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.

A prospective investigation of neighborhood socioeconomic deprivation and physical activity and sedentary behavior in older adults.

Neighborhood conditions may have an important impact on physical activity and sedentary behaviors in the older population. Most previous studies in this area are cross-sectional and report mixed findings regarding the effects of neighborhood environment on different types of physical activity. Moreover, little is known about the prospective relationship between neighborhood environment and sedentary behaviors. Our analysis included 136,526 participants from the NIH-AARP Diet and Health Study (age 51-70). Neighborhood socioeconomic deprivation was measured with an index based on census variables and developed using principal component analysis. Physical activity and sedentary behaviors were measured both at baseline (1995-1996) and follow-up (2004-2006). Multiple regression analyses were conducted to examine the prospective relationship between neighborhood deprivation and exercise, non-exercise physical activity, and sedentary behaviors, adjusting for baseline physical activity and sedentary behaviors as well as potential confounders. We found that more severe neighborhood socioeconomic deprivation was prospectively associated with reduced time for exercise (ß Q5 vs Q1 (95% confidence interval), hour, -0.85 (-0.95, -0.75)) but increased time spent in non-exercise physical activities (1.16 (0.97, 1.34)), such as household activities, outdoor chores, and walking for transportation. Moreover, people from more deprived neighborhoods were also more likely to engage in prolonged (≥5 h/day) TV viewing (Odds ratio Q5 vs Q1 (95% confidence interval), 1.21 (1.15, 1.27)). In conclusion, neighborhood socioeconomic deprivation is associated with physical activity and sedentary behavior in the older population. These associations may differ for different types of physical activities.

Fitness Trackers to Guide Advice on Activity Prescription.

This Viewpoint discusses whether older self-report questionnaire studies and newer studies of fitness-tracking devices reach different conclusions about the dose of physical activity needed for health.

Sleep duration and total and cause-specific mortality in a large US cohort: interrelationships with physical activity, sedentary behavior, and body mass index.

Both short and long durations of sleep are associated with higher mortality, but little is known about the interrelationship between sleep and other modifiable factors in relation to mortality. In the National Institutes of Health-AARP Diet and Health Study (1995-1996), we examined associations between sleep duration and total, cardiovascular disease (CVD), and cancer mortality among 239,896 US men and women aged 51-72 years who were free of cancer, CVD, and respiratory disease. We evaluated the influence of moderate-to-vigorous physical activity, television viewing, and body mass index (BMI; weight (kg)/height (m)(2)) on the sleep-mortality association and assessed their combined association with mortality. During an average of 14 years of follow-up, we identified 44,100 deaths. Compared with 7-8 hours of sleep per day, both shorter and longer sleep durations were associated with higher total and CVD mortality. We found a greater elevation in CVD mortality associated with shorter sleep among overweight and obese people, suggesting a synergistic interaction between sleep and BMI. People in the unhealthy categories of all 4 risk factors (sleep <7 hours/day, moderate-to-vigorous physical activity ≤1 hour/week, television viewing ≥3 hours/day, and BMI ≥25) had significantly higher all-cause (relative risk (RR) = 1.42, 95% confidence interval (CI): 1.34, 1.52), CVD (RR = 1.90, 95% CI: 1.67, 2.17), and cancer (RR = 1.21, 95% CI: 1.09, 1.34) mortality. Short sleep duration may predict higher mortality, particularly CVD mortality, among overweight and obese people.

Maternal 24-h movement patterns across pregnancy and postpartum: The LIFE-Moms consortium.

Objective: Time spent among the 24-h movement behaviors (physical activity [PA], sleep, sedentary behavior [SB]) in the perinatal period is important for maternal and child health. We described changes to 24-h movement behaviors and behavior guideline attainment during pregnancy and postpartum and identified correlates of behavior changes. Methods: This secondary data analysis included the standard of care group (n = 439) from the U.S.-based Lifestyle Interventions For Expectant Moms (LIFE-Moms) consortium, including persons with overweight and obesity. Wrist-worn accelerometry was used to measure movement behaviors early (9-15 weeks) and late (35-36 weeks) pregnancy, and âˆ¼ 1-year postpartum. Sleep and moderate-to-vigorous PA (MVPA) were compared to adult and pregnancy-specific guidelines, respectively. SB was classified into quartiles. PA and SB context were quantified using questionnaires. Mixed models were used to examine changes in behaviors and guidelines and identify correlates. Results: Participants were 31.3 ± 3.5 years, 53.5 % were Black or Hispanic, and 45.1 % had overweight. Sleep duration decreased across time, but participants consistently met the guideline (range: 85.0-93.6 %). SB increased during pregnancy and decreased postpartum, while light PA and MVPA followed the inverse pattern. Participants met slightly fewer guidelines late pregnancy (1.2 ± 0.7 guidelines) but more postpartum (1.7 ± 0.8 guidelines) than early pregnancy (1.4 ± 0.8 guidelines). Black or Hispanic race/ethnicity, higher pregravid body mass index, and non-day work-shift (e.g., night-shift) were identified correlates of lower guideline adherence and varying PA and SB context. Conclusion: Perinatal interventions should consider strategies to prevent SB increase and sustain MVPA to promote guideline adherence.

