Associations of 24 h time-use compositions of sitting, standing, physical activity and sleeping with optimal cardiometabolic risk and glycaemic control: The Maastricht Study.

AIMS/HYPOTHESIS: The associations of sitting, standing, physical activity and sleep with cardiometabolic health and glycaemic control markers are interrelated. We aimed to identify 24 h time-use compositions associated with optimal metabolic and glycaemic control and determine whether these varied by diabetes status. METHODS: Thigh-worn activPAL data from 2388 participants aged 40-75 years (48.7% female; mean age 60.1 [SD = 8.1] years; n=684 with type 2 diabetes) in The Maastricht Study were examined. Compositional isometric log ratios were generated from mean 24 h time use (sitting, standing, light-intensity physical activity [LPA], moderate-to-vigorous physical activity [MVPA] and sleeping) and regressed with outcomes of waist circumference, fasting plasma glucose (FPG), 2 h plasma glucose, HbA1c, the Matsuda index expressed as z scores, and with a clustered cardiometabolic risk score. Overall analyses were adjusted for demographics, smoking, dietary intake and diabetes status, and interaction by diabetes status was examined separately. The estimated difference when substituting 30 min of one behaviour with another was determined with isotemporal substitution. To identify optimal time use, all combinations of 24 h compositions possible within the study footprint (1st-99th percentile of each behaviour) were investigated to determine those cross-sectionally associated with the most-optimal outcome (top 5%) for each outcome measure. RESULTS: Compositions lower in sitting time and with greater standing time, physical activity and sleeping had the most beneficial associations with outcomes. Associations were stronger in participants with type 2 diabetes (p<0.05 for interactions), with larger estimated benefits for waist circumference, FPG and HbA1c when sitting was replaced by LPA or MVPA in those with type 2 diabetes vs the overall sample. The mean (range) optimal compositions of 24 h time use, considering all outcomes, were 6 h (range 5 h 40 min-7 h 10 min) for sitting, 5 h 10 min (4 h 10 min-6 h 10 min) for standing, 2 h 10 min (2 h-2 h 20 min) for LPA, 2 h 10 min (1 h 40 min-2 h 20 min) for MVPA and 8 h 20 min (7 h 30 min-9 h) for sleeping. CONCLUSIONS/INTERPRETATION: Shorter sitting time and more time spent standing, undergoing physical activity and sleeping are associated with preferable cardiometabolic health. The substitutions of behavioural time use were significantly stronger in their associations with glycaemic control in those with type 2 diabetes compared with those with normoglycaemic metabolism, especially when sitting time was balanced with greater physical activity.

The association between device-measured sitting time and cardiometabolic health risk factors in children.

BACKGROUND: There is limited evidence of the associations between postural-derived sitting time, waist-worn derived sedentary time and children's health and the moderation effect of physical activity (PA). This study examined associations of children's device-measured sitting time with cardiometabolic health risk factors, including moderation by physical activity. METHODS: Cross-sectional baseline data from children (mean-age 8.2 ± 0.5 years) in Melbourne, Australia (2010) participating in the TransformUs program were used. Children simultaneously wore an activPAL to assess sitting time and an ActiGraph GT3X to assess sedentary time and physical activity intensity. Cardiometabolic health risk factors included: adiposity (body mass index [BMI], waist circumference [WC]), systolic and diastolic blood pressure (SBP, DBP), high-density lipoprotein (HDL), low-density lipoprotein (LDL), cholesterol, triglycerides, fasting plasma glucose (FPG), serum insulin, and 25-hydroxyvitaminD (25[OH]D). Linear regression models (n = 71-113) assessed associations between sitting time with each health risk factor, adjusted for different PA intensities (i.e. light [LIPA], moderate-vigorous intensities [MVPA], separately on each model), age, sex, adiposity, and clustering by school. Interaction terms examined moderation. The analyses were repeated using device-measured sedentary time (i.e. ActiGraph GT3X) for comparison. RESULTS: Sitting time was positively associated with SBP (b = 0.015; 95%CI: 0.004, 0.026), DBP (b = 0.012; 95%CI:0.004, 0.020), and FPG (b = 0.001; 95%CI: 0.000, 0.000), after adjusting for higher PA intensities. The association between sitting time and insulin (b = 0.003; 95%CI: 0.000, 0.006) was attenuated after adjusting for higher PA intensities. When the models were adjusted for LIPA and MVPA, there was a negative association with LDL (b=-0.001; 95%CI: -0.002, -0.000 and b=-0.001; 95%CI: -0.003, -0.000, respectively). There was a negative association of sedentary time with WCz (b=-0.003; 95%CI: -0.005, 0.000) and BMIz (b=-0.003; 95%CI: -0.006, -0.000) when the models were adjusted by MVPA. Sedentary time was positively associated with triglycerides (b = 0.001; 95%CI: 0.000, 0.001) but attenuated after adjusting for MVPA. No evidence of moderation effects was found. CONCLUSIONS: Higher volumes of sitting and sedentary time were associated with some adverse associations on some cardiometabolic health risk factors in children. These associations were more evident when sitting time was the predictor. This suggests that reducing time spent sitting may benefit some cardiometabolic health outcomes, but future experimental research is needed to confirm causal relationships and identify the biological mechanisms that might be involved. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12609000715279.

