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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».