Using a Betabinomial distribution to estimate the prevalence of adherence to physical activity guidelines among children and youth.

BACKGROUND: Estimates of the prevalence of adherence to physical activity guidelines in the population are generally the result of averaging individual probability of adherence based on the number of days people meet the guidelines and the number of days they are assessed. Given this number of active and inactive days (days assessed minus days active), the conditional probability of meeting the guidelines that has been used in the past is a Beta (1 + active days, 1 + inactive days) distribution assuming the probability p of a day being active is bounded by 0 and 1 and averages 50%. A change in the assumption about the distribution of p is required to better match the discrete nature of the data and to better assess the probability of adherence when the percentage of active days in the population differs from 50%. DATA AND METHODS: Using accelerometry data from the Canadian Health Measures Survey, the probability of adherence to physical activity guidelines is estimated using a conditional probability given the number of active and inactive days distributed as a Betabinomial(n, a + active days , ß + inactive days) assuming that p is randomly distributed as Beta(a, ß) where the parameters a and ß are estimated by maximum likelihood. RESULTS: The resulting Betabinomial distribution is discrete. For children aged 6 or older, the probability of meeting physical activity guidelines 7 out of 7 days is similar to published estimates. For pre-schoolers, the Betabinomial distribution yields higher estimates of adherence to the guidelines than the Beta distribution, in line with the probability of being active on any given day. INTERPRETATION: In estimating the probability of adherence to physical activity guidelines, the Betabinomial distribution has several advantages over the previously used Beta distribution. It is a discrete distribution and maximizes the richness of accelerometer data.

Commercially available wearable health monitors in dogs only had a very strong correlation during longer durations of time: a pilot study.

OBJECTIVE: To compare a commercially available accelerometer, FitBark 2 (FitBark 2nd Generation; FitBark) with a previously validated accelerometer, Actical (Actical; Respironics Inc) during periods of activity and rest. We hypothesized that the FitBark 2 would correlate strongly with the Actical during periods of activity and rest. METHODS: 20 dogs between the ages of 1 and 9 years of variable sex, breed, and body weight were enrolled from April through August 2022 in a 1-week pilot trial. Dogs were fitted with a neck collar with both devices mounted on it and wore it continuously for 1 week. Six time points were established to evaluate varying degrees of activity and periods of rest, which included the week, a 1-mile walk, the day of the walk, the hour the walk occurred in, 6 am to 7 am on the morning of the walk, and 11 pm to 12 am on the evening of the walk. Actical and FitBark 2 were compared using linear regression and correlation analysis. RESULTS: Correlation analysis revealed a very strong correlation between the Actical and FitBark 2 during the entire week of the study, with a moderate correlation at other time points. Linear regression revealed the strength of the relationship by the r2 = 0.85, F1,13 = 76.08. CONCLUSIONS: FitBark 2 can be used to evaluate the activity and rest of dogs with varying degrees of correlation when compared to the Actical. CLINICAL RELEVANCE: The time period assessed impacts the correlation of the FitBark 2 and Actical. This should be considered when using the FitBark 2 for data collection.

Agreement Between Apple Watch and Actical Step Counts in a Community Setting: Cross-Sectional Investigation From the Framingham Heart Study.

BACKGROUND: Step counting is comparable among many research-grade and consumer-grade accelerometers in laboratory settings. OBJECTIVE: The purpose of this study was to compare the agreement between Actical and Apple Watch step-counting in a community setting. METHODS: Among Third Generation Framingham Heart Study participants (N=3486), we examined the agreement of step-counting between those who wore a consumer-grade accelerometer (Apple Watch Series 0) and a research-grade accelerometer (Actical) on the same days. Secondarily, we examined the agreement during each hour when both devices were worn to account for differences in wear time between devices. RESULTS: We studied 523 participants (n=3223 person-days, mean age 51.7, SD 8.9 years; women: n=298, 57.0%). Between devices, we observed modest correlation (intraclass correlation [ICC] 0.56, 95% CI 0.54-0.59), poor continuous agreement (29.7%, n=957 of days having steps counts with ≤15% difference), a mean difference of 499 steps per day higher count by Actical, and wide limits of agreement, roughly ±9000 steps per day. However, devices showed stronger agreement in identifying who meets various steps per day thresholds (eg, at 8000 steps per day, kappa coefficient=0.49), for which devices were concordant for 74.8% (n=391) of participants. In secondary analyses, in the hours during which both devices were worn (n=456 participants, n=18,760 person-hours), the correlation was much stronger (ICC 0.86, 95% CI 0.85-0.86), but continuous agreement remained poor (27.3%, n=5115 of hours having step counts with ≤15% difference) between devices and was slightly worse for those with mobility limitations or obesity. CONCLUSIONS: Our investigation suggests poor overall agreement between steps counted by the Actical device and those counted by the Apple Watch device, with stronger agreement in discriminating who meets certain step thresholds. The impact of these challenges may be minimized if accelerometers are used by individuals to determine whether they are meeting physical activity guidelines or tracking step counts. It is also possible that some of the limitations of these older accelerometers may be improved in newer devices.