Relative associations of behavioral and physiological risks for cardiometabolic disease with cognition in bipolar disorder during mid and later-life: findings from the UK biobank.

BACKGROUND: Cardiometabolic disease risk factors are disproportionately prevalent in bipolar disorder (BD) and are associated with cognitive impairment. It is, however, unknown which health risk factors for cardiometabolic disease are relevant to cognition in BD. This study aimed to identify the cardiometabolic disease risk factors that are the most important correlates of cognitive impairment in BD; and to examine whether the nature of the relationships vary between mid and later life. METHODS: Data from the UK Biobank were available for 966 participants with BD, aged between 40 and 69 years. Individual cardiometabolic disease risk factors were initially regressed onto a global cognition score in separate models for the following risk factor domains; (1) health risk behaviors (physical activity, sedentary behavior, smoking, and sleep) and (2) physiological risk factors, stratified into (2a) anthropometric and clinical risk (handgrip strength, body composition, and blood pressure), and (2b) cardiometabolic disease risk biomarkers (CRP, lipid profile, and HbA1c). A final combined multivariate regression model for global cognition was then fitted, including only the predictor variables that were significantly associated with cognition in the previous models. RESULTS: In the final combined model, lower mentally active and higher passive sedentary behavior, higher levels of physical activity, inadequate sleep duration, higher systolic and lower diastolic blood pressure, and lower handgrip strength were associated with worse global cognition. CONCLUSIONS: Health risk behaviors, as well as blood pressure and muscular strength, are associated with cognitive function in BD, whereas other traditional physiological cardiometabolic disease risk factors are not.

Associations of device-measured and self-reported physical activity with alcohol consumption: Secondary analyses of a randomized controlled trial (FitForChange).

BACKGROUND: Physical activity (PA) is increasingly used as an adjunct treatment for alcohol use disorder (AUD). Previous studies have relied on self-report measures of PA, which are prone to measurement error. In the context of a randomized controlled trial of PA for AUD, we examined: (1) associations between device-measured and self-reported PA, (2) associations between PA measurements and alcohol use, and (3) the feasibility of obtaining device-measured PA data in this population. METHOD: One-hundred and forty individuals with clinician-diagnosed AUD participated in a 12-week intervention comparing usual care (phone counselling) to yoga-based exercise and aerobic exercise. Device-measured PA (Actigraph GT3x), self-reported PA (International Physical Activity Questionnaire) and alcohol consumption (Timeline Follow Back Method) were assessed before and after the trial. Effects of the interventions on PA levels were assessed using linear mixed models. RESULTS: In total, 42% (n=59) of participants returned usable device-measured PA data (mean age= 56±10 years, 73% male). Device-measured and self-reported vigorous-intensity PA were correlated (ß= -0.02, 95%CI= -0.03, -0.00). No associations were found for moderate-intensity PA. Compared to usual care, time spent in device-measured light-intensity PA increased in the aerobic exercise group (∆= 357, 95%CI= 709, 5.24). Increases in device-measured light-intensity PA were associated with fewer standard drinks (∆= -0.24, 95%CI= -0.03, -0.44), and fewer heavy drinking days (∆= -0.06, 95%CI=-0.01, -0.10). CONCLUSION: Increases in light-intensity/habitual PA were associated with less alcohol consumption in adults with AUD. Self-reported PA data should be interpreted with caution. Incentives are needed to obtain device-measured PA data in AUD populations.

Sedentary behavior and lifespan brain health.

