Using Computer Vision to Annotate Video-Recoded Direct Observation of Physical Behavior.

Direct observation is a ground-truth measure for physical behavior, but the high cost limits widespread use. The purpose of this study was to develop and test machine learning methods to recognize aspects of physical behavior and location from videos of human movement: Adults (N = 26, aged 18-59 y) were recorded in their natural environment for two, 2- to 3-h sessions. Trained research assistants annotated videos using commercially available software including the following taxonomies: (1) sedentary versus non-sedentary (two classes); (2) activity type (four classes: sedentary, walking, running, and mixed movement); and (3) activity intensity (four classes: sedentary, light, moderate, and vigorous). Four machine learning approaches were trained and evaluated for each taxonomy. Models were trained on 80% of the videos, validated on 10%, and final accuracy is reported on the remaining 10% of the videos not used in training. Overall accuracy was as follows: 87.4% for Taxonomy 1, 63.1% for Taxonomy 2, and 68.6% for Taxonomy 3. This study shows it is possible to use computer vision to annotate aspects of physical behavior, speeding up the time and reducing labor required for direct observation. Future research should test these machine learning models on larger, independent datasets and take advantage of analysis of video fragments, rather than individual still images.

The impact of the COVID-19 pandemic on physical activity and sedentary behavior during pregnancy: a prospective study.

BACKGROUND: Prior studies evaluating the impact of the COVID-19 pandemic on pregnancy physical activity (PA) have largely been limited to internet-based surveys not validated for use in pregnancy. METHODS: This study used data from the Pregnancy PA Questionnaire Validation study conducted from 2019-2021. A prospective cohort of 50 pregnant women completed the Pregnancy PA Questionnaire (PPAQ), validated for use in pregnancy, in early, mid, and late pregnancy and wore an ActiGraph GT3X-BT for seven days. COVID-19 impact was defined using a fixed date of onset (March 13, 2020) and a self-reported date. Multivariable linear mixed effects regression models adjusted for age, early pregnancy BMI, gestational age, and parity. RESULTS: Higher sedentary behavior (14.2 MET-hrs/wk, 95% CI: 2.3, 26.0) and household/caregiving PA (34.4 MET-hrs/wk, 95% CI: 8.5, 60.3 and 25.9 MET-hrs/wk, 95% CI: 0.9, 50.9) and lower locomotion (-8.0 h/wk, 95% CI: -15.7, -0.3) and occupational PA (-34.5 MET-hrs/wk, 95% CI: -61.9, -7.0 and -30.6 MET-hrs/wk, 95% CI: -51.4, -9.8) was observed in middle and late pregnancy, respectively, after COVID-19 vs. before. There was no impact on steps/day or meeting American College of Obstetricians and Gynecologists guidelines. CONCLUSIONS: Proactive approaches for the promotion of pregnancy PA during pandemic-related restrictions are critically needed.

Methods for estimating physical activity and energy expenditure using raw accelerometry data or novel analytical approaches: a repository, framework, and reporting guidelines.

The proliferation of approaches for analyzing accelerometer data using raw acceleration or novel analytic approaches like machine learning ('novel methods') outpaces their implementation in practice. This may be due to lack of accessibility, either because authors do not provide their developed models or because these models are difficult to find when included as supplementary material. Additionally, when access to a model is provided, authors may not include example data or instructions on how to use the model. This further hinders use by other researchers, particularly those who are not experts in statistics or writing computer code.Objective: We created a repository of novel methods of analyzing accelerometer data for the estimation of energy expenditure and/or physical activity intensity and a framework and reporting guidelines to guide future work.Approach: Methods were identified from a recent scoping review. Available code, models, sample data, and instructions were compiled or created.Main Results: Sixty-three methods are hosted in the repository, in preschoolers (n = 6), children/adolescents (n = 20), and adults (n = 42), using hip (n = 45), wrist (n = 25), thigh (n = 4), chest (n = 4), ankle (n = 6), other (n = 4), or a combination of monitor wear locations (n = 9). Fifteen models are implemented in R, while 48 are provided as cut-points, equations, or decision trees.Significance: The developed tools should facilitate the use and development of novel methods for analyzing accelerometer data, thus improving data harmonization and consistency across studies. Future advances may involve including models that authors did not link to the original published article or those which identify activity type.

