Cross-sectional and longitudinal associations between the 24-hour movement behaviours, including muscle and bone strengthening activity, with bone and lean mass from childhood to adolescence.

BACKGROUND: This study aimed to assess whether moderate-to-vigorous physical activity (MVPA), sport and exercise as a proxy measure of muscle and bone strengthening activity, sedentary behaviour, and sleep were associated with total-body-less-head (TBLH) bone mineral content (BMC) and TBLH lean mass cross-sectionally and longitudinally from age 6 to 9 years and age 9 to 11 years to age 15 to 17 years. METHODS: We used longitudinal data from a population sample of Finnish children from the Physical Activity and Nutrition in Children study (age 6 to 9 years: n = 478, 229 females; age 9 to 11 years: n = 384, 197 females; age 15 to 17 years: n = 222, 103 females). Linear regression analysed the cross-sectional and longitudinal associations between accelerometer-assessed MVPA, sedentary time and sleep, and questionnaire-assessed sport and exercise participation and screen time with dual-energy X-ray absorptiometry-assessed TBLH BMC and lean mass. RESULTS: In females, MVPA at age 6 to 9 years was positively associated with TBLH BMC at age 15 to 17 years (ß = 0.008, p = 0.010). Sport and exercise at age 9 to 11 years was positively associated with TBLH BMC (ß = 0.020, p = 0.002) and lean mass (ß = 0.343, p = 0.040) at age 15 to 17 years. MVPA at age 9 to 11 years was positively associated with TBLH lean mass (ß = 0.272, p = 0.004) at age 15 to 17 years. In males, sleep at age 6 to 9 years was positively associated with TBLH lean mass (ß = 0.382, p = 0.003) at age 15 to 17 years. Sport and exercise at age 9 to 11 years was positively associated with TBLH BMC (ß = 0.027, p = 0.012) and lean mass (ß = 0.721, p < 0.001) at age 15 to 17 years. CONCLUSIONS: Promoting engagement in the 24-hour movement behaviours in childhood, particularly sport and exercise to strengthen muscle and bone, is important in supporting bone and lean mass development in adolescence. TRIAL REGISTRATION: NCT01803776; first trial registration date: 04/03/2013.

Shorter sleep among adolescents is associated with lower fruit and vegetable consumption the following day.

BACKGROUND: Insufficient sleep has been associated with weight gain and metabolic dysregulation, with one suggested mechanism being through reduction in diet quality. Experimental evidence supports a causal effect of sleep timings on diet but this may not be applicable to a free-living adolescent population. In this analysis we use daily measures of sleep timings and diet quality, to examine the effect of sleep duration and timing on diet quality the following day among free-living adolescents. METHODS: The ROOTS study is a prospective cohort recruited from secondary schools in Cambridgeshire and Suffolk (UK). Participants (n = 815) at mean age 15.0y (SD 0.3y) completed a diet diary and wore a combined heart rate and accelerometer device over 4 consecutive days. Sleep duration and timing (midpoint) were derived from acceleration and heart rate traces, while daily energy density and fruit and vegetable intake were calculated from dietary data. Analyses were performed at day-level (1815 person-days). Multilevel random effects models were used to test associations between sleep each night and subsequent day diet, with daily sleep and diet measures nested within individuals and schools, and adjusted for day-level and individual-level confounding variables. RESULTS: Adolescents slept a mean of 7.88 hrs (SD 1.10) per night, reporting a mean energy density of 2.12 kcal/g (SD 0.48) and median energy-adjusted daily fruit and vegetable intake of 137.3 g (IQR 130.4). One hour shorter sleep duration was associated with lower intake of fruit and vegetables (-6.42 g, 95%CI -1.84, -10.99) the following day. An association with higher dietary energy density (0.016 kcal/g, 95%CI 0.034, -0.002) the following day was observed but did not reach statistical significance. Sleep timing was not associated with either fruit and vegetable intake (-2.52 g/d, 95%CI -7.66, 2.62) or dietary energy density (-0.001 kcal/g, 95%CI -0.022, 0.020). CONCLUSIONS: Our observational findings from a free-living adolescent population support the experimental evidence for a causal role of sleep on diet, with shorter sleep duration at night leading to a small decrease in diet quality the following day. These findings support experimental evidence to suggest inclusion of sleep duration as one component of interventions designed to improve diet quality and weight status in adolescents.

Associations between change in physical activity and sedentary time and health-related quality of life in older english adults: the EPIC-Norfolk cohort study.

