Prospective association between descriptive accelerometer-derived physical behaviour metrics and cardiometabolic risk indicators in Dutch children: The ABCD study

To investigate gaze and behavioural metrics at different viewing distances with multifocal contact lenses (MFCLs), single vision contact lenses (SVCLs) and progressive addition lenses (PALs). Fifteen presbyopic contact lens wearers participated over five separate study visits. At each visit, participants were randomly assigned to wear one of five refractive corrections: habitual PAL spectacles, delefilcon A (Alcon Inc.) MFCLs and three separate pairs of delefilcon A single vision lenses worn as distance, intermediate and near corrections. Participants wore a Pupil Core headset to record eye and head movements while performing three visual tasks: reading, visual search and scene observation. Data were investigated using linear regression and post-hoc testing. Parameters of interest included gaze (fixation duration, head movement) and behavioural (reading speed, reading accuracy, visual search time) metrics. Reading speed in SVCLs was significantly faster than in MFCLs and PAL spectacles (F = 16.3, p < 0.0001). Refractive correction worn did not influence visual search times (F = 0.16, p = 0.85). Fixation duration was significantly affected by the type of visual task (F = 60.2, p < 0.001), and an interaction effect was observed between viewing distance and refractive correction (F = 4.3, p = 0.002). There was significantly more horizontal and vertical head movement (F = 3.2, p = 0.01 and F = 3.3, p = 0.01, respectively) during visual search tasks when wearing PAL spectacles compared to SVCLsor MFCLs. This work showed that the type of refractive correction affects behavioural metrics such as reading speed and gaze behaviour by affecting horizontal and vertical head movements. The findings of this study suggest that under certain conditions, wearers of MFCLs make fewer head movements compared to PAL spectacles. Gaze behaviour metrics offer a new approach to compare and understand contact lens and spectacle performance, with potential applications including peripheral optical designs for myopia management.

To describe active and sedentary time in children with spina bifida and to compare their physical activity on weekdays versus weekends. In this exploratory cross-sectional study, data from 13 Canadian and 22 Dutch children with spina bifida (14 females, 21 males; mean age 10y 11mo, standard deviation [SD] 3y 6mo, range 5y 6mo-18y; Hoffer classification distribution: community [n=28], household [n=3], non-functional [n=3], and non-ambulator [n=1]) were analysed. Objective measures of physical activity and sedentary behaviour were obtained by using ActiGraph or Actiheart activity monitors. Data for the participants wearing the ActiGraph were compared with age- and sex-matched controls that were developing typically using independent-samples t-tests. Activity data collected on weekdays was compared to those on weekends. ActiGraph data demonstrated children with spina bifida spent more time sedentary (mean [SD] 49.5min/h [5.78]) and less time in moderate to vigorous physical activity (mean [SD] 2.33min/h [1.61]) compared with the typically developing group (mean [SD] 41.0min/h [5.76] and 5.46min/h [2.13], p=0.001 and p<0.001 respectively). For both ActiGraph- and Actiheart-derived data, physical activity and sedentary time were not significantly different between weekdays and weekends. Children with spina bifida have reduced levels of physical activity and increased sedentary behaviour, with no statistical differences seen between weekdays and weekends. Several methodological issues related to activity monitoring warrant consideration when choosing the appropriate method to quantify physical activity and sedentary behaviour. Reduced levels of physical activity and sedentary time were quantified in children with spina bifida. Objective quantification of physical behaviour in ambulatory and non-ambulatory school-aged children with spina bifida is possible.

