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Purpose: Traditional summary metrics provided by accelerometer device manufacturers, known as counts, are proprietary and manufacturer specific, making it difficult to compare studies using different devices. Alternative summary metrics based on raw accelerometry data have been introduced in recent years. However, they were often not calibrated on ground truth measures of activity-related energy expenditure for direct translation into continuous activity intensity levels. Our purpose is to calibrate, derive, and validate thresholds among women 60 years and older based on a recently proposed transparent raw data-based accelerometer activity index (AAI) and to demonstrate its application in association with cardiometabolic risk factors. Methods: We first built calibration equations for estimating metabolic equivalents continuously using AAI and personal characteristics using internal calibration data (N = 199). We then derived AAI cutpoints to classify epochs into sedentary behavior and physical activity intensity categories. The AAI cutpoints were applied to 4,655 data units in the main study. We then utilized linear models to investigate associations of AAI sedentary behavior and physical activity intensity with cardiometabolic risk factors. Results: We found that AAI demonstrated great predictive accuracy for estimating metabolic equivalents (R ² = .74). AAI-Based physical activity measures were associated in the expected directions with body mass index, blood glucose, and high-density lipoprotein cholesterol. Conclusion: The calibration framework for AAI and the cutpoints derived for women older than 60 years can be applied to ongoing epidemiologic studies to more accurately define sedentary behavior and physical activity intensity exposures, which could improve accuracy of estimated associations with health outcomes.

Background: Depression is a significant health problem, whereas higher physical activity (PA) associates with fewer depressive symptoms. We examined how self-reported depressive symptoms are associated with accelerometer-measured PA, standing, sedentary behavior, and time in bed (TIB) among 20- to 69-year-old men and women. Methods: The study is a part of the cross-sectional, population-based FinFit2017 study, in which depressive symptoms were assessed by modified nine-item Finnish version of the Patient Health Questionnaire, and physical behavior in terms of PA, sedentary behavior, standing, and TIB was assessed 24/7 by a triaxial accelerometer. During waking hours, the accelerometer was hip worn. Intensity of PA was analyzed by mean amplitude deviation and body posture by angle for posture estimation algorithms. During TIB, the device was wrist worn, and the analysis was based on the wrist movements. A total of 1,823 participants answered the nine-item Finnish version of the Patient Health Questionnaire and used the accelerometer 24 hr at least 4 days per week. Results: Men without depressive symptoms had on average more standing, light, and moderate to vigorous PA and steps, and less low and high movement TIB than the men with at least moderate symptoms, when age group, education, work status, marital status, and fitness were adjusted for. The asymptomatic women had more moderate to vigorous PA and steps and less high movement TIB than the women with at least moderate symptoms. Conclusions: Depressive symptoms were associated with lower levels of PA and longer TIB. It is important to identify these symptoms as early as possible to be able to initiate and target preventive actions, including PA promotion, to these symptomatic persons on time.

Consumer activity devices use prompts to alter sedentary and physical activity (PA) behaviors. However, it is unclear if PA prompts are effective. Purpose: To evaluate the effectiveness of PA prompts from a consumer wearable device in reducing sitting time and increasing PA in university employees. Methods: Thirty-three university employees without a history of consumer activity device wear were randomly assigned a Fitbit Alta HR that administered PA prompts (Prompt group) or had the prompt feature deactivated (No Prompt group). Participants wore an activPAL for 5–7 days to measure baseline sitting time and PA behaviors. Participants then wore the Fitbit for 12 days during waking hours and an activPAL during the last 5–7 days of the Fitbit wear period. Changes in activPAL sitting time and PA were compared across groups. Mean Fitbit steps taken in the first 50 min and the last 10 min of each hour were calculated and compared across groups during “Inactive” hours (<250 steps in the first 50 min), where a prompt was given (Prompt group) or would have been given (No Prompt group). Results: Mean activPAL sitting time increased in the Prompt group (0.66 ± 1.70 hr/day) and remained stable in the No Prompt group (−0.04 ± 1.29 hr/day), with no statistically significant differences between groups (d = 0.33, p = .36). Moderate to vigorous PA increased modestly in both groups, but no significant differences were observed. Fitbit steps during the last 10 min of inactive hours did not differ across groups. Conclusion: Fitbit PA prompts did not alter sitting time or PA behaviors in university employees.

The purpose of this scoping review was to examine publications using accelerometers in children aged 6 months to <6 years and report on current methodologies used for data collection and analyses. We examined device make and model, device placement, sampling frequency, data collection protocol, definition of nonwear time, inclusion criteria, epoch duration, and cut points. Five online databases and three gray literature databases were searched. Studies were included if they were published in English between January 2009 and March 2021. A total of 627 articles were included for descriptive analyses. Of the reviewed articles, 75% used ActiGraph devices. The most common device placement was hip or waist. More than 80% of articles did not report a sampling frequency, and 7-day protocols during only waking hours were the most frequently reported. Fifteen-second epoch durations and the cut points developed by Pate et al. in 2006 were the most common. A total of 203 articles did not report which definition of nonwear time was used; when reported, “20 minutes of consecutive zeros” was the most frequently used. Finally, the most common inclusion criteria were “greater or equal to 10 hr/day for at least 3 days” for studies conducted in free-living environments and “greater than 50% of the school day” for studies conducted in preschool or childcare environments. Results demonstrated a major lack of reporting of methods used to analyze accelerometer data from young children. A list of recommended reporting practices was developed to encourage increased reporting of key methodological details for research in this area.