Compendium Of Physical Activities Research Articles

Energy expenditure and physiological markers during a city mountain hike in the heat: A case study.

Background: Energy expenditure may be difficult to assess when hiking difficult trails. Case presentation: We measured physical activity exercise energy expenditure (PAEE) directly from oxygen uptake using a mobile device (cardiopulmonary exercise testing, CPET), and by using a formula based on heart rate (HR), or metabolic equivalent values from the Compendium of Physical Activity, and other physiological outcomes. Outcomes and implications: Total PAEE (1342 kcal) using CPET showed a two-fold difference between ascending and descending (887 vs. 455 kcal) during a 124-min hike. For HR, PAEE was 1893kcal (+551 kcal overreporting), while compendium-based scenarios ranged from 1179 to 1446 kcal, which was in closer range (-163 to +104 kcal/min) compared to the CPET data. Fluid consumption was 1300 mL/hour, with 1.1% bodyweight loss, peak skin temperature of 35.2°C and core body temperature of 39.2°C. Recommendations: Tables reasonably predict energy expenditure while not precisely reflecting the actual situation.

The role of physical activity in animal-assisted interventions for autism: A systematic review

Purpose: Animal-assisted interventions (AAIs) may have therapeutic effects for autism. Physical activity (PA) has many established benefits for autism. AAIs appear to involve PA, which may drive some of their beneficial effects. The purpose of this review was to quantify the extent to which AAIs involve PA and to describe the PA dose. Methods: A systematic search was conducted for relevant articles published between January 2015 and May 2022. A total of 1,949 articles were identified with 38 articles eligible for inclusion. PA involvement and dose were determined by matching the intervention description to PA values in the Compendium of Physical Activities. Results: PA was involved in 31 of the studies. Animal species included horses ( n = 17), dogs ( n = 17), dolphins ( n = 3), and cats ( n = 1). All horse, 70% of dog and 67% of dolphin studies included PA. PA dose varied; however, horse and dolphin studies involved moderate-to-vigorous intensity PA and dog studies involved light-intensity PA. PA was assessed in only one study. Conclusion: AAIs for autism involve PA but are not designed as PA interventions nor is PA measured. AAIs could be designed as animal-assisted PA interventions to capitalize on the benefits of PA for both overall health and characteristics of autism.

Estimating physical activity levels for regular and non-regular bike share users in Charleston, South Carolina

IntroductionThe growth of bike share systems has increased opportunities for active transportation and recreation. Using Global positioning system data for all trips in two years for the Charleston, South Carolina bike share system, the objectives of the study were to 1) demonstrate a physical activity estimation process for bike share trips, 2) describe the estimated activity levels for the bike share system, and 3) examine differences in bike share physical activity levels by bike share membership type. MethodsAll bike share trips taken in 2018 and 2019 were analyzed in this study (n = 93,470). Global positioning system data was used to record the distance and duration, which was used to calculate average speed for all trips. Based on this speed, a metabolic equivalent (MET) value derived from the Compendium of Physical Activities (2010) was assigned to each bike share trip and total MET-minutes were estimated for each trip. Differences in MET-minutes by bike share membership type, day of the week, and seasonality were analyzed using linear regression. ResultsOn average, each bike share trip resulted in 147.8 MET-minutes, or about 30% of the minimum weekly recommended physical activity (i.e., 500–1000 MET-minutes). Trips were evenly split into light and moderate intensity based on MET values. Bike share members classified as regular users accumulated greater MET-minutes per trip compared to those classified as non-regular users (b=147.9, p<0.001). ConclusionsBike share systems can estimate the physical activity levels for trips using global positioning system data and an evidence-based approach. Both regular and non-regular bike share use can contribute to meeting daily physical activity recommendations, quantifying a health promotion component for bike share systems.