BACKGROUND: No previous studies have examined the associations between changes in objectively-measured physical behaviours with follow-up QoL in older adults. Based on cross-sectional evidence, it is biologically plausible that such associations exist. If so, this bolsters the case for the commissioning of activity interventions and for including QoL as an outcome in trials of such interventions. METHODS: We assessed physical behaviours (total physical activity, moderate-to-vigorous physical activity (MVPA), light physical activity, total sedentary time and prolonged sedentary bout time) for 7 days using hip-worn accelerometers at baseline (2006-2011) and follow-up (2012-2016) and health-related quality-of-life (QoL) using EQ-5D questionnaires at follow-up in 1433 participants (≥ 60 years) of the EPIC (European Prospective Investigation into Cancer)-Norfolk study. The EQ-5D summary score was used, with 0 as the worst to 1 as best perceived quality-of-life. We evaluated the prospective associations of baseline physical behaviours with follow-up QoL, and of changes in behaviours with follow-up QoL using multi-level regression. RESULTS: On average, MVPA decreased by 4.0 min/day/year (SD 8.3) for men and 4.0 min/day/year for women (SD 12.0) between baseline and follow-up. Total sedentary time increased by an average 5.5 min/day/yr (SD 16.0) for men and 6.4 min/day/yr (SD 15.0) for women between baseline and follow-up. Mean (SD) follow-up time was 5.8 (1.8) years. We found that higher baseline MVPA and lower sedentary time was associated with higher subsequent QoL (e.g. 1 h/day greater baseline MVPA was associated with 0.02 higher EQ-5D score, 95% CI 0.06, 0.36). More pronounced declines in activity were associated with worse Hr-QoL (0.005 (95% CI 0.003, 0.008) lower EQ-5D per min/day/yr decrease in MVPA). Increases in sedentary behaviours were also associated with poorer QoL (0.002 lower EQ-5D, 95% CI -0.003, -0.0007 per hour/day/yr increase in total sedentary time). CONCLUSIONS: Promotion of physical activity and limiting sedentary time among older adults may improve quality-of-life, and therefore this relationship ought to be included in future cost effectiveness analyses so that greater commissioning of activity interventions can be considered.

Energy expenditure and intensity of ritual jumping-dancing in male Maasai.

OBJECTIVES: Traditional jumping-dance rituals performed by Maasai men involve prolonged physical exertion that may contribute significantly to overall physical activity level. We aimed to objectively quantify the metabolic intensity of jumping-dance activity and assess associations with habitual physical activity and cardiorespiratory fitness (CRF). METHODS: Twenty Maasai men (18-37 years) from rural Tanzania volunteered to participate in the study. Habitual physical activity was monitored using combined heart rate (HR) and movement sensing over 3 days, and jumping-dance engagement was self-reported. A 1-h jumping-dance session resembling a traditional ritual was organized, during which participants' vertical acceleration and HR were monitored. An incremental, submaximal 8-min step test was performed to calibrate HR to physical activity energy expenditure (PAEE) and assess CRF. RESULTS: Mean (range) habitual PAEE was 60 (37-116) kJ day-1 kg-1 , and CRF was 43 (32-54) mL O2 min-1 kg-1 . The jumping-dance activity was performed at an absolute HR of 122 (83-169) beats·min-1 , and PAEE of 283 (84-484) J min-1 kg-1 or 42 (18-75)% when expressed relative to CRF. The total PAEE for the session was 17 (range 5-29) kJ kg-1 , ~28% of the daily total. Self-reported engagement in habitual jumping-dance frequency was 3.8 (1-7) sessions/week, with a total duration of 2.1 (0.5-6.0) h/session. CONCLUSIONS: Intensity during traditional jumping-dance activity was moderate, but on average sevenfold higher than habitual physical activity. These rituals are common, and can make a substantial contribution to overall physical activity in Maasai men, and thus be promoted as a culture-specific activity to increase energy expenditure and maintain good health in this population.

Physical activity volume and intensity distribution in relation to bone, lean and fat mass in children.

Considering physical activity (PA) volume and intensity may provide novel insights into the relationships of PA with bone, lean, and fat mass. This study aimed to assess the associations of PA volume, PA intensity distribution, including moderate-to-vigorous PA (MVPA) with total-body-less-head bone mineral content (BMC), lean, and fat mass in children. A population sample of 290 Finnish children (158 females) aged 9-11 years from the Physical Activity and Nutrition in Children (PANIC) Study was studied. PA, including MVPA, was assessed with a combined heart rate and movement sensor, and the uniaxial acceleration was used to calculate average-acceleration (a proxy metric for PA volume) and intensity-gradient (reflective of PA intensity distribution). Linear regression analyzed the associations of PA volume, PA intensity and MVPA with BMC, lean mass, and fat mass assessed by dual-energy X-ray absorptiometry. PA volume was positively associated with BMC in females (unstandardised regression coefficient [ß] = 0.26) and males (ß = 0.47), and positively associated with lean (ß = 7.33) and negatively associated with fat mass in males (ß = -20.62). PA intensity was negatively associated with BMC in males (ß = -0.13). MVPA was positively associated with lean mass in females and males (ß = 0.007 to 0.012), and negatively associated with fat mass in females and males (ß = -0.030 to -0.029). PA volume may be important for improving BMC in females and males, and increasing lean and reducing fat mass in males, whereas MVPA may be important for favorable lean and fat outcomes in both sexes.

Engagement With mHealth COVID-19 Digital Biomarker Measurements in a Longitudinal Cohort Study: Mixed Methods Evaluation.

