The WorkingWell Mobile Phone App for Individuals With Serious Mental Illnesses: Proof-of-Concept, Mixed-Methods Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: The disparities in employment for individuals with serious mental illnesses have been well documented, as have the benefits of work. Mobile technology can provide accessible in-the-moment support for these individuals. The WorkingWell mobile app was developed to meet the need for accessible follow-along supports for individuals with serious mental illnesses in the workplace. OBJECTIVE: We explore the usability, usage, usefulness, and overall feasibility of the WorkingWell mobile app with individuals with serious mental illnesses who are actively employed and receiving community-based services. METHODS: In this proof-of-concept, mixed-methods, 2-month feasibility study (N=40), employed individuals with serious mental illnesses were recruited in mental health agencies. Participants completed surveys regarding background characteristics and cellphone use at enrollment and responded to interview items regarding app usability, usage, and usefulness in technical assistance calls at 1, 2, 4, and 6 weeks of participation and in the exit interview at 8 weeks. Data on the frequency of app usage were downloaded on a daily basis. A version of the System Usability Scale (SUS) was administered in the exit interview. Overall feasibility was determined by the percent of users completing the study, responses to an interview item regarding continued use, and findings on usability, usage, and usefulness. General impressions were obtained from users regarding user support materials, technical assistance, and study procedures. RESULTS: Most participants were male (60%, 24/40), aged 55 or younger (70%, 28/40), white (80%, 32/40), had less than a 4-year college education (78%, 31/40), were employed part-time (98%, 39/40), had been working more than 6 months (60%, 24/40), and indicated a diagnosis of bipolar, schizoaffective, or depressive disorder (84%, 16/25). The majority of participants owned cellphones (95%, 38/40) and used them multiple times per day (83%, 33/40). Their average rating on SUS usability items was 3.93 (SD 0.77, range 1.57-5.00), reflecting positive responses. In general, participants indicated WorkingWell was "very easy," "straightforward," "simple," and "user friendly." Usability challenges were related to personal issues (eg, memory) or to difficulties with the phone or app. Data on app usage varied considerably. The most frequent navigations were to the home screen, followed by Rate My Day and My Progress, and then by Manage the Moment and Remind Me. The app was described as useful by most participants; 86% (30/35) agreed the app would help them manage better on the job. Of the 40 original participants, 35 (87%) completed the study. CONCLUSIONS: The WorkingWell app is a feasible approach to providing accessible, as-needed employment support for individuals with serious mental illnesses. The app would benefit from modifications to address recommendations from feasibility testing. Controlled research with larger samples, more diverse in individual characteristics and workplace settings, is essential to demonstrating the effectiveness of the app.
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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 | 0,034 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».