Association of Accelerometer-Measured Physical Activity and Cardiovascular Health in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

INTRODUCTION: Evidence regarding the associations between accelerometer-measured moderate-vigorous physical activity (MVPA) and cardiovascular health (CVH) indicators among Hispanic/Latino adults are unavailable. METHODS: Examined cross-sectional data from 12,008 Hispanic/Latino adults aged 18-74 years participating in the Hispanic Community Health Study/Study of Latinos. Accelerometer-measured MVPA was assessed categorically and dichotomously per 2008 PA guidelines. Adverse and ideal CVH indicators were determined by standard cut-points for blood glucose, total cholesterol, blood pressure, body mass index (BMI), and smoking. A composite of low CV risk, defined as achieving all ideal CVH indicators, was included. Adjusted Poisson regression models and complex survey design methods were used for all analyses. RESULTS: Compared to high MVPA, lower MVPA categories were associated with higher prevalence of all adverse CVH indicators, except hypertension, and with lower prevalence of low CV risk and ideal blood glucose, blood pressure, and BMI. Similarly, non-adherence to PA guidelines was associated with a higher prevalence of diabetes (16%), hypercholesterolemia (9%), obesity (28%), and smoking (9%); and lower prevalence of low CV risk (24%), ideal blood glucose (6%), ideal blood pressure (6%), and ideal BMI (22%). CONCLUSION: Overall, high accelerometer-measured MVPA and meeting PA guidelines were associated with favorable CVH in Hispanic/Latino adults.

How Many Valid Days Are Necessary to Assess Physical Activity Data From Accelerometry During Pregnancy?

BACKGROUND: The authors examined whether or not ≤3 days wearing Actical® accelerometers provided acceptable results in comparison with the recommendation of ≥4 days in women across gestation. METHODS: A total of 26, 76, and 57 participants at early, mid, and late pregnancy, respectively, were assessed. Participants were instructed to wear the device for 7 days and women who wore it for ≥4 days were included. For each participant, 3, 2, and 1 day(s) were randomly selected. Paired comparisons, intraclass correlations coefficients, and kappa statistics were performed for ≥4 days (criterion) versus 3, 2, and 1 day(s). Averages (in minutes per day) of sedentary time, light, moderate, vigorous, moderate to vigorous physical activity (PA) and steps per day were examined. RESULTS: When 3 valid days were compared with the criterion, no significant differences were found for any gestational period. The intraclass correlations coefficients were "high" for all PA-related variables. The k values varied from .819 to .838 across pregnancy ("strong"). Two and 1 valid day(s) versus the criterion showed significant differences in some PA intensities, reduced intraclass correlations coefficients, "moderate" k values for 2 valid days (.638-.788) and "minimal-to-moderate" k values for 1 valid day (.367-.755). CONCLUSION: In pregnant women during early, mid, and late pregnancy, PA data obtained from 3 valid days of wear was equivalent and agreed with ≥4 valid days.

Variety Is the Spice of Late Life: Social Integration and Daily Activity.

OBJECTIVES: Social integration (involvement with a diverse array of social ties) has been linked to positive outcomes including better physical health. Research has not investigated whether encounters with diverse social ties enhance individuals' daily behaviors. The objectives of this study were to assess whether social ties connect individuals to more diverse daily behaviors, physical activity, and nonsedentary time as well as more positive mood. METHOD: Older adults (aged 65+, n = 313) provided information about their 10 closest social ties. Then they completed ecological momentary assessment (EMA) surveys every 3 hr for 5-6 days where they reported on social encounters and behaviors. They also wore Actical accelerometers to objectively measure physical activity. RESULTS: Multilevel models revealed that encounters with a greater variety of social ties was associated with engaging in a greater variety of behaviors, more objectively measured physical activity, and a smaller proportion of time spent sedentary. Encounters with weak ties/peripheral social ties accounted for this increased activity (compared to being alone or with close friends or family). Moreover, involvement with diverse ties or diverse behaviors was associated with better mood. DISCUSSION: Findings are discussed in terms of social engagement theory, network diversity, and the benefits of weak ties.

Software thresholds alter the bias of actigraphy for monitoring sleep in team-sport athletes.