To establish whether associations between sedentary behavior and cardiometabolic health differ when assessed by thigh-worn and waist-worn accelerometry. Participants were recruited from several areas in the United Kingdom. Sedentary behavior was assessed using the activPAL worn on the thigh and ActiGraph worn on the waist. Average total (TST), prolonged (bouts ≥30 min; PST) and breaks (BST) in sedentary time were calculated. Cardiometabolic health markers included: adiposity (body fat) and surrogate markers of adiposity ((waist circumference, body mass index [BMI]), lipids (total, low density lipoprotein, and high-density lipoprotein [HDL] cholesterol, triglycerides), blood pressure, and glucose (fasting, 2 h and glycated hemoglobin A1c). A clustered cardiometabolic risk score was calculated. Linear regression analysis examined the associations with cardiometabolic health. There were 1457 participants (mean age [± standard deviation], 59.38 ± 11.85 yr; 51.7% male; mean BMI, 30.19 ± 5.59 kg·m) included in the analyses. ActivPAL and ActiGraph sedentary variables were moderately correlated (0.416-0.511, P < 0.01); however, all variables were significantly different from each other (P < 0.05). Consistency was observed across devices in the direction and magnitude of associations of TST and PST with adiposity, surrogate markers of adiposity, HDL, triglycerides, and cardiometabolic risk score and for BST with adiposity, surrogate markers of adiposity, and cardiometabolic risk. Differences across devices were observed in associations of TST and PST with diastolic blood pressure, for TST with 2-h glucose and for BST with HDL. No other associations were observed for any other health marker for either device. Results suggest that associations with cardiometabolic health are largely comparable across the two common assessments of sedentary behavior but some small differences may exist for certain health markers.

Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Big Medilytics - Horizon 2020 Background Physical behaviour is a multidimensional construct comprising physical activity (PA) and sedentary behaviour. PA adherence, during and after cardiac rehabilitation (CR), is problematic with less than 55% of the patients meeting recommended levels. Furthermore, CR only results in limited improvements in sedentary behaviour. To optimize CR programs, more information is needed on which patients could benefit from additional physical behaviour counselling. Purpose To explore trajectories (improvement, stabilization, or worsening) of moderate-to-vigorous PA (MVPA) and sedentary behaviour during and after CR, and to identify predictors for trajectory membership in patients with acute coronary syndrome (ACS). Methods The study was performed among 533 patients (mean age 57.9 ± 8.9years; 18.2% women) who participated in a 12-week multi-dimensional CR program that started (median) 35 days after hospitalization for ACS. Physical behaviour was measured using accelerometry at CR start, CR completion and 12 and 18 months follow-up. Latent class trajectory modelling was applied to explore trajectories for MVPA and sedentary behaviour. Separate trajectories were determined for the CR period and post-CR period. Using multinomial logistic regression, potential demographic, psychological, and cardiovascular predictors for each of the trajectories were explored. Results Using trajectory analyses, three classes of patients were identified for MVPA and sedentary behaviour both during and after CR: an increasing group, a declining group, and a steady group with only minor changes (see figure 1). Baseline physical behaviour level was the main predictor for declining trajectories, where, interestingly, patients with a higher starting level of the specific physical behaviour were more likely to be in the declining group as compared to the steady group (odds ratio (OR) for MVPA and sedentary behaviour during and post-CR ranging from 1.06-1.45, all p &amp;lt; 0.05). During CR, smokers were less likely to be in the declining group for sedentary behaviour (OR 0.29 (0.09-0.96)) as compared to the steady group. Post-CR, participants with a higher age were less likely to be in the increasing group for MVPA and more likely to be in the increasing group for sedentary behaviour (OR respectively 0.96 (0.93-1.00), 1.04 (1.01-1.07)). Furthermore, participants with a higher BMI were also less likely to be in the increasing group for MVPA post-CR (0.91 (0.84-1.00)). Conclusion: Distinct trajectories for MVPA and sedentary behaviour exist for CR patients, which are mainly distinguished by baseline physical behaviour level, where patients with a higher starting level of the specific physical behaviour were more likely to be in the declining class. We did not see this for the increasing group, suggesting that this phenomenon was not only explained by regression to the mean. Abstract Figure.

