Patient Smartphone Ownership and Interest in Mobile Apps to Monitor Symptoms of Mental Health Conditions: A Survey in Four Geographically Distinct Psychiatric Clinics
Bibliographic record
Abstract
BACKGROUND: Despite growing interest in mobile mental health and utilization of smartphone technology to monitor psychiatric symptoms, there remains a lack of knowledge both regarding patient ownership of smartphones and their interest in using such to monitor their mental health. OBJECTIVE: To provide data on psychiatric outpatients' prevalence of smartphone ownership and interest in using their smartphones to run applications to monitor their mental health. METHODS: We surveyed 320 psychiatric outpatients from four clinics around the United States in order to capture a geographically and socioeconomically diverse patient population. These comprised a state clinic in Massachusetts (n=108), a county clinic in California (n=56), a hybrid public and private clinic in Louisiana (n=50), and a private/university clinic in Wisconsin (n=106). RESULTS: Smartphone ownership and interest in utilizing such to monitor mental health varied by both clinic type and age with overall ownership of 62.5% (200/320), which is slightly higher than the average United States' rate of ownership of 58% in January 2014. Overall patient interest in utilizing smartphones to monitor symptoms was 70.6% (226/320). CONCLUSIONS: These results suggest that psychiatric outpatients are interested in using their smartphones to monitor their mental health and own the smartphones capable of running mental healthcare related mobile applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".