Physical Activity Levels (PAL) in US Adults-2019.

PURPOSE: Physical activity levels (PAL) are associated with mortality risk and were instrumental in estimating national energy requirements, but we are unaware of population-based estimates of PAL in US adults. Thus, we conducted a nationwide survey using a validated previous-day recall method to estimate PAL and the behavioral determinants of low and higher PAL. METHODS: Participants from the AmeriSpeak panel 20-75 yr of age ( N = 2640) completed Activities Completed over Time in 24-hours previous-day recalls. PAL values were estimated as the average metabolic equivalent value over 24 h. Recalls were conducted on randomly selected days in October and November 2019. Survey sample design weights were applied to reflect the US population. RESULTS: Mean age was 45.3 yr, 51% were female, 67% were non-Hispanic White, and 37% had a body mass index of ≥30 kg·m -2 . US adults reported a mean PAL of 1.63 (95% confidence interval, 1.60-1.65), and 39% (37%-42%) of adults reported PAL ≥1.6 on a given day. Men reported higher PAL than women (1.67 vs 1.59), and older adults reported lower PAL. Adults with PAL <1.4 spent 81% (12.1 h·d -1 ) of their waking day sedentary and 19% (2.7 h·d -1 ) in total physical activity. Adults with PAL considered to be "active"(1.6-1.89) spent only 49% (8.0 h·d -1 ) of their waking day sedentary, and 51% (8.3 h·d -1 ) physically active. CONCLUSIONS: Our study provides novel estimates of PAL in a nationwide sample of US adults and a description of the type and intensity of sedentary and physically active behaviors contributing to low and higher PAL. These findings may inform public health messages aimed at increasing physical activity in adults and potentially contribute to obesity prevention efforts.

Rethinking physical activity assessment in cancer survivors: a multi-component approach using NHANES data.

PURPOSE: To confirm that a multi-component physical activity index (PAI), with physical activity (PA)-related behaviors and performance measures, enhances PA assessment for adults with cancer history ("survivors") vs. for the general population for key health variables. METHODS: Data from 2011 to 2014 NHANES were analyzed (n = 9620 adults, including 866 survivors). We generated PAI scores by calculating subscales for activity-related behaviors (i.e., moderate-to-vigorous PA and TV viewing hours/sedentary time) (n/8) and performance (i.e., cardiorespiratory fitness and muscle strength) (n/8), then combined for an overall PAI score (n/16 points). Dependent variables (self-reported health, multimorbidity, and functional limitations) were dichotomized and tested with logistic regression to estimate associations with PAI variables. RESULTS: Survivors obtaining 22.5 + MET hours/week of PA were >5× more likely to report excellent health than non-exercisers (OR = 5.5, 95% CI 3.27-9.28). We observed a general decrease in likelihood of multimorbidity and functional limitations with increasing PA. Models with the combined score showed that higher PAI scores had larger positive associations with all three for all adults. Survivors with higher PAI scores were >30% more likely to report excellent self-rated health (OR = 1.3, 95% CI 1.2-1.4) and 20% less likely to report multimorbidity and functional limitations compared to survivors with lower PAI scores, considering covariates. CONCLUSIONS: Greater PA-related behavior and performance was beneficial for adults irrespective of cancer history. However, using a combined score elucidates unique needs and benefits for survivors vs. for general population. IMPLICATIONS FOR CANCER SURVIVORS: Using a multi-component approach to PA assessment could help in developing validated tools to plan exercise programs and interventions for survivors.

Changes in physical activity and sedentary time in United States adults in response to COVID-19.