Background Little is known how combined weekly patterns of physical activity and sedentary behaviour are associated with cardiometabolic health. The objective of this paper is to identify weekly patterns of physical activity and sedentary behaviour and to examine cardiometabolic health status associated with different activity patterns. Methods Data are from a subsample of the Mitchelstown cohort; 475 (59.7 ± 5.5 years) middle-aged adults. Participants wore the wrist GENEActiv accelerometer for 7-consecutive days. Data was summarised into 60s epochs and each time interval categorised based on thresholds. Latent profile analysis (LPA) defined classes based on observed clustering of sedentary behaviour and physical activity variables while multivariate latent class regression was used to compare cardiometabolic health status across classes. Results LPA revealed 4 distinct physical behaviour patterns; Sedentary Group (20.9%), moderate-to-vigorous physical activity (MVPA) and High-Sedentary Activity Group (40.9%), MVPA and High-Light Activity Group (24.7%) and a Physically Active Group (13.5%). Overall the Sedentary Group had poorer outcomes, characterised by high Body Mass Index, triglycerides, fasting plasma glucose and insulin levels, and low high density lipoprotein-cholesterol levels. The remaining classes were characterised by healthier cardiometabolic profiles as sedentary behaviour decreased. Discussion The classification of groups of adults with similar physical behaviour patterns offers important information for the identification and tailoring of public health and health promotion messages and intervention strategies. These findings could help identify optimal patterns of physical behaviour that improves cardiometabolic health as health policy should be directed towards altering patterns of behaviour rather than concentrating on a single type of behaviour.

Most of the studies applying the accelerometers to quantify physical activity (PA) and sedentary time (ST) require participants to wear the devices during waking hours only. There is no consensus on how many wearing hours are enough to reflect daily activity behavior in free-living conditions. Whether wear time has substantial impact on accelerometer-based estimates remains unclear. PURPOSE: To examine whether accelerometer-derived metrics were dependent on daily wear time. METHODS: Baseline data from 120 children (24.2% boys) aged 8 to 11 years who participated in the longitudinal study on active travel were analyzed. They were instructed to wear an ActiGraph wGT3x accelerometry on the waist for 7 to 10 consecutive days, only removing it while swimming and bathing. Accelerometer-derived metrics included daily wear time, total activity counts (TAC), ST, light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), moderate-to-vigorous PA (MVPA), moderate-to-vigorous PA (MVPA) bouts ≥ 10 minutes (MVPA-10), and proportion of VPA within MVPA (%VPA). Linear mixed models were performed to examine the relationships of these metrics with daily wear time which was categorized into quarters. RESULTS: Majority of the children (96%) provided at least 3 valid accelerometer wear days (defined as ≥ 480 minutes of valid wear time per day), with a total of 797 valid wear days included in analyses. After adjustment for age, sex, and body mass index, none of the PA metrics was completely independent on wear time. A linear relationship was found between wear time and four PA metrics including TAC, MPA, LPA, and ST. However, MVPA estimates were comparable between the first (8 to 11 hours per day) and second quarters (11 to 13 hours per day) of wear time (mean ± standard error: 31.4 ± 1.8 vs 35.7 ± 1.8 minutes, NS). Similar results were found for MVPA-10, VPA, and %VPA. CONCLUSIONS: Accelerometer-derived PA metrics were largely dependent on wear time. MVPA minutes seem to be comparable across wear time of 13 hours per day or less. Comparisons of accelerometer-derived PA estimates between studies need to be cautious and taken into account of differences in wear duration.