Higher levels of physical activity are known to benefit aspects of brain health across the lifespan. However, the role of sedentary behavior (SB) is less well understood. In this review we summarize and discuss evidence on the role of SB on brain health (including cognitive performance, structural or functional brain measures, and dementia risk) for different age groups, critically compare assessment approaches to capture SB, and offer insights into emerging opportunities to assess SB via digital technologies. Across the lifespan, specific characteristics of SB (particularly whether they are cognitively active or cognitively passive) potentially act as moderators influencing the associations between SB and specific brain health outcomes. We outline challenges and opportunities for future research aiming to provide more robust empirical evidence on these observations.

Sitting Time Reduction and Blood Pressure in Older Adults: A Randomized Clinical Trial.

Importance: Practical health promotion strategies for improving cardiometabolic health in older adults are needed. Objective: To examine the efficacy of a sedentary behavior reduction intervention for reducing sitting time and improving blood pressure in older adults. Design, Setting, and Participants: This parallel-group randomized clinical trial was conducted in adults aged 60 to 89 years with high sitting time and body mass index of 30 to 50 from January 1, 2019, to November 31, 2022, at a health care system in Washington State. Intervention: Participants were randomized 1:1 to the sitting reduction intervention or a healthy living attention control condition for 6 months. Intervention participants received 10 health coaching contacts, sitting reduction goals, and a standing desk and fitness tracker to prompt sitting breaks. The attention control group received 10 health coaching contacts to set general healthy living goals, excluding physical activity or sedentary behavior. Main Outcomes and Measures: The primary outcome, measured at baseline, 3 months, and 6 months, was sitting time assessed using accelerometers worn for 7 days at each time point. Coprimary outcomes were systolic and diastolic blood pressure measured at baseline and 6 months. Results: A total of 283 participants (140 intervention and 143 control) were randomized (baseline mean [SD] age, 68.8 [6.2] years; 186 [65.7%] female; mean [SD] body mass index, 34.9 [4.7]). At baseline, 147 (51.9%) had a hypertension diagnosis and 97 (69.3%) took at least 1 antihypertensive medication. Sitting time was reduced, favoring the intervention arm, with a difference in the mean change of -31.44 min/d at 3 months (95% CI, -48.69 to -14.19 min/d; P < .001) and -31.85 min/d at 6 months (95% CI, -52.91 to -10.79 min/d; P = .003). Systolic blood pressure change was lower by 3.48 mm Hg, favoring the intervention arm at 6 months (95% CI, -6.68 to -0.28 mm Hg; P = .03). There were 6 serious adverse events in each arm and none were study related. Conclusions and Relevance: In this study of a 6-month sitting reduction intervention, older adults in the intervention reduced sedentary time by more than 30 min/d and reduced systolic blood pressure. Sitting reduction could be a promising approach to improve health in older adults. Trial Registration: ClinicalTrials.gov Identifier: NCT03739762.

Quantifying intuition: Bayesian approach to figures of merit in EXAFS analysis of magic size clusters.

Analysis of the extended X-ray absorption fine structure (EXAFS) can yield local structural information in magic size clusters even when other structural methods (such as X-ray diffraction) fail, but typically requires an initial guess - an atomistic model. Model comparison is thus one of the most crucial steps in establishing atomic structure of nanoscale systems and relies critically on the corresponding figures of merit (delivered by the data analysis) to make a decision on the most suitable model of atomic arrangements. However, none of the currently used statistical figures of merit take into account the significant factor of parameter correlations. Here we show that ignoring such correlations may result in a selection of an incorrect structural model. We then report on a new metric based on Bayes theorem that addresses this problem. We show that our new metric is superior to the currently used in EXAFS analysis as it reliably yields correct structural models even in cases when other statistical criteria may fail. We then demonstrate the utility of the new figure of merit in comparison of structural models for CdS magic-size clusters using EXAFS data.

Cost-effectiveness of reducing children's sedentary time and increasing physical activity at school: the Transform-Us! intervention.

