Technology Acceptance and Usability of a Mobile App to Support the Workflow of Health Care Aides Who Provide Services to Older Adults: Pilot Mixed Methods Study (Preprint)
Notice bibliographique
Résumé
BACKGROUND Health care aides are unlicensed support personnel who provide direct care, personal assistance, and support to people with health conditions. The shortage of health care aides has been attributed to recruitment challenges, high turnover, an aging population, the COVID-19 pandemic, and low retention rates. Mobile apps are among the many information communication technologies that are paving the way for eHealth solutions to help address this workforce shortage by enhancing the workflow of health care aides. In collaboration with Clinisys EMR Inc, we developed a mobile app (Mobile Smart Care System [mSCS]) to support the workflow of health care aides who provide services to older adult residents of a long-term care facility. OBJECTIVE The purpose of this study was to investigate the technology acceptance and usability of a mobile app in a real-world environment, while it is used by health care aides who provide services to older adults. METHODS This pilot study used a mixed methods design: sequential mixed methods (QUANTITATIVE, qualitative). Our study included a pre– and post–paper-based questionnaire with no control group (QUAN). Toward the end of the study, 2 focus groups were conducted with a subsample of health care aides (qual, qualitative description design). Technology acceptance and usability questionnaires used a 5-point Likert scale ranging from disagree (1) to agree (5). The items included in the questionnaires were validated in earlier research as having high levels of internal consistency for the Unified Theory of Acceptance and Use of Technology constructs. A total of 60 health care aides who provided services to older adults as part of their routine caseloads used the mobile app for 1 month. Comparisons of the Unified Theory of Acceptance and Use of Technology constructs’ summative scores at pretest and posttest were calculated using a paired t test (2-tailed). We used the partial least squares structural regression model to determine the factors influencing mobile app acceptance and usability for health care aides. The α level of significance for all tests was set at P≤.05 (2-tailed). RESULTS We found that acceptance of the mSCS was high among health care aides, performance expectancy construct was the strongest predictor of intention to use the mSCS, intention to use the mSCS predicted usage behavior. The qualitative data support the quantitative findings and showed health care aides’ strong belief that the mSCS was useful, portable, and reliable, although there were still opportunities for improvement, especially with regard to the mSCS user interface. CONCLUSIONS Overall, these results support the assertion that mSCS technology acceptance and usability are high among health care aides. In other words, health care aides perceived that the mSCS assisted them in addressing their workflow issues.
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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 | 0,013 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».