Comparison Of Wearable Camera Image Physical Activity Behavior Estimates To Direct Observation

PURPOSE: To assess the accuracy and precision of physical activity (PA) behavior estimates from wearable camera (WC) still-images (IMGs) to video-recorded direct observation (DO). METHODS: Participants (n = 19) wore a WC above the xiphoid process while simultaneously being video recorded by researchers, during 3 visits, in a place of the participants choice. WC was set to the highest capture rate. A PA behavior annotation protocol was developed from the Compendium of Physical Activities. Videos and IMGs were annotated using Noldus Observer XT14 and Image Browser software, respectively. For analysis, PA behavior annotations were collapsed into 8 domains. A linear mixed effects model was used to calculate statistical bias and 95% CIs of IMGs to DO behavior times to determine accuracy and precision. Confusion matrices were computed to determine trends in behavior misclassification by IMGs against DO. One-way random effects interclass correlation coefficient (ICC) was calculated between domains to assess reliability. RESULTS: 53 visits were analyzed with a total time of 4381.3 min (mean 82.7 ± 19.2 min). Fig. 1 shows bias and 95% confidence intervals between IMGs and DO for each behavior domain. Discrepancies of PA behavior min estimation by IMGs against DO annotations were statistically insignificant at p > 0.05. Household had the smallest bias (0.09 ± 6.2 min) whereas, sport & exercise had the largest (1.61 ± 9.32 min). From confusion matrices, the domain “other” had the largest misclassification, as IMG annotation scored such events as “household” 26.3% of the time. ICC between IMGs and DO was excellent (ICC = 0.8945 [0.8938, 0.8953]). CONCLUSIONS: WC IMG annotation is accurate and precise when estimating PA behavior compared to annotated, DO videos. Future work examining WC use in more heterogeneous populations and over longer periods of time would further advance the application of this technology in measurement research.

Perceived Exertion in Physical Activity Measurement Across the Lifecourse: Results from SWAN

Physical activity (PA) guidelines recommend 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity PA, in addition to muscle strengthening activities, each week. Many questionnaires ascertain PA frequency, duration, and intensity to benchmark achievement of PA recommendations. However, most scoring algorithms utilize absolute intensity estimates when exertion may be influenced by age or other sociodemographic or health characteristics. This study compared PA estimates with and without adjustments for perceived exertion and determined if that difference was associated with individual characteristics. Women (n=2,711) from the longitudinal Study of Women’s Health Across the Nation who completed ≥3 Kaiser Physical Activity Surveys (KPAS) across 8 biennial visits were included (baseline age: 46.4±2.7 years). KPAS responses were converted to metabolic equivalent of a task (METs) using the Compendium of Physical Activities to estimate absolute and perceived intensity-adjusted MET values. Latent class growth modeling identified subgroups of participants following similar patterns of change in the difference between absolute intensity-based and perceived intensity-adjusted estimates across time. Four major trajectory classes emerged with patterns reflecting: (1) lessening high-intensity exercise (2.2%); (2) increasingly high-intensity exercise (3.1%); (3) consistently moderate-intensity exercise (92.0%); and (4) consistently low-intensity exercise (2.8%). Consistently low-intensity exercisers, for whom absolute intensity-based estimates exceeded perceived intensity-adjusted estimates, were more likely to be Japanese or Chinese (p<0.001) and have lower BMI (p=0.05). However, for most participants, absolute intensity-based estimates approximated perceived intensity-adjusted estimates over time, suggesting that traditional PA scoring techniques may provide sufficient estimates for PA in longitudinal cohort studies of mid-life and older adult women.

Effect of Exercise Volume on Plantar Microcirculation and Tissue Hardness in People With Type 2 Diabetes.