BACKGROUND: Improving physical activity and reducing sedentary behavior represent important areas for intervention in childhood in order to reduce the burden of chronic disease related to obesity and physical inactivity in later life. This paper aims to determine the cost-effectiveness of a multi-arm primary school-based intervention to increase physical activity and/or reduce sedentary time in 8-9 year old children (Transform-Us!). METHODS: Modelled cost-utility analysis, using costs and effects from a cluster randomized controlled trial of a 30-month intervention that used pedagogical and environmental strategies to reduce and break up sedentary behaviour (SB-I), promote physical activity (PA-I), or a combined approach (PA + SB-I), compared to current practice. A validated multiple-cohort lifetable model (ACE-Obesity Policy model) estimated the obesity and physical activity-related health outcomes (measured as change in body mass index and change in metabolic equivalent task minutes respectively) and healthcare cost-savings over the cohort's lifetime from the public-payer perspective, assuming the intervention was delivered to all 8-9 year old children attending Australian Government primary schools. Sensitivity analyses tested the impact on cost-effectiveness of varying key input parameters, including maintenance of intervention effect assumptions. RESULTS: Cost-effectiveness results demonstrated that, when compared to control schools, the PA-I and SB-I intervention arms were "dominant", meaning that they resulted in net health benefits and healthcare cost-savings if the intervention effects were maintained. When the costs and effects of these intervention arms were extrapolated to the Australian population, results suggested significant potential as obesity prevention measures (PA-I: 60,780 HALYs saved (95% UI 15,007-109,413), healthcare cost-savings AUD641M (95% UI AUD165M-$1.1B); SB-I: 61,126 HALYs saved (95% UI 11,770 - 111,249), healthcare cost-savings AUD654M (95% UI AUD126M-1.2B)). The PA-I and SB-I interventions remained cost-effective in sensitivity analysis, assuming the full decay of intervention effect after 10 years. CONCLUSIONS: The PA-I and SB-I Transform-Us! intervention arms represent good value for money and could lead to health benefits and healthcare cost-savings arising from the prevention of chronic disease in later life if intervention effects are sustained. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN83725066). Australia and New Zealand Clinical Trials Registry Number (ACTRN12609000715279).

MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery.

We herein describe the development and application of a modular technology platform which incorporates recent advances in plate-based microscale chemistry, automated purification, in situ quantification, and robotic liquid handling to enable rapid access to high-quality chemical matter already formatted for assays. In using microscale chemistry and thus consuming minimal chemical matter, the platform is not only efficient but also follows green chemistry principles. By reorienting existing high-throughput assay technology, the platform can generate a full package of relevant data on each set of compounds in every learning cycle. The multiparameter exploration of chemical and property space is hereby driven by active learning models. The enhanced compound optimization process is generating knowledge for drug discovery projects in a time frame never before possible.

A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study.

BACKGROUND: This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants. OBJECTIVE: The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL. METHODS: A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement. RESULTS: Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=-0.3% to 0.9%), 1.19% for standing (LoA=-1.5% to 3.42%), and -4.71% for walking (LoA=-9.26% to -0.16%). The mean biases between SORD and activPAL were -3.45% for sitting and reclining (LoA=-11.59% to 4.68%), 7.45% for standing (LoA=-5.04% to 19.95%), and -5.40% for walking (LoA=-11.44% to 0.64%). CONCLUSIONS: Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior.

Participant and workplace champion experiences of an intervention designed to reduce sitting time in desk-based workers: SMART work & life.

BACKGROUND: A cluster randomised controlled trial demonstrated the effectiveness of the SMART Work & Life (SWAL) behaviour change intervention, with and without a height-adjustable desk, for reducing sitting time in desk-based workers. Staff within organisations volunteered to be trained to facilitate delivery of the SWAL intervention and act as workplace champions. This paper presents the experiences of these champions on the training and intervention delivery, and from participants on their intervention participation. METHODS: Quantitative and qualitative feedback from workplace champions on their training session was collected. Participants provided quantitative feedback via questionnaires at 3 and 12 month follow-up on the intervention strategies (education, group catch ups, sitting less challenges, self-monitoring and prompts, and the height-adjustable desk [SWAL plus desk group only]). Interviews and focus groups were also conducted at 12 month follow-up with workplace champions and participants respectively to gather more detailed feedback. Transcripts were uploaded to NVivo and the constant comparative approach informed the analysis of the interviews and focus groups. RESULTS: Workplace champions rated the training highly with mean scores ranging from 5.3/6 to 5.7/6 for the eight parts. Most participants felt the education increased their awareness of the health consequences of high levels of sitting (SWAL: 90.7%; SWAL plus desk: 88.2%) and motivated them to change their sitting time (SWAL: 77.5%; SWAL plus desk: 85.77%). A high percentage of participants (70%) reported finding the group catch up session helpful and worthwhile. However, focus groups highlighted mixed responses to the group catch-up sessions, sitting less challenges and self-monitoring intervention components. Participants in the SWAL plus desk group felt that having a height-adjustable desk was key in changing their behaviour, with intrinsic as well as time based factors reported as key influences on the height-adjustable desk usage. In both intervention groups, participants reported a range of benefits from the intervention including more energy, less fatigue, an increase in focus, alertness, productivity and concentration as well as less musculoskeletal problems (SWAL plus desk group only). Work-related, interpersonal, personal attributes, physical office environment and physical barriers were identified as barriers when trying to sit less and move more. CONCLUSIONS: Workplace champion and participant feedback on the intervention was largely positive but it is clear that different behaviour change strategies worked for different people indicating that a 'one size fits all' approach may not be appropriate for this type of intervention. The SWAL intervention could be tested in a broader range of organisations following a few minor adaptations based on the champion and participant feedback. TRIAL REGISTRATION: ISCRCTN registry (ISRCTN11618007).

