mHealth Interventions to Support Caregivers of Older Adults: Equity-Focused Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Informal caregivers, hereafter referred to as caregivers, provide support to older adults so that they can age safely at home. The decision to become a caregiver can be influenced by individual factors, such as personal choice, or societal factors such as social determinants of health, including household income, employment status, and culture-specific gender roles. Over time, caregivers' health can be negatively affected by their caregiving roles. Although programs exist to support caregivers, the availability and appropriateness of services do not match caregivers' expressed needs. Research suggests that supportive interventions offered through mobile health (mHealth) technologies have the potential to increase caregivers' access to supportive services. However, a knowledge gap remains regarding the extent to which social determinants of health are considered in the design, implementation, and evaluation of mHealth interventions intended to support the caregivers of older adults. OBJECTIVE: This study aimed to conduct a systematic review to determine how health equity is considered in the design, implementation, and evaluation of mHealth interventions for caregivers of older adults using Cochrane Equity's PROGRESS-Plus (place of residence, race, ethnicity, culture, language, occupation, gender, religion, education, social capital, socioeconomic status-plus age, disability, and sexual orientation) framework and synthesize evidence of the impacts of the identified caregiver-focused mHealth interventions. METHODS: A systematic review was conducted using 5 databases. Articles published between January 2010 and June 2021 were included if they evaluated or explored the impact of mHealth interventions on the health and well-being of informal caregivers of older adults. mHealth interventions were defined as supportive services, for example, education, that caregivers of older adults accessed via mobile or wireless devices. RESULTS: In total, 28 articles met the inclusion criteria and were included in the review. The interventions evaluated sought to connect caregivers with services, facilitate caregiving, and promote caregivers' health and well-being. The PROGRESS-Plus framework factors were mainly considered in the results, discussion, and limitations sections of the included studies. Some PROGRESS-Plus factors such as sexual orientation, religion, and occupation, received little to no consideration across any phase of the intervention design, implementation, or evaluation. Overall, the findings of this review suggest that mHealth interventions were positively received by study participants. Such interventions have the potential to reduce caregiver burden and positively affect caregivers' physical and mental health while supporting them as caregivers. The study findings highlight the importance of making support available to help facilitate caregivers' use of mHealth interventions, as well as in the use of appropriate language and text. CONCLUSIONS: The successful uptake and spread of mHealth interventions to support caregivers of older adults will depend on creating opportunities for the inclusive involvement of a broad range of stakeholders at all stages of design, implementation, and evaluation.
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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 | 0,010 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,006 |
| Bibliométrie | 0,010 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| 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 | 0,000 |
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 ».