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Comparison of Two Generations of ActiGraph Accelerometers: The CARDIA Study.

ABSTRACT: PURPOSE:This study aimed to examine the comparability of the ActiGraph 7164 and wGT3X-BT wear time, count-based estimates, and average time per day in physical activity of different intensities. METHODS:We studied 87 Coronary Artery Risk Development in Young Adults (CARDIA) participants 48-60 yr of age who simultaneously wore the 7164 and wGT3X-BT accelerometers at the waist in 2015-2016, with wear time of ?4 of 7 d, ?10 h·d for both monitors. Freedson cutpoints (counts per minute) were used to define sedentary (<100), light (100-1951), moderate (1952-5724), and vigorous activity (?5725). Agreement was evaluated using paired-difference tests, intraclass correlation coefficients, and Bland-Altman plots. Given systematic differences in count-based estimates between monitors, a calibration formula applied to the wGT3X-BT values was obtained by linear regression. RESULTS:Total detected wear time minutes per day was nearly identical between the 7164 and the wGT3X-BT (881.5 ± 70.9 vs 880.3 ± 78.1, P = 0.72). The wGT3X-BT values were calibrated to the 7164 values by dividing counts by 1.088. After calibration, no differences were observed between the 7164 and the wGT3X-BT in total counts per day (310,184 ± 129,189 vs 307,085 ± 135,362, P = 0.48), average counts per min per day (349.5 ± 139.5 vs 346.5 ± 147.2, P = 0.54), sedentary (513.2 ± 93.6 vs 509.6 ± 98.6, P = 0.23), light (335.3 ± 81.5 vs 338.7 ± 81.1, P = 0.22), moderate (31.0 ± 21.9 vs 30.3 ± 23.4, P = 0.31), or moderate-to-vigorous minutes per day (33.1 ± 24.6 vs 32.0 ± 26.0, P = 0.13). A significant difference was observed for vigorous minutes per day (0.2 ± 1.0 vs 0.0 ± 0.3, P < 0.01); however, the absolute difference was marginal. Intraclass correlation coefficients showed excellent agreement for all measures (0.95-0.99). CONCLUSIONS:After applying a calibration formula, the 7164 and wGT3X-BT were comparable for total wear time, count-based estimates, and average minutes per day in sedentary, light, moderate, and moderate-to-vigorous activity. Findings illustrate a novel methodological approach to facilitate accelerometer data harmonization.

SUBMITTER: Whitaker KM

PROVIDER: S-EPMC5953790 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Project description:ContextAnterior cruciate ligament reconstruction (ACLR) and gait speed are risk factors for developing knee osteoarthritis (OA). Measuring minute-level cadence during free-living activities may aid in identifying individuals at elevated risk of developing slow habitual gait speed and, in the long term, OA.ObjectiveTo assess differences in peak 1-minute cadence and weekly time in different cadence intensities between individuals with and without ACLR.DesignCross-sectional study.SettingShort-term, free-living conditions.Patients or other participantsA total of 57 participants with ACLR (34 women, 23 men; age = 20.9 ± 3.2 years, time since surgery = 28.7 ± 17.7 months) and 42 healthy control participants (22 women, 20 men; age = 20.7 ± 1.7 years).Main outcome measure(s)Each participant wore a physical activity monitor for 7 days. Data were collected at 30 Hz, processed in 60-second epochs, and included in the analyses if the activity monitor was worn for at least 10 hours per day over 4 days. Mean daily steps, peak 1-minute cadence, and weekly minutes spent at 60 to 79 (slow walking), 80 to 99 (medium walking), 100 to 119 (brisk walking), ≥100 (moderate- to vigorous-intensity ambulation), and ≥130 (vigorous-intensity ambulation) steps per minute were calculated. One-way analyses of covariance were conducted to determine differences between groups, controlling for height and activity-monitor wear time.ResultsThose with ACLR took fewer daily steps (8422 ± 2663 versus 10 033 ± 3046 steps; P = .005) and spent fewer weekly minutes in moderate- to vigorous-intensity cadence (175.8 ± 116.5 minutes versus 218.5 ± 137.1 minutes; P = .048) than participants without ACLR. We observed no differences in minutes spent at slow (ACLR = 77.4 ± 40.5 minutes versus control = 83.9 ± 34.3 minutes; P = .88), medium (ACLR = 71.6 ± 40.2 minutes versus control = 82.9 ± 46.8 minutes; P = .56), brisk (ACLR = 115.3 ± 70.3 minutes versus control = 138.3 ± 73.3 minutes; P = .18), or vigorous-intensity (ACLR = 24.3 ± 36.5 minutes versus control = 38.1 ± 60.9 minutes; P = .10) cadences per week.ConclusionsParticipants with ACLR walked approximately 40 fewer minutes per week in moderate- to vigorous-intensity cadence than participants without ACLR. Increasing the time spent at cadence ≥100 steps per minute and overall volume of physical activity may be useful as interventional targets to help reduce the risk of early development of OA after ACLR.