Accessibility and use of novel methods for predicting physical activity and energy expenditure using accelerometry: a scoping review.

Use of raw acceleration data and/or 'novel' analytic approaches like machine learning for physical activity measurement will not be widely implemented if methods are not accessible to researchers.Objective: This scoping review characterizes the validation approach, accessibility and use of novel analytic techniques for classifying energy expenditure and/or physical activity intensity using raw or count-based accelerometer data.Approach: Three databases were searched for articles published between January 2000 and February 2021. Use of each method was coded from a list of citing articles compiled from Google Scholar. Authors' provision of access to the model (e.g., by request, sample code) was recorded.Main Results: Studies (N = 168) included adults (n = 143), and/or children (n = 38). Model use ranged from 0 to 27 uses/year (average 0.83) with 101 models that have never been used. Approximately half of uses occurred in a free-living setting (52%) and/or by other authors (56%). Over half of included articles (n = 107) did not provide complete access to their model. Sixty-one articles provided access to their method by including equations, coefficients, cut-points, or decision trees in the paper (n = 48) and/or by providing access to code (n = 13).Significance: The proliferation of approaches for analyzing accelerometer data outpaces the use of these models in practice. As less than half of the developed models are made accessible, it is unsurprising that so many models are not used by other researchers. We encourage researchers to make their models available and accessible for better harmonization of methods and improved capabilities for device-based physical activity measurement.

The Impact of Stand-Biased Desks on Afterschool Physical Activity Behaviors of Elementary School Children.

The purpose of this secondary analysis was to assess whether students' use of stand-biased desks during the school day influenced physical activity (PA) and sedentary behaviors (SB) during the afterschool period. By using a crossover design consisting of two 9-week intervention periods, 99 participants from grades 3, 4, and 6 were randomly assigned by their teacher to either a traditional (Group 1; sit−stand) or stand-biased (Group 2; stand−sit) desk in the classroom. The desk type then switched between intervention periods. Afterschool PA and SB were measured by accelerometry at baseline (fall) and following both intervention periods at post I (winter) and post II (spring). Independent sample t-tests and mixed-effects modeling were applied at a significance value of p < 0.05 to detect differences between groups. No significant differences in afterschool SB, light-intensity PA (LPA), or moderate- to vigorous-intensity PA (MVPA) were found between groups. There were also no significant two- or three-way interaction effects detected between desk assignment, time, and afterschool SB, LPA, or MVPA. Stand-biased desks in the classroom were not detrimental to children's afterschool PA and SB.

Objective Physical Activity Levels, Sedentary Time, and Muscle Mass, Strength, and Function: Impact on Physical and Mental Health-Related Quality of Life in Older Adults.

Limited physical activity, sedentary behavior, and reduction in muscle mass, strength, and function are accompanied by negative outcomes. The relationship between these factors and physical and mental health-related quality of life (HRQoL) has yet to be elucidated in continuing care retirement community (CCRC) residents. The current study investigated the relationships among physical activity, sedentary behavior, and muscle mass, strength, and function to physical and mental HRQoL. A sample of 105 CCRC residents participated in this cross-sectional correlational study. Findings demonstrated that low physical activity and muscle function were significantly associated with low physical HRQoL and explained 38% of the variance in physical HRQoL. Handgrip strength was significantly associated with low mental HRQoL and explained 8% of the variance in mental HRQoL. Older adults living in CCRCs may benefit from interventions targeting physical activity and the distinct muscle components of sarcopenia that have the potential to improve physical and mental HRQoL. [Research in Gerontological Nursing, 15(3), 131-139.].

Validation of a series of walking and stepping tests to predict maximal oxygen consumption in adults aged 18-79 years.

