Purpose: To compare steps counts recorded by consumer activity trackers when worn on the non-dominant and dominant wrist against a waist-worn pedometer during free-living. Methods: 30 participants wore six consumer wrist-worn physical activity trackers and a pedometer. On day 1, three trackers were worn on the non-dominant wrist (ND) and three on the dominant (D) wrist. On day 2 trackers were worn on the opposite wrist. On both days, a pedometer (New-Lifestyles NL-800) was worn at the waist. Mean absolute percent error (MAPE) and the Bland-Altman method assessed tracker agreement with the pedometer. Repeated measures ANOVA examined whether MAPEs were significantly different between wrist trackers (i.e., brand comparison) and between wrist location (i.e., non-dominant vs. dominant). Results: MAPEs were higher for the D wrist trackers. Five out of six trackers on the D wrist over-counted, while four out of six trackers on the ND wrist under-counted. MAPE errors were significant (p ≤ .001) between trackers but not across wrist location (p > .05). Fitbit Flex_ND, Mi Band_ND and D, Garmin Vivofit3_D and Jawbone UP24_D had a mean bias of <500 steps. 95% limits of agreement were narrowest for Mi Band_ND. Conclusions: Tracker agreement with the waist-worn pedometer varied widely but trackers on the ND wrist had better agreement. The Mi Band was the most comparable to the pedometer.
Charlotte L. Edwardson, Melanie Davies, Kamlesh Khunti, Thomas Yates, and Alex V. Rowlands
Alex V. Rowlands, Tatiana Plekhanova, Tom Yates, Evgeny M. Mirkes, Melanie Davies, Kamlesh Khunti, and Charlotte L. Edwardson
Introduction: To capitalize on the increasing availability of accelerometry data for epidemiological research it is desirable to compare and/or pool data from surveys worldwide. This study aimed to establish whether free-living physical activity outcomes can be considered equivalent between three research-grade accelerometer brands worn on the dominant and non-dominant wrist. Of prime interest were the average acceleration (ACC) and the intensity gradient (IG). These two metrics describe the volume and intensity of the complete activity profile; further, they are comparable across populations making them ideal for comparing and/or pooling activity data. Methods: Forty-eight adults wore a GENEActiv, Axivity, and ActiGraph on both wrists for up to 7-days. Data were processed using open-source software (GGIR) to generate physical activity outcomes, including ACC and IG. Agreement was assessed using pairwise 95% equivalence tests (±10% equivalence zone) and intra-class correlation coefficients (ICC). Results: ACC was equivalent between brands when measured at the non-dominant wrist (ICC ≥ 0.93), but approximately 10% higher when measured at the dominant wrist (GENEActiv and Axivity only, ICC ≥ 0.83). The IG was equivalent irrespective of monitor brand or wrist (ICC ≥ 0.88). After adjusting ACC measured at the dominant wrist by −10% (GENEActiv and Axivity only), ACC was also within (or marginally outside) the 10% equivalence zone for all monitor pairings. Conclusion: If average acceleration is decreased by 10% for studies deploying monitors on the dominant wrist (GENEActiv and Axivity only), ACC and IG may be suitable for comparing and/or collating physical activity outcomes across accelerometer datasets, regardless of monitor brand and wrist.
Tatiana Plekhanova, Alex V. Rowlands, Tom Yates, Andrew Hall, Emer M. Brady, Melanie Davies, Kamlesh Khunti, and Charlotte L. Edwardson
Introduction: This study examined the equivalency of sleep estimates from Axivity, GENEActiv, and ActiGraph accelerometers worn on the nondominant and dominant wrists and with and without using a sleep log to guide the algorithm. Methods: 47 young adults wore an Axivity, GENEActiv, and ActiGraph accelerometer continuously on both wrists for 4–7 days. Sleep time, sleep window, sleep efficiency, sleep onset, and wake time were produced using the open-source software (GGIR). For each outcome, agreement between accelerometer brands, dominant and nondominant wrists, and with and without use of a sleep log, was examined using pairwise 95% equivalence tests (±10% equivalence zone) and intraclass correlation coefficients (ICCs), with 95% confidence intervals and limits of agreement. Results: All sleep outcomes were within a 10% equivalence zone irrespective of brand, wrist, or use of a sleep log. ICCs were poor to good for sleep time (ICCs ≥ .66) and sleep window (ICCs ≥ .56). Most ICCs were good to excellent for sleep efficiency (ICCs ≥ .73), sleep onset (ICCs ≥ .88), and wake time (ICCs ≥ .87). There were low levels of mean bias; however, there were wide 95% limits of agreement for sleep time, sleep window, sleep onset, and wake time outcomes. Sleep time (up to 25 min) and sleep window (up to 29 min) outcomes were higher when use of the sleep log was not used. Conclusion: The present findings suggest that sleep outcomes from the Axivity, GENEActiv, and ActiGraph, when analyzed identically, are comparable across studies with different accelerometer brands and wear protocols at a group level. However, caution is advised when comparing studies that differ on sleep log availability.