Patient and Health Care Professional Perspectives About Referral, Self-Reported Use, and Perceived Importance of Digital Mental Health App Attributes in a Diverse Integrated Health System: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Digital mental health applications (DMHAs) are emerging, novel solutions to address gaps in behavioral health care. Accordingly, Kaiser Permanente Mid-Atlantic States (KPMAS) integrated referrals for 6 unique DMHAs into clinical care in 2019. OBJECTIVE: This study investigated patient and health care professional (HCP) experiences with DMHA referral; DMHA use; and perceived importance of engagement, functionality, design, and information attributes in real-world practice. METHODS: Separate cross-sectional surveys were developed and tested for patients and HCPs. Surveys were administered to KPMAS participants through REDCap (Research Electronic Data Capture), and completed between March 2022 and June 2022. Samples included randomly selected patients who were previously referred to at least 1 DMHA between April 2021 and December 2021 and behavioral health and primary care providers who referred DMHAs between December 2019 and December 2021. RESULTS: Of the 119 patients e-mailed a survey link, 58 (48.7%) completed the survey and 44 (37%) confirmed receiving a DMHA referral. The mean age of the sample was 42.21 (SD 14.08) years (29/44, 66%); 73% (32/44) of the respondents were female, 73% (32/44) of the respondents had at least a 4-year college degree, 41% (18/44) of the respondents were Black or African American, and 39% (17/44) of the respondents were White. Moreover, 27% (12/44) of the respondents screened positive for anxiety symptoms, and 23% (10/44) of the respondents screened positive for depression. Overall, 61% (27/44) of the respondents reported DMHA use for ≤6 months since referral, 36% (16/44) reported use within the past 30 days, and 43% (19/44) of the respondents reported that DMHAs were very or extremely helpful for improving mental and emotional health. The most important patient-reported DMHA attributes by domain were being fun and interesting to use (engagement); ease in learning how to use (functionality); visual appeal (design); and having well-written, goal- and topic-relevant content (information). Of the 60 sampled HCPs, 12 (20%) completed the survey. Mean HCP respondent age was 46 (SD 7.75) years, and 92% (11/12) of the respondents were female. Mean number of years since completing training was 14.3 (SD 9.94) years (10/12, 83%). Of the 12 HCPs, 7 (58%) were physicians and 5 (42%) were nonphysicians. The most important HCP-reported DMHA attributes by domain were personalized settings and content (engagement); ease in learning how to use (functionality); arrangement and size of screen content (design); and having well-written, goal- and topic-relevant content (information). HCPs described "typical patients" referred to DMHAs based on perceived need, technical capability, and common medical conditions, and they provided guidance for successful use. CONCLUSIONS: Individual patient needs and preferences should match the most appropriate DMHA. With many DMHA choices, decision support systems are essential to assist patients and HCPs with selecting appropriate DMHAs to optimize uptake and sustained use.
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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 | 0.004 | 0.009 |
| 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) | 0.002 | 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".