Physical activity is associated lower risk for a broad range of non-communicable diseases and early mortality, and even small changes in daily activity levels could have a profound effect on public health at the population level. The COVID-19 pandemic reshaped daily life for United States (US) adults resulting in reductions in physical activity early in the pandemic, but its longer-term effects on daily activities are unknown. To examine the longer-term impact of the pandemic on daily activity levels, we conducted a nationwide longitudinal study of 1,635 adults (20-75 years) in AmeriSpeak. Previous-day recalls of time-use, sedentary time, and physical activity were completed on randomly selected days in Fall 2019 (pre-pandemic) and Fall 2020. Overall, US adults reported less time in transportation (-0.47 hrs/d), more total discretionary time (0.40 hrs/d), but no changes in total sedentary time (0.10 hrs/d) or leisure-time physical activity (-0.06 hrs/d). Women reported significantly less total activity (-0.36 hrs/d) and participants with children < 13 yrs reported more sedentary time (0.60 to 0.82 hrs/d) and less moderate-to-vigorous intensity activity (-0.84 to -0.72 hrs/d). Adults without children reported no changes in sedentary time (0.02 hrs/d) or moderate-vigorous intensity activity (-0.06 hrs/d). Adults who started working from home reported no changes in physical activity, but they were among the most sedentary and least active population groups at both timepoints. Our findings describe the complex inter-play between competing behaviors as time-use demands have changed in response to the pandemic, particularly for adults with younger children. Many US adults are likely to continue working from home; therefore, implementation of evidence-based approaches to increase physical activity and reduce sedentary time in this growing population subgroup appears warranted.

Sitting Time, Type, and Context Among Long-Term Weight-Loss Maintainers.

OBJECTIVE: This study aimed to investigate sitting time, the home sedentary environment, and physical activity among weight-loss maintainers in WW (formerly Weight Watchers). METHODS: Participants were 4,305 weight-loss maintainers who had maintained ≥9.1 kg of weight loss (24.7 kg on average) for 3.3 years and had an average current BMI of 27.6 kg/m2 . A control group of weight-stable individuals with obesity (n = 619) had an average BMI of 38.9 kg/m2 . The Multicontext Sitting Time Questionnaire and Paffenbarger physical activity questionnaire were administered. RESULTS: Weight-loss maintainers versus controls spent 3 hours less per day sitting during the week (10.9 vs. 13.9; η p 2 = 0.039; P = 0.0001) and weekends (9.7 vs. 12.6; η p 2 = 0.038). Weight-loss maintainers versus controls spent 1 hour less per day in non-work-related sitting using a computer or video games during the week (1.4 vs. 2.3; η p 2 = 0.03; P = 0.0001) and weekends (1.5 vs. 2.5; η p 2 = 0.03; P = 0.0001). Weight-loss maintainers versus controls had similar numbers of sedentary-promoting devices (15.8 vs. 14.8) and expended significantly more calories per week in physical activity (1,835 vs. 785; η p 2 = 0.036; P = 0.0001). CONCLUSIONS: Weight-loss maintainers reported less time sitting than weight-stable individuals with obesity. Future research should test the efficacy of targeting sitting time to help promote long-term weight-loss maintenance.

Sedentary Behavior in U.S. Adults: 2019.

PURPOSE: Higher levels of sedentary behavior are associated with early mortality, but the distribution of sedentary time by classes of behavior and demographic groups is poorly described in U.S. adults. To quantify the amount and sources of sedentary time in U.S. adults, we conducted a nationwide survey using a novel validated self-administered previous-day recall method and compared these values with a commonly used sitting time question. METHODS: Participants from the AmeriSpeak panel 20 to 75 yr of age (N = 2640) completed up to two activities completed over time in 24 h (ACT24) previous-day recalls. Recalls were conducted on randomly selected days in October and November 2019. Survey sample designs were applied to reflect the U.S. population. RESULTS: Mean age was 45.3 yr, 51% were female, 67% non-Hispanic White, and 37% had a body mass index of ≥30 kg·m-2. U.S. adults reported a mean 9.5 h·d-1 of sedentary time (95% confidence interval = 9.4, 9.7 h·d-1), which was 34% more than reported using a common surveillance measure (P < 0.01). Most daily sedentary time was accumulated in the leisure and work life domains, with leisure accounting for 47% (4.3 h·d-1, 95% confidence interval = 4.2, 4.5 h·d-1) of the total sedentary time. Eighty-two percent of leisure time was spent sedentary, mainly watching television/videos or engaged in Internet/computer use. CONCLUSIONS: U.S. adults appear to spend more time in sedentary behavior than previously thought, and the majority of this time is accumulated at work and in leisure time. Reducing sedentary screen time during leisure in favor of physically active could be an important intervention target in the effort to increase physical activity in U.S. adults.

