Physical Activity Surveillance in Children and Adolescents Using Smartphone Technology: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Self-reported physical activity (PA) questionnaires have traditionally been used for PA surveillance in children and adolescents, especially in free-living conditions. Objective measures are more accurate at measuring PA, but high cost often creates a barrier for their use in low- and middle-income settings. The advent of smartphone technology has greatly influenced mobile health and has offered new opportunities in health research, including PA surveillance. OBJECTIVE: This review aimed to systematically explore the use of smartphone technology for PA surveillance in children and adolescents, specifically focusing on the use of smartphone apps. METHODS: A literature search was conducted using 5 databases (PubMed, Scopus, CINAHL, MEDLINE, and Web of Science) and Google Scholar to identify articles relevant to the topic that were published from 2008 to 2023. Articles were included if they included children and adolescents within the age range of 5 to 18 years; used smartphone technology as PA surveillance; had PA behavioral outcomes such as energy expenditure, step count, and PA levels; were written in English; and were published between 2008 and 2023. RESULTS: We identified and analyzed 8 studies (5 cross-sectional studies and 3 cohort studies). All participants were aged 12-18 years, and all studies were conducted in high-income countries only. Participants were recruited from schools, primary care facilities, and voluntarily. Five studies used mobile apps specifically and purposely developed for the study, whereas 3 studies used mobile apps downloadable from the Apple App Store and Android Play Store. PA surveillance using these apps was conducted from 24 hours to 4 weeks. CONCLUSIONS: Evidence of PA surveillance using smartphone technology in children and adolescents was insufficient, which demonstrated the knowledge gap. Additional research is needed to further study the feasibility and validity of smartphone apps for PA surveillance among children and adolescents, especially in low- and middle-income countries.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".