Implementation and engagement of the SMART Work & Life sitting reduction intervention: an exploratory analysis on intervention effectiveness.

BACKGROUND: To enhance the impact of interventions, it is important to understand how intervention engagement relates to study outcomes. We report on the level of implementation and engagement with the SMART Work & Life (SWAL) programme (delivered with (SWAL plus desk) and without a height-adjustable desk (SWAL)) and explore the effects of different levels of this on change in daily sitting time in comparison to the control group. METHODS: The extent of intervention delivery by workplace champions and the extent of engagement by champions and participants (staff) with each intervention activity was assessed by training attendance logs, workplace champion withdrawal dates, intervention activities logs and questionnaires. These data were used to assess whether a cluster met defined criteria for low, medium, or high implementation and engagement or none of these. Mixed effects linear regression analyses tested whether change in sitting time varied by: (i) the number of intervention activities implemented and engaged with, and (ii) the percentage of implementation and engagement with all intervention strategies. RESULTS: Workplace champions were recruited for all clusters, with 51/52 (98%) attending training. Overall, 12/27 (44.4%) SWAL and 9/25 (36.0%) SWAL plus desk clusters implemented all main intervention strategies. Across remaining clusters, the level of intervention implementation varied. Those in the SWAL (n = 8 (29.6%) clusters, 80 (32.1%) participants) and SWAL plus desk (n = 5 (20.0%) clusters, 41 (17.1%) participants) intervention groups who implemented and engaged with the most intervention strategies and had the highest percentage of cluster implementation and engagement with all intervention strategies sat for 30.9 (95% CI -53.9 to -7.9, p = 0.01) and 75.6 (95% CI -103.6 to -47.7, p < 0.001) fewer minutes/day respectively compared to the control group at 12 month follow up. These differences were larger than the complete case analysis. The differences in sitting time observed for the medium and low levels were similar to the complete case analysis. CONCLUSIONS: Most intervention strategies were delivered to some extent across the clusters although there was large variation. Superior effects for sitting reduction were seen for those intervention groups who implemented and engaged with the most intervention components and had the highest level of cluster implementation and engagement. TRIAL REGISTRATION: ISRCTN11618007. Registered on 24 January 2018. https://www.isrctn.com/ISRCTNISRCTN11618007 .

A multicomponent intervention to reduce daily sitting time in office workers: the SMART Work & Life three-arm cluster RCT.