BACKGROUND: The COVID-19 pandemic accelerated the interest in implementing mobile health (mHealth) in population-based health studies, but evidence is lacking on engagement and adherence in studies. We conducted a fully remote study for ≥6 months tracking COVID-19 digital biomarkers and symptoms using a smartphone app nested within an existing cohort of adults. OBJECTIVE: We aimed to investigate participant characteristics associated with initial and sustained engagement in digital biomarker collection from a bespoke smartphone app and if engagement changed over time or because of COVID-19 factors and explore participants' reasons for consenting to the smartphone substudy and experiences related to initial and continued engagement. METHODS: Participants in the Fenland COVID-19 study were invited to the app substudy from August 2020 to October 2020 until study closure (April 30, 2021). Participants were asked to complete digital biomarker modules (oxygen saturation, body temperature, and resting heart rate [RHR]) and possible COVID-19 symptoms in the app 3 times per week. Participants manually entered the measurements, except RHR that was measured using the smartphone camera. Engagement was categorized by median weekly frequency of completing the 3 digital biomarker modules (categories: 0, 1-2, and ≥3 times per week). Sociodemographic and health characteristics of those who did or did not consent to the substudy and by engagement category were explored. Semistructured interviews were conducted with 35 participants who were purposively sampled by sex, age, educational attainment, and engagement category, and data were analyzed thematically; 63% (22/35) of the participants consented to the app substudy, and 37% (13/35) of the participants did not consent. RESULTS: A total of 62.61% (2524/4031) of Fenland COVID-19 study participants consented to the app substudy. Of those, 90.21% (2277/2524) completed the app onboarding process. Median time in the app substudy was 34.5 weeks (IQR 34-37) with no change in engagement from 0 to 3 months or 3 to 6 months. Completion rates (≥1 per week) across the study between digital biomarkers were similar (RHR: 56,517/77,664, 72.77%; temperature: 56,742/77,664, 73.06%; oxygen saturation: 57,088/77,664, 73.51%). Older age groups and lower managerial and intermediate occupations were associated with higher engagement, whereas working, being a current smoker, being overweight or obese, and high perceived stress were associated with lower engagement. Continued engagement was facilitated through routine and personal motivation, and poor engagement was caused by user error and app or equipment malfunctions preventing data input. From these results, we developed key recommendations to improve engagement in population-based mHealth studies. CONCLUSIONS: This mixed methods study demonstrated both high initial and sustained engagement in a large mHealth COVID-19 study over a ≥6-month period. Being nested in a known cohort study enabled the identification of participant characteristics and factors associated with engagement to inform future applications in population-based health research.

Joint associations between objectively measured physical activity volume and intensity with body fatness: the Fenland study.

BACKGROUND/OBJECTIVES: Physical activity energy expenditure (PAEE) represents the total volume of all physical activity. This can be accumulated as different underlying intensity profiles. Although volume and intensity have been studied in isolation, less is known about their joint association with health. We examined this association with body fatness in a population-based sample of middle-aged British adults. METHODS: In total, 6148 women and 5320 men from the Fenland study with objectively measured physical activity from individually calibrated combined heart rate and movement sensing and DXA-derived body fat percentage (BF%) were included in the analyses. We used linear and compositional isocaloric substitution analysis to examine associations of PAEE and its intensity composition with body fatness. Sex-stratified models were adjusted for socio-economic and dietary covariates. RESULTS: PAEE was inversely associated with body fatness in women (beta = -0.16 (95% CI: -0.17; -0.15) BF% per kJ day-1 kg-1) and men (beta = -0.09 (95% CI: -0.10; -0.08) BF% per kJ day-1 kg-1). Intensity composition was significantly associated with body fatness, beyond that of PAEE; the reallocation of energy to vigorous physical activity (>6 METs) from other intensities was associated with less body fatness, whereas light activity (1.5-3 METs) was positively associated. However, light activity was the main driver of overall PAEE volume, and the relative importance of intensity was marginal compared to that of volume; the difference between PAEE in tertile 1 and 2 in women was associated with 3 percentage-point lower BF%. Higher vigorous physical activity in the same group to the maximum observed value was associated with 1 percentage-point lower BF%. CONCLUSIONS: In this large, population-based cohort study with objective measures, PAEE was inversely associated with body fatness. Beyond the PAEE association, greater levels of intense activity were also associated with lower body fatness. This contribution was marginal relative to PAEE. These findings support current guidelines for physical activity which emphasise that any movement is beneficial, rather than specific activity intensity or duration targets.

Physical activity intensity profiles associated with cardiometabolic risk in middle-aged to older men and women.

