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Physical behavior can be assessed using a range of competing methods. The Free-Living Activity Study for Health (FLASH) is an ongoing study that facilitates the comparison of such methods. The purpose of this report is to describe the FLASH, with a particular emphasis on a subsample of participants who have consented to have their deidentified data released in a shared repository. Participants in the FLASH wear seven physical activity monitors for a 24-hr period and then complete a detailed recall using the Activities Completed Over Time in 24-hr online assessment tool. The participants can optionally agree to be video recorded for 30–60 min, which allows for direct observation as a criterion indicator of their behavior during that period. As of version 0.1.0, the repository includes data from 38 participants, and the sample size will grow as data are collected, processed, and released in future versions. The repository makes it possible to combine sensor data (e.g., from ActiGraph and SenseWear) with minute-by-minute contextual data (from the Activities Completed Over Time in 24-hr recall system), which enables the FLASH to generate benchmark data for a wide range of future research. The repository itself provides an example of how a powerful open-source tool (GitHub) can be used to share data and code in a way that encourages communication and collaboration among a variety of scientists (e.g., algorithm developers and end users). The FLASH data set will provide long-term benefits to researchers interested in advancing the science of physical behavior monitoring.

Evidence suggests that video captured with a wearable video device (WVD) may augment or supplant traditional methods for assessing park use. Unmanned aerial systems (UASs) are used to assess human activity, but research employing them for park assessments is sparse. Therefore, this study compared park user counts between a WVD and UAS. A diverse set of 33 amenities (e.g., playground) in three parks were videoed simultaneously by one researcher wearing a WVD and another operating the UAS. Assessments were done at 12 p.m. and 7 p.m. on weekends, with one park evaluated on two occasions 7 days apart. Two investigators independently reviewed videos and reached consensus on the counts of individuals at each amenity. Intraclass correlation coefficients (ICCs) were used to determine intra- and interrater reliabilities. A total of 404 (M = 4.7; SD = 9.6) and 389 (M = 4.5; SD = 9.0) individuals were counted in the UAS and WVD videos, respectively. Absolute agreement was 86% (74/86) and 100% when no individuals were using the amenity. Whether using all 86 videos or only videos having people (48 videos), ICCs indicated excellent reliability (ICC = .99; p < .001). The totals seen for the repeated measures were UAS = 146 and WVD = 136 for Day 1 and UAS = 169 and WVD = 161 for Day 2. Intrarater reliability was excellent for the UAS (ICC = .92; p < .001) and good for the WVD (ICC = .89; p < .001). Disagreement was mainly due to obstructions—people behind or under structures. This study provides support for the use of UASs for counting park users and future research examining the potential benefits of video analysis for assessing park use.

Background: The authors assessed agreement between participant diaries and two automated algorithms applied to activPAL (PAL Technologies Ltd, Glasgow, United Kingdom) data for classifying awake wear time in three age groups. Methods: Study 1 involved 20 youth and 23 adults who, by protocol, removed the activPAL occasionally to create nonwear periods. Study 2 involved 744 older adults who wore the activPAL continuously. Both studies involved multiple assessment days. In-bed, out-of-bed, and nonwear times were recorded in the participant diaries. The CREA (in PAL processing suite) and ProcessingPAL (secondary application) algorithms estimated out-of-bed wear time. Second- and day-level agreement between the algorithms and diary was investigated, as were associations of sedentary variables with self-rated health. Results: The overall accuracy for classifying out-of-bed wear time as compared with the diary was 89.7% (Study 1) to 95% (Study 2) for CREA and 89.4% (Study 1) to 93% (Study 2) for ProcessingPAL. Over 90% of the nonwear time occurring in nonwear periods >165 min was detected by both algorithms, while <11% occurring in periods ≤165 min was detected. For the daily variables, the mean absolute errors for each algorithm were generally within 0–15% of the diary mean. Most Spearman correlations were very large (≥.81). The mean absolute errors and correlations were less favorable for days on which any nonwear time had occurred. The associations between sedentary variables and self-rated health were similar across processing methods. Conclusion: The automated awake wear-time classification algorithms performed similarly to the diary information on days without short (≤2.5–2.75 hr) nonwear periods. Because both diary and algorithm data can have inaccuracies, best practices likely involve integrating diary and algorithm output.

