Comparing the Accuracy of Different Wearable Activity Monitors in Patients With Lung Cancer and Providing Initial Recommendations: Protocol for a Pilot Validation Study
Bibliographic record
Abstract
BACKGROUND: Wearable activity monitors (WAMs) provide insights into physical activity (PA) and are widely used in behavioral interventions and cancer survivorship research. However, validation studies of wearable devices in populations with cancer are scarce, and existing studies using activity monitors in patients with cancer lack standardization. This gap is particularly significant in patients with lung cancer (LC), who often experience unique mobility challenges and gait impairments that may affect device accuracy. This study addresses this gap by validating the Fitbit Charge 6, ActiGraph LEAP, and activPAL3 micro in patients with LC in both laboratory and free-living conditions and developing a standardized framework for assessing wearable devices in populations with cancer and impaired mobility. OBJECTIVE: This study aims to validate and compare the accuracy of consumer-grade (Fitbit Charge 6) and research-grade (activPAL3 micro and ActiGraph LEAP) WAMs in patients with LC under both laboratory and free-living conditions. Moreover, this protocol aims to establish standardized procedures that can be adapted for validating current and future generations of wearable devices while accounting for disease-specific factors that may impact measurement accuracy. METHODS: In total, 15 adults diagnosed with LC (stages 1-4) will participate in laboratory and free-living protocols, wearing Fitbit Charge 6, activPAL3 micro, and ActiGraph LEAP devices simultaneously. The laboratory protocol will consist of a series of structured activities, including variable-time walking trials, sitting and standing tests, posture changes, and gait speed assessments. Activities will be video recorded for validation. In the free-living protocol, participants will wear the devices continuously for 7 days except during water-based activities. WAM-based outcome measures will include step count; time spent at light, moderate, and vigorous PA intensity levels; posture; and posture changes (only the activPAL3 micro measured posture). Validated survey instruments will be administered both before and after WAM data collection to control for potential confounding factors that may influence movement patterns and device accuracy. Laboratory-based validity measures will compare WAM data to video-recorded observations. Sensitivity, specificity, positive predictive value, and agreement will also be determined. Free-living agreement between devices will be assessed using Bland-Altman plots, intraclass correlation analysis, and 95% limits of agreement. RESULTS: Data collection is ongoing, with 11 participants enrolled and 7 (64%) having completed both in-laboratory and free-living protocols. On average, enrolled participants are aged 63.0 (SD 7.8; range 50.0-73.0) years, with 8 (73%) participants being women. Participant enrollment is expected to conclude in mid-2025, and initial findings are expected to be disseminated by the end of 2025. CONCLUSIONS: This is the first study that validates WAM accuracy for populations with LC while providing comprehensive recommendations for future validation studies. This study will provide critical insights into the accuracy and reliability of WAMs for assessing PA in LC survivors, which are essential for interpreting clinical research and informing future interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/70472.
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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 | 0.051 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".