Accelerometers provide detailed data about physical activity (PA) across the full intensity spectrum. However, when examining associations with health, results are often aggregated to only a few summary measures [e.g. time spent "sedentary" or "moderate-to-vigorous" intensity PA]. Using multivariate pattern analysis, which can handle collinear exposure variables, we examined associations between the full PA intensity spectrum and cardiometabolic risk (CMR) in a population-based sample of middle-aged to older adults. Participants (n = 3660; mean ± SD age = 69 ± 8y and BMI = 26.7 ± 4.2 kg/m2; 55% female) from the EPIC-Norfolk study (UK) with valid accelerometry (ActiGraph-GT1M) data were included. We used multivariate pattern analysis with partial least squares regression to examine cross-sectional multivariate associations (r) across the full PA intensity spectrum [minutes/day at 0-5000 counts-per-minute (cpm); 5 s epoch] with a continuous CMR score (reflecting waist, blood pressure, lipid, and glucose metabolism). Models were sex-stratified and adjusted for potential confounders. There was a positive (detrimental) association between PA and CMR at 0-12 cpm (maximally-adjusted r = 0.08 (95%CI 0.06-0.10). PA was negatively (favourably) associated with CMR at all intensities above 13 cpm ranging between r = -0.09 (0.07-0.12) at 800-999 cpm and r = -0.14 (0.11-0.16) at 75-99 and 4000-4999 cpm. The strongest favourable associations were from 50 to 800 cpm (r = 0.10-0.12) in men, but from ≥2500 cpm (r = 0.18-0.20) in women; with higher proportions of model explained variance for women (R2 = 7.4% vs. 2.3%). Most of the PA intensity spectrum was beneficially associated with CMR in middle-aged to older adults, even at intensities lower than what has traditionally been considered "sedentary" or "light-intensity" activity. This supports encouragement of PA at almost any intensity in this age-group.

Associations between abdominal adiposity, body size and objectively measured physical activity in infants from Soweto, South Africa.

OBJECTIVES: Considering the importance of the early life period, in conjunction with the increasing prevalence of adiposity and insufficient physical activity already evident in early childhood, this study aimed to determine associations between abdominal adiposity, body size, and objectively measured physical activity in infancy. METHODS: Infants (n = 138, aged 3-24 months) from Soweto, South Africa were recruited to this cross-sectional study. Visceral (VAT) and subcutaneous abdominal fat (SAT) were measured using ultrasound. Physical activity was assessed using accelerometry and analysed at the hourly level. Multilevel linear regression analyses were run with body composition exposures adjusted for age, sex, and length; models with VAT and SAT were also adjusted for total abdominal fat. RESULTS: Mean (SD) age was 11.8 (7.6) months; 86% were normal weight, 7% were underweight and 7% overweight. In linear models, no body composition variable was significantly associated with physical activity. Physical activity was higher with each increasing length tertile (ANOVA p < 0.01); with a mean(95%CI) 29(60-60)mg in the lowest tertile, 39(71-71)mg in the middle tertile, and 50(81-82)mg in the highest tertile. Infants with normal weight had higher mean(95%CI) physical activity (40(70-80)mg) than underweight (34(73-85)mg, p = 0.01) or overweight infants (31(63-78)mg, ANOVA p < 0.01). When also adjusting for total abdominal fat, infants in the lowest SAT tertile had higher physical activity than those in the middle or highest SAT tertiles (p < 0.01). CONCLUSIONS: These findings lend support for higher physical activity as a marker of healthy growth in the first two years of life.

A pragmatic and scalable strategy using mobile technology to promote sustained lifestyle changes to prevent type 2 diabetes in India and the UK: a randomised controlled trial.

AIMS/HYPOTHESIS: This randomised controlled trial was performed in India and the UK in people with prediabetes to study whether mobile phone short message service (SMS) text messages can be used to motivate and educate people to follow lifestyle modifications, to prevent type 2 diabetes. METHODS: The study was performed in people with prediabetes (n = 2062; control: n = 1031; intervention: n = 1031) defined by HbA1c ≥42 and ≤47 mmol/mol (≥6.0% and ≤6.4%). Participants were recruited from public and private sector organisations in India (men and women aged 35-55 years) and by the National Health Service (NHS) Health Checks programme in the UK (aged 40-74 years without pre-existing diabetes, cardiovascular disease or kidney disease). Allocation to the study groups was performed using a computer-generated sequence (1:1) in India and by stratified randomisation in permuted blocks in the UK. Investigators in both countries remained blinded throughout the study period. All participants received advice on a healthy lifestyle at baseline. The intervention group in addition received supportive text messages using mobile phone SMS messages 2-3 times per week. Participants were assessed at baseline and at 6, 12 and 24 months. The primary outcome was conversion to type 2 diabetes and secondary outcomes included anthropometry, biochemistry, dietary and physical activity changes, blood pressure and quality of life. RESULTS: At the 2 year follow-up (n = 2062; control: n = 1031; intervention: n = 1031), in the intention-to-treat population the HR for development of type 2 diabetes calculated using a discrete-time proportional hazards model was 0.89 (95% CI 0.74, 1.07; p = 0.22). There were no significant differences in the secondary outcomes. CONCLUSIONS/INTERPRETATION: This trial in two countries with varied ethnic and cultural backgrounds showed no significant reduction in the progression to diabetes in 2 years by lifestyle modification using SMS messaging. TRIAL REGISTRATION: The primary study was registered on www.ClinicalTrials.gov (India, NCT01570946; UK, NCT01795833). FUNDING: The study was funded jointly by the Indian Council for Medical Research and the UK Medical Research Council.

