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Background Behaviour has diverse economic, social and health consequences. Linking time spent in different daily activities to energy expenditure (EE) is one way of investigating the health and physiological consequences of behaviour and identifying targets to improve population health and well-being. Methods We estimated behaviour-related EE for respondents to time use surveys (TUS) from three countries: UK 2001, Poland 2012 and US 2003–13. The Harmonised Multinational Time Use Survey (MTUS) activity categories were matched to MET estimates from the 2011 Compendium of Physical Activities. We attach METs values to each successive activity in the TUS, together with both the original UK, Polish and US activity classifications and the 68-category MTUS activity classification. We used TUS estimates of activity durations across 24-h to estimate the Physical Activity Level (PAL) for respondents from the three countries and the average time spent and MET values for different activity categories. Results PAL values ranged from 1.59 in the US to 1.74 in Poland. The main sources of daily EE from PA were paid and unpaid work activities. Discretionary PA accounted for only a very small part (~ 3%) of adult daily energy expenditures. Using the harmonised MTUS 68-activity classification reduced the variability of the aggregate PAEE measure by ~ 20%, but the patterns of association between key demographics (age, sex, educational attainment) were unaffected. TUS data were further used to (1) identify sources of daily PA, and (2) assess adherence to physical activity guidelines (PAG) on a single-day basis. Estimated adherence levels were similar to those reported from other TUS as well as frequency based estimates. Conclusions Comparative studies of energy expenditure based on harmonised time use activity categories could provide insight into the relative importance of different activities for energy expenditure across different countries and demographic groups. However, new observational studies combining TUS data with accelerometer, direct observation and other measures of activity intensity are required for more accurate MET assignments to activity categories in TUS.

Background Past studies of associations between measures of the built environment, particularly street connectivity, and active transportation (AT) or leisure walking/bicycling have largely failed to account for spatial autocorrelation of connectivity variables and have seldom examined both the propensity for AT and its duration in a coherent fashion. Such efforts could improve our understanding of the spatial and behavioral aspects of AT. We analyzed spatially identified data from Los Angeles and San Diego Counties collected as part of the 2001 California Health Interview Survey. Results Principal components analysis indicated that ~85% of the variance in nine measures of street connectivity are accounted for by two components representing buffers with short blocks and dense nodes (PRIN1) or buffers with longer blocks that still maintain a grid like structure (PRIN2). PRIN1 and PRIN2 were positively associated with active transportation (AT) after adjustment for diverse demographic and health related variables. Propensity and duration of AT were correlated in both Los Angeles (r = 0.14) and San Diego (r = 0.49) at the zip code level. Multivariate analysis could account for the correlation between the two outcomes. After controlling for demography, measures of the built environment and other factors, no spatial autocorrelation remained for propensity to report AT (i.e., report of AT appeared to be independent among neighborhood residents). However, very localized correlation was evident in duration of AT, particularly in San Diego, where the variance of duration, after accounting for spatial autocorrelation, was 5% smaller within small neighborhoods (~0.01 square latitude/longitude degrees = 0.6 mile diameter) compared to within larger zip code areas. Thus a finer spatial scale of analysis seems to be more appropriate for explaining variation in connectivity and AT. Conclusions Joint analysis of the propensity and duration of AT behavior and an explicitly geographic approach can strengthen studies of the built environment and physical activity (PA), specifically AT. More rigorous analytical work on cross-sectional data, such as in the present study, continues to support the need for experimental and longitudinal study designs including the analysis of natural experiments to evaluate the utility of environmental interventions aimed at increasing PA.