INTRODUCTION: Field tests to estimate maximal oxygen consumption (VO2max) are an alternative to traditional exercise testing methods. Published field tests and their accompanying estimation equations account for up to 80% of the variance in VO2max with an error rate of ~4.5 ml.kg-1.min-1. These tests are limited to very specific age-range populations. The purpose of this study was to create and validate a series of easily administered walking and stepping field equations to predict VO2max across a range of healthy 18-79-year-old adults. METHODS: One-hundred-fifty-seven adults completed a graded maximal exercise test to assess VO2max. Five separate walking and three separate stepping tests of varying durations, number of stages, and intensities were completed. VO2max estimation equations were created using hierarchal multiple regression. Covariates including age, sex, body mass, resting heart rate, distance walked, gait speed, stepping cadence, and recovery heart rate were entered into each model using a stepwise approach. Each full model created had the same base model consisting of age, sex, and body mass. Validity of each model was assessed using a Jackknife cross-validation analysis, and percent bias and root mean square error (RMSE) were calculated. RESULTS: Base models accounted for ~72% of the total variance of VO2max. Full model variance ranged from ~79-83% and bias was minimal (<±1.0%) across models. RMSE for all models were approximately 4.5 ml.kg-1.min-1. Stepping tests performed better than walking tests by explaining ~2.5% more of the variance and displayed smaller RMSE. CONCLUSION: All eight models accounted for a large percentage of VO2max variance (~81%) with a RMSE of ~4.5 ml.kg-1.min-1. The variance and level of error of models examined highlight good group mean prediction with greater error expected at the individual level. All the models perform similarly across a broad age range, highlighting flexibility in application of these tests to a more general population.

Self-management processes, sedentary behavior, physical activity and dietary self-management behaviors: impact on muscle outcomes in continuing care retirement community residents.

BACKGROUND: Despite the known benefits of non-sedentary behavior, physical activity, and protein and caloric intake to health and muscle mass, strength, and function, many older adults do not meet physical activity and dietary recommendations. A better understanding of the factors associated with sedentary behavior, physical activity and dietary self-management behaviors, and muscle outcomes (muscle mass, strength, and function) is needed, particularly among continuing care retirement community residents. The objective of this study was to examine the factors associated with sedentary behavior, physical activity and dietary self-management behaviors, and muscle outcomes among continuing care retirement community residents. It also aimed to determine whether sedentary behavior and physical activity and dietary self-management behaviors mediate the relationships between self-efficacy, goal congruence, aging expectations, social support, and muscle outcomes. METHODS: A sample of 105 continuing care retirement community residents (age > 70 years) participated in this correlational, cross-sectional study. Questionnaires on pain, self-efficacy, goal congruence, aging expectation, social support, and daily protein and caloric intake were administered. Physical activity and sedentary behavior (ActiGraph wGT3X-BT), muscle mass (ImpediMed SFB7), muscle strength (Jamar Smart Digital Hand Dynamometer), and muscle function (Short Physical Performance Battery) were measured. Multiple regression, logistic regression, and mediation analyses were performed. RESULTS: Low goal congruence predicted engagement in sedentary behavior and light physical activity. Higher levels of self-efficacy and social support were associated with increased likelihoods of achieving greater moderate physical activity and meeting daily recommendations for caloric intake, respectively. Self-efficacy and goal congruence predicted muscle function and strength. Moreover, sedentary behavior and achieving greater moderate physical activity were found to partially but significantly mediate the relationship between self-efficacy and muscle function. CONCLUSION: Future research should evaluate whether attempts to reduce sedentary behavior and promote physical activity and dietary self-management behaviors and muscle outcomes are more successful when modifications to the self-management process factors are also targeted.

Older adults' physical activity-related social control and social support in the context of personal norms.

This study investigated whether perceived physical activity norms moderated the effects of physical activity-related social interactions on intentions to engage in physical activity among community-residing older adults (N = 217). Structural equation modeling tested whether two types of social support and social control interacted with personal norms in predicting intentions to be active. Emotional and informational support were associated with higher intentions, and negative social control was associated with lower intentions to engage in activity. Each of these effects was more prominent in the context of weak personal norms, suggesting future research and interventions should consider joint effects of support and norms.

