Measurements and Digital Technology Solutions to Monitor Physical Activity in Patients With Pediatric Cancer: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Patients with pediatric cancer often experience reduced physical activity (PA) due to treatment-related fatigue, functional limitations, and lack of structured exercise programs. Digital health solutions, including wearable sensors and augmented reality (AR)-based interventions, may offer new possibilities for monitoring and improving PA in this population. OBJECTIVE: This scoping review aims to address existing research gaps by identifying the instruments-both conventional and digital-used to monitor PA in patients with pediatric cancer during treatment. In addition, this study examines PA monitoring methods, identifies the variables collected, and explores the applicability of digital health solutions in facilitating PA engagement among patients with pediatric cancer. METHODS: In accordance with the Joanna Briggs Institute methodology, a systematic search was conducted across 8 scientific databases-ProQuest, Web of Science, EBSCO Complete, Google Scholar, ScienceDirect, Scopus, MEDLINE (PubMed), and Cochrane-on April 18 and 19, 2024. Studies were screened using the Rayyan AI-assisted review tool based on predefined inclusion criteria targeting children aged 7-19 years who were undergoing cancer treatment or were within 2 years posttreatment. Eligible studies included clinical trials and observational studies that examined objective (eg, wearable sensors) and subjective (eg, questionnaires and self-reports) approaches to PA monitoring. Keywords and controlled vocabulary (eg, MeSH [Medical Subject Headings] terms) were identified through a review of relevant literature. Data were extracted systematically to capture study characteristics, intervention types, and outcome measures. Extracted data were charted and synthesized narratively to identify patterns, technological applications, and research gaps in PA monitoring among patients with pediatric cancer. RESULTS: Twelve studies met the inclusion criteria and employed a range of PA monitoring tools. Digital health solutions, including Actical and Garmin VivoFit 3 devices, were used in 5 studies to assess step counts, gait cycles, and movement intensity. Self-reported measures were identified in 11 studies, most commonly the Activities Scale for Kids and the Pediatric Quality of Life Inventory-Multidimensional Fatigue Scale, which provided insights into mobility and fatigue. Despite their feasibility, subjective assessments were limited by recall bias and motivational factors. Although digital health solutions-such as wearable sensors, gamification, and mobile applications-showed potential to improve PA adherence, their application remains underutilized, and evidence regarding their integration in pediatric oncology is limited. CONCLUSIONS: Existing objective and subjective methods for monitoring PA provide valuable insights; however, gaps remain in the use of interactive digital health solutions, such as AR-based interventions, for PA monitoring and engagement. Future research should focus on integrating digital tools that not only track PA but also actively engage patients, enhance motivation, and support rehabilitation across both clinical and home settings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".