Willingness to Adopt mHealth Among Chinese Parents During the COVID-19 Outbreak: Cross-sectional Questionnaire Study
Bibliographic record
Abstract
Background Parental involvement in mobile health (mHealth) to consult with medical professionals appears to be prevalent in China with the rapid development of the internet. More parents with busy jobs have chosen to use mHealth. During the ongoing COVID-19 outbreak, mHealth can assist with health promotion, directions for medication use, and disease diagnosis via online chat and video consultation without contacting others. To our knowledge, no studies have been performed to explore the role of mHealth in parents’ attitudes toward child health care at home during the COVID-19 outbreak. Objective This study aims to identify the associated factors of willingness to adopt mHealth among Chinese parents during the COVID-19 outbreak and to explore the correlation between the frequency of adopting mHealth and parents’ attitudes toward child health care at home. Methods Chinese parents were asked to complete an online survey from January 25 to February 15, 2020. The questionnaire comprised of two parts with a total of 16 items, including parents’ demographic variables and attitudes toward child health care at home. By multivariate logistic regression, we explored factors associated with parents’ willingness to adopt mHealth during the COVID-19 outbreak. Pearson chi-square tests were used to reveal the correlation between the frequency of adopting mHealth and parents’ attitudes toward child health care at home. Results A total of 254 parents enrolled, and 202 (79.5%) parents were willing to adopt mHealth during the COVID-19 outbreak. Parents’ age (26-35 years: adjusted odds ratio [AOR] 8.114, 95% CI 1.471-44.764), parents’ interest in the COVID-19 pandemic (moderate: AOR 8.753, 95% CI 2.009-38.127; high: AOR 22.194, 95% CI 5.509-89.411), the source that recommended mHealth (medical health providers: AOR 4.257, 95% CI 1.439-12.596), the presence of chronic disease in their children (yes: AOR 20.844, 95% CI 4.600-94.443), parents’ duration of daily internet use (4-6 hours: AOR 6.487, 95% CI 1.870-22.495; >6 hours: AOR 8.766, 95% CI 1.883-40.804), and adoption of mHealth before the COVID-19 outbreak (yes: AOR 3.413, 95% CI 1.234-9.444) were significantly correlated with the parents’ willingness to adopt mHealth during the COVID-19 outbreak. The frequency of mHealth use among parents was correlated with their behaviors in regard to handwashing (χ26=18.967, P=.004), mask wearing (χ26=45.364, P<.001), frequency of leaving the home (χ26=16.767, P=.01), room disinfection and ventilation (χ26=19.515, P=.003), temperature checking (χ26=17.47, P=.007), and mental health care of children (χ26=63.810, P<.001) during the COVID-19 pandemic. Conclusions We found various objective factors that were associated with parents’ willingness to adopt mHealth during the COVID-19 outbreak. Overall, parents’ willingness to adopt mHealth was high. The frequency of mHealth use among parents was correlated with their attitudes toward child health care at home. The option of mHealth to patients at home during the COVID-19 outbreak would be beneficial for education and improvement in self-management of child health care at home.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".