THE USE OF INDIVIDUALIZED EXERCISE PRESCRIPTION AND ACTIVITY TRACKERS TO PROMOTE PHYSICAL ACTIVITY IN CHILDREN WITH CONGENITAL HEART DISEASE

Covid-19 and measures to contain spreading the disease have led to changed physical activity behavior. This study aims to investigate the relationship between socioeconomic status (SES) and changes in the amount of moderate to vigorous physical activity (MVPA) during the Covid-19 crisis. Using the Dutch Lifelines Covid-19 cohort study (n = 17,749), the amount of MVPA was measured at 15 time-points between March and December 2020, and compared with the amount before the Covid19 pandemic. For SES, the population was stratified in three education and income levels. Logistic regression models were used to estimate the odds ratio (OR) and confidence interval (CI) of altered MVPA for low and high SES groups, with the middle SES category as the reference group. A clear socioeconomic gradient in changes in MVPA behavior was observed. Low educated individuals had significantly higher odds (OR = 1.14; CI: 1.03–1.27) of decreasing MVPA, while the high educated had significantly lower odds of decreased MVPA (OR = 0.84, CI: 0.79–0.90). Both low education (OR = 0.87; CI: 0.77–0.98) and low income (OR = 0.85; CI 0.78–0.92) had significantly lower odds to increase MVPA, while high education (OR = 1.21, CI: 1.12–1.30) and high income (OR = 1.17; CI: 1.07–1.28) had significantly higher odds to increase MVPA. Most findings were consistent over the full research period. Socioeconomic inequalities in MVPA have increased during the Covid-19 pandemic, even when Covid-19 containment measures were relaxed. Our findings suggest that future public health policies need to increase efforts to improve physical activity behavior with an even larger focus on low SES groups.

Data from ActiGraph accelerometers have long been imported into ActiLife software, where the company’s proprietary “activity counts” were generated in order to understand physical behavior metrics. In 2022, ActiGraph released an open-source method to generate activity counts from any raw, triaxial accelerometer data using Python, which has been translated into RStudio packages. However, it is unclear if outcomes are comparable when generated in ActiLife and RStudio. Therefore, the authors’ technical note systematically compared activity counts and related physical behavior metrics generated from ActiGraph accelerometer data using ActiLife or available packages in RStudio and provides example code to ease implementation of such analyses in RStudio. In addition to comparing triaxial activity counts, physical behavior outputs (sleep, sedentary behavior, light-intensity physical activity, and moderate- to vigorous-intensity physical activity) were compared using multiple nonwear algorithms, epochs, cut points, sleep scoring algorithms, and accelerometer placement sites. Activity counts and physical behavior outcomes were largely the same between ActiLife and the tested packages in RStudio. However, peculiarities in the application of nonwear algorithms to the first and last portions of a data file (that occurred on partial, first or last days of data collection), differences in rounding, and handling of counts values on the borderline of activity intensities resulted in small but inconsequential differences in some files. The hope is that researchers and both hardware and software manufacturers continue to push efforts toward transparency in data analysis and interpretation, which will enhance comparability across devices and studies and help to advance fields examining links between physical behavior and health.

Weight–activity associations with cardiometabolic risk factors among U.S. youth

To develop healthy ageing interventions, longitudinal associations between objectively assessed physical behaviours and physical function need to be better understood. We assessed associations between accelerometer-assessed total physical activity (PA), moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary time and prolonged sedentary bout time, and clinically assessed physical function (grip strength, usual walking speed (UWS), chair stand speed) at two time-points in 3188 participants (≥ 60 years) of the EPIC-Norfolk study. Bidirectional associations were assessed using multivariable linear regression. Over an average of 6.1 years, baseline physical behaviours (greater total PA, MVPA and LPA, and less sedentary time) were associated with better subsequent walking and chair stand speed. Better baseline physical function was associated with better follow-up physical behaviours. There were no bidirectional associations between changes in physical behaviours and grip strength. Improvements in UWS were associated with improvements in all physical behaviours. Improvements in chair stand speed were associated with improvements in total PA, MVPA, and sedentary bout time. Improvements in physical behaviours were associated with improvements in UWS (3.1 cm/s/yr per 100 cpm/yr total PA, 3.6 cm/s/yr per hr/day/yr MVPA, 2.5 cm/s/yr per hr/day/yr LPA, − 2.9 cm/s/yr per hour/day/yr sedentary time, and − 1.6 cm/s/yr per hr/day/yr prolonged sedentary bout time). Only improvements in total PA, MVPA and sedentary bout time were associated with improvements in chair stand speed. In conclusion, we found bidirectional associations between changes in some physical behaviours and physical function and between baseline physical behaviours and subsequent physical function, highlighting the importance of considering the full range of physical behaviours to promote healthy ageing.