Evaluation of a very brief pedometer-based physical activity intervention delivered in NHS Health Checks in England: The VBI randomised controlled trial.

BACKGROUND: The majority of people do not achieve recommended levels of physical activity. There is a need for effective, scalable interventions to promote activity. Self-monitoring by pedometer is a potentially suitable strategy. We assessed the effectiveness and cost-effectiveness of a very brief (5-minute) pedometer-based intervention ('Step It Up') delivered as part of National Health Service (NHS) Health Checks in primary care. METHODS AND FINDINGS: The Very Brief Intervention (VBI) Trial was a two parallel-group, randomised controlled trial (RCT) with 3-month follow-up, conducted in 23 primary care practices in the East of England. Participants were 1,007 healthy adults aged 40 to 74 years eligible for an NHS Health Check. They were randomly allocated (1:1) using a web-based tool between October 1, 2014, and December 31, 2015, to either intervention (505) or control group (502), stratified by primary care practice. Participants were aware of study group allocation. Control participants received the NHS Health Check only. Intervention participants additionally received Step It Up: a 5-minute face-to-face discussion, written materials, pedometer, and step chart. The primary outcome was accelerometer-based physical activity volume at 3-month follow-up adjusted for sex, 5-year age group, and general practice. Secondary outcomes included time spent in different intensities of physical activity, self-reported physical activity, and economic measures. We conducted an in-depth fidelity assessment on a subsample of Health Check consultations. Participants' mean age was 56 years, two-thirds were female, they were predominantly white, and two-thirds were in paid employment. The primary outcome was available in 859 (85.3%) participants. There was no significant between-group difference in activity volume at 3 months (adjusted intervention effect 8.8 counts per minute [cpm]; 95% CI -18.7 to 36.3; p = 0.53). We found no significant between-group differences in the secondary outcomes of step counts per day, time spent in moderate or vigorous activity, time spent in vigorous activity, and time spent in moderate-intensity activity (accelerometer-derived variables); as well as in total physical activity, home-based activity, work-based activity, leisure-based activity, commuting physical activity, and screen or TV time (self-reported physical activity variables). Of the 505 intervention participants, 491 (97%) received the Step it Up intervention. Analysis of 37 intervention consultations showed that 60% of Step it Up components were delivered faithfully. The intervention cost £18.04 per participant. Incremental cost to the NHS per 1,000-step increase per day was £96 and to society was £239. Adverse events were reported by 5 intervention participants (of which 2 were serious) and 5 control participants (of which 2 were serious). The study's limitations include a participation rate of 16% and low return of audiotapes by practices for fidelity assessment. CONCLUSIONS: In this large well-conducted trial, we found no evidence of effect of a plausible very brief pedometer intervention embedded in NHS Health Checks on objectively measured activity at 3-month follow-up. TRIAL REGISTRATION: Current Controlled Trials (ISRCTN72691150).

Estimating physical activity from self-reported behaviours in large-scale population studies using network harmonisation: findings from UK Biobank and associations with disease outcomes.

BACKGROUND: UK Biobank is a large prospective cohort study containing accelerometer-based physical activity data with strong validity collected from 100,000 participants approximately 5 years after baseline. In contrast, the main cohort has multiple self-reported physical behaviours from > 500,000 participants with longer follow-up time, offering several epidemiological advantages. However, questionnaire methods typically suffer from greater measurement error, and at present there is no tested method for combining these diverse self-reported data to more comprehensively assess the overall dose of physical activity. This study aimed to use the accelerometry sub-cohort to calibrate the self-reported behavioural variables to produce a harmonised estimate of physical activity energy expenditure, and subsequently examine its reliability, validity, and associations with disease outcomes. METHODS: We calibrated 14 self-reported behavioural variables from the UK Biobank main cohort using the wrist accelerometry sub-cohort (n = 93,425), and used published equations to estimate physical activity energy expenditure (PAEESR). For comparison, we estimated physical activity based on the scoring criteria of the International Physical Activity Questionnaire, and by summing variables for occupational and leisure-time physical activity with no calibration. Test-retest reliability was assessed using data from the UK Biobank repeat assessment (n = 18,905) collected a mean of 4.3 years after baseline. Validity was assessed in an independent validation study (n = 98) with estimates based on doubly labelled water (PAEEDLW). In the main UK Biobank cohort (n = 374,352), Cox regression was used to estimate associations between PAEESR and fatal and non-fatal outcomes including all-cause, cardiovascular diseases, respiratory diseases, and cancers. RESULTS: PAEESR explained 27% variance in gold-standard PAEEDLW estimates, with no mean bias. However, error was strongly correlated with PAEEDLW (r = -.98; p < 0.001), and PAEESR had narrower range than the criterion. Test-retest reliability (Λ = .67) and relative validity (Spearman = .52) of PAEESR outperformed two common approaches for processing self-report data with no calibration. Predictive validity was demonstrated by associations with morbidity and mortality, e.g. 14% (95%CI: 11-17%) lower mortality for individuals meeting lower physical activity guidelines. CONCLUSIONS: The PAEESR variable has good reliability and validity for ranking individuals, with no mean bias but correlated error at individual-level. PAEESR outperformed uncalibrated estimates and showed stronger inverse associations with disease outcomes.

