Smartphone Apps and Wearables for Health Parameters in Young Adulthood: Cross-Sectional Study
Bibliographic record
Abstract
Background: Fostering innovative and more effective interventions to support active aging strategies from youth is crucial to help this population adopt healthier lifestyles using technologies they are already familiar with. Mobile health (mHealth), particularly apps and wearables, represents a promising approach due to its versatility, ease of use, and ability to monitor multiple health variables simultaneously. Moreover, these devices offer opportunities for personalization and support in health behavior change, making them valuable tools for shaping healthy habits from a young age. Objective: This study aims to (1) investigate whether young adults (18-26 years old) use apps or wearables to monitor or improve their health variables (ie, physical activity, diet, and mental health); (2) examine how they use them; (3) identify the most commonly used apps and wearables and the most frequently monitored health variables across these domains; and (4) evaluate the importance of different characteristics and functions of apps and wearables for health purposes. Methods: This cross-sectional study used a public involvement framework to enhance the research quality and was conducted through an anonymous web survey disseminated across Italy over a 3-month period. The survey consisted of 5 sections: (1) demographics, (2) mobile apps and wearable devices for physical activity and sports, (3) mobile apps and wearable devices for diet, (4) mobile apps and wearable devices for mental health, and (5) preferences regarding mobile apps and wearable devices. Participants were eligible if they were young adults who reported using at least one app or wearable device to monitor at least one health variable (eg, steps, training, sleep, calorie intake). No additional eligibility criteria were applied. Results: A total of 693 questionnaires were analyzed for aims 1 and 4, with the sample showing an equal gender distribution (females: 363/693, 52.4%). For aims 2 and 3, a total of 317 questionnaires were included. Participants using an app or wearable for physical activity accounted for 320 (46.2%), while 60 (8.7%) and 156 (22.5%) reported use for diet and mental health, respectively. Moreover, the frequency of use was predominantly on a daily basis, particularly for wearables. The app and wearable characteristics identified as most important were user-friendliness, free access to content, loading speed, and icon clarity. Conclusions: Findings suggest that Italian young adults, particularly women, predominantly use wearables over apps to track health data, with both being checked on a daily basis. Physical activity is the most frequently monitored domain, likely due to its ease of tracking, while diet and mental health receive less attention. Overall, these tools are used more for monitoring than for actively improving health-related variables. The most valued characteristics identified by young adults include ease of use, free access to all content, and fast loading speed. These insights should guide the design and refinement of digital health interventions targeting this population.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".