Objective: Exercise has been reported to be beneficial for people with type 2 diabetes (T2DM), but exercise, especially weight-bearing exercise, may increase the risk of diabetic foot ulcers (DFUs). This study aimed to explore the associations between different volumes of weight-bearing physical activities and plantar microcirculation and tissue hardness in people with T2DM. Methods: 130 elderly people with T2DM were enrolled for this cross-sectional study. They were classified into the high exercise volume group and the low exercise volume group based on their weekly energy expenditure (metabolic equivalents per week) in the past year. Weekly energy expenditure was calculated using the International Physical Activity Questionnaire and the Compendium of Physical Activities. The plantar oxygen saturation (SO2) and soft tissue hardness of each participant’s right foot were measured. Results: A total of 80 participants completed the trial. The average exercise energy expenditure of the high exercise volume group and the low exercise volume group were significantly different (p < 0.05). The results showed that the SO2 of the high exercise volume group (67.25 ± 6.12%) was significantly higher than the low exercise volume group (63.75 ± 8.02%, p < 0.05). The plantar tissue hardness of the high exercise volume group was lower than the low exercise volume group in the big toe, midfoot and hindfoot regions (p < 0.05). Conclusion: This study demonstrates that higher volumes of exercise are associated with better plantar microcirculation and lower plantar tissue hardness in people with T2DM. The findings of this study indicate that weight-bearing exercise may not increase risk of developing diabetic foot ulcers.

The forgotten agriculture-nutrition link: farm technologies and human energy requirements

In the quest to reduce global under- and malnutrition, which are particularly high among smallholder farmers, agriculture-nutrition linkages are receiving increasing attention. Researchers have analyzed the link between the quantity and diversity of food that farmers produce and nutritional outcomes but paid limited attention to a third agriculture-nutrition link: the link between how food is produced and nutritional outcomes. This neglect persists despite the majority of smallholder farmers relying on hand tools for farming, which implies heavy physical work and, thus, high energy requirements. To address this research gap, this study compares the energy requirements of farm households in rural Zambia that are characterized by three different levels of mechanization: hand tools, animal drought power, and tractors. 1638 days of detailed time-use and nutrition data were collected from 186 male and female adults and boys and girls during different seasons (land preparation, weeding, and harvesting/processing) using an innovative picture-based smartphone app called “Timetracker”. This data served to calculate different proxies for physical activity and energy requirements using “Ainsworth’s Compendium of Physical Activities”. The results suggest that detailed time-use data offers great potentials to study physical activity and energy requirements. The findings show strong linkages between farm technologies, physical activity levels, and energy requirements, suggesting that this agriculture-nutrition link deserves more scientific and political attention to reduce under- and malnutrition among smallholder farmers.

Relationship Between Resting Heart Rate And Physical Activity Mode In College Students After Covid-19

Resting heart rate (RHR) is a simple and effective clinical index for predicting heart function. Good physical activity (PA) habits are associated with an optimal RHR. PURPOSE: To explore the relationship between the RHR and physical activity mode among the first batch of college students who have been at home for at least half a year before returning to normal college life after COVID-19 in Shanghai, China. METHODS: 176 healthy college students (mean ± SD, age = 22.69 ± 3.14 yr, 39.2% female, BMI = 21.93 ± 2.95 kg/m2) volunteered to participate in this study. RHR was estimated by self-measurement of pulse beats in 30s in the classroom in the second teaching week. Students should keep quiet and simulate at least 2 times before measurement. An online PA questionnaire was used to collect frequency, type, and weekly PA time according to "Compendium of Physical Activities" (Ainsworth B E, et al., 2011). The optimal RHR cut-off value is ≤70 bpm for men and ≤ 80 bpm for women (David Nanchen, 2018) when analyzing the relationship between RHR and PA. In order to compare the mean values of the subscales (males and females), ANOVA with repeated measures was applied. The assumed significance level was set at p < 0.05. RESULTS: The average RHR was 73.60 ± 8.61 bpm and the main PA mode were "inactive rest" and "low-intensity activity" such as walking, talking with others, sitting, and learning, etc. (more than 70%). Generally, weekly PA frequency (2.84 ± 1.78 times) and time (92.31 ± 76.15 min) were not meeting minimum recommended PA guidelines for adults. The male students performed better in weekly PA frequency (3.31 ± 1.9 times) and time (113.72 ± 79.08 min) than female (1.84 ± 1.34 times and 61.96 ± 57.61 min) (p < 0.01 and p < 0.01). In addition, weekly PA time (132.44 ± 93.23 min) was significantly higher in male students with RHR ≤ 70 bpm when compared to that with RHR > 70 bpm (96.53 ± 59.32 min) (p = 0.03). However, it didn’t appear in the corresponding female subgroups. CONCLUSIONS: After COVID-19, the PA mode of college students was mainly low-intensity activity with a relatively low level of weekly frequency and PA time, the phenomenon of which was more pronounced among female students. For male students, the optimal RHR (≤70 bpm) after COVID-19 might be associated with the high weekly total PA time.