Purpose : Physical activity (PA) is crucial for health, but there is insufficient evidence about PA patterns and their operationalization. The authors developed two algorithms (SPORT_constant and SPORT_linear) to quantify PA patterns and check whether pattern information yields additional explained variance (compared with a compositional data approach [CoDA]). Methods : To measure PA, 397 (218 females) adolescents with a mean age of 12.4 (SD = 0.6) years wore an ActiGraph on their lower back for 1 week. The SPORT algorithms are based on a running value, each day starting with 0 and minutely adapting depending on the behavior being performed. The authors used linear regression models with a behavior-dependent constant (SPORT_constant) and a function of time-in-bout (SPORT_linear) as predictors and body mass index z scores (BMI _z ) and fat mass percentages (%FM) as exemplary outcomes. For generalizability, the models were validated using five-fold cross-validation where data were split up in five groups, and each of them was a test data set in one of five iterations. Results : The CoDA and the SPORT_constant models explained low variance in BMI _z (2% and 1%) and low to moderate variance in %FM (both 5%). The variance being explained by the SPORT_linear models was 6% (BMI _z ) and 9% (%FM), which was significantly more than the CoDA models (p < .001) according to likelihood ratio tests. Conclusion : Among this group of adolescents, SPORT_linear explained more variance of BMI _z and %FM than CoDA. These results suggest a way to enable research about PA patterns. Future research should apply the SPORT_linear algorithm in other target groups and with other health outcomes.

Short sleep duration is associated with poorer outcomes for adolescents; however, sleep duration is often assessed (either by questionnaire or device) using self-reported bedtime (i.e., the time a person goes to bed). With sedentary activities, such as screen time, being common presleep in-bed behaviors, the use of “bedtime” may introduce error to the estimates of sleep duration. It has been proposed that self-reported “shuteye time” (i.e., the time a person starts trying to go to sleep) is used instead of bedtime. This study aimed to compare the bedtimes and shuteye times of a sample of 15- to 18-year-old female adolescents recruited from 13 high schools across New Zealand. The influence on sleep duration estimates and associations with healthy lifestyle habits was also examined. Sleep data were collected from 136 participants using actigraphy and self-report. On average, 52 min (95% confidence interval [43, 60] min) of sedentary time was misclassified as sleep when bedtime was used instead of shuteye time with actigraph data. Mean bedtimes on weekdays and weekends were 9:56 p.m. (SD = 58 min) and 10:40 p.m. (SD = 77 min), respectively. The relationship between bedtime and shuteye time was not linear—indicating that bedtime cannot be used as a proxy for shuteye time. Earlier shuteye times were more strongly associated with meeting fruit and vegetable intake and sleep and physical activity guidelines than earlier bedtimes. Using bedtime instead of shuteye time to estimate sleep duration may introduce substantial error to estimates of both sleep and sedentary time.

