. All information was stored in aggregated, anonymized data files on Google Analytics' storage database. Google's software development kit was used to securely capture data about the number of downloads, location of downloads, number of app sessions, frequency and duration of app sessions, time between app sessions, and user retention, allowing for examination of which app's tools were viewed and for how long, including Information (psychoeducation), Test (self-monitoring using the Patient Health Questionnaire [PHQ-9]), Thought Diary (targeting negative cognitions), Activities (behavioral activation), Videos (curated from YouTube), and Safety Plan (safety plan development and links to quickly access crisis management resources). RESULTS: MoodTools was used by 158,930 people from 198 countries, including countries where English was not the primary language and in low- and middle-income countries. After the initial download, 51.14% (n=81,277) of users returned to the app after the initial download, and retention rates decreased with each subsequent app session. The typical person used the app for 3 sessions for a total of 12 minutes over 90 days. The most frequently visited tools were Test and Thought Diary (n=393,549, 24.32%). On average, users completed and reviewed the results of the PHQ-9 for 49 seconds and 53 seconds, respectively, and spent 3 minutes and 5 seconds on Thought Diary. CONCLUSIONS: Self-guided mHealth apps could be one approach (among the many needed) to reduce the burden of depression. Observational data collected in this study show a global interest in MoodTools, including in low- and middle-income countries and countries where English is not the primary language. Future research is needed to determine whether people who use self-guided apps experience improvement in depressive symptoms, and if so, what "dosage" provides a meaningful benefit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".