Relationship of the Pro12Ala Polymorphism on the PPARy2 Gene With the Body Composition of Practitioners of Cyclic Exercises

This study aimed to verify the association between the genotypic of the receptor gene activated by peroxisome proliferators gamma 2 (PPARy2) and the body composition and the specific indicators of adiposity in practitioners physical exercises, considering nutritional intake, age, and training load as influencing factors. It was conducted a cross-sectional study with 335 adults (47.9 ± 12.7 years, 138 men, body mass index/BMI = 27.0 ± 4.9 kg/m2), practitioners of aerobic exercises in cyclical modalities (running, walking and/or cycling, who spent 328.3 ± 193.6 kcal/day on physical training). The genotyping of the Pro12Ala polymorphism was performed using the PCR-RFLP technique and the body composition measured by bioimpedance (InBody 720). Energy expenditure was based on the compendium of physical activities and caloric intake was measured by 24 h recall questionnaire. The higher prevalence was for the Pro/Pro genotype (76.1% vs. 23.9% of Pro/Ala). Pro/Pro genotypic group showed significant higher mean values for body mass (BM) (p < 0.03 for men and p < 0.02 for women) and BMI (p < 0.00 for men and p < 0.02 for women) and %FAT (p < 0.00), waist-hip ratio (WHR) (p < 0.04), and visceral fat (VF) (p < 0.00) only in men compared to Pro/Ala. Higher frequency of Pro/Pro was observed in the category indicating BMI (p < 0.00 for men and p < 0.03 for women), WRH (p < 0.03 for men and p < 0.00 for women), and %FAT (p < 0.03) (in the latter case, only among men. It was also observed that the frequency of distribution of Pro/Ala in the eutrophic category of the BMI remained independent of all influencers, while WHR and %FAT were independent of the training load, but influenced by nutritional intake and age. In women, the frequency of Pro/Ala distribution at the lowest BMI and WHR values remained independent of all confounding variables. It is concluded that the Pro12Ala polymorphism in the PPARy2 gene consistently influences indicators of body composition and adiposity, regardless of the practitioners of physical training, but the relationship needs to be considered according to age and nutritional intake.

Development of physical activity classification table for Koreans: using the Compendium of physical activities in the United States

Development of physical activity classification table for Koreans: using the Compendium of physical activities in the United States

Physical activity assessment by accelerometry in people with heart failure

BackgroundInternational guidelines for physical activity recommend at least 150 min per week of moderate-to-vigorous physical activity (MVPA) for adults, including those with cardiac disease. There is yet to be consensus on the most appropriate way to categorise raw accelerometer data into behaviourally relevant metrics such as intensity, especially in chronic disease populations. Therefore the aim of this study was to estimate acceleration values corresponding to inactivity and MVPA during daily living activities of patients with heart failure (HF), via calibration with oxygen consumption (VO2) and to compare these values to previously published, commonly applied PA intensity thresholds which are based on healthy adults.MethodsTwenty-two adults with HF (mean age 71 ± 14 years) undertook a range of daily living activities (including laying down, sitting, standing and walking) whilst measuring PA via wrist- and hip-worn accelerometers and VO2 via indirect calorimetry. Raw accelerometer output was used to compute PA in units of milligravity (mg). Energy expenditure across each of the activities was converted into measured METs (VO2/resting metabolic rate) and standard METs (VO2/3.5 ml/kg/min). PA energy costs were also compared with predicted METs in the compendium of physical activities. Location specific activity intensity thresholds were established via multilevel mixed effects linear regression and receiver operator characteristic curve analysis. A leave-one-out method was used to cross-validate the thresholds.ResultsAccelerometer values corresponding with intensity thresholds for inactivity (< 1.5METs) and MVPA (≥3.0METs) were > 50% lower than previously published intensity thresholds for both wrists and waist accelerometers (inactivity: 16.7 to 18.6 mg versus 45.8 mg; MVPA: 43.1 to 49.0 mg versus 93.2 to 100 mg). Measured METs were higher than both standard METs (34–35%) and predicted METs (45–105%) across all standing and walking activities.ConclusionHF specific accelerometer intensity thresholds for inactivity and MVPA are lower than previously published thresholds based on healthy adults, due to lower resting metabolic rate and greater energy expenditure during daily living activities for HF patients.Trial registrationClinical trials.gov NCT03659877, retrospectively registered on September 6th 2018.