Estimating energy expenditure from wrist and thigh accelerometry in free-living adults: a doubly labelled water study.

BACKGROUND: Many large studies have implemented wrist or thigh accelerometry to capture physical activity, but the accuracy of these measurements to infer activity energy expenditure (AEE) and consequently total energy expenditure (TEE) has not been demonstrated. The purpose of this study was to assess the validity of acceleration intensity at wrist and thigh sites as estimates of AEE and TEE under free-living conditions using a gold-standard criterion. METHODS: Measurements for 193 UK adults (105 men, 88 women, aged 40-66 years, BMI 20.4-36.6 kg m-2) were collected with triaxial accelerometers worn on the dominant wrist, non-dominant wrist and thigh in free-living conditions for 9-14 days. In a subsample (50 men, 50 women) TEE was simultaneously assessed with doubly labelled water (DLW). AEE was estimated from non-dominant wrist using an established estimation model, and novel models were derived for dominant wrist and thigh in the non-DLW subsample. Agreement with both AEE and TEE from DLW was evaluated by mean bias, root mean squared error (RMSE), and Pearson correlation. RESULTS: Mean TEE and AEE derived from DLW were 11.6 (2.3) MJ day-1 and 49.8 (16.3) kJ day-1 kg-1. Dominant and non-dominant wrist acceleration were highly correlated in free-living (r = 0.93), but less so with thigh (r = 0.73 and 0.66, respectively). Estimates of AEE were 48.6 (11.8) kJ day-1 kg-1 from dominant wrist, 48.6 (12.3) from non-dominant wrist, and 46.0 (10.1) from thigh; these agreed strongly with AEE (RMSE ~12.2 kJ day-1 kg-1, r ~ 0.71) with small mean biases at the population level (~6%). Only the thigh estimate was statistically significantly different from the criterion. When combining these AEE estimates with estimated REE, agreement was stronger with the criterion (RMSE ~1.0 MJ day-1, r ~ 0.90). CONCLUSIONS: In UK adults, acceleration measured at either wrist or thigh can be used to estimate population levels of AEE and TEE in free-living conditions with high precision.

Correction to: Physical activity intensity, bout-duration, and cardiometabolic risk markers in children and adolescents.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Descriptive epidemiology of physical activity energy expenditure in UK adults (The Fenland study).

BACKGROUND: Physical activity (PA) plays a role in the prevention of a range of diseases including obesity and cardiometabolic disorders. Large population-based descriptive studies of PA, incorporating precise measurement, are needed to understand the relative burden of insufficient PA levels and to inform the tailoring of interventions. Combined heart and movement sensing enables the study of physical activity energy expenditure (PAEE) and intensity distribution. We aimed to describe the sociodemographic correlates of PAEE and moderate-to-vigorous physical activity (MVPA) in UK adults. METHODS: The Fenland study is a population-based cohort study of 12,435 adults aged 29-64 years-old in Cambridgeshire, UK. Following individual calibration (treadmill), participants wore a combined heart rate and movement sensor continuously for 6 days in free-living, from which we derived PAEE (kJ•day- 1•kg- 1) and time in MVPA (> 3 & > 4 METs) in bouts greater than 1 min and 10 min. Socio-demographic information was self-reported. Stratum-specific summary statistics and multivariable analyses were performed. RESULTS: Women accumulated a mean (sd) 50(20) kJ•day- 1•kg- 1 of PAEE, and 83(67) and 33(39) minutes•day- 1 of 1-min bouted and 10-min bouted MVPA respectively. By contrast, men recorded 59(23) kJ•day- 1•kg- 1, 124(84) and 60(58) minutes•day- 1. Age and BMI were also important correlates of PA. Association with age was inverse in both sexes, more strongly so for PAEE than MVPA. Obese individuals accumulated less PA than their normal-weight counterparts, whether considering PAEE or allometrically-scaled PAEE (- 10 kJ•day- 1•kg- 1 or - 15 kJ•day- 1•kg-2/3 in men). Higher income and manual work were associated with higher PA; manual workers recorded 13-16 kJ•kg- 1•day- 1 more PAEE than sedentary counterparts. Overall, 86% of women and 96% of men accumulated a daily average of MVPA (> 3 METs) corresponding to 150 min per week. These values were 49 and 74% if only considering bouts > 10 min (15 and 31% for > 4 METs). CONCLUSIONS: PA varied by age, sex and BMI, and was higher in manual workers and those with higher incomes. Light physical activity was the main driver of PAEE; a component of PA that is currently not quantified as a target in UK guidelines.

Physical activity energy expenditure and cardiometabolic health in three rural Kenyan populations.