Background Youth use different forms of screen time (e.g., streaming, gaming) that may be related to body mass index (BMI). Screen time is non-independent from other behaviors, including physical activity and sleep duration. Statistical approaches such as isotemporal substitution or compositional data analysis (CoDA) can model associations between these non-independent behaviors and health outcomes. Few studies have examined different types of screen time, physical activity, and sleep duration simultaneously in relation to BMI. Methods Data were baseline (2017–2018) and one-year follow-up (2018–2019) from the Adolescent Brain Cognitive Development Study, a multi-site study of a nationally representative sample of U.S. youth ( N  = 10,544, mean [SE] baseline age = 9.9 [0.03] years, 48.9% female, 45.4% non-White). Participants reported daily minutes of screen time (streaming, gaming, socializing), physical activity, and sleep. Sex-stratified models estimated the association between baseline behaviors and follow-up BMI z -score, controlling for demographic characteristics, internalizing symptoms, and BMI z -score at baseline. Results In females, isotemporal substitution models estimated that replacing 30 min of socializing (β [95% CI] = -0.03 [-0.05, -0.002]), streaming (-0.03 [-0.05, -0.01]), or gaming (-0.03 [-0.06, -0.01]) with 30 min of physical activity was associated with a lower follow-up BMI z -score. In males, replacing 30 min of socializing (-0.03 [-0.05, -0.01]), streaming (-0.02 [-0.03, -0.01]), or gaming (-0.02 [-0.03, -0.01]) with 30 min of sleep was associated with a lower follow-up BMI z -score. In males, replacing 30 min of socializing with 30 min of gaming was associated with a lower follow-up BMI z -score (-0.01 [-0.03, -0.0001]). CoDA estimated that in males, a greater proportion of time spent in baseline socializing, relative to the remaining behaviors, was associated with a higher follow-up BMI z -score (0.05 [0.02, 0.08]). In females, no associations between screen time and BMI were observed using CoDA. Conclusions One-year longitudinal associations between screen time and BMI may depend on form of screen time, what behavior it replaces (physical activity or sleep), and participant sex. The alternative statistical approaches yielded somewhat different results. Experimental manipulation of screen time and investigation of biopsychosocial mechanisms underlying the observed sex differences will allow for causal inference and can inform interventions.

Sedentary behavior has emerged as a novel health risk factor independent of moderate to vigorous physical activity (MVPA). Previous studies have shown self-reported sedentary time to be associated with mortality; however, no studies have investigated the effect of objectively measured sedentary time on mortality independent of MVPA. The objective our study was to examine the association between objectively measured sedentary time and all-cause mortality. 7-day accelerometry data of 1906 participants aged 50 and over from the U.S. nationally representative National Health and Nutrition Examination Survey (NHANES) 2003-2004 were analyzed. All-cause mortality was assessed from the date of examination through December 31, 2006. Over an average follow-up of 2.8 years, there were 145 deaths reported. In a model adjusted for sociodemographic factors, lifestyle factors, multiple morbidities, mobility limitation, and MVPA, participants in third quartile (hazard ratio (HR):4.05; 95%CI:1.55-10.60) and fourth quartile (HR:5.94; 95%CI: 2.49-14.15) of having higher percent sedentary time had a significantly increased risk of death compared to those in the lowest quartile. Our study suggests that sedentary behavior is a risk factor for mortality independent of MVPA. Further investigation, including studies with longer follow-up, is needed to address the health consequences of sedentary behavior.

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.

Diverse biochemical and physiological adaptations enable different species of ectotherms to survive and reproduce in very different temperature regimes, but whether these adaptations fully compensate for the thermodynamically depressing effects of low temperature on rates of biological processes is debated. If such adaptations are fully compensatory, then temperature‐dependent processes (e.g., digestion rate, population growth rate) of cold‐adapted species will match those of warm‐adapted species when each is measured at its own optimal temperature. Here we show that cold‐adapted insect species have much lower maximum rates of population growth than do warm‐adapted species, even when we control for phylogenetic relatedness. This pattern also holds when we use a structural‐equation model to analyze alternative hypotheses that might otherwise explain this correlation. Thus, although physiological adaptations enable some insects to survive and reproduce at low temperatures, these adaptations do not overcome the “tyranny” of thermodynamics, at least for rates of population increase. Indeed, the sensitivity of population growth rates of insects to temperature is even greater than predicted by a recent thermodynamic model. Our findings suggest that adaptation to temperature inevitably alters the population dynamics of insects. This result has broad evolutionary and ecological consequences.

Concurrent with the global increase in obesity, 1 numerous studies and reviews have been published concerning associations of overweight and obesity with mortality. Their findings have prompted considerable public health debate. There is ongoing discussion as to whether cutoff points for body-mass index (BMI) categories should differ across regions or racial or ethnic groups. 2 Additionally, studies differ in their assessment of the relation between BMI and mortality.