A Method To Estimate Free-living Behaviors Using High-frequency Wrist Accelerometer Data

PURPOSE: To develop a novel method to estimate free-living sedentary behavior and activity intensity using high-frequency wrist accelerometer data. METHODS: Forty-nine participants (mean ± SD; age: 20.4±1.3 yrs, 45.8% male) completed four, 1-hour sessions of free-living behaviors in home, school, community, and exercise environments. Each session was video-recorded (DO) and participants wore an ActiGraph wGT3X-BT (AG) accelerometer on the non-dominant wrist. Videos were coded for whole body movement, contextual activity type, and activity intensity from the Compendium of Physical Activities (e.g. walking, shopping, 2.8 METs). The novel two-step method (SojWrist) first segments AG data into bouts, or “sojourns”, of inactivity (i.e. sedentary and standing behaviors) or activity using an acceleration standard deviation threshold and random forest model. The second step estimates the intensity of inactive (sedentary, light) and active (light, moderate, vigorous) sojourns. Separate inactive and active sojourn RF models were fit to estimate intensity using bout duration and time- and frequency-domain AG signal characteristics. A 90-10 sample split was used for SojWrist development (N = 44) and cross-validation (N=5). Percent agreement between DO and SojWrist was evaluated at each step using second-by-second data. RESULTS: In the cross-validation sample, 91.8% [95%CI: 87.0%, 96.5%] of inactive and active periods were classified correctly from step 1 of SojWrist. After step 2, overall percent agreement between DO and SojWrist was 86.9% [95%CI: 78.9%, 95.0%] across all intensity categories (Table). CONCLUSION: The new SojWrist performed well at estimating free-living activity intensity categories from a wrist worn accelerometer. Future work should strive to improve method performance for predicting activity intensity categories and test validity on a diverse, independent, free-living sample. Supported by NIH NIDDK 1R01DK110148

Can Yoga Meet the Requirement of the Physical Activity Guideline of India? A Descriptive Review.

Background:Physical inactivity is the fourth major risk factor for global mortality accounting for 6% of all deaths globally and it is a key risk factor for noncommunicable disease occurrences. About 54.4% of Indians are physically inactive and <10% engage in recreational activities. On the one hand, India is spreading the message of Yoga, as a form of physical activity (PA) to the whole world. On the other hand, until now, the Physical Activity Guideline (PAG) in India is not yet fully developed. Therefore, we conducted a descriptive review of the rationality of yoga as one of the PA tools with two hypotheses - Does yoga qualify as a PA tool? Moreover, can yoga help to meet the requirement of Indian PAG?.Methods:An in-depth literature review was carried out using databases such as PubMed, ScopeMed, Google Scholar, and Cochrane Library. All the published articles, government reports and policy documents, which met inclusion criteria with specific reference to yoga and energy expenditure, were gathered.Results:The search strategy yielded 838 articles, of which 16 documents were considered for review. The review included 7 policy documents and/or studies that discussed PAGs/policy/strategy globally and 9 research studies targeted toward the energy expenditure and yoga. Huge variability was documented in the recommended PAGs globally and yoga found to be the moderate metabolic equivalents of tasks in the form of energy expenditure in this review.Conclusion:The compendium of physical activities should add a separate category for energy expenditure by yoga. This will help build-up newer exercise formats involving yogic physical activities to comply with the daily-recommended PA dose. In the national PA plan for India, yoga should get a prominent place. Further, in the Indian perspective, an exclusive PA plan is justified instead of a PA embedded within the national health programs in lieu of wider scope.

