A Pilot Study Validating Select Research-Grade and Consumer-Based Wearables Throughout a Range of Dynamic Exercise Intensities in Persons With and Without Type 1 Diabetes: A Novel Approach
Bibliographic record
Abstract
Background: The increasing popularity of wearable technology necessitates the evaluation of their accuracy to differentiate physical activity (PA) intensities. These devices may play an integral role in customizing PA interventions for primary prevention and secondary management of chronic diseases. For example, in persons with type 1 diabetes (T1D), PA greatly affects glucose concentrations depending on the intensity, mode (ie, aerobic, anaerobic, mixed), and duration. This variability in glucose responses underscores the importance of implementing dependable wearable technology in emerging avenues such as artificial pancreas systems. Methods: Participants completed three 40-minute, dynamic non-steady-state exercise sessions, while outfitted with multiple research (Fitmate, Metria, Bioharness) and consumer (Garmin, Fitbit) grade wearables. The data were extracted according to the devices’ maximum sensitivity (eg, breath by breath, beat to beat, or minute time stamps) and averaged into minute-by-minute data. The variables of interest, heart rate (HR), breathing frequency, and energy expenditure (EE), were compared to validated criterion measures. Results: Compared to deriving EE by laboratory indirect calorimetry standard, the Metria activity patch overestimates EE during light-to-moderate PA intensities (L-MI) and moderate-to-vigorous PA intensities (M-VI) (mean ± SD) (0.28 ± 1.62 kilocalories· minute -1 , P < .001, 0.64 ± 1.65 kilocalories· minute -1 , P < .001, respectively). The Metria underestimates EE during vigorous-to-maximal PA intensity (V-MI) (–1.78 ± 2.77 kilocalories · minute -1 , P < .001). Similarly, compared to Polar HR monitor, the Bioharness underestimates HR at L-MI (–1 ± 8 bpm, P < .001) and M-VI (5 ± 11 bpm, P < .001), respectively. A significant difference in EE was observed for the Garmin device, compared to the Fitmate ( P < .001) during continuous L-MI activity. Conclusions: Overall, our study demonstrates that current research-grade wearable technologies operate within a ~10% error for both HR and EE during a wide range of dynamic exercise intensities. This level of accuracy for emerging research-grade instruments is considered both clinically and practically acceptable for research-based or consumer use. In conclusion, research-grade wearable technology that uses EE kilocalories · minute -1 and HR reliably differentiates PA intensities.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".