Identification of changes in sleep across pregnancy and the impact on cardiometabolic health and energy intake in women with obesity.

This prospective, observational study investigated changes in sleep and the effect on energy intake, gestational weight gain, and cardiometabolic health across pregnancy in 52 healthy pregnant women with obesity. Habitual sleep was assessed by wrist-worn actigraphy (time spent in bed; TIB, total sleep time; TST, and sleep efficiency) in early (130-156 weeks) and late (350-366) pregnancy. A change to habitual sleep was defined as change of one-half of the standard deviation of TIB and TST across six consecutive nights from early pregnancy. Energy intake and changes in weight, fasting glucose, insulin, and lipids across pregnancy were compared between women who changed sleep. During early pregnancy, TIB was 9:24 ± 0:08 h and varied by 1:37 ± 0:07 h across the six nights. TST and sleep efficiency significantly declined from early to late pregnancy (7:03 ± 0:08 h to 6:28 ± 0:09 h, p < 0.001) and (76 ± 0.1% to 71 ± 0.2%, p < 0.001), respectively. For women who increased TIB (n = 11), fasting glucose decreased (-11.6 ± 4.3%, p < 0.01) across pregnancy and they had a trend towards decreased insulin (-57.8 ± 33.5%; p = 0.09) and HOMA-IR (-72.4 ± 37.3%; p = 0.06) compared to women who decreased TIB (n = 13). Women who increased TIB had a significantly lower daily energy intake across pregnancy (-540 ± 163 kcal; p < 0.01) and tended to have less gestational weight gain (-147 ± 88 g/week; p = 0.10). Changes in TST did not affect plasma markers, energy intake or weight gain. The positive relationship between sleep and cardiometabolic health during pregnancy is explained in part by lower energy intake. We hypothesize lower energy intake is due to a prolonged overnight fast and a decrease in the time available for eating.

Elevated insulin levels following 7 days of increased sedentary time are due to lower hepatic extraction and not higher insulin secretion.

Higher insulin following sedentary behavior may be due to increased insulin secretion (IS), decreased hepatic insulin extraction (HIE), or a combination of both. Ten healthy adults completed glucose tolerance tests following 7 days of normal activity and 7 days of increased sitting. There were no differences in IS; however, HIE at 120 min after ingestion (85.4% ± 7.2% vs. 74.6% ± 6.6%, p < 0.05) and the area under the curve (73.6% ± 9.4% vs. 67.5% ± 11.3%, p < 0.05) were reduced following 7 days of increased sedentary time.

Demographic-specific Validity of the Cancer Prevention Study-3 Sedentary Time Survey.

PURPOSE: This study examined the 1-yr test-retest reliability and criterion validity of sedentary time survey items in a subset of participants from a large, nationwide prospective cohort. METHODS: Participants included 423 women and 290 men age 31 to 72 yr in the Cancer Prevention Study-3. Reliability was assessed by computing Spearman correlation coefficients between responses from prestudy and poststudy surveys. Validity was assessed by comparing survey-estimated sedentary time with a latent variable representing true sedentary time estimated from the 7-d diaries, accelerometry, and surveys through the method of triads. Sensitivity analyses were restricted to 566 participants with an average of 14+ h of diary and accelerometer data per day for 7 d per quarter. RESULTS: Reliability estimates for total sitting time were moderate or strong across all demographic strata (Spearman ρ ≥ 0.6), with significant differences by race (P = 0.01). Reliability estimates were strongest for the TV-related sedentary time item (Spearman ρ, 0.74; 95% confidence interval, 0.70-0.77). The overall validity coefficient (VC) for survey-assessed total sedentary time was 0.62 (95% confidence interval, 0.55-0.69), although VC varied by age group and activity level (P < 0.05). However, VC were similar across groups (P < 0.05) when restricting to highly compliant participants in a sensitivity analysis. CONCLUSIONS: The Cancer Prevention Study-3 sedentary behavior questionnaire has acceptable reliability and validity for ranking or categorizing participants according to sedentary time. Acceptable reliability and validity estimates persist across various demographic subgroups.

Association of Leisure-Time Physical Activity Across the Adult Life Course With All-Cause and Cause-Specific Mortality.

