Association Between Accelerometer and Parental Reported Weekend and Weekday Sleeping Patterns and Adiposity Among Preschool-Aged Children

Sleep is vital for healthy development of young children; however, it is not understood how the quality and quantity vary between the weekends and weekdays (WE–WD). Research focused on older children has demonstrated that there is significant WE–WD variability and that this is associated with adiposity. It is unclear how this is experienced among preschoolers. This study explored: (a) the accuracy of WE–WD sleep as reported in parental logbooks compared with accelerometers; (b) the difference between WE and WD total sleep time, sleep efficiency, and timing, as assessed by accelerometers; and (c) the association between the variability of these metrics and adiposity. Eighty-seven preschoolers (M = 46; 4.48 ± 0.89 years) wore an accelerometer on their right hip for 7 days. Parents were given logbooks to track “lights out” times (sleep onset) and out of bed time (sleep offset). Compared with accelerometers, parental logbook reports indicated earlier sleep onset and later sleep offset times on both WEs and WDs. Accelerometer-derived total sleep time, sleep efficiency, and onset/offset were not significantly different on the WEs and WDs; however, a sex effect was observed, with males going to bed and waking up earlier than females. Correlation analyses revealed that variability of sleep onset times throughout the week was positively correlated with percentage of fat mass in children. Results suggest that variability of sleep onset may be associated with increased adiposity in preschool children. Additional research with larger and more socioeconomically and racially diverse samples is needed to confirm these findings.

Comparison of Fitbit One and ActivPAL3TM in Adults With Multiple Sclerosis in a Free-Living Environment

Walking is the most common and preferred way for people with multiple sclerosis (MS) to be active. Consumer-grade wearable activity monitors may be used as a tool to assist people with MS to track their walking by counting the number of steps. The authors evaluated the validity of Fitbit One activity tracker in individuals with MS by comparing step counts measured over a 7-day period against ActivPAL3TM (AP). Twenty-five ambulatory adults with MS with an average age 51.7 (10.2) years and gait speed 0.98 (0.47) m/s, median Expanded Disability Status Scale 5.5 (2.5–6.5), and 15 years post-MS diagnosis wore Fitbit One (using both waist and ankle placement) and AP for 7 consecutive days. Validity of Fitbit One for measuring step counts against AP was assessed using intraclass correlation coefficients (ICCs), Bland–Altman plots, and t tests. Regardless of wearing location (waist or ankle), there was good agreement between steps recorded by Fitbit One and AP (ICC: .86 [.82, .90]). The ankle-worn Fitbit measured steps more accurately (ICC: .91 [.81, .95]) than the waist-worn Fitbit (ICC: .81 [.62, .85]) especially in individuals (n = 12) who walked slowly (gait speed = 0.74 m/s). Fitbit One as a user-friendly, inexpensive, consumer-grade activity tracker can accurately record steps in persons with MS in a free-living environment.

Comparison of a Thigh-Worn Accelerometer Algorithm With Diary Estimates of Time in Bed and Time Asleep: The 1970 British Cohort Study

Background: Thigh-worn accelerometers have established reliability and validity for measurement of free-living physical activity-related behaviors. However, comparisons of methods for measuring sleep and time in bed using the thigh-worn accelerometer are rare. The authors compared the thigh-worn accelerometer algorithm that estimates time in bed with the output of a sleep diary (time in bed and time asleep). Methods: Participants (N = 5,498), from the 1970 British Cohort Study, wore an activPAL device on their thigh continuously for 7 days and completed a sleep diary. Bland–Altman plots and Pearson correlation coefficients were used to examine associations between the algorithm derived and diary time in bed and asleep. Results: The algorithm estimated acceptable levels of agreement with time in bed when compared with diary time in bed (mean bias of −11.4 min; limits of agreement −264.6 to 241.8). The algorithm-derived time in bed overestimated diary sleep time (mean bias of 55.2 min; limits of agreement −204.5 to 314.8 min). Algorithm and sleep diary are reasonably correlated (ρ = .48, 95% confidence interval [.45, .52] for women and ρ = .51, 95% confidence interval [.47, .55] for men) and provide broadly comparable estimates of time in bed but not for sleep time. Conclusions: The algorithm showed acceptable estimates of time in bed compared with diary at the group level. However, about half of the participants were outside of the ±30 min difference of a clinically relevant limit at an individual level.

Agreement of Sedentary Behavior Metrics Derived From Hip- and Thigh-Worn Accelerometers Among Older Adults: With Implications for Studying Physical and Cognitive Health

Little is known about how sedentary behavior (SB) metrics derived from hip- and thigh-worn accelerometers agree for older adults. Thigh-worn activPAL (AP) micro monitors were concurrently worn with hip-worn ActiGraph (AG) GT3X+ accelerometers (with SB measured using the 100 counts per minute [cpm] cut point; AG100cpm) by 953 older adults (age 77 ± 6.6, 54% women) for 4–7 days. Device agreement for sedentary time and five SB pattern metrics was assessed using mean error and correlations. Logistic regression tested associations with four health outcomes using standardized (i.e., z scores) and unstandardized SB metrics. Mean errors (AP − AG100cpm) and 95% limits of agreement were: sedentary time −54.7 [−223.4, 113.9] min/day; time in 30+ min bouts 77.6 [−74.8, 230.1] min/day; mean bout duration 5.9 [0.5, 11.4] min; usual bout duration 15.2 [0.4, 30] min; breaks in sedentary time −35.4 [−63.1, −7.6] breaks/day; and alpha −.5 [−.6, −.4]. Respective Pearson correlations were: .66, .78, .73, .79, .51, and .40. Concordance correlations were: .57, .67, .40, .50, .14, and .02. The statistical significance and direction of associations were identical for AG100cpm and AP metrics in 46 of 48 tests, though significant differences in the magnitude of odds ratios were observed among 13 of 24 tests for unstandardized and five of 24 for standardized SB metrics. Caution is needed when interpreting SB metrics and associations with health from AG100cpm due to the tendency for it to overestimate breaks in sedentary time relative to AP. However, high correlations between AP and AG100cpm measures and similar standardized associations with health outcomes suggest that studies using AG100cpm are useful, though not ideal, for studying SB in older adults.