Background: Office workers spend 70-85% of their time at work sitting. High levels of sitting have been linked to poor physiological and psychological health. Evidence shows the need for fully powered randomised controlled trials, with long-term follow-up, to test the effectiveness of interventions to reduce sitting time. Objective: Our objective was to test the clinical effectiveness and cost-effectiveness of the SMART Work & Life intervention, delivered with and without a height-adjustable workstation, compared with usual practice at 12-month follow-up. Design: A three-arm cluster randomised controlled trial. Setting: Councils in England. Participants: Office workers. Intervention: SMART Work & Life is a multicomponent intervention that includes behaviour change strategies, delivered by workplace champions. Clusters were randomised to (1) the SMART Work & Life intervention, (2) the SMART Work & Life intervention with a height-adjustable workstation (i.e. SMART Work & Life plus desk) or (3) a control group (i.e. usual practice). Outcome measures were assessed at baseline and at 3 and 12 months. Main outcome measures: The primary outcome was device-assessed daily sitting time compared with usual practice at 12 months. Secondary outcomes included sitting, standing, stepping time, physical activity, adiposity, blood pressure, biochemical measures, musculoskeletal issues, psychosocial variables, work-related health, diet and sleep. Cost-effectiveness and process evaluation data were collected. Results: A total of 78 clusters (756 participants) were randomised [control, 26 clusters (n = 267); SMART Work & Life only, 27 clusters (n = 249); SMART Work & Life plus desk, 25 clusters (n = 240)]. At 12 months, significant differences between groups were found in daily sitting time, with participants in the SMART Work & Life-only and SMART Work & Life plus desk arms sitting 22.2 minutes per day (97.5% confidence interval -38.8 to -5.7 minutes/day; p = 0.003) and 63.7 minutes per day (97.5% confidence interval -80.0 to -47.4 minutes/day; p < 0.001), respectively, less than the control group. Participants in the SMART Work & Life plus desk arm sat 41.7 minutes per day (95% confidence interval -56.3 to -27.0 minutes/day; p < 0.001) less than participants in the SMART Work & Life-only arm. Sitting time was largely replaced by standing time, and changes in daily behaviour were driven by changes during work hours on workdays. Behaviour changes observed at 12 months were similar to 3 months. At 12 months, small improvements were seen for stress, well-being and vigour in both intervention groups, and for pain in the lower extremity and social norms in the SMART Work & Life plus desk group. Results from the process evaluation supported these findings, with participants reporting feeling more energised, alert, focused and productive. The process evaluation also showed that participants viewed the intervention positively; however, the extent of engagement varied across clusters. The average cost of SMART Work & Life only and SMART Work & Life plus desk was £80.59 and £228.31 per participant, respectively. Within trial, SMART Work & Life only had an incremental cost-effectiveness ratio of £12,091 per quality-adjusted life-year, with SMART Work & Life plus desk being dominated. Over a lifetime, SMART Work & Life only and SMART Work & Life plus desk had incremental cost-effectiveness ratios of £4985 and £13,378 per quality-adjusted life-year, respectively. Limitations: The study was carried out in one sector, limiting generalisability. Conclusions: The SMART Work & Life intervention, provided with and without a height-adjustable workstation, was successful in changing sitting time. Future work: There is a need for longer-term follow-up, as well as follow-up within different organisations. Trial registration: Current Controlled Trials ISRCTN11618007.

Office workers spend a large proportion of their day sitting. High levels of sitting have been linked to diseases, such as type 2 diabetes, heart disease and some cancers. The SMART Work & Life intervention is designed to reduce office workers' sitting time inside and outside work. The SMART Work & Life intervention involves organisational, environmental, group and individual strategies to encourage a reduction in sitting time and was designed to be delivered with and without a height-adjustable workstation (which allows the user to switch between sitting and standing while working). To test whether or not the SMART Work & Life intervention worked, we recruited 756 office workers from councils in Leicester/Leicestershire, Greater Manchester and Liverpool, UK. Participants were from 78 office groups. One-third of the participants received the intervention, one-third received the intervention with a height-adjustable workstation and one-third were a control group (and carried on as usual). Workplace champions in each office group were given training and resources to deliver the intervention. Data were collected at the start of the study, with follow-up measurements at 3 and 12 months. We measured sitting time using a small device worn on the thigh and collected data on weight, body fat, blood pressure, blood sugar and cholesterol levels. We asked participants about their health and work and spoke to participants to find out what they thought of the intervention. Our results showed that participants who received the intervention without workstation sat for 22 minutes less per day, and participants who received the intervention with workstation sat for 64 minutes less per day, than participants in the control group. Levels of stress, well-being, vigour (i.e. personal and emotional energy and cognitive liveliness) and pain in the lower extremity appeared to improve in the intervention groups. Participants viewed the intervention positively and reported several benefits, such as feeling more energised, alert, focused and productive; however, the extent to which participants engaged with the intervention varied across groups.

Diverse strategies are needed to support physical activity engagement in women who have had breast cancer.

PURPOSE: Physical activity can improve health in people living with and beyond breast cancer; however, how to best support physical activity participation in this population is unclear. This qualitative study sought to identify important physical activity program components for breast cancer. METHODS: Women with previous breast cancer (n = 11) and allied health professionals (n = 7) participated in one-on-one semi-structured interviews (n = 15) or focus groups (n = 1). Qualitative data were analyzed using reflexive thematic analysis methods. RESULTS: Four main themes were generated including (1) the need for physical activity programs; (2) person-centered programs; (3) flexible physical activity programs; and (4) systems factors. These reflected the health and non-health benefits of physical activity, the need to facilitate agency, the diversity in individual characteristics, preferences, abilities, and commitments of people with lived experience of cancer, as well as the need for physical activity programs to be integrated within the broader health system. CONCLUSION: Strategies to support physical activity engagement for breast cancer should embrace the diversity of those who are diagnosed with cancer as well as the diversity in which physical activity can be achieved.

