What Drives Young Vietnamese to Use Mobile Health Innovations? Implications for Health Communication and Behavioral Interventions
Bibliographic record
Abstract
BACKGROUND: Mobile phone use in Vietnam has become increasingly popular in recent years, with youth (people aged 15-24 years) being one of the groups with the heaviest use. Health-related apps on mobile phones (mobile health [mHealth] apps) appear to be a feasible approach for disease and health management, especially for self-management. However, there has been a scarcity of research on mobile phone usage for health care among youth and adolescents. OBJECTIVE: This study aims to identify the patterns of usage of mobile phone apps and the preferences for functionalities of mobile phone-based health-related apps among Vietnamese youth. METHODS: An online cross-sectional study was conducted in Vietnam in August to October 2015. Web-based respondent-driven sampling technique was adopted to recruit participants. The online questionnaire was developed and distributed using Google Forms. Chi square and Mann-Whitney tests were used to investigate the difference in attitude and preference for mobile phone apps between the two genders. RESULTS: Among 356 youths (age from 15 to 25 years) sampled, low prevalence was found of using mHealth apps such as beauty counseling (6.5%, 23/356), nutrition counseling (7.9%, 28/356), disease prevention (9.8%, 35/356), and disease treatment (7.6%, 27/356). The majority of users found the app(s) they used to be useful (72.7%, 48/356) and reported satisfaction with these apps (61.9%, 39/356). No significant differences were found between the genders in their perception of the usefulness of apps and their satisfaction with mobile health apps. Most of the participants (68.2%, 238/356) preferred apps which are conceptualized and designed to run on a mobile phone compared to Web-based apps, and 50% (176/356) preferred visual materials. Approximately 53.9% (188/356) reported that it was integral for the mobile phone apps to have a sharing/social network functionality. Participants with a higher perceived stress score and EuroQol-5 Dimensions (EQ-5D) index were significantly less likely to use mHealth apps. CONCLUSIONS: This study found a low proportion using mHealth-related mobile phone apps, but a high level of receptiveness and satisfaction among Vietnamese youth. Acceptance level and preferences toward mHealth apps as well as specifically preferred functionalities discovered in this study are essential not only in conceptualizing and developing appropriate mobile phone interventions targeting youth and adolescents, but also in the application of technically advanced solutions in disease prevention and health management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".