Mobile Health Hearing Aid Acclimatization and Support Program in Low-Income Communities: Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: The most common management option for hearing loss is hearing aids. In addition to devices, patients require information and support, including maintenance and troubleshooting. Mobile health (mHealth) technologies can support hearing aid management, acclimatization, and use. This study developed an mHealth acclimatization and support program for first-time hearing aid users and subsequently implemented and pilot-tested the feasibility of the program. The program was facilitated by community health workers (CHWs) in low-income communities in South Africa. OBJECTIVE: This study aimed to evaluate the feasibility of an mHealth acclimatization and support program supported by CHWs in low-income communities. METHODS: An application-based acclimatization and support was adapted and translated for use in low- and middle-income countries. This program was delivered in the form of 20 different voice notes accompanied by graphical illustrations via WhatsApp or 20 different SMS text messages. The program was provided to first-time hearing aid users immediately after a community-based hearing aid fitting in March 2021 in 2 low-income communities in the Western Cape, South Africa. The 20 messages were sent over a period of 45 days. Participants were contacted telephonically on days 8, 20, and 43 of the program and via open-ended paper-based questionnaires translated to isiXhosa 45 days and 6 months after the program started to obtain information on their experiences, perceptions, and accessibility of the program. Their responses were analyzed using inductive thematic analysis. RESULTS: A total of 19 participants fitted with hearing aids received the mHealth acclimatization and support program. Most participants (15/19, 79%) received the program via WhatsApp, with 21% (4/19) of them receiving it via SMS text message. Participants described the program as helpful, supportive, informative, sufficient, and clear at both follow-ups. A total of 14 participants reported that they were still using their hearing aids at the 6-month follow-up. Three participants indicated that not all their questions about hearing aids were answered, and 5 others had minor hearing aid issues. This included feedback (n=1), battery performance (n=1), physical fit (n=2), and issues with hearing aid accessories (n=1). However, CHWs successfully addressed all these issues. There were no notable differences in responses between the participants who received the program via WhatsApp compared with those who received it through SMS text message. Most participants receiving WhatsApp messages reported that the voice notes were easier to understand, but the graphical illustrations supplemented the voice notes well. CONCLUSIONS: An mHealth acclimatization and support program is feasible and potentially assists hearing aid acclimatization and use for first-time users in low-income communities. Scalable mHealth support options can facilitate increased access and improve outcomes of hearing care.
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 | 0,005 |
| 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 | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 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 ».