The Association Between App-Administered Depression Assessments and Suicidal Ideation in User Comments: Retrospective Observational Study
Bibliographic record
Abstract
Background Many people use apps to help understand and manage their depression symptoms. App-administered questionnaires for the symptoms of depression, such as the Patient Health Questionnaire-9, are easy to score and implement in an app, but may not be accompanied by essential resources and access needed to provide proper support and avoid potential harm. Objective Our primary goal was to evaluate the differences in risks and helpfulness associated with using an app to self-diagnose depression, comparing assessment-only apps with multifeatured apps. We also investigated whether, what, and how additional app features may mitigate potential risks. Methods In this retrospective observational study, we identified apps in the Google Play store that provided a depression assessment as a feature and had at least five user comments. We separated apps into two categories based on those having only a depression assessment versus those that offered additional supportive features. We conducted theoretical thematic analyses over the user reviews, with thematic coding indicating the helpfulness of the app, the presence of suicidal ideation, and how and why the apps were used. We compared the results across the two categories of apps and analyzed the differences using chi-square statistical tests. Results We evaluated 6 apps; 3 provided only a depression assessment (assessment only), and 3 provided features in addition to self-assessment (multifeatured). User comments for assessment-only apps indicated significantly more suicidal ideation or self-harm (n=31, 9.4%) compared to comments for multifeatured apps (n=48, 2.3%; X21=43.88, P<.001). Users of multifeatured apps were over three times more likely than assessment-only app users to comment in favor of the app’s helpfulness, likely due to features like mood tracking, journaling, and informational resources (n=56, 17% vs n=1223, 59% respectively; X21=200.36, P<.001). The number of users under the age of 18 years was significantly higher among assessment-only app users (n=40, 12%) than multifeatured app users (n=9, 0.04%; X21=189.09, P<.001). Conclusions Apps that diagnose depression by self-assessment without context or other supportive features are more likely to be used by those under 18 years of age and more likely to be associated with increased user distress and potential harm. Depression self-assessments in apps should be implemented with caution and accompanied by evidence-based capabilities that establish proper context, increase self-empowerment, and encourage users to seek clinical diagnostics and outside help.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".