Importance: Although the benefits of leisure-time physical activity (LTPA) in middle age are established, the health effects of long-term participation and changes in LTPA between adolescence and middle age have not been documented. Objective: To determine whether an association exists between LTPA life course patterns and mortality. Design, Setting, and Participants: This prospective cohort study used data from the National Institutes of Health-AARP (formerly American Association of Retired Persons) Diet and Health Study established in 1995 to 1996. Data analysis was conducted from March 2017 through February 2018. Data were analyzed for 315 059 adult AARP members living in 6 states, namely, California, Florida, Louisiana, New Jersey, North Carolina, or Pennsylvania, or 2 metropolitan areas, Atlanta, Georgia, or Detroit, Michigan. Exposures: Self-reported LTPA (hours per week) at the baseline interview for ages grouped as 15 to 18, 19 to 29, 35 to 39, and 40 to 61 years. Main Outcomes and Measures: All-cause, cardiovascular disease (CVD)-related, and cancer-related mortality records available through December 31, 2011. Results: Of 315 059 participants, 183 451 (58.2%) were men, and the participants were 50 to 71 years of age at enrollment. Ten LTPA trajectories (categorized as maintaining, increasing, and decreasing LTPA across time) were identified, and 71 377 deaths due to all causes, 22 219 deaths due to CVD, and 16 388 deaths due to cancer occurred. Compared with participants who were consistently inactive throughout adulthood, participants who maintained the highest amount of LTPA in each age period were at lower risks for all-cause, CVD-related, and cancer-related mortality. For example, compared with participants who were consistently inactive, maintaining higher amounts of LTPA was associated with lower all-cause (hazard ratio [HR], 0.64; 95% CI, 0.60-0.68), CVD-related (HR, 0.58; 95% CI, 0.53-0.64), and cancer-related (HR, 0.86; 95% CI, 0.77-0.97) mortality. Adults who were less active throughout most of the adult life course but increased LTPA in later adulthood (40-61 years of age) also had lower risk for all-cause (HR, 0.65; 95% CI, 0.62-0.68), CVD-related (HR, 0.57; 95% CI, 0.53-0.61), and cancer-related (HR, 0.84; 95% CI, 0.77-0.92) mortality. Conclusions and Relevance: Maintaining higher LTPA levels and increasing LTPA in later adulthood were associated with comparable low risk of mortality, suggesting that midlife is not too late to start physical activity. Inactive adults may be encouraged to be more active, whereas young adults who are already active may strive to maintain their activity level as they get older.

Neighborhood Socioeconomic Deprivation and Weight Change in a Large U.S. Cohort.

INTRODUCTION: Both excessive weight gain and weight loss are important risk factors in the older population. Neighborhood environment may play an important role in weight change, but neighborhood effects on weight gain and weight loss have not been studied separately. This study examined the associations between neighborhood socioeconomic deprivation and excessive weight gain and weight loss. METHODS: This analysis included 153,690 men and 105,179 women (aged 51-70 years). Baseline addresses were geocoded into geographic coordinates and linked to the 2000 U.S. Census at the Census tract level. Census variables were used to generate a socioeconomic deprivation index by principle component analysis. Excessive weight gain and loss were defined as gaining or losing >10% of baseline (1995-1996) body weight at follow-up (2004-2006). The analysis was performed in 2015. RESULTS: More severe neighborhood socioeconomic deprivation was associated with higher risks of both excessive weight gain and weight loss after adjusting for individual indicators of SES, disease conditions, and lifestyle factors (Quintile 5 vs Quintile 1: weight gain, OR=1.36, 95% CI=1.28, 1.45 for men and OR=1.20, 95% CI=1.13, 1.27 for women; weight loss, OR=1.09, 95%% CI=1.02, 1.17 for men and OR=1.23, 95% CI=1.14, 1.32 for women). The findings were fairly consistent across subpopulations with different demographics and lifestyle factors. CONCLUSIONS: Neighborhood socioeconomic deprivation predicts higher risk of excessive weight gain and weight loss.

Television Viewing Time and Inflammatory-Related Mortality.

