{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hilden P"],"funding":["National Heart, Lung, and Blood Institute"],"pagination":["e0282162"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9956594"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(2)"],"pubmed_abstract":["

Introduction/purpose

Physical activity studies often utilize wearable devices to measure participants' habitual activity levels by averaging values across several valid observation days. These studies face competing demands-available resources and the burden to study participants must be balanced with the goal to obtain reliable measurements of a person's longer-term average. Information about the number of valid observation days required to reliably measure targeted metrics of habitual activity is required to inform study design.

Methods

To date, the number of days required to achieve a desired level of aggregate long-term reliability (typically 0.80) has often been estimated by applying the Spearman-Brown Prophecy formula to short-term test-retest reliability data from studies with single, relatively brief observation windows. Our work, in contrast, utilizes a resampling-based approach to quantify the long-term test-retest reliability of aggregate measures of activity in a cohort of 79 participants who were asked to wear a FitBit Flex every day for approximately one year.

Results

The conventional approach can produce reliability estimates that substantially overestimate the actual test-retest reliability. Six or more valid days of observation for each participant appear necessary to obtain 0.80 reliability for the average amount of time spent in light physical activity; 8 and 10 valid days are needed for sedentary time and moderate/vigorous activity respectively.

Conclusion

Protocols that result in 7-10 valid observation days for each participant may be needed to obtain reliable measurements of key physical activity metrics."],"journal":["PloS one"],"pubmed_title":["How many days are needed? Measurement reliability of wearable device data to assess physical activity."],"pmcid":["PMC9956594"],"funding_grant_id":["R01HL115941"],"pubmed_authors":["Schwartz JE","Diaz KM","Goldsmith J","Hilden P","Pascual C"],"additional_accession":[]},"is_claimable":false,"name":"How many days are needed? Measurement reliability of wearable device data to assess physical activity.","description":"

Introduction/purpose

Physical activity studies often utilize wearable devices to measure participants' habitual activity levels by averaging values across several valid observation days. These studies face competing demands-available resources and the burden to study participants must be balanced with the goal to obtain reliable measurements of a person's longer-term average. Information about the number of valid observation days required to reliably measure targeted metrics of habitual activity is required to inform study design.

Methods

To date, the number of days required to achieve a desired level of aggregate long-term reliability (typically 0.80) has often been estimated by applying the Spearman-Brown Prophecy formula to short-term test-retest reliability data from studies with single, relatively brief observation windows. Our work, in contrast, utilizes a resampling-based approach to quantify the long-term test-retest reliability of aggregate measures of activity in a cohort of 79 participants who were asked to wear a FitBit Flex every day for approximately one year.

Results

The conventional approach can produce reliability estimates that substantially overestimate the actual test-retest reliability. Six or more valid days of observation for each participant appear necessary to obtain 0.80 reliability for the average amount of time spent in light physical activity; 8 and 10 valid days are needed for sedentary time and moderate/vigorous activity respectively.

Conclusion

Protocols that result in 7-10 valid observation days for each participant may be needed to obtain reliable measurements of key physical activity metrics.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023","modification":"2025-05-29T19:42:59.985Z","creation":"2025-05-29T19:42:59.985Z"},"accession":"S-EPMC9956594","cross_references":{"pubmed":["36827427"],"doi":["10.1371/journal.pone.0282162"]}}