Validity and Interinstrument Reliability of a Medical Grade Physical Activity Monitor in Older Adults

Wearable physical activity monitors are associated with an increase in user’s habitual physical activity levels. Most of the older adult population do not meet the national moderate- to vigorous-intensity physical activity (MVPA) recommendations and may benefit from being prescribed a physical activity monitor. The PiezoRx is a class one medical grade device that uses step rate thresholds to measure MVPA. The validity and reliability of the PiezoRx in measuring MVPA has yet to be determined in older persons. We assessed the validity and interinstrument reliability of the PiezoRx to measure steps and MVPA in older adults. Participants (n = 19; 68.8 ± 2.3 years) wore an Omron HJ-320 pedometer, ActiGraph GT3X accelerometer, and four PiezoRx monitors during a five-stage treadmill walking protocol. The PiezoRx devices were set at moderate physical activity and vigorous physical activity step rate thresholds (steps per minute) of 100/120, 110/130, adjusted for height and adjusted for height + fitness. The PiezoRx exhibited a stronger correlation (intraclass correlation coefficient = .82) with manually counted steps than the ActiGraph (intraclass correlation coefficient = .53) and Omron (intraclass correlation coefficient = .54) and had a low absolute percentage error (3 ± 6%). The PiezoRx with moderate physical activity/vigorous physical activity step thresholds adjusted to 110/130 was strongly correlated to indirect calorimetry (0.84, p < .001) and best distinguished each walking stage as MVPA or not (sensitivity: 88%; specificity: 95%). The PiezoRx monitor is a valid and reliable measure of step count and MVPA among older adults. The device’s ability to measure MVPA in absolute terms was improved when step rate thresholds for moderate physical activity/vigorous physical activity were increased to 110/130 steps per minute in this population.

Validation of Wearable Camera Still Images to Assess Posture in Free-Living Conditions

Purpose: To assess the convergent validity of body-worn wearable camera still images (IMGs) for determining posture compared with activPAL (AP) classifications. Methods: The participants (n = 16, mean age 46.7 ± 23.8 years, 9 F) wore an Autographer wearable camera and an AP during three 2-hr free-living visits. IMGs were on average 8.47 s apart and were annotated with output consisting of events, transitory states, unknown, and gaps. The events were annotations that matched AP classifications (sit, stand, and move), consisting of at least three IMGs; the transitory states were posture annotations fewer than three IMGs; the unknowns were IMGs that could not be accurately classified; and the gaps were the time between annotations. For the analyses, the annotation and AP output were converted to 1-s epochs. The total and average length of visits and events were reported in minutes. Bias and 95% confidence intervals for event posture times from IMGs to AP were calculated to determine accuracy and precision. Confusion matrices using total AP posture times were computed to determine misclassification. Results: Forty-three visits were analyzed, with a total visit and event time of 5,027.73 and 4,237.23 min, respectively, and the average visit and event lengths being 116.92 and 98.54 min, respectively. Bias was not statistically significant for sitting, but was significant for standing and movement (0.84, −6.87, and 6.04 min, respectively). From confusion matrices, IMGs correctly classified sitting, standing, and movement (85.69%, 54.87%, and 69.41%, respectively) of total AP time. Conclusion: Wearable camera IMGs provide a good estimation of overall sitting time. Future work is warranted to improve posture classifications and examine the validity of IMGs in assessing activity-type behaviors.

Comparison of Three Algorithms Using Thigh-Worn Accelerometers for Classifying Sitting, Standing, and Stepping in Free-Living Office Workers

Accurate measurement of time spent sitting, standing, and stepping is important in studies seeking to evaluate interventions to reduce sedentary behavior. In this study, the authors evaluated the agreement in classification of these activities from three algorithms applied to thigh-worn ActiGraph accelerometers using predictions from the widely used activPAL device as a criterion. Participants (n = 29, 72% female, age 23–68 years) wore the activPAL3™ micro (processed by PAL software, version 7.2.32) and the ActiGraph™ GT9X accelerometer on the right front thigh concurrently for working hours on one full workday (7.2 ± 1.2 hr). ActiGraph output was classified via the three test algorithms: ActiGraph’s ActiLife software (inclinometer); an open source method; and, a machine-learning algorithm reported in the literature (Acti4). Performance at an instance level was evaluated by computing classification accuracy (F scores) for 15-s windows. The F scores showed high accuracy relative to the criterion for identifying sitting (96.7–97.1) and were 84.7–85.1 for identifying standing and 78.1–80.6 for identifying stepping. The four methods agreed strongly in total time spent sitting, standing, and stepping, with intraclass correlation coefficients of .96 (95% confidence interval [.92, .96]), .92 (95% confidence interval [.81, .96]), and .87 (95% confidence interval [.53, .95]) but sometimes overestimated sitting time and underestimated standing time relative to activPAL. These algorithms for identifying sitting, standing, and stepping from thigh-worn accelerometers provide estimates that are very similar to those obtained using the activPAL.

Comparing Counts of Park Users With a Wearable Video Device and an Unmanned Aerial System

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.

Shuteye Time Compared With Bedtime: Misclassification of Sleep in Adolescent Females

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.