Examining the Light Heart Mobile Device App for Assessing Human Pulse Interval and Heart Rate Variability: Validation Study
Bibliographic record
Abstract
BACKGROUND: Pulse interval is a biomarker of psychological and physiological health. Pulse interval can now be assessed using mobile phone apps, which expands researchers' ability to assess pulse interval in the real world. Prior to implementation, measurement accuracy should be established. OBJECTIVE: This investigation evaluated the validity of the Light Heart mobile app to measure pulse interval and pulse rate variability in healthy young adults. METHODS: Validity was assessed by comparing the pulse interval and SD of normal pulse intervals obtained by Light Heart to the gold standard, electrocardiogram (ECG), in 14 young healthy individuals (mean age 24, SD 5 years; n=9, 64% female) in a seated posture. RESULTS: Mean pulse interval (Light Heart: 859, SD 113 ms; ECG: 857, SD 112 ms) demonstrated a strong positive linear correlation (r=0.99; P<.001) and strong agreement (intraclass correlation coefficient=1.00, 95% CI 0.99-1.00) between techniques. The Bland-Altman plot demonstrated good agreement for the mean pulse interval measured with Light Heart and ECG with evidence of fixed bias (-1.56, SD 1.86; 95% CI -5.2 to 2.1 ms), suggesting that Light Heart overestimates pulse interval by a small margin. When Bland-Altman plots were constructed for each participant's beat-by-beat pulse interval data, all participants demonstrated strong agreement between Light Heart and ECG with no evidence of fixed bias between measures. Heart rate variability, assessed by SD of normal pulse intervals, demonstrated strong agreement between techniques (Light Heart: mean 73, SD 23 ms; ECG: mean 73, SD 22 ms; r=0.99; P<.001; intraclass correlation coefficient=0.99, 95% CI 0.97-1.00). CONCLUSIONS: This study provides evidence to suggest that the Light Heart mobile app provides valid measures of pulse interval and heart rate variability in healthy young adults.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".