Awareness, Perceptions, Willingness, and Feasibility of mHealth Apps Among People Living With Epilepsy: Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: The rapid expansion of mobile health (mHealth) apps has transformed health care delivery worldwide. Despite their potential to improve epilepsy care, a substantial treatment gap remains, especially in low- and middle-income countries, due to limited resources, stigma, and low adoption of digital technologies. Although mHealth apps can bridge these disparities, their impact depends on acceptance and use by the target population. OBJECTIVE: We aimed to assess the awareness, feasibility, willingness, perception, and factors influencing these behaviors for the usage of mHealth apps among people living with epilepsy in Pakistan. METHODS: We conducted a cross-sectional analytical survey between March and July 2024 among people living with epilepsy attending the Pakistan Institute of Medical Sciences (PIMS). Participants completed a validated, self-administered questionnaire with 33 items across 5 domains. We recruited 406 participants through convenience sampling and analyzed the data using SPSS version 23.0 (IBM Corp). Through multivariable linear regression analysis, we explored factors associated with people living with epilepsy willingness to use mHealth apps. Correlation analysis was used to elucidate the association among awareness, perception, feasibility, and willingness. RESULTS: Among 406 participants, 53.7% (n=218) were male, 64.5% (n=262) were married, and 89.2% (n=362) were identified as Muslim. Although 86.2% (n=350) of participants have heard about mHealth apps for epilepsy management, 78.1% (n=317) expressed negative perceptions of their use. More than half, 69% (n=280), reported concerns about the privacy of their medical information online, and 78.1% (n=317) were not comfortable using mHealth apps on smartphones or tablets. Multivariable linear regression analysis revealed that rural residents (P=.05), those with a college education (P<.001), and participants with a treatment duration of 2-3 years (P<.001) significantly influenced participants' willingness. Correlation analysis showed a weak negative relationship between awareness and feasibility (ρ=-0.124; P=.01) and a weak positive relationship between awareness and willingness (ρ=0.013; P=.07). CONCLUSIONS: To expand mHealth use for epilepsy care in Pakistan, stakeholders must address concerns about digital literacy, data privacy, and trust. Collaborative efforts involving government, technologists, nongovernmental organizations, academia, and health care providers can improve education, enhance data security, and adapt mHealth tools to local needs, ultimately improving treatment access and outcomes for people living with epilepsy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".