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

PURPOSE: To assess the convergent validity of body worn wearable camera (WC) still-images (IMGs) for determining posture compared with activPAL (AP) classifications. METHODS: Participants (n=16, mean age 46.7±23.8yrs, 9F) wore an Autographer WC above the xyphoid process and an AP during three, 2hr free-living visits. IMGs were captured on average 8.47 seconds apart and were annotated with output consisting of events, transitory states, unknown and gaps. Events were annotations that matched AP classifications (sit, stand and move) consisting of at least 3 IMGs, transitory states were posture annotations fewer than 3 IMGs, unknown were IMGs that could not be accurately classified, and gaps were time between annotations. For analyses, annotation and AP output were converted to one-sec epochs and matched second-by-second. Total and average length of visits and events are reported in minutes. Bias and 95% CIs for event posture times from IMGs to AP posture times were calculated to determine accuracy and precision. Confusion matrices using total AP posture times were computed to determine misclassification. RESULTS: 43 visits were analyzed with a total visit and event time of 5027.73 and 4237.23 minutes and average visit and event lengths being 116.92 and 98.54 minutes, respectively. Bias was not statistically significant for sitting but significant for standing and movement (0.84, -6.87 and 6.04 minutes). From confusion matrices, IMGs correctly classified sitting, standing and movement 85.69%, 54.87%, and 69.41% of total AP time, respectively. CONCLUSION: WC IMGs provide a good estimation of overall sitting time but underestimate standing and overestimate movement time. Future work is warranted to improve posture classifications and examine IMG accuracy and precision in assessing activity type behaviors.

Accelerometer Calibration: The Importance of Considering Functionality.

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.

The Use of Consumer Wearable Physical Activity Monitors in Clinical Populations with Functional Limitations.

Functionally limiting health conditions have a high rate of prevalence worldwide and incur a significant amount of economic burden. Physical activity (PA) can prevent the onset of these conditions and alleviate economic burden by reducing symptoms, but a large portion of these individuals do not engage in health enhancing PA. Consumer wearable physical activity monitors (WPAM) are tools that have become increasingly popular within the past few years and could provide a means to improve PA levels for individuals with health conditions that cause functional limitations. This review reports on the validity of PA outcomes, feasibility and utility, and intervention/promotion effectiveness for consumer WPAM in functionally limited clinical populations. 2250 records from January 2018 to July 2021 were retrieved from PubMed, Web of Science, SPORTDiscus and CINAHL with 656 records being duplicates and 23 records passing a full-text article review. Studies included within the review looked at individuals with osteoarthritis, rheumatoid arthritis, axial spondyloarthritis, multiple sclerosis, Parkinson's disease, ischemic stroke and peripheral arterial disease. The most popular brand of consumer WPAM was Fitbit. Validation studies for consumer WPAM were primarily focused on step counts showing overestimations for daily step counts and over- and under-estimations occurring within shorter time durations depending on step cadence. Wrist worn WPAM are the most feasible for functionally limited clinical populations with widespread utilization for associating clinically relevant outcomes with PA levels but they have limited validation to confirm their accuracy and precision in measurement. Interventions included used a mixture of a WPAM and other behavior change techniques to improve PA levels for clinical populations and show promising effectiveness. Future work is warranted on determining the validity of PA outcomes from WPAM determined to be feasible in select clinical populations and creating interventions looking at which features of a consumer WPAM intervention promote PA.

Attentiveness and Fidgeting While Using a Stand-Biased Desk in Elementary School Children.

Standing desks are a viable option to decrease sedentary time in the classroom. However, it is important that standing desks are not detrimental to classroom behavior or learning. The purpose of this study was to evaluate the impact of stand-biased desks on fidgeting and attentiveness. Ninety-seven students in grades 3, 4, and 6 (ages 8-12 years) volunteered to participate in this study. The intervention employed a within-classroom crossover design, with teacher-determined allocation for seating within each classroom and included the replacement of one-half of the traditional sitting desks with stand-biased desks. Direct observation of student's attentive and fidgeting behaviors occurred at three assessment periods, at baseline when all students were in a sitting desk condition and at the end of each nine-week intervention. Stand-biased desks did not influence fidgeting behavior, but did have an impact on attentive behavior. Students that were less attentive at baseline had a 40-80% increase incidence rate in non-attentive behavior while in the traditional desk as compared to the stand-biased desk after the intervention. While fidgeting and non-attentive episodes (p = 0.034) were significantly related, the type of desk did not significantly moderate this relationship (p = 0.810). Standing desks can be incorporated into the classroom without negatively influencing classroom behavior.

Acceptance and Feasibility of Seated Elliptical Pedaling to Replace Sedentary Behavior in Older Adults.

