Wearable acceleration sensors are increasingly used for the assessment of free-living physical activity. Acceleration sensor calibration is a potential source of error. This study aims to describe and evaluate an autocalibration method to minimize calibration error using segments within the free-living records (no extra experiments needed). The autocalibration method entailed the extraction of nonmovement periods in the data, for which the measured vector magnitude should ideally be the gravitational acceleration (1 g); this property was used to derive calibration correction factors using an iterative closest-point fitting process. The reduction in calibration error was evaluated in data from four cohorts: UK (n = 921), Kuwait (n = 120), Cameroon (n = 311), and Brazil (n = 200). Our method significantly reduced calibration error in all cohorts (P < 0.01), ranging from 16.6 to 3.0 mg in the Kuwaiti cohort to 76.7 to 8.0 mg error in the Brazil cohort. Utilizing temperature sensor data resulted in a small nonsignificant additional improvement (P > 0.05). Temperature correction coefficients were highest for the z-axis, e.g., 19.6-mg offset per 5°C. Further, application of the autocalibration method had a significant impact on typical metrics used for describing human physical activity, e.g., in Brazil average wrist acceleration was 0.2 to 51% lower than uncalibrated values depending on metric selection (P < 0.01). The autocalibration method as presented helps reduce the calibration error in wearable acceleration sensor data and improves comparability of physical activity measures across study locations. Temperature ultization seems essential when temperature deviates substantially from the average temperature in the record but not for multiday summary measures.

Ication from wrist accelerometer data. Methods Three samples were used: 1) free living (n = 13, ages 20–60 yr); 2) laboratory based (n = 25, ages 30–65 yr); and 3) hospital inpatients (n = 10, ages 60–90 yr). All participants wore a GENEActiv on their wrist and activPAL on their thigh. The free-living sample wore an additional GENEActiv on the thigh and completed the Multimedia Activity Recall for Children and Adults. The laboratory-based sample wore the monitors while seated at a desk for 7 h, punctuated by 2 min of walking every 20 min. The free-living and inpatient samples wore the monitors for 24 h. Posture was classified from wrist-worn accelerometry using the Sedentary Sphere concept. Results Sitting time did not differ between the wrist GENEActiv and the activPAL in the free-living sample and was correlated in the three samples combined (rho = 0.9, P < 0.001), free-living and inpatient samples (r ≃ 0.8, P < 0.01). Mean intraindividual agreement was 85% ± 7%. In the laboratory-based and inpatient samples, sitting time was underestimated by the wrist GENEActiv by 30 min and 2 h relative to the activPAL, respectively (P < 0.05). Posture classification disagreed during reading while standing, cooking while standing, and brief periods during driving. Posture allocation validity was excellent when the GENEActiv was worn on the thigh, evidenced by the near-perfect agreement with the activPAL (96% ± 3%). Conclusions The Sedentary Sphere enables determination of the most likely posture from the wrist-worn GENEActiv. Visualizing behaviors on the sphere displays the pattern of wrist movement and positions within that behavior....

Introduction: Low energy availability (EA) may impede adaptation to exercise, suppressing reproductive function and bone turnover. Exercise energy expenditure (EEE) measurements lack definition and consistency. This study aimed to compare EA measured from moderate and vigorous physical activity from accelerometry (EEE_mpva) with EA from total physical activity (EEE_tpa) from doubly labeled water in women. The secondary aim was to determine the relationship of EA with physical fitness, body composition by dual-energy x-ray absorptiometry, heart rate variability (HRV), and eating behavior (Brief Eating Disorder in Athletes Questionnaire [BEDA-Q]). Methods: This was a prospective, repeated-measures study, assessing EA measures and training adaptation during 11-month basic military training. Forty-seven women (23.9 ± 2.6 yr) completed three consecutive 10-d assessments of EEE_mvpa, EEE_tpa, and energy intake (EI). EA measures were compared using linear regression and Bland–Altman analyses; relationships of EA with fat mass, HRV, 1.5-mile run times, and BEDA-Q were evaluated using partial correlations. Results: EA from EEE_mvpa demonstrated strong agreement with EA from EEE_tpa across the measurement range (R ² = 0.76, r = 0.87, P < 0.001) and was higher by 10 kcal·kg⁻¹ FFM·d⁻¹. However, EA was low in absolute terms because of underreported EI. Higher EA was associated with improved 1.5-mile run time (r = 0.28, P < 0.001), fat mass loss (r = 0.38, P < 0.001), and lower BEDA-Q score (r = −0.37, P < 0.001) but not HRV (all P > 0.10). Conclusion: Accelerometry-based EEE demonstrated validity against doubly labeled water during multistressor training, the difference representing 10 kcal·kg⁻¹ FFM·d⁻¹ EEE from nonexercise activity. Beneficial physical but not autonomic adaptations were associated with higher EA. EA_mvpa and BEDA-Q warrant consideration for low EA assessment and screening.