OBJECTIVES: Physical activity is beneficial for metabolic health but the extent to which this may differ by ethnicity is still unclear. Here, the objective was to characterize the association between physical activity energy expenditure (PAEE) and cardiometabolic risk among the Luo, Kamba, and Maasai ethnic groups of rural Kenya. METHODS: In a cross-sectional study of 1084 rural Kenyans, free-living PAEE was objectively measured using individually-calibrated heart rate and movement sensing. A clustered metabolic syndrome risk score (zMS) was developed by averaging the sex-specific z-scores of five risk components measuring central adiposity, blood pressure, lipid levels, glucose tolerance, and insulin resistance. RESULTS: zMS was 0.08 (-0.09; -0.06) SD lower for every 10 kJ/kg/day difference in PAEE after adjustment for age and sex; this association was modified by ethnicity (interaction with PAEE P < 0.05). When adjusted for adiposity, each 10 kJ/kg/day difference in PAEE was predicted to lower zMS by 0.04 (-0.05, -0.03) SD, without evidence of interaction by ethnicity. The Maasai were predicted to have higher cardiometabolic risk than the Kamba and Luo at every quintile of PAEE, with a strong dose-dependent decreasing trend among all ethnicities. CONCLUSION: Free-living PAEE is strongly inversely associated with cardiometabolic risk in rural Kenyans. Differences between ethnic groups in this association were observed but were explained by differences in central adiposity. Therefore, targeted interventions to increase PAEE are more likely to be effective in subgroups with high central adiposity, such as Maasai with low levels of PAEE.

Longitudinal associations of physical activity and sedentary time with cardiometabolic risk factors in children.

BACKGROUND: There are few prospective studies on the associations of changes in objectively measured vigorous physical activity (VPA∆ ), moderate-to-vigorous physical activity (MVPA∆ ), light physical activity (LPA∆ ), and sedentary time (ST∆ ) with changes in cardiometabolic risk factors (∆ ) in children. We therefore investigated these relationships among children. METHODS: The participants were a population sample of 258 children aged 6-8 years followed for 2 years. We assessed PA and ST by a combined heart rate and movement sensor; computed continuous age- and sex-adjusted z-scores for waist circumference, blood pressure, and fasting insulin, glucose, triglycerides, and high-density lipoprotein (HDL) cholesterol; and constructed a cardiometabolic risk score (CRS) of these risk factors. Data were analyzed using linear regression models adjusted for age, sex, the explanatory and outcome variables at baseline, and puberty. RESULTS: VPA∆ associated inversely with CRS∆ (ß = -0.209, P = 0.001), body fat percentage (BF%)∆ (ß = -0.244, P = 0.001), insulin∆ (ß = -0.220, P = 0.001), and triglycerides∆ (ß = -0.164, P = 0.012) and directly with HDL cholesterol∆ (ß = 0.159, P = 0.023). MVPA∆ associated inversely with CRS∆ (ß = -0.178, P = 0.012), BF%∆ (ß = -0.298, P = <0.001), and insulin∆ (ß = -0.213, P = 0.006) and directly with HDL cholesterol∆ (ß = 0.184, P = 0.022). LPA∆ only associated negatively with CRS∆ (ß = -0.163, P = 0.032). ST∆ associated directly with CRS∆ (ß = 0.218, P = 0.003), BF%∆ (ß = 0.212, P = 0.016), and insulin∆ (ß = 0.159, P = 0.049). CONCLUSIONS: Increased VPA and MVPA and decreased ST were associated with reduced overall cardiometabolic risk and major individual risk factors. Change in LPA had weaker associations with changes in these cardiometabolic risk factors. Our findings suggest that increasing at least moderate-intensity PA and decreasing ST decrease cardiometabolic risk in children.

Associations of physical activity, sedentary time, and cardiorespiratory fitness with heart rate variability in 6- to 9-year-old children: the PANIC study.

PURPOSE: To study the associations of physical activity (PA), sedentary time (ST), and cardiorespiratory fitness (CRF) with heart rate variability (HRV) in children. METHODS: The participants were a population sample of 377 children aged 6-9 years (49% boys). ST, light PA (LPA), moderate PA (MPA), vigorous PA (VPA), and moderate-to-vigorous PA (MVPA), and PA energy expenditure (PAEE) were assessed using a combined heart rate and movement sensor, maximal power output per kilograms of lean body mass as a measure of CRF by maximal cycle ergometer exercise test, and HRV variables (SDNN, RMSSD, LF, and HF) using 5 min resting electrocardiography. Data were analysed by linear regression adjusted for years from peak height velocity. RESULTS: In boys, ST was inversely associated (ß = - 0.185 to - 0.146, p ≤ 0.049) and MVPA, VPA, PAEE, and CRF were directly associated (ß = 0.147 to 0.320, p ≤ 0.048) with HRV variables. CRF was directly associated with all HRV variables and PAEE was directly associated with RMSSD after mutual adjustment for ST, PAEE, and CRF (ß = 0.169 to 0.270, p ≤ 0.046). In girls, ST was inversely associated (ß = - 0.382 to - 0.294, p < 0.001) and LPA, MPA, VPA, MVPA, and PAEE were directly associated with HRV variables (ß = 0.144 to 0.348, p ≤ 0.049). After mutual adjustment for ST, PAEE, and CRF, only the inverse associations of ST with HRV variables remained statistically significant. CONCLUSIONS: Higher ST and lower PA and CRF were associated with poorer cardiac autonomic nervous system function in children. Lower CRF in boys and higher ST in girls were the strongest correlates of poorer cardiac autonomic function.