OBJECTIVES: Actical® actigraphy is commonly used to monitor athlete sleep. The proprietary software, called Actiware®, processes data with three different sleep-wake thresholds (Low, Medium or High), but there is no standardisation regarding their use. The purpose of this study was to examine validity and bias of the sleep-wake thresholds for processing Actical® sleep data in team sport athletes. DESIGN: Validation study comparing actigraph against accepted gold standard polysomnography (PSG). METHODS: Sixty seven nights of sleep were recorded simultaneously with polysomnography and Actical® devices. Individual night data was compared across five sleep measures for each sleep-wake threshold using Actiware® software. Accuracy of each sleep-wake threshold compared with PSG was evaluated from mean bias with 95% confidence limits, Pearson moment-product correlation and associated standard error of estimate. RESULTS: The Medium threshold generated the smallest mean bias compared with polysomnography for total sleep time (8.5min), sleep efficiency (1.8%) and wake after sleep onset (-4.1min); whereas the Low threshold had the smallest bias (7.5min) for wake bouts. Bias in sleep onset latency was the same across thresholds (-9.5min). The standard error of the estimate was similar across all thresholds; total sleep time ∼25min, sleep efficiency ∼4.5%, wake after sleep onset ∼21min, and wake bouts ∼8 counts. CONCLUSIONS: Sleep parameters measured by the Actical® device are greatly influenced by the sleep-wake threshold applied. In the present study the Medium threshold produced the smallest bias for most parameters compared with PSG. Given the magnitude of measurement variability, confidence limits should be employed when interpreting changes in sleep parameters.

Deriving the reference value from the circadian motor active patterns in the "non-dementia" population, compared to the "dementia" population: What is the amount of physical activity conducive to the good circadian rhythm.

BACKGROUND: The circadian rhythm in older adults is commonly known to change with a decrease in physical activity. However, the association between circadian rhythm metrics and physical activity remains unclear. The objective of this study was to examine circadian activity patterns in older people with and without dementia and to determine the amount of physical activity conducive to a good circadian measurement. METHOD: Circadian parameters were collected from 117 older community-dwelling people (66 subjects without dementia and 52 subjects with dementia); the parameters were measured continuously using actigraphy for 7 days. A receiver operating characteristic (ROC) curve was applied to determine reference values for the circadian rhythm parameters, consisting of interdaily stability (IS), intradaily variability (IV), and relative amplitude (RA), in older subjects. RESULTS: The ROC curve revealed reference values of 0.55 for IS, 1.10 for IV, and 0.82 for RA. In addition, as a result of the ROC curve in the moderate-to-vigorous physical Activity (MVPA) conducive to the reference value of the Non-parametric Circadian Rhythm Analysis per day, the optimal reference values were 51 minutes for IV and 55 minutes for RA. However, the IS had no classification accuracy. CONCLUSIONS: Our results demonstrated the reference values derived from the circadian parameters of older Japanese population with or without dementia. Also, we determined the MVPA conducive to a good circadian rest-active pattern. This reference value for physical activity conducive to a good circadian rhythm might be useful for developing a new index for health promotion in the older community-dwelling population.

Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure.

Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland-Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity.

Ankle Accelerometry for Assessing Physical Activity Among Adolescent Girls: Threshold Determination, Validity, Reliability, and Feasibility.

PURPOSE: Ankle accelerometry allows for 24-hr data collection and improves data volume/integrity versus hip accelerometry. Using Actical ankle accelerometry, the purpose of this study was to (a) develop sensitive/specific thresholds, (b) examine validity/reliability, (c) compare new thresholds with those of the manufacturer, and (d) examine feasibility in a community sample (low-income, urban adolescent girls). METHOD: Two studies were conducted with 6th- through 7th-grade girls (aged 10-14 years old): First was a laboratory study (n = 24), in which 2 Actical accelerometers were placed on the ankle and worn while measuring energy expenditure (Cosmed K4b2, metabolic equivalents [METs]) during 10 prescribed activities. Analyses included device equivalence reliability (intraclass correlation coefficient [ICC], activity counts of 2 Acticals), criterion-related validity (correlation, activity counts and METs), and calculations of sensitivity, specificity, kappa, and receiver-operating characteristic curves for thresholds. The second was a free-living study (n = 459), in which an Actical was worn for more than 7 days on the ankle (full 24-hr days retained). Analyses included feasibility (frequencies, missing data) and paired t tests (new thresholds vs. those of the manufacturer). RESULTS: In the laboratory study, the Actical demonstrated reliability (ICC = .92) and validity (r = .81). Thresholds demonstrated sensitivity (91%), specificity (84%), kappa = .73 (p = .043), area under curve range = .81-.97. In the free-living study, 99.6% of participants wore the accelerometer; 84.1% had complete/valid data (mean = 5.7 days). Primary reasons for missing/invalid data included: improper programming/documentation (5.2%), failure to return device (5.0%), and wear-time ≤ 2 days (2.8%). The moderate-to-vigorous physical activity threshold (> 3,200 counts/minute) yielded 37.2 min/day, 2 to 4.5 times lower than that of the manufacturer's software (effect size = 0.74-4.05). CONCLUSIONS: Validity, reliability, and feasibility evidences support Actical ankle accelerometry to assess physical activity in community studies of adolescent girls. When comparing manufacturers' software versus new thresholds, a major difference was observed.

