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The Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of clinical and community studies that employ common digital mobile protocols and collect common clinical and biological measures across participating studies. At a high level, a key scientific goal which spans mMARCH studies is to develop a better understanding of the interrelationships between physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. mMARCH studies employ wrist-worn accelerometry to obtain objective measures of PA/SL/CR. However, there is currently no consensus on a standard data processing pipeline for raw accelerometry data and few open-source tools which facilitate their development. The R package GGIR is the most prominent open-source software package for processing raw accelerometry data, offering great functionality and substantial user flexibility. However, even with GGIR, processing done in a harmonized and reproducible fashion across multiple analytical centers requires a nontrivial amount of expertise combined with a careful implementation. In addition, there are many statistical methods useful for analyzing PA/SL/CR patterns using accelerometry data which are implemented in non-GGIR R packages, including methods from multivariate statistics, functional data analysis, distributional data analysis, and time series analyses. To address the issues of multisite harmonization and additional feature creation, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data via GGIR, merging GGIR, and non-GGIR features of PA/SL/CR together, implementing several additional data and feature quality checks, and performing multiple analyses including Joint and Individual Variation Explained, an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. The pipeline is easily modified to calculate additional features of interest, and allows for studies not affiliated with mMARCH to apply a pipeline which facilitates direct comparisons of scientific results in published work by mMARCH studies. This manuscript describes the pipeline and illustrates the use of combined GGIR and non-GGIR features by applying Joint and Individual Variation Explained to the accelerometry component of CoLaus|PsyCoLaus, one of mMARCH sites. The pipeline is publicly available via open-source R package mMARCH.AC.

Background: Over 75% of American adults are not meeting aerobic and muscular physical activity recommendations, with the majority being females. Equestrian activities are a potential avenue to increase physical activity, especially in females who account for approximately 90% of sport participants. This study describes perceptions of equestrian activities and establishes the patterns of self-reported equestrian, barn work, and nonequestrian physical activity engagement to understand participation in activities that may sustain physical activity across the lifespan. Methods: American equestrians (n = 2551) completed an anonymous online survey with questions about perceptions and benefits of equestrian activities, demographics, and engagement in equestrian activities, barn work, and nonequestrian activities. Results: There were 2039 completed responses, (95.6% female), with representation from all regions of the United States. Professionals (20.6%), amateurs (39.1%), and recreational (40.3%) comprised participation status. Significantly fewer recreational participants perceived equestrian as physical activity and as a sport than amateurs (P < .05) and professionals (P < .05). Engagement in equestrian and barn work physical activity was significantly higher in professionals (P < .0001), followed by amateurs (P < .0001), with the lowest in recreational equestrians (P = .001). Professional and amateur equestrians engaged in significantly more nonequestrian physical activity than recreational participants (P < .05). Conclusions: Equestrian physical activity engagement is dependent on the status of participation. Equestrian, barn work, and nonequestrian physical activity do meet physical activity aerobic and muscular recommendations and should be encouraged as a physical activity for females across the lifespan.

Stepping cadence is an important determinant of activity intensity, with faster stepping associated with the most health benefits. The Stryd monitor provides real-time feedback on stepping cadence. The limited existing literature has neither validated the Stryd across slow walking to fast running speeds nor strictly followed statistical guidelines for monitor validation studies. We assessed the criterion validity of the Stryd monitor to detect stepping cadence across multiple walking and jogging/running speeds. It was hypothesized that the Stryd monitor would be an accurate measure of stepping cadence across all measured speeds. Forty-six participants (23 ± 5 years, 26 females) wore the Stryd monitor on their shoelaces during a 10-stage progressive treadmill walking (Speeds 1–5) and jogging/running (Speeds 6–10) protocol (criterion: manually counted video-recorded cadence; total stages: 438). Standardized guidelines for physical activity monitor statistical analyses were followed. A two-way repeated-measure analysis of variance revealed the Stryd monitor recorded a slightly higher cadence (<1 steps/min difference, all p < .001) at 2 miles/hr (92.1 ± 6.2 steps/min vs. 91.5 ± 6.4 steps/min, p < .001), 2.5 miles/hr (101.3 ± 6.1 steps/min vs. 100.7 ± 6.4 steps/min), and 3.5 miles/hr (117.4 ± 5.9 steps/min vs. 117.0 ± 6.0 steps/min). However, equivalence testing demonstrated high equivalence of the Stryd and manually counted cadence (equivalence zone required: ≤± 2.6%) across all speeds. The Stryd activity monitor is a valid measure of stepping cadence across walking, jogging, and running speeds. By providing real-time cadence feedback, the Stryd monitor has strong potential to help guide the general public monitor their stepping intensity to promote more habitual activity at faster cadences.