Development of the Impacts of Cycling Tool (ICT): A modelling study and web tool for evaluating health and environmental impacts of cycling uptake.

BACKGROUND: A modal shift to cycling has the potential to reduce greenhouse gas emissions and provide health co-benefits. Methods, models, and tools are needed to estimate the potential for cycling uptake and communicate to policy makers the range of impacts this would have. METHODS AND FINDINGS: The Impacts of Cycling Tool (ICT) is an open source model with a web interface for visualising travel patterns and comparing the impacts of different scenarios of cycling uptake. It is currently applied to England. The ICT allows users to visualise individual and trip-level data from the English National Travel Survey (NTS), 2004-2014 sample, 132,000 adults. It models scenarios in which there is an increase in the proportion of the population who cycle regularly, using a distance-based propensity approach to model which trips would be cycled. From this, the model estimates likely impact on travel patterns, health, and greenhouse gas emissions. Estimates of nonoccupational physical activity are generated by fusing the NTS with the English Active People Survey (APS, 2013-2014, 559,515 adults) to create a synthetic population. Under 'equity' scenarios, we investigate what would happen if cycling levels increased equally among all age and gender categories, as opposed to in proportion to the profile of current cyclists. Under electric assist bike (pedelecs or 'e-bike') scenarios, the probability of cycling longer trips increases, based on the e-bike data from the Netherlands, 2013-2014 Dutch Travel Survey (50,868 adults).Outcomes are presented across domains including transport (trip duration and trips by mode), health (physical activity levels, years of life lost), and car transport-related CO2 emissions. Results can be visualised for the whole population and various subpopulations (region, age, gender, and ethnicity). The tool is available at www.pct.bike/ict. If the proportion of the English population who cycle regularly increased from 4.8% to 25%, then there would be notable reductions in car miles and passenger related CO2 emissions (2.2%) and health benefits (2.1% reduction in years of life lost due to premature mortality). If the new cyclists had access to e-bikes, then mortality reductions would be similar, while the reduction in car miles and CO2 emissions would be larger (2.7%). If take-up of cycling occurred equally by gender and age (under 80 years), then health benefits would be marginally greater (2.2%) but reduction in CO2 slightly smaller (1.8%). The study is limited by the quality and comparability of the input data (including reliance on self-report behaviours). As with all modelling studies, many assumptions are required and potentially important pathways excluded (e.g. injury, air pollution, and noise pollution). CONCLUSION: This study demonstrates a generalisable approach for using travel survey data to model scenarios of cycling uptake that can be applied to a wide range of settings. The use of individual-level data allows investigation of a wide range of outcomes, and variation across subgroups. Future work should investigate the sensitivity of results to assumptions and omissions, and if this varies across setting.

Physical activity intensity, bout-duration, and cardiometabolic risk markers in children and adolescents.

OBJECTIVES: To determine the role of physical activity intensity and bout-duration in modulating associations between physical activity and cardiometabolic risk markers. METHODS: A cross-sectional study using the International Children's Accelerometry Database (ICAD) including 38,306 observations (in 29,734 individuals aged 4-18 years). Accelerometry data was summarized as time accumulated in 16 combinations of intensity thresholds (≥500 to ≥3000 counts/min) and bout-durations (≥1 to ≥10 min). Outcomes were body mass index (BMI, kg/m2), waist circumference, biochemical markers, blood pressure, and a composite score of these metabolic markers. A second composite score excluded the adiposity component. Linear mixed models were applied to elucidate the associations and expressed per 10 min difference in daily activity above the intensity/bout-duration combination. Estimates (and variance) from each of the 16 combinations of intensity and bout-duration examined in the linear mixed models were analyzed in meta-regression to investigate trends in the association. RESULTS: Each 10 min positive difference in physical activity was significantly and inversely associated with the risk factors irrespective of the combination of intensity and bout-duration. In meta-regression, each 1000 counts/min increase in intensity threshold was associated with a -0.027 (95% CI: -0.039 to -0.014) standard deviations lower composite risk score, and a -0.064 (95% CI: -0.09 to -0.038) kg/m2 lower BMI. Conversely, meta-regression suggested bout-duration was not significantly associated with effect-sizes (per 1 min increase in bout-duration: -0.002 (95% CI: -0.005 to 0.0005) standard deviations for the composite risk score, and -0.005 (95% CI: -0.012 to 0.002) kg/m2 for BMI). CONCLUSIONS: Time spent at higher intensity physical activity was the main determinant of variation in cardiometabolic risk factors, not bout-duration. Greater magnitude of associations was consistently observed with higher intensities. These results suggest that, in children and adolescents, physical activity, preferably at higher intensities, of any bout-duration should be promoted.