How physically active are women in the third trimester of pregnancy? A cross-sectional study of physical activity in pregnancy and its outcomes among women delivering at a rural maternity hospital in South India

Background: Physical activity influences perinatal outcomes. Moderate-intensity aerobic activity of 150 min/week during pregnancy is recommended. Objective: This study aimed at assessing the physical activity levels during pregnancy and determining the factors associated with physical activity among rural women. Methods: This was a cross-sectional study conducted in a rural maternity hospital, in a village in Ramanagara district located in the south of Karnataka among pregnant women >29 weeks of gestation and newly delivered mothers availing obstetric services at the hospital. A structured questionnaire with sociodemographic characteristics, pregnancy details, and neonate details was used. International Physical Activity Questionnaire Short Form was used for measuring physical activity. Physical activity was classified as low, moderate, and high intensity by calculating metabolic equivalent minutes per week based on the Compendium of Physical Activities. Results: Among the 158 study participants, 113 were antenatal women and 45 had just delivered. The mean age of the study participants was 23.31 ± 3.14 years and the mean weight gain per week was 0.35 ± 0.11 kg. About 96% of the subjects had high-intensity physical activity while 4% had moderate-intensity physical activity. Women from nuclear families and older mothers had significantly high physical activity. Women with high-intensity physical activity were significantly more likely to deliver a low birth weight baby in the early neonatal period. Conclusion: Being a rural area, majority of the pregnant mothers had high-intensity level of physical activity, which was significantly associated with lower birth weight among neonates. It is recommended to avoid high-intensity physical activity during pregnancy and focus advice on moderate physical activity for pregnant women.

The Forgotten Agriculture-Nutrition Link: Estimating the Energy Requirements of Different Farming Technologies in Rural Zambia With Time-Use Data

In the quest to reduce global under- and malnutrition, which is particularly high among smallholder farmers, agriculture-nutrition linkages have received a lot of attention in recent years. Researchers have analysed the link between the quantity of food that farmers produce and nutritional outcomes and the link between farm diversity and consumption diversity. A third agriculture-nutrition link has been largely neglected in recent years: the impact of how food is produced on human energy requirements, and, consequently, nutritional outcomes. This neglect persists despite the fact that the majority of smallholder farmers in Sub-Saharan Africa rely on hand tools for farming, which implies heavy physical work and, thus, high energy requirements. To address this research gap, the present study compares the energy requirements of farm households in rural Zambia that are characterized by three different levels of mechanization: hand tools, animal draught power and tractors. Detailed time-use as well as food and nutrition data was collected from male and female adults and from children during different seasons: land preparation, weeding and harvesting/processing. Subjects recorded time-use themselves using an innovative picture-based smartphone app called “Timetracker”. The time-use data served to calculate daily energy requirements using “Ainsworth’s Compendium of Physical Activities”. To analyse the link between mechanization and energy use as well as nutritional outcomes, linear mixed models and multiple linear regressions were used. The results show that during land preparation, individuals in non-mechanized households are, on the average, not able to meet their dietary energy requirements. In subsequent farming periods, results are more mixed. Gender differences are noteworthy throughout, with men mostly having higher physical activity levels and energy requirements compared to women The findings suggest that farm technologies affect nutritional outcomes substantially and that this neglected agriculture-nutrition linkage deserves more scientific and political attention in order to reduce the prevalence of both under- and malnutrition among smallholder farmers, while safeguarding against emerging double burden of nutrition.

Association between neighborhood greenspace and fasting plasma glucose from a large cohort study in Taiwan