The purpose of this study was to explore the feasibility and acceptability of a seated pedaling device to reduce sedentary behavior (SB) in the homes of older adults. METHODS: Each participant (N = 20) was outfitted with an activity monitor and seated pedaling device in the home for 7 days and randomly assigned to one of four light-intensity pedaling groups (15, 30, 45, and 60 min/day). RESULTS: There was 100% adherence in all groups and significant group differences in the minutes pedaled per day (p < .001), with no significant difference in the total pedaling days completed (p = .241). The 15-, 30-, 45-, and 60-min groups experienced a 4.0%, 5.4%, 10.6%, and 11.3% reduction in SB on the days pedaled, respectively. CONCLUSION: Clinically relevant reductions in SB time were achievable in this 1-week trial. Long-term adherence and the impact of replacing SB with seated light activities on geriatric-relevant health outcomes should be investigated.

Pacemaker detected active minutes are superior to pedometer-based step counts in measuring the response to physical activity counseling in sedentary older adults.

BACKGROUND: In patients with permanent pacemakers (PPM), physical activity (PA) can be monitored using embedded accelerometers to measure pacemaker detected active hours (PDAH), a strong predictor of mortality. We examined the impact of a PA Counseling (PAC) intervention on increasing activity as measured by PDAH and daily step counts. METHODS: Thirteen patients (average age 80 ± 6 years, 84.6% women) with implanted Medtronic PPMs with a ≤ 2 PDAH daily average were included in this study. Patients were randomized to Usual Care (UC, N = 6) or a Physical Activity Counseling Intervention (PACI, N = 7) groups. Step count and PDAH data were obtained at baseline, following a 12-week intervention, then 12 weeks after intervention completion. Data were analyzed using independent t-tests, Pearson's r, chi-square, and general linear models for repeated measures. RESULTS: PDAH significantly differed by time point for all subject combined (P = 0.01) but not by study group. Subjects with baseline gait speeds of > 0.8 m/sec were responsible for the increases in PDAH observed. Step counts did not differ over time in the entire cohort or by study group. Step count and PDAH significantly correlated at baseline (r = 0.60, P = 0.03). This correlation disappeared by week 12. CONCLUSION(S): PDAH can be used to monitor PA and PA interventions and may be superior to hip-worn pedometers in detecting activity. A significant increase in PA, regardless of treatment group, suggests that patient awareness of the ability to monitor PA through a PPM increases PA in these patients, particularly in patients with gait speeds of < 0.8 m/sec. TRIAL REGISTRATION: ClincalTrials.gov NCT03052829. Date of Registration: 2/14/2017.

Pedometer Feedback Interventions Increase Daily Physical Activity in Phase III Cardiac Rehabilitation Participants.

PURPOSE: To determine the effects of individually tailored interventions designed to increase physical activity (PA) in cardiac patients. METHODS: A total of 99 (77 men and 22 women, 61.5 ± 10.7 yr) patients entering a phase III cardiac rehabilitation program completed a 12-wk PA intervention. Patients were randomized to usual care (UC, time-based recommendation), pedometer feedback (PF), newsletter-based motivational messaging (MM), or PF + MM. Both PF groups were given a goal of increasing steps/d by 10% of individual baseline value each week. If the goal for the week was not reached, the same goal was used for the next week. Physical activity was assessed for 7 d before beginning and after completing the program. The change in steps/d, moderate to vigorous intensity PA minutes, and sedentary time were compared among intervention groups. RESULTS: Average change in steps/d was found to be significantly greater (P < .01) in the PF (2957 ± 3185) and the PF + MM (3150 ± 3007) compared with UC (264 ± 2065) and MM (718 ± 2415) groups. No group experienced changes in moderate to vigorous intensity PA time and only the PF intervention group decreased sedentary time (baseline 470.2 ± 77.1 to postintervention 447.8 ± 74.9 min/d, P = .01). CONCLUSION: The findings from this study demonstrate that using PF was superior to the usual time-based PA recommendations and to newsletter-based MM in patients starting a phase III CR program. Cardiac rehabilitation programs are encouraged to implement PA feedback with individualized PA goals in order to support the increase in PA.

Identification of Latent Classes of Motor Performance in a Heterogenous Population of Adults.