PURPOSE: Television (TV) viewing time is associated with increased risk of all-cause, cardiovascular and cancer mortality. Although TV time is detrimentally associated with key inflammatory markers, the associations of TV time with other inflammatory-related mortality (with a predominant inflammatory, oxidative or infectious component, but not attributable to cancer or cardiovascular causes), are unknown. METHODS: Among 8933 Australian adults (4593 never-smokers) from the baseline (1999-2000) Australian Diabetes, Obesity and Lifestyle Study (median follow-up, 13.6 yr), we examined TV time in relation to noninflammatory and inflammatory-related mortality (not attributable to cancer or cardiovascular causes, hereafter "inflammatory-related" mortality). Because smoking has a significant inflammatory component, we also examined this relationship in never-smokers. RESULTS: Of 896 deaths, 248 were attributable to cardiovascular disease, 346 to cancer, 130 to other inflammatory-related causes (71 for never-smokers), and 172 to noninflammatory-related causes (87 for never-smokers). After multivariate adjustment for age, sex, education, household income, smoking status, alcohol intake, energy intake, diet, and cardiometabolic risk biomarkers (model 3), every additional hours per day of TV time was associated with increased risk of inflammatory-related mortality in the overall population (hazard ratio, 1.12; 95% confidence interval, 1.00-1.25) and in never-smokers (1.18; 1.00, 1.40). These results were attenuated after additional adjustment for leisure-time physical activity. After multivariate adjustment (model 3), no association was observed for noninflammatory mortality in the overall population (0.95; 0.85, 1.07), but risk tended to decrease for never-smokers (0.85; 0.75, 1.02). CONCLUSIONS: In summary, before adjustment for leisure-time physical activity, TV time was associated with increased risk of inflammatory-related mortality. This is consistent with the hypothesis that high TV viewing may be associated with a chronic inflammatory state.

Targeting Reductions in Sitting Time to Increase Physical Activity and Improve Health.

: New evidence suggests that reductions in sedentary behavior may increase physical activity and improve health. These findings point to new behavioral targets for intervention and new ways to think about intervening to increase overall physical activity in the population. This report provides a knowledge update reflecting the rapid accumulation of new evidence related to sedentary behavior and health among adults. Recent observational studies suggest that leveraging the time-inverse relationship between sedentary and active behaviors by replacing sitting with standing, light- or moderate-intensity activity can have important health benefits, particularly among less active adults. Clinical studies are providing evidence of the probable physiologic mechanisms underlying these associations, as well as insights into the cardiometabolic impact of breaking up and reducing sedentary behavior. In contrast to the well-established behavioral theories that guide the development and dissemination of evidence-based interventions to increase moderate- to vigorous-intensity physical activity, much less is known about how to reduce sedentary time to increase daily activities. It has become clear that the environmental, social, and individual level determinants for sedentary time are distinct from those linked to the adoption and maintenance of moderate- to vigorous-intensity physical activity. As a result, novel intervention strategies that focus on sitting and lower-intensity activities by leveraging the surrounding environment (e.g., workplace, school, and home) as well as individual-level cues and habits of sedentary behavior are being tested to increase the potency of interventions designed to increase overall physical activity. Herein we summarize the solutions-oriented research across the behavioral research framework, with a focus on highlighting areas of synergy across disciplines and identifying gaps for future research.

Objectively measured physical activity and plasma metabolomics in the Shanghai Physical Activity Study.

BACKGROUND: Physical activity is associated with a variety of health benefits, but the biological mechanisms that explain these associations remain unclear. Metabolomics is a powerful tool to comprehensively evaluate global metabolic signature associated with physical activity and helps to pinpoint the pathways that mediate the health effects of physical activity. There has been limited research on metabolomics and habitual physical activity, and no metabolomics study has examined sedentary behaviour and physical activity of different intensities. METHODS: In a group of Chinese adults (N = 277), we used an untargeted approach to examine 328 plasma metabolites in relation to accelerometer-measured physical activity, including overall volume of physical activity (physical activity energy expenditure (PAEE) and duration of physically active time) and sedentary time, and measures related to different intensities of physical activity (moderate-to-vigorous activity (MVPA), light activity, average physical activity intensity). RESULTS: We identified 11 metabolites that were associated with total activity, with a false discovery rate of 0.2 or lower. Notably, we observed generally lower levels of amino acids in the valine, leucine and isoleucine metabolism pathway and of carbohydrates in sugar metabolism among participants with higher activity levels. Moreover, we found that PAEE, time spent in light activity and duration of physically active time were associated with a similar metabolic pattern, whereas the metabolic signature associated with sedentary time mirrored this pattern. In contrast, average activity intensity and time spent in MVPA appeared to be associated with somewhat different metabolic patterns. CONCLUSIONS: Overall, the metabolomics patterns support a beneficial role of higher volume of physical activity in cardiometabolic health. Our findings identified candidate pathways and provide insight into the mechanisms underlying the health effects of physical activity.