Introduction: The authors’ objective was to identify the minimum number of days required to measure sedentary behavior and physical activity in children during school hours. Methods: Fifty-three children from four classes of the second year of elementary school in a public school in Brazil were selected. Sedentary behavior and physical activity were evaluated using activPAL in the thigh and ActiGraph GT3X on the hip. The devices were used for 4 days during the 4 hr of school. Intraclass correlation coefficient (ICC) and Bland–Altman plots were used for statistical analysis (p < .05). Results: For sedentary/stationary behavior indicators, 1 day showed good agreement with 4 days (sitting time, ICC = .89; bias [limits of agreement 95%, LA95%] = 1.6 [45.1 to −41.9], standing time, ICC = .93; bias [LA95%] 1.1 [30.2 to −28.0], and stationary behavior, ICC = .56; bias [LA95%] = 0.2 [37.2 to −36.7]). However, 2 days were necessary for good agreement, with 4 days for physical activity indicators (walking time, ICC = .91; bias [LA95%] = 1.1 [12.0 to −9.7], light physical activity, ICC = .97; bias [LA95%] = 0.3 [7.6 to −7.0], moderate physical activity, ICC = .93; bias [LA95%] = 0.3 [2.3 to −1.6], and vigorous physical activity, ICC = .93; bias [LA95%] = 0.3 [3.1 to −2.5]). Conclusion: Therefore, 1 evaluation day seems enough to obtain representative data of school sedentary/stationary behavior, while 2 days are necessary for the evaluation of physical activity indicators during school hours.

As a default setting, many body-worn research-grade activity monitors rely on software algorithms developed for young adults using waist-worn devices. ActiGraph offers the low-frequency extension (LFE) filter, which reduces the movement threshold to capture low acceleration activity, which is more common in older adults. It is unclear how this filter changes activity estimates and whether it is appropriate for all older adults. The authors compared activity estimates with and without the LFE filter on wrist-worn devices in a sample of 34 older adults who wore the ActiGraph GT9X on their nondominant wrist for 7 days in a free-living environment. The authors used participant characteristics to predict discrepancy in step count estimates generated with and without the LFE filter to determine which individuals are most accurately characterized. Estimates of steps per minute were higher (M = 21, SD = 1), and more activity was classified as moderate to vigorous intensity (M = 5.03%, SD = 3.92%) with the LFE filter (M = 11, SD = 1; M = 4.27%, SD = 3.52%) versus without the LFE filter (all ps < .001). The findings suggest that axes-based variables should be interpreted with caution when generated with wrist-worn data, and future studies should develop separate wrist and waist-worn standard estimates in older adults. Participation in a greater amount of moderate to vigorous intensity physical activity predicted a larger discrepancy in step counts generated with and without the filter (p < .009), suggesting that the LFE filter becomes increasingly inappropriate for use in highly active older individuals.

Purpose: To compare the accuracy and precision of a hip-worn accelerometer to predict energy cost during structured activities across motor performance and disease conditions. Methods: 118 adults self-identifying as healthy (n = 44) and those with arthritis (n = 23), multiple sclerosis (n = 18), Parkinson’s disease (n = 17), and stroke (n = 18) underwent measures of motor performance and were categorized into groups: Group 1, usual; Group 2, moderate impairment; and Group 3, severe impairment. The participants completed structured activities while wearing an accelerometer and a portable metabolic measurement system. Accelerometer-predicted energy cost (metabolic equivalent of tasks [METs]) were compared with measured METs and evaluated across functional impairment and disease conditions. Statistical significance was assessed using linear mixed effect models and Bayesian information criteria to assess model fit. Results: All activities’ accelerometer counts per minute (CPM) were 29.5–72.6% less for those with disease compared with those who were healthy. The predicted MET bias was similar across disease, −0.49 (−0.71, −0.27) for arthritis, −0.38 (−0.53, −0.22) for healthy, −0.44 (−0.68, −0.20) for MS, −0.34 (−0.58, −0.09) for Parkinson’s, and −0.30 (−0.54, −0.06) for stroke. For functional impairment, there was a graded reduction in CPM for all activities: Group 1, 1,215 CPM (1,129, 1,301); Group 2, 789 CPM (695, 884); and Group 3, 343 CPM (220, 466). The predicted MET bias revealed similar results across the Group 1, −0.37 METs (−0.52, −0.23); Group 2, −0.44 METs (−0.60, −0.28); and Group 3, −0.33 METs (−0.55, −0.13). The Bayesian information criteria showed a better model fit for functional impairment compared with disease condition. Conclusion: Using functionality to improve accelerometer calibration could decrease variability and warrants further exploration to improve accelerometer prediction of physical activity.