Background:Dialysis patients have reduced moderate to vigorous physical activity, andlight physical activity. This has been shown in self-reported surveys andobjective accelerometer studies. Less attention has been directed towardsedentary behavior, which is characterized by low energy expenditure (≤1.5metabolic equivalents). Furthermore, locations where physical activity andsedentary behavior occur are largely unknown for dialysis patients.Objectives:The objectives of this study were (1) to determine the minutes per day ofmoderate to vigorous physical activity, light physical activity, andsedentary behavior for hemodialysis patients; (2) to describe differences inmoderate to vigorous physical activity, light physical activity, andsedentary behavior comparing dialysis versus nondialysis days; and (3) todescribe the locations where moderate to vigorous physical activity, lightphysical activity, and sedentary behavior occur using global positioningsystem (GPS) data.Design:Cross-sectional study.Setting:The study was performed at a tertiary care hospital in Nova Scotia,Canada.Patients:A total of 50 adult in-center hemodialysis patients consented to thestudy.Measurements:Physical activity and sedentary behavior were measured with an Actigraph-GT3Xaccelerometer. Location was determined using a Qstarz BT-Q1000X GPSreceiver.Methods:Minutes of daily activity were described as was percentage of wear time foreach activity level across different locations during waking hours. Physicalactivity intensity, quantity, and location were also analyzed according todialysis vs nondialysis days.Results:Forty-three patients met requirements for accelerometer analysis, of whom 42had GPS data. Median wear time was 836.5 min/day (interquartile range [IQR]:788.3-918.3). Median minutes of daily wear time spent in sedentary behavior,light physical activity, and moderate to vigorous physical activity was 636minutes (IQR: 594.1-730.1), 178 minutes (IQR: 144-222.1), and 1.6 minutes(IQR: 0.6-7.7), respectively. Proportion of daily wear time spent insedentary behavior, light physical activity, and moderate to vigorousphysical activity was 78.4% (IQR: 70.7-84.0), 21.5% (IQR: 16.0-26.9), and0.2% (IQR: 0.1-1.1), respectively. Home was the dominant location for totallinked accelerometer-GPS time (59.4%, IQR: 46.9-69.5) as well as for eachprespecified level of activity. Significantly more sedentary behavior andless light physical activity occurred on dialysis days compared withnondialysis days (P ≤ .01, respectively). Moderate tovigorous physical activity did not differ significantly between dialysis andnondialysis days.Limitations:Small sample size from a single academic center may limit generalizability.Difficult to engage population as less than half of eligible dialysispatients provided consent. Physical activity may have been underestimated asdevices were not worn for all waking hours or aquatic activities, andhip-based accelerometers may not capture stationary exercise.Conclusions:Ambulatory, in-center hemodialysis patients exhibit substantial sedentarybehavior and minimal physical activity across a limited range of locations.Given the sedentary tendencies of this population, focus should be directedon increasing physical activity at any location frequented. Home-basedexercise programs may serve as a potential adjunct to establishedintradialytic-based therapies given the amount of time spent in the homeenvironment.

