Individual and Parental Factors of Adolescents’ mHealth App Use: Nationally Representative Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Knowledge of the characteristics of adolescents who use mobile health (mHealth) apps to monitor health and how these characteristics differ from those of app nonusers is limited. OBJECTIVE: We aimed to determine mHealth app use based on adolescent and parental factors, including sociodemographics, digital skills, and health indicators, in a nationally representative sample of Czech adolescents (N=2500). METHODS: Adolescents aged 11 to 16 years and one of their parents participated in an online survey in 2021. A professional research agency recruited the participants. Quotas were used to ensure the sample's representativeness. The sociodemographic factors were the adolescents' age, gender, and parental perceived financial security. The adolescents also provided information about their screen time, eHealth literacy, BMI, health anxiety, physical activity, and sleep quality. Parents reported their digital skills, mobile phone attitudes, and the mediation of their children's online health information-seeking behaviors. We evaluated the differences between the users and nonusers of mHealth apps and identified the significant predictors of mHealth app use. Next, we separately examined how these factors were associated with the use of mHealth apps that track calorie intake or expenditure, number of steps, weight, or sports activity (eg, exercise, running, and working out), as well as other mHealth apps (eg, those that track sleep and heart rate). RESULTS: More than half of the adolescents (1429/2455, 58.21%) reported using mHealth apps. App users were relatively older and, more often, girls. Apps that counted the number of steps were used most frequently, and adolescents whose parents reported higher perceived financial security used them more regularly. Overall, being older and physically active and having higher eHealth literacy skills were associated with using mHealth apps. Adolescents with higher BMI, health anxiety, and lower sleep quality more frequently used mHealth apps to track calorie intake or expenditure, weight, and health indicators. mHealth apps to track physical activity were used more regularly by girls. There was a positive association between parental mediation of online health information-seeking behaviors and adolescents' mHealth app use. CONCLUSIONS: These findings demonstrated that older age, physical activity, and eHealth literacy skills were the common underlying factors of adolescents' mHealth app use. We initially showed parents as significant role models for their children's adoption of, and engagement with, mHealth apps when they actively mediate their online health information-seeking behaviors. Improving the eHealth literacy skills of adolescents through parental guidance might enhance health technology use in this population. Tracking eating behaviors, weight, and health were more prevalent for adolescents who reported higher BMI, health anxiety, and lower sleep quality. Future research studies should examine the determinants and health outcomes of adolescents' mHealth app use longitudinally.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".