Physical Activity Assessed by Wrist and Thigh Worn Accelerometry and Associations with Cardiometabolic Health.

Physical activity is increasingly being captured by accelerometers worn on different body locations. The aim of this study was to examine the associations between physical activity volume (average acceleration), intensity (intensity gradient) and cardiometabolic health when assessed by a thigh-worn and wrist-worn accelerometer. A sample of 659 office workers wore an Axivity AX3 on the non-dominant wrist and an activPAL3 micro on the right thigh concurrently for 24 h a day for 8 days. An average acceleration (proxy for physical activity volume) and intensity gradient (intensity distribution) were calculated from both devices using the open-source raw accelerometer processing software GGIR. Clustered cardiometabolic risk (CMR) was calculated using markers of cardiometabolic health, including waist circumference, triglycerides, HDL-cholesterol, mean arterial pressure and fasting glucose. Linear regression analysis assessed the associations between physical activity volume and intensity gradient with cardiometabolic health. Physical activity volume derived from the thigh-worn activPAL and the wrist-worn Axivity were beneficially associated with CMR and the majority of individual health markers, but associations only remained significant after adjusting for physical activity intensity in the thigh-worn activPAL. Physical activity intensity was associated with CMR score and individual health markers when derived from the wrist-worn Axivity, and these associations were independent of volume. Associations between cardiometabolic health and physical activity volume were similarly captured by the thigh-worn activPAL and the wrist-worn Axivity. However, only the wrist-worn Axivity captured aspects of the intensity distribution associated with cardiometabolic health. This may relate to the reduced range of accelerations detected by the thigh-worn activPAL.

Changes in Desk-Based Workers' Sitting, Standing, and Stepping Time: Short- and Longer-Term Effects on Musculoskeletal Pain.

PURPOSE: Sitting at work can be associated with musculoskeletal pain, but the effect of reductions in sitting is not well understood. We examined relationships with musculoskeletal pain of changes in sitting, standing, stepping, and short and long bouts of these behaviors. METHODS: Analyses pooled data from 224 desk workers (68.4% women; mean ± SD age = 45.5 ± 9.4 yr; body mass index = 28.1 ± 6.1 kg⋅m -2 ) in intervention trial. Device-assessed (activPAL3) sitting, standing, and stepping time and multisite musculoskeletal pain (Nordic Questionnaire; 12 body areas) were assessed at baseline, 3 months, and 12 months. Compositional data analyses in linear mixed-effects regressions examined relationships within 16 waking hours of the behaviors and their short and long bouts, with changes from baseline in acute and chronic multisite musculoskeletal pain at 3 and 12 months. Analyses were adjusted for initial group randomization and relevant covariates. RESULTS: At 3 months, increased standing relative to changes in other compositions was significantly associated with increased multisite musculoskeletal pain (acute: ß = 1.54, 95% confidence interval [CI] = 0.10 to 2.98; chronic: ß = 1.49, 95% CI = 0.12 to 2.83). By contrast, increased stepping relative to changes in other compositions was significantly associated with reduced multisite musculoskeletal pain (acute: ß = -1.49, 95% CI = -2.97 to -0.02; chronic: ß = -1.87, 95% CI = -3.75 to -0.01). Neither sitting reduction relative to changes in other compositions nor changes in short bouts relative to long bouts of the behaviors were significantly associated with multisite musculoskeletal pain changes. At 12 months, there were no significant associations for any of the compositional changes. CONCLUSIONS: In the short term, while increasing standing with reduced sitting time can be unfavorable, concurrently increasing stepping could potentially reduce musculoskeletal pain. In the longer term, musculoskeletal pain may not be increased by moderate reductions in sitting time through spending more time standing or stepping.

Just-In-Time Adaptive Intervention to Sit Less and Move More in People With Type 2 Diabetes: Protocol for a Microrandomized Trial.