A multilevel approach to examining time-specific effects in accelerometer-assessed physical activity.

OBJECTIVES: Popular methods for analyzing accelerometer data often use a single physical activity outcome variable such as average-weekly or total physical activity. These approaches limit the types of research questions that can be answered and fail to utilize the detailed, time-specific information available from accelerometers. This study proposes the use of multilevel modeling, which tested intervention effects at specific time periods. DESIGN: The motivating example was the Active by Choice Today trial. Simulations were used to test whether the application of time-specific hypotheses about when physical activity intervention treatment effects were expected to occur (e.g., after-school hours) increased power to detect effects compared to traditional methods. METHODS: Six simulation conditions were tested: (1) no treatment effects (to test the type 1 error rate), (2) time-specific effects, but no traditionally-tested effects, (3) traditionally-tested effects, but no time-specific effects, and (4) combinations of traditional and time-specific effects in 3 proportions. RESULTS: Results showed the proposed multilevel approach demonstrated appropriate type 1 error rates and increased power to detect treatment effects during hypothesized times by 31-38 percentage points compared to traditional approaches. This was consistent across varying proportions of traditional versus time-specific effects, and there was no loss of power using the multilevel approach when only traditional effects were present. CONCLUSIONS: The current study showed potential advantages of testing time-specific hypotheses about intervention effects using a multilevel time-specific approach. This approach may show intervention effects when traditional approaches do not. Future research should explore the application of this additional analytic tool for accelerometer physical activity estimates.

Evaluating the Responsiveness of Accelerometry to Detect Change in Physical Activity.

The responsiveness to change of the Actical and ActiGraph accelerometers was assessed in children and adolescents. Participants (n=208) aged 6-16 years completed two simulated free-living protocols, one with primarily light-to-moderate physical activities (PA) and one with mostly moderate-to-vigorous PA. Time in sedentary, light, moderate, and vigorous PA was estimated using 8 previously developed cut-points (4 for Actical and 4 for ActiGraph) and 15-s and 30-s epochs. Accelerometer responsiveness for detecting differences in PA between protocols was assessed using standardized response means (SRM). SRM values ≥0.8 represented high responsiveness to change. Both accelerometers showed high responsiveness for all PA intensities (SRMs = 1.2-4.7 for Actical and 1.1-3.3 for ActiGraph). All cut-points and epoch lengths yielded high responsiveness, and choice of cut-points and epoch length had little effect on responsiveness. Thus, both the Actical and ActiGraph can detect change in PA in a simulated free-living setting, irrespective of cut-point selection or epoch length.

Habitual sleep/wake patterns in the Old Order Amish: heritability and association with non-genetic factors.

STUDY OBJECTIVES: We sought to evaluate the contribution of genetic and non-genetic factors on habitual sleep/wake patterns in a community-dwelling agrarian population using a physical activity monitoring device, the Actical. DESIGN: Cross-sectional population-based study of healthy Old Order Amish enrolled in the Heredity and Phenotype Intervention (HAPI) Heart Study. SETTING: Lancaster County, PA, USA. PARTICIPANTS: 723 healthy adults (54% men) with a mean age of 43.3 ± 13.8 years (range: 20-80). 96% of the subjects were connected into one 5-generation pedigree. INTERVENTIONS: N/A. MEASUREMENTS: Participants wore Actical accelerometers 24 hours/day for 7 days to determine physical activity level, as well as habitual wake time, bedtime, and sleep duration. Participants completed the Horne-Östberg Morningness-Eveningness Questionnaire (MEQ), a modified Epworth Sleepiness Scale (ESS), and a lifestyle questionnaire. A sub-study of 164 participants kept sleep diaries. RESULTS: Habitual wake time and bedtime determined by Actical were highly correlated with results from sleep diaries (r = 0.82 for wake time and 0.72 for bedtime, both P < 0.0001). After adjustment for age, sex, occupation, and season, higher activity level was associated with earlier wake time but not with bedtime, and correspondingly with shorter sleep duration. After adjustment for the aforementioned factors and the effects of a shared household, habitual wake time, MEQ score, and ESS score showed significant heritability (wake time h(2) = 0.20, MEQ h(2) = 0.21, and ESS h(2) = 0.17). CONCLUSIONS: Objectively measured wake time, self-reported morningness-eveningness preference, and daytime sleepiness appear heritable and wake time may be associated with physical activity level.