Attitudes and Preferences on the Use of Mobile Health Technology and Health Games for Self-Management: Interviews With Older Adults on Anticoagulation Therapy
Notice bibliographique
Résumé
BACKGROUND: Older adults are at substantial risk for cardiovascular disorders that may require anticoagulation therapy. Those on warfarin therapy report dissatisfaction and reduced quality of life (QOL) resulting from the treatment. Advances in the area of mobile health (mHealth) technology have resulted in the design and development of new patient-centric models for the provision of personalized health care services to improve care delivery. However, there is a paucity of research examining the effectiveness of mHealth tools on knowledge, attitudes, and patient satisfaction with treatment, as well as self-management, adherence to therapy, and QOL in older adults with chronic illness conditions requiring long-term warfarin therapy. OBJECTIVE: The objective of the study was to explore the attitudes and preferences of older adults on warfarin therapy regarding the use of mHealth technology and health games to gain skills for self-management. METHODS: We conducted group and individual interviews with patients (60 years or older) on warfarin therapy at two anticoagulation clinics affiliated with an academic medical center. We held 4 group and 2 individual interviews, resulting in 11 patient participants and 2 family caregiver participants. We used structured questions on three topic areas including medication self-management strategies, mHealth technology use, and health games for exercise. We demonstrated some commercial health apps related to medication management, vitamin K content of food, and a videogame for balance exercise. Discussions were audiotaped and transcribed verbatim. Common themes were drawn using content analysis. RESULTS: The participants reported awareness of the importance of staying on schedule with warfarin therapy. They also acknowledged that negative experiences of friends or family members who were taking warfarin influenced their desire to keep on schedule with warfarin therapy. In addition, the participants expressed that the use of mHealth technology may be helpful for medication management. They also expressed the need for family support in the use of health technology devices. Moreover, the participants discussed concerns and challenges to use health technology and health games, and provided suggestions on ways to make mHealth technology and health games elder-friendly. CONCLUSIONS: These findings indicate that our older adults on warfarin therapy are interested in mHealth technology specific to warfarin medication management and health games. Further research needs to be done to validate these findings. Elder-friendly designs, technology support, and physical safety using mHealth technology may be useful in this population. These findings can be used to inform a larger study to design and test an elder-centered mHealth technology in this target population.
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 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».