OBJECTIVE: To determine classes of motor performance based on community deployable motor impairment and functional tests in a heterogeneous adult population. DESIGN: Sixteen tests of limb-specific and whole-body measures of motor impairment and function were obtained. Linear regression analysis was used to dichotomize performance on each test as falling within or outside the age- and sex-predicted values. Latent class analysis was used to determine 3 classes of motor performance. The chi-square test of association and the Fisher exact test were used for categorical variables, and analysis of variance and the Kruskal-Wallis test were used for continuous variables to evaluate the relationship between demographic characteristics and latent classes. SETTING: General community. PARTICIPANTS: Individuals (N=118; 50 men) participated in the study. Quota sampling was used to recruit individuals who self-identified as healthy (n=44) or currently living with a preexisting chronic health condition, including arthritis (n=19), multiple sclerosis (n=18), Parkinson disease (n=17), stroke (n=18), or low functioning (n=2). INTERVENTION: Not applicable. MAIN OUTCOME MEASURE: Latent classes of motor performance. RESULTS: Across the entire sample, 3 latent classes of motor performance were determined that clustered individuals with motor performance falling: (1) within predicted values on most of the tests (expected class), (2) outside predicted values on some of the tests (moderate class), and (3) outside predicted values on most of the tests (severe class).The ability to distinguish between the respective classes based on the percent chance of falling outside predicted values was achieved using the following community deployable motor performance tests: 10-meter walk test (22%, 80%, and 100%), 6-minute walk test (14.5%, 37.5%, and 100%), grooved pegboard test (23%, 38%, and 100%), and modified physical performance test (3%, 54%, and 96%). CONCLUSIONS: In this heterogeneous group of adults, we found 3 distinct classes of motor performance, with the sample clustering into an expected test score group, a moderate test score deficiency group, and a severed test score deficiency group. Based on the motor performance tests, we established that community deployable, easily administered testing could accurately predict the established clusters of motor performance.

A Framework to Evaluate Devices That Assess Physical Behavior.

Body-worn devices that estimate physical behavior have tremendous potential to address key research gaps. However, there is no consensus on how devices and processing methods should be developed and evaluated, resulting in large differences in summary estimates and confusion for end users. We propose a phase-based framework for developing and evaluating devices that emphasizes robust validation studies in naturalistic conditions.

Energy Cost of Slow and Normal Gait Speeds in Low and Normally Functioning Adults.

OBJECTIVE: Slow walking speed paired with increased energy cost is a strong predictor for mortality and disability in older adults but has yet to be examined in a heterogeneous sample (ie, age, sex, disease status). The aim of this study was to examine energy cost of slow and normal walking speeds among low- and normal-functioning adults. DESIGN: Adults aged 20-90 yrs were recruited for this study. Participants completed a 10-m functional walk test at a self-selected normal walking speed and were categorized as low functioning or normal functioning based on expected age- and sex-adjusted average gait speed. Participants completed two successive 3-min walking stages, at slower than normal and normal walking speeds, respectively. Gas exchange was measured and energy cost per meter (milliliter per kilogram per meter) was calculated for both walking speeds. RESULTS: Energy cost per meter was higher (P < 0.0001) in the low-functioning group (n = 76; female = 59.21%; mean ± SD age = 61.13 ± 14.68 yrs) during the slower than normal and normal (P < 0.0001) walking speed bouts compared with the normal-functioning group (n = 42; female = 54.76%; mean ± SD age = 51.55 ± 19.51 yrs). CONCLUSIONS: Low-functioning adults rely on greater energy cost per meter of walking at slower and normal speeds. This has implications for total daily energy expenditure in low-functioning, adult populations.

Do Stand-Biased Desks in the Classroom Change School-Time Activity and Sedentary Behavior?

The purpose of this study was to determine the effect of stand-biased desks on the physical activity and sedentary behavior of third, fourth and sixth grade students across the school year. Methods: This within classroom crossover design study used teacher-determined allocation for seating within each classroom. Half of the students used a stand-biased desk and half used a sitting desk. Five-day hip-worn accelerometer assessments were completed at baseline and at the end of each nine-week intervention period. A mixed effects model was used to determine the differences in the percentage of time spent active and sedentary. Results: A total of 22, 36 and 41 students in 3rd, 4th and 6th grades, respectively, completed this study (57.1% male, 79.3% White). Regardless of the desk type, students became more sedentary (p < 0.001) and less active (p < 0.001) in the classroom as the school year progressed. After controlling for baseline activity, there was a significant interaction between the type of desk and time (p = 0.029). Students who spent a higher percentage of their classroom time sedentary engaged in less sedentary behavior when using a stand-biased desk compared to the traditional desk. Conclusion: The standing desk intervention was effective in mitigating the increase in sedentary behavior for those who started the school year more sedentary.