Smartphone, Social Media, and Mental Health App Use in an Acute Transdiagnostic Psychiatric Sample
Bibliographic record
Abstract
BACKGROUND: Despite high rates of smartphone ownership in psychiatric populations, there are very little data available characterizing smartphone use in individuals with mental illness. In particular, few studies have examined the interest and use of smartphones to support mental health. OBJECTIVE: This study aimed to (1) characterize general smartphone app and social media usage in an acute transdiagnostic psychiatric sample with high smartphone ownership, (2) characterize current engagement and interest in the use of smartphone apps to support mental health, and (3) test demographic and clinical predictors of smartphone use. METHODS: The survey was completed by all patients attending an adult partial hospital program, with no exclusion criteria. The primary outcomes were frequency of use of general and mental health smartphone apps (smartphone use survey) and the frequency of social media use and phone-checking behavior (mobile technology engagement scale). RESULTS: Overall, 322 patients (aged mean 33.49, SD 13.87 years; 57% female) reported that their most frequently used app functions were texting, email, and social media. Younger individuals reported more frequent use across most types of apps. Baseline depression and anxiety symptoms were not associated with the frequency of app use. Participants reported health care, calendar, and texting apps as most supportive of their mental health and social media apps as most negatively affecting their mental health. Most patients reported an interest in (73.9% [238/322]) and willingness to use (81.3% [262/322]) a smartphone app to monitor their mental health condition. Less than half (44%) of the patients currently had a mental health app downloaded on their smartphone, with mindfulness and meditation apps being the most common type. CONCLUSIONS: The high interest in and willingness to use mental health apps, paired with the only moderate current reported usage, indicate a potential unmet treatment opportunity in psychiatric populations. There is potential to optimize non-mental health-specific apps to better support the needs of those with mental illness and to design a new wave of mental health apps that match the needs of these populations as well as the way they use smartphones in daily life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".