Introduction: The COVID-19 lockdown introduced restrictions to free-living activities. Changes to these activities can be accurately quantified using combined measurement. Using activPAL3 and self-reports to collect activity data, the study aimed to quantify changes that occurred in physical activity and sedentary behavior between prelockdown and lockdown. The study also sought to determine changes in indoor and outdoor stepping. Methods: Using activPAL3, four participants recorded physical activity data prelockdown and during lockdown restrictions (February–June 2020). Single events (sitting, standing, stepping, lying) were recorded and analyzed by the CREA algorithm using an event-based approach. The analysis focused on step count, sedentary time, and lying (in bed) time; median and interquartile range were calculated. Daily steps classified as taking place indoors and outdoors were calculated separately. Results: 33 prelockdown and 92 in-lockdown days of valid data were captured. Median daily step count across all participants reduced by 14.8% (from 5,828 prelockdown to 4,963 in-lockdown), while sedentary and lying time increased by 4% and 8%, respectively (sedentary: 9.98–10.30 hr; lying: 9.33–10.05 hr). Individual variations were observed in hours spent sedentary (001: 8.44–8.66, 002: 7.41–8.66, 003: 11.97–10.59, 004: 6.29–7.94, and lying (001: 9.69–9.49, 002: 11.46–11.66, 003: 7.63–9.34, 004: 9.7–11.12) pre- and in-lockdown. Discrepancies in self-report versus algorithm classification of indoor/outdoor stepping were observed for three participants. Conclusion: The study quantitively showed lockdown restrictions negatively impacted physical activity and sedentary behavior; two variables closely linked to health outcomes. This has important implications for public health policies to help develop targeted interventions and mandates that encourage additional physical activity and lower sedentary behavior.

Background: Smartphones and wrist-worn activity trackers are increasingly popular for step counting purposes and physical activity promotion. Although trackers from popular brands have frequently been validated, the accuracy of low-cost devices under free-living conditions has not been adequately determined. Objective: To investigate the criterion validity of smartphones and low-cost wrist-worn activity trackers under free-living conditions. Methods: Participants wore a waist-worn Yamax pedometer and seven different low-cost wrist-worn activity trackers continuously over 3 days, and an activity log was completed at the end of each day. At the end of the study, the number of step counts reflected on the participants’ smartphone for each of the 3 days was also recorded. To establish criterion validity, step counts from smartphones and activity trackers were compared with the pedometers using Pearson’s correlation coefficient, mean absolute percentage error, and intraclass correlation coefficient. Results: Five of the seven activity trackers underestimated step counts and the remaining two and the smartphones overestimated step counts. Criterion validity was consistently higher for the activity trackers (r = .78–.92; mean absolute percentage error 14.5%–36.1%; intraclass correlation coefficient: .51–.91) than the smartphone (r = .37; mean absolute percentage error 55.7%; intraclass correlation coefficient: .36). Stratified analysis showed better validity of smartphones among female than for male participants. Phone wearing location also affected accuracy. Conclusions: Low-cost trackers demonstrated high accuracy in recording step counts and can be considered with confidence for research purposes or large-scale health promotion programs. The accuracy of using a smartphone for measuring step counts was substantially lower. Factors such as phone wear location and gender should also be considered when using smartphones to track step counts.

Office workers who transitioned to working from home are spending an even higher percentage of their workday sitting compared with being “in-office” and this is an emerging health concern. With many office workers continuing to work from home since the onset of the COVID-19 pandemic, it is imperative to have a validated self-report questionnaire to assess sedentary behavior, break frequency, and duration, to reduce the cost and burden of using device-based assessments. This secondary analysis study aimed to validate the modified Last 7-Day Sedentary Behavior Questionnaire (SIT-Q 7d) against an activPAL4™ device in full-time home-based “office” workers (n = 148; mean age = 44.90). Participants completed the modified SIT-Q 7d and wore an activPAL4 for a full work week. The findings showed that the modified SIT-Q 7d had low (ρ = .35–.37) and weak (ρ = .27–.28) criterion validity for accurate estimates of break frequency and break duration, respectively. The 95% limits of agreement were large for break frequency (26.85–29.01) and medium for break duration (5.81–8.47), indicating that the modified SIT-Q 7d may not be appropriate for measuring occupational sedentary behavior patterns at the individual level. Further validation is still required before confidently recommending this self-report questionnaire to be used among this population to assess breaks in sedentary time.

Older adults continue to spend little time engaged in physical activity when hospitalized. The purpose of this study was to (a) describe activity among hospitalized older adults with dementia and (b) identify the association between specific factors (gender, ambulation independence, comorbidities, race, and hospital setting) and their physical activity. This descriptive study utilized baseline data on the first 79 participants from the Function Focused Care for Acute Care using the Evidence Integration Triangle. Multiple linear regression models were run using accelerometry data from the first full day of hospitalization. The participants spent an average of 83.7% of their time being sedentary. Male gender, ambulation independence, and hospital setting (the hospital in which the patient was admitted) were associated with greater activity. This study reports on the limited time spent in activity for older adults with dementia when hospitalized and highlights patient profiles that are particularly vulnerable to sedentary behavior in the hospital setting.

This prospective cohort study aimed to investigate the association between physical activity (PA) as measured using accelerometers, and functional improvement measured using a short physical performance battery in older patients undergoing rehabilitation. After admission to the rehabilitation hospital, patients were categorized into quartile groups based on their level of PA measured using accelerometers. The primary outcome was physical function measured using the short physical performance battery at hospital discharge. A total of 204 patients were included in the analysis. After adjusting for confounding factors, light-intensity PA (p < .001) and moderate-to-vigorous-intensity PA (p < .001) were associated with a short physical performance battery at hospital discharge. In conclusion, PA at admission is positively associated with functional improvement in older patients undergoing hospital rehabilitation.