This investigation examined parental influence on children’s moderate to vigorous physical activity (MVPA) participation via an expectancy-value model that included parents’ behavior, parents’ beliefs about their children’s MVPA, and children’s beliefs about their MVPA. The influence of parents on their children’s MVPA was investigated via questionnaires tapping the belief systems of fourth- and fifth-grade children (n=71) and their parents (n=69). Self-reported MVPA was assessed for parents and children. Correlational analyses demonstrated a number of significant relationships between parents’ belief systems and children’s MVPA behavior and children’s belief systems and their physical activity participation. Based on hierarchical regression analyses, there was no evidence of a positive relationship between parents’ physical activity behavior (role modeling) and children’s physical activity behavior. Parents’ perceptions of their children’s MVPA competence was the only parent belief system variable related to children’s MVPA participation. In addition, children’s task orientation and expectancies significantly predicted their MVPA participation.

Introduction: Self-reported physical inactivity and sitting time have been associated with a greater prevalence of chronic kidney disease (CKD) in middle-aged populations. However, data are limited regarding the association of objectively-assessed physical activity and sedentary behavior with prevalence of CKD among older adults. Hypothesis: We hypothesized that higher physical activity and less sedentary time will individually and conjointly be associated with a lower prevalence of CKD in older adults. Methods: We evaluated 1,318 Framingham Offspring cohort participants (mean age 69.3 years, 53.9% women) with accelerometry-derived physical activity data (wear time ≥ 10 hours/day for at least 4 days) at examination 9 (2011-2014). CKD was defined as an eGFR &lt; 60 ml/min/1.73 2 and/or urine albumin-to-creatinine ratio (UACR) ≥ 25/35 micrograms/mg (men/women) at examination 9. Multivariable logistic regression models were used to relate physical activity and sedentary time with the prevalence of CKD adjusting for covariates (Table). In sensitivity analysis to mitigate reverse causality, we excluded participants with more advanced CKD (eGFR&lt;30 ml/min/1.73 2 ). Results: Men spent more time in moderate to vigorous physical activity (MVPA) (mean 21.7 min/day) and less time in sedentary time (mean, 660.7±76.9 min/day), compared to women (mean MVPA, 15.6 min/day; mean sedentary time, 675.8±70.5 min/day). Overall, 279 participants had prevalent CKD (20.8%; 127 women). Higher sedentary time was associated with a higher prevalence of CKD after adjusting for risk factors and MVPA in sensitivity analysis, the association remained significant. MVPA was inversely associated with CKD prevalence in minimally-adjusted models only. Conclusions: Our cross-sectional observations on a sample of older adults are consistent with the notion that reduction of sedentary time in the elderly may have salutary effects on kidney function. Additional studies of multiethnic cohorts are warranted to confirm our observations.

Introduction: Pregnant women with diabetes often show low levels of physical activity (PA) and high sedentary behavior (SED). Longitudinal studies with objective measures are needed to understand the relationships of daily PA with daily and next-day blood glucose (BG). Hypothesis: Increased steps or moderate to vigorous PA (MVPA) and decreased SED are linked with lower post-meal BG and next day fasting BG in pregnant women. Methods: Participants were 10 pregnant women with diabetes [mean age= 29.3 (SD= 3.6); mean gestational age= 21.9 (SD= 3.9); 90% (9 of 10) Latina] enrolled in a 12-week pilot PA intervention. Participants self-reported demographic and BG data (morning fasting BG, up to 3 daily post-meal BGs). Steps, MVPA (mins/day), and SED (mins/day) were measured using a Fitbit Alta HR. Participants had on average 49 (range: 21 to 77) days with valid PA and BG data, for a total of 469 observations. Multi-level models (MLMs) were fit to examine mean and day-level effects of steps, MVPA, and SED on post-meal and next-day fasting BG after adjusting for age, gestational age, education, and participant mean PA or SED. Due to the small sample size, effect sizes are emphasized in results instead of statistical significance. Results: The mean post-meal BG was 122.5 mg/dL and mean fasting BG was 92.81 mg/dL. After adjustment, an increase of mean steps by 1000 was linked to a lower mean post-meal BG by 11.79 mg/dL (p=0.22) