Effects of Missing Data on Heart Rate Variability Measured From A Smartwatch: Exploratory Observational Study
Notice bibliographique
Résumé
BACKGROUND: Measuring heart rate variability (HRV) through wearable photoplethysmography sensors from smartwatches is gaining popularity for monitoring many health conditions. However, missing data caused by insufficient wear compliance or signal quality can degrade the performance of health metrics or algorithm calculations. Research is needed on how to best account for missing data and to assess the accuracy of metrics derived from photoplethysmography sensors. OBJECTIVE: This study aimed to evaluate the influence of missing data on HRV metrics collected from smartwatches both at rest and during activity in real-world settings and to evaluate HRV agreement and consistency between wearable photoplethysmography and gold-standard wearable electrocardiogram (ECG) sensors in real-world settings. METHODS: Healthy participants were outfitted with a smartwatch with a photoplethysmography sensor that collected high-resolution interbeat interval (IBI) data to wear continuously (day and night) for up to 6 months. New datasets were created with various amounts of missing data and then compared with the original (reference) datasets. 5-minute windows of each HRV metric (median IBI, SD of IBI values [STDRR], root-mean-square of the difference in successive IBI values [RMSDRR], low-frequency [LF] power, high-frequency [HF] power, and the ratio of LF to HF power [LF/HF]) were compared between the reference and the missing datasets (10%, 20%, 35%, and 60% missing data). HRV metrics calculated from the photoplethysmography sensor were compared with HRV metrics calculated from a chest-worn ECG sensor. RESULTS: At rest, median IBI remained stable until at least 60% of data degradation (P=.24), STDRR remained stable until at least 35% of data degradation (P=.02), and RMSDRR remained stable until at least 35% data degradation (P=.001). During the activity, STDRR remained stable until 20% data degradation (P=.02) while median IBI (P=.01) and RMSDRR P<.001) were unstable at 10% data degradation. LF (rest: P<.001; activity: P<.001), HF (rest: P<.001, activity: P<.001), and LF/HF (rest: P<.001, activity: P<.001) were unstable at 10% data degradation during rest and activity. Median IBI values calculated from photoplethysmography sensors had a moderate agreement (intraclass correlation coefficient [ICC]=0.585) and consistency (ICC=0.589) and LF had moderate consistency (ICC=0.545) with ECG sensors. Other HRV metrics demonstrated poor agreement (ICC=0.071-0.472). CONCLUSIONS: This study describes a methodology for the extraction of HRV metrics from photoplethysmography sensor data that resulted in stable and valid metrics while using the least amount of available data. While smartwatches containing photoplethysmography sensors are valuable for remote monitoring of patients, future work is needed to identify best practices for using these sensors to evaluate HRV in medical settings.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».