Mobile Health Hearing Aid Acclimatization and Support Program in Low-Income Communities: Feasibility Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".