Growing evidence suggests that greenness exposure improves health status, but few studies address diabetes, and research mainly comes from Western countries. This study hypothesizes that higher greenness exposure benefits glucose control with increased intensity of physical activity. We used data from a longitudinal health examination database in Taiwan during 2000–2014. The normalized difference vegetation index (NDVI) was used as an objective indicator of greenness in the neighborhood. Fasting plasma glucose served as an indicator of early diabetes diagnosis. A metabolic equivalent value (MET) based on a compendium of physical activities was used to estimate the intensity of physical activity. We applied a linear mixed-effect model with an individual-level random effect to investigate the relationship between neighborhood greenness and the fasting plasma glucose value stratified by four levels of physical activity. There are 341,211 participants with no diabetes, and 773,602 check-up visits included here. The fasting glucose among participants living in areas with higher green space was lower than that of those living in less green areas. The beneficial effects of greenspace on glucose were stronger for those with more physical activity, especially in the 500-m buffer. In the physically inactive group, compared with the lowest quantile (Q1) of cumulative average greenness, people living in the highest quantile (Q4) of greenness had 0.35 mg less glucose in each dl of plasma [95% confidence interval: −0.50, −0.20]. In the physically active group, people residing in the highest quantile (Q4) of greenness had 0.48 mg less glucose in each dl of plasma [95% confidence interval: −0.73, −0.23]. Higher residential greenness appears to be associated with lower glucose. Higher physical activity can mediate this association. Our results provide evidence that ensuring access to greenspace in residential neighborhoods may provide positive health outcomes and improve public health, and should be implemented in urban planning.

Estimating Sedentary Time from a Hip- and Wrist-Worn Accelerometer.

This study aimed to determine the validity of existing methods to estimate sedentary behavior (SB) under free-living conditions using ActiGraph GT3X+ accelerometers (AG). Forty-eight young (18-25 yr) adults wore an AG on the right hip and nondominant wrist and were video recorded during four 1-h sessions in free-living settings (home, community, school, and exercise). Direct observation videos were coded for postural orientation, activity type (e.g., walking), and METs derived from the Compendium of Physical Activities, which served as the criterion measure of SB (sitting or lying posture, <1.5 METs). Thirteen methods using cut points from vertical counts per minute (CPM), counts per 15-s (CP15s), and vector magnitude (VM) counts (e.g., CPM1853VM), raw acceleration and arm angle (sedentary sphere), Euclidean norm minus one (ENMO) corrected for gravity (mg) thresholds, uni- or triaxial sojourn hybrid machine learning models (Soj1x and Soj3x), random forest (RF), and decision tree (TR) models were used to estimate SB minutes from AG data. Method bias, mean absolute percent error, and their 95% confidence intervals were estimated using repeated-measures linear mixed models. On average, participants spent 34.1 min per session in SB. CPM100, CPM150, Soj1x, and Soj3x were the only methods to accurately estimate SB from the hip. Sedentary sphere and ENMO44.8 overestimated SB by 3.9 and 6.1 min, respectively, whereas the remaining wrist methods underestimated SB (range, 9.5-2.5 min). In general, mean absolute percent error was lower using hip methods compared with wrist methods. Accurate group-level estimates of SB from a hip-worn AG can be achieved using either simpler count-based approaches (CPM100 and CPM150) or machine learning models (Soj1x and Soj3x). Wrist methods did not provide accurate or precise estimates of SB. The development of large open-source free-living calibration data sets may lead to improvements in SB estimates.

Temporal Relationships Between The Act24 And A Monitor-based Method For Estimating Energy Expenditure Over A 24 Hour Period

The ability to quantify physical activity (PA) behavior and energy expenditure (EE) remains an important metric in public health and fitness research. While monitor-based methods remain popular, they cannot provide the context needed to understand relationships between PA, sedentary behavior, and sleep. The ACT24 is a promising online, self-guided recall survey that enables activities to be summarized and processed by day, activity or time. PURPOSE: To compares EE estimates from the ACT24 with estimates from the established Sensewear armband (SWA) across full days as well as at the minute-by-minute level. METHODS: 91 adults (36% male, mean±SD age 26.0±10.2 years) wore a SWA for 24 hours and completed the ACT24 the following morning. The compendium of physical activities was used to quantify self-reported activity levels based on ACT24 responses. To compare overall relationships, estimates of total daily EE from the SWA and ACT24 were correlated, and the mean absolute percent error (MAPE) of the ACT24 estimate was calculated relative to the SWA values. To assess the potential for contextualizing monitor data, data from Act24 were temporally matched with the SWA files and similar correlation and descriptive analyses were performed and averaged across individuals. RESULTS: The daily estimates were highly correlated r= 0.88, p<0.0001) and similar in magnitude for total daily EE (SWA: 2929 +/- 1106kcal; ACT24: 2902 +/- 950kcal) with a group level MAPE of 13.6%. The associations of temporally matched estimates revealed individual correlations ranging from r=0.08 to r=0.93 (mean of r =0.54 +/- 0.15). The individual MAPE values for the temporally matched data ranged from 0.02% to 56.1% (mean MAPE = 14%+/-11.6%). CONCLUSION: Previous-day recalls such as ACT24 may be useful alternatives to questionnaires or wearable devices for assessment of daily activities over 24-h periods. The robust export options also enable data to be temporally matched with other data sources to provide contextual information to be merged with monitor data. The results reveal good overall agreement between the two methods at both the group and individual level and provides a promising way to investigate PA context; however, additional research is needed to understand the factors influencing error between report-based and monitor-based methods.