Project description:The behavior of many wild animals remains a mystery, as it is difficult to quantify behavior of species that cannot be easily followed throughout their daily or seasonal movements. Accelerometers can solve some of these mysteries, as they collect activity data at a high temporal resolution (<1 s), can be relatively small (<1 g) so they minimally disrupt behavior, and are increasingly capable of recording data for long periods. Nonetheless, there is a need for increased validation of methods to classify animal behavior from accelerometers to promote widespread adoption of this technology in ecology. We assessed the accuracy of six different behavioral assignment methods for two species of seabird, thick-billed murres (Uria lomvia) and black-legged kittiwakes (Rissa tridactyla). We identified three behaviors using tri-axial accelerometers: standing, swimming, and flying, after classifying diving using a pressure sensor for murres. We evaluated six classification methods relative to independent classifications from concurrent GPS tracking data. We used four variables for classification: depth, wing beat frequency, pitch, and dynamic acceleration. Average accuracy for all methods was >98% for murres, and 89% and 93% for kittiwakes during incubation and chick rearing, respectively. Variable selection showed that classification accuracy did not improve with more than two (kittiwakes) or three (murres) variables. We conclude that simple methods of behavioral classification can be as accurate for classifying basic behaviors as more complex approaches, and that identifying suitable accelerometer metrics is more important than using a particular classification method when the objective is to develop a daily activity or energy budget. Highly accurate daily activity budgets can be generated from accelerometer data using multiple methods and a small number of accelerometer metrics; therefore, identifying a suitable behavioral classification method should not be a barrier to using accelerometers in studies of seabird behavior and ecology.

Project description:Objectives: Our count-scaled algorithm automatically scores sleep across 24 hours to process sleep timing, quantity, and quality. The aim of this study was to validate the algorithm against overnight PSG in children to determine the best site placement for sleep. Methods: 28 children (5-8 years) with no history of sleep disturbance wore two types of accelerometers (ActiGraph GT3X+ and Actical) at two sites (left hip, non-dominant wrist) for 24-h. Data were processed using the count-scaled algorithm. PSG data were collected using an in-home Type 2 device. PSG-actigraphy epoch sensitivity (sleep agreement) and specificity (wake agreement) were determined and sleep outcomes compared for timing (onset and offset), quantity [sleep period time (SPT) and total sleep time (TST)], and quality metrics [sleep efficiency and waking after sleep onset (WASO)]. Results: Overall, sensitivities were high (89.1% to 99.5%) and specificities low (21.1% to 45.7%). Sleep offset was accurately measured by actigraphy, regardless of brand or placement site. By contrast, sleep onset agreed with PSG using hip-positioned but not wrist-positioned devices (difference ActiGraph : PSG 21 min, P < .001; Actical : PSG 14 min, P < .001). The ActiGraph at the wrist accurately detected WASO and sleep efficiency, but under (-34 min, P < .001) and overestimated (5.8%, P < .001) these at the hip. The Actical under- and over-estimated these variables respectively at both sites. Results for TST varied ranging from significant differences to PSG of -26 to 21 min (ActiGraph wrist and hip respectively) and 9 min (ns) to 59 min for Actical (wrist and hip respectively). Conclusion: Overall the count-scaled algorithm produced high sensitivity at the expense of low specificity in comparison with PSG. A best site placement for estimates of all sleep variables could not be determined, but overall the results suggested ActiGraph GT3X+ at the hip may be superior for sleep timing and quantity metrics, whereas the wrist may be superior for sleep quality metrics. Both devices placed at the hip performed well for sleep timing but not for sleep quality. Differences are likely linked to freedom of movement of the wrist vs the trunk (hip) during overnight sleep.

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Comparison of Two Generations of ActiGraph Accelerometers: The CARDIA Study.

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