Accuracy of Fitbit devices: a systematic review and narrative syntheses of quantitative data (Preprint)
Notice bibliographique
Résumé
UNSTRUCTURED OBJECTIVE: To systematically evaluate and report measurement accuracy for FitbitT activity trackers in controlled and free-living settings. DATA SOURCES: Electronic searches using PubMed, Embase, CINAHL and SportsDiscus databases with a supplementary Google Scholar search. ELIGIBILITY: Original research published in English comparing Fitbit to a gold- or research-standard criterion in healthy adults and those living with any health condition or disability. APPRAISAL: Risk of bias was assessed using a modification of the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN). SYNTHESES: We explored measurement accuracy for steps, energy expenditure, sleep, time in activity and distance using group percent differences as the common rubric for error comparisons. We conducted descriptive analyses for frequency of accuracy comparisons within a +/-3% error in controlled and +/-10% error in free-living settings and assessed for potential bias of over- or under-estimation. We secondarily explored how variations in body placement, ambulation speed or type of activity influenced accuracy. RESULTS: Sixty-seven studies were included. Consistent evidence indicated that Fitbit devices were likely to meet acceptable accuracy for step count approximately half the time, with a tendency to underestimate steps in controlled-testing and overestimate steps in free-living settings. Findings also suggest a greater tendency to provide accurate measures for steps during normal/self-paced walking with torso placement, during jogging with wrist placement, and during slow/very slow walking with ankle placement in adults with no mobility limitations. Whereas, consistent evidence indicated that Fitbit devices were unlikely to provide accurate measures for EE in any testing condition. Evidence from a limited number of studies also suggest that compared to research-grade accelerometers Fitbit devices may provide similar measures to for time in bed or time sleeping, while likely markedly overestimating time spent in higher intensity activities. LIMITATIONS: Our point estimations for potential bias (mean or median percent error) gives equal weighting to all accuracy comparisons, possibly mis-representing the true point-estimate for measurement bias for some of the testing conditions we examined. CONCLUSION:Fitbit devices are most likely to provide accurate measures of steps in adults with no mobility limitations when the device is worn on the torso while walking at normal or self-paced walking speeds. Whereas, Fitbit devices are unlikely to provide accurate measures of EE. Limited evidence suggests that Fitbit activity trackers may not provide accurate measures for sleep, distance or time spent in activity, however, further accuracy studies are warranted. IMPLICATIONS: Other than for measures of steps in adults with no limitations in mobility, discretion should be used when considering the use of Fitbit devices as an outcome measurement tool in research or to inform health care decisions as there are seemingly a limited number of situations where the device is likely to provide accurate measurement. REGISTRATION: n/a
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,127 | 0,466 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,009 | 0,009 |
| Bibliométrie | 0,030 | 0,027 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,007 | 0,009 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».