Metabolic Equivalent Values of Common Daily Activities in Middle-Age and Older Adults in Free-Living Environments: A Pilot Study.

The purpose was to assess metabolic equivalent (MET) values of common daily activities in middle-age and older adults in free-living environments and compare these with MET values listed in the compendium of physical activities (CPA). Sixty participants (mean age = 71.5, SD = 10.8) completed a semistructured protocol of sitting, lying, self-paced walking, and 4 self-selected activities in their residences. Oxygen consumption was measured using portable indirect calorimetry, to assess METs for each activity relative to VO2 at rest (VO2 during activity/VO2 at rest). Measured MET values for 20 different activities were compared with those in the CPA, for the total sample and for participants aged 55-64, 65-74, and 75-99years. Measured METs for sitting, walking, sweeping, trimming, and laundry were significantly different from the CPA values. Measured MET values for sedentary activities were lower in all age groups, and those for walking and household activities were higher in the youngest age group, than the CPA values. For gardening activities, there was a significant decline in measured METs with age. Some measured MET values in older people differed from those in the CPA. The values reported here may be useful for future research with younger, middle-age, and older-old people.

Successful Smoking Cessation Associated with Walking Behavior in Patients with Claudication

Behavioral economics theories suggest that a preference for delayed benefits promotes positive behavioral change, a concept relevant to both smoking cessation and community-based exercise regimens for claudication. Given the high rate of smoking among older veterans, we were interested in examining the association between smoking cessation, exercise regimen adherence, and preferences for delayed versus immediate benefits. Between April 2017 and March 2018, patients with claudication at the Michael E. DeBakey Veterans Affairs Medical Center in Houston, Texas, received questionnaires collecting information on social, behavioral, and psychological characteristics. A dual validation system, via the electronic medical record and survey data, measured the primary outcome-smoking cessation versus current smoking. Self-reported physical activity was measured through the validated Ainsworth's compendium of Physical Activities and binary survey questions. The Walking Impairment and Barratt's Impulsivity Questionnaires measured subjective symptom severity and behavioral economics factors, respectively. Multivariable, logistic regression models identified significant associations. The survey was mailed to 500 patients who met the eligibility criteria. We received responses from 148 individuals (30%), and 67 of 141 (48%) indicated that they had successfully quit smoking. In unadjusted comparisons, the median cognitive complexity score in the smoking cessation group was higher than that in the current smoking group. A greater proportion of patients who reported walking for exercise (n=46) also reported successful smoking cessation (28/46, 61%). Among those who were not walking for exercise (n=88), more individuals reported current smoking (49/88, 56%). In the multivariable model, individuals who had successfully stopped smoking were older (odds ratio [OR]: 7.59, P<0.001), more likely to walk for exercise (OR: 3.94, P=0.009), more interested in the future than in the present (OR: 1.73, P=0.030), and more likely to regularly save money (OR: 3.49, P=0.046). We found that participants who reported successful smoking cessation were more likely to report walking for exercise. Our findings suggest that adherence to walking may be less challenging for patients who have already successfully implemented and continue to implement another beneficial health behavior (smoking cessation). Patients with claudication who are current smokers may be less likely to adopt exercise recommendations.