Housing is a major social determinant of health that affects health status and outcomes across the lifespan. An interagency portfolio analysis assessed the level of funding invested in \"health and housing research\" from fiscal years (FY) 2016-2020 across the National Institutes of Health (NIH), the United States Department of Housing and Urban Development (HUD), and the Centers for Disease Control and Prevention (CDC) to characterize the existing health and housing portfolio and identify potential areas for additional research and collaboration. We identified NIH, HUD, and CDC research projects that were relevant to both health and housing and characterized them by housing theme, health topic, population, and study design. We organized the assessment of the individual housing themes by four overarching housing-to-health pathways. From FY 2016-2020, NIH, HUD, and CDC funded 565 health and housing projects combined. The Neighborhood pathway was most common, followed by studies of the Safety and Quality pathway. Studies of the Affordability and Stability pathways were least common. Health topics such as substance use, mental health, and cardiovascular disease were most often studied. Most studies were observational (66%); only a little over one fourth (27%) were intervention studies. This review of the research grant portfolios of three major federal funders of health and housing research in the United States describes the diversity and substantial investment in research at the intersection between housing and health. Analysis of the combined portfolio points to gaps in studies on causal pathways linking housing to health outcomes. The findings highlight the need for research to better understand the causal pathways from housing to health and prevention intervention research, including rigorous evaluation of housing interventions and policies to improve health and well-being.

The aim of the study was to examine the relationship between employment status and job activity level with physical activity (PA) and sedentary time, stratified by gender and country. Cross-sectional study design. Data from working age adults (18−65 years) from two cross-sectional studies, the Swedish 2001−2002 and 2007−2008 Attitude Behavior and Change Study (ABC; n=1165) and the 2003−2006 US National Health and Nutrition Examination Survey (NHANES; n=4201), were stratified by employment status (employed and not employed) and job activity level (active, sedentary and mixed). PA in counts×min−1 and time spent in sedentary, low and moderate or higher intensity were measured with accelerometers. Analyses were conducted in 2012–2013. In NHANES, the employed had significantly higher counts×min−1 and spent more time in moderate or higher intensity PA than those not employed. In ABC, no significant differences were observed between employed and unemployed. Adults with active versus sedentary occupations had higher counts×min−1 and less sedentary time in both the USA and Sweden and in both men and women. For example, counts×min−1 were 20−40% greater in active versus sedentary jobs. Employment status is related to PA and sedentary time among men and women in the USA but not in Sweden. Among the employed, occupational PA is associated with total PA and sedentary time for both genders and in both countries. Comparisons of PA levels based on objective measurements can refine understanding of country differences in activity.

Background This study examined whether message reactance mediates the cognitive effects of messages about the causal relationship between alcohol and cancer. Methods We randomly assigned US adults ( N  = 799) recruited from a commercial online research panel to receive one of two types of messages about the alcohol-cancer link: (1) high-certainty (e.g., “Drinking alcohol causes cancer”), and (2) low-certainty, employing the modal verb “may” (e.g., “Drinking alcohol may cause cancer”). We evaluated whether message reactance mediated the effects of message certainty vs. uncertainty on (1) perceived causal certainty about the alcohol-cancer link, and (2) perceived alcohol-related cancer risk. We also explored whether heavy vs. non-heavy alcohol consumption moderated these effects. Results Message reactance mediated the effects of high-certainty (vs. low) messages on perceived causal certainty (direct: b =0.54; indirect: b =-0.11; both p s<0.001) and alcohol-related cancer risk (direct: b =0.37; indirect: b =-0.13; both p s<0.001)—suppressing the positive effects of expressed certainty on both outcomes. The suppressive effects of message reactance on perceived causal certainty were stronger for participants reporting heavy ( b =-0.50) vs. non-heavy alcohol consumption ( b =-0.35; p =.034). Conditional indirect effects for the perceived causal certainty model only remained significant for participants with non-heavy alcohol consumption ( b =-0.10; p =.004). Conclusions Effects of high-certainty (vs. low) messages were negatively mediated by message reactance and moderated by alcohol consumption. Findings suggest that although high-certainty risk messages may increase cancer risk perceptions, their effect is attenuated by inciting negative reactance to the messages, particularly for those who consume a heavy amount of alcohol. These mixed effects should be considered in future messaging initiatives aimed at communicating the alcohol-cancer relationship to the public.