BACKGROUND: Reducing sedentary behavior and increasing physical activity in people with type 2 diabetes (T2D) are associated with various positive health benefits. Just-in-time adaptive interventions offer the potential to target both of these behaviors through more contextually aware, tailored, and personalized support. We have developed a just-in-time adaptive intervention to promote sitting less and moving more in people with T2D. OBJECTIVE: This paper presents the study protocol for a microrandomized trial to investigate whether motivational messages are effective in reducing time spent sitting in people with T2D and to determine what behavior change techniques are effective and in which context (eg, location, etc). METHODS: We will use a 6-week microrandomized trial design. A total of 22 adults with T2D will be recruited. The intervention aims to reduce sitting time and increase time spent standing and walking and comprises a mobile app (iMove), a bespoke activity sensor called Sedentary Behavior Detector (SORD), a messaging system, and a secured database. Depending on the randomization sequence, participants will potentially receive motivational messages 5 times a day. RESULTS: Recruitment was initiated in October 2022. As of now, 6 participants (2 female and 4 male) have consented and enrolled in the study. Their baseline measurements have been completed, and they have started using iMove. The mean age of 6 participants is 56.8 years, and they were diagnosed with T2D for 9.4 years on average. CONCLUSIONS: This study will inform the optimization of digital behavior change interventions to support people with T2D Sit Less and Move More to increase daily physical activity. This study will generate new evidence about the immediate effectiveness of sedentary behavior interventions, their active ingredients, and associated factors. TRIAL REGISTRATION: Australian New Zealand Clinical Trial Registry ACTRN12622000426785; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=383664. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41502.

Aspects of Program Engagement in an Online Physical Activity Intervention and Baseline Predictors of Engagement.

PURPOSE: Participant engagement in an online physical activity (PA) intervention is described and baseline factors related to engagement are identified. DESIGN: Longitudinal Study Within Randomized Controlled Trial. SETTING: Online/Internet. SAMPLE: Primary care patients (21-70 years). INTERVENTION: ActiveGOALS was a 3-month, self-directed online PA intervention (15 total lessons, remote coaching support, and a body-worn step-counter). MEASURES: Engagement was measured across six outcomes related to lesson completion (total number and time to complete), coach contact, and behavior tracking (PA, sedentary). Self-reported baseline factors were examined from seven domains (confidence, environment, health, health care, demographic, lifestyle, and quality of life). ANALYSIS: General linear and nonlinear mixed models were used to examine relationships between baseline factors and engagement outcomes within and across all domains. RESULTS: Seventy-nine participants were included in the sample (77.2% female; 74.7% white non-Hispanic). Program engagement was high (58.2% completed all lessons; PA was tracked ≥3 times/week for 11.3 ± 4.0 weeks on average). Average time between completed lessons (days) was longer than expected and participants only contacted their coach about 1 of every 3 weeks. Individual predictors related to health, health care, demographics, lifestyle, and quality of life were significantly related to engagement. CONCLUSION: Examining multiple aspects of engagement and a large number of potential predictors of engagement is likely needed to determine facilitators and barriers for high engagement in multi-faceted online intervention programs.

Physiology of sedentary behavior.

Sedentary behaviors (SB) are characterized by low energy expenditure while in a sitting or reclining posture. Evidence relevant to understanding the physiology of SB can be derived from studies employing several experimental models: bed rest, immobilization, reduced step count, and reducing/interrupting prolonged SB. We examine the relevant physiological evidence relating to body weight and energy balance, intermediary metabolism, cardiovascular and respiratory systems, the musculoskeletal system, the central nervous system, and immunity and inflammatory responses. Excessive and prolonged SB can lead to insulin resistance, vascular dysfunction, shift in substrate use toward carbohydrate oxidation, shift in muscle fiber from oxidative to glycolytic type, reduced cardiorespiratory fitness, loss of muscle mass and strength and bone mass, and increased total body fat mass and visceral fat depot, blood lipid concentrations, and inflammation. Despite marked differences across individual studies, longer term interventions aimed at reducing/interrupting SB have resulted in small, albeit marginally clinically meaningful, benefits on body weight, waist circumference, percent body fat, fasting glucose, insulin, HbA1c and HDL concentrations, systolic blood pressure, and vascular function in adults and older adults. There is more limited evidence for other health-related outcomes and physiological systems and for children and adolescents. Future research should focus on the investigation of molecular and cellular mechanisms underpinning adaptations to increasing and reducing/interrupting SB and the necessary changes in SB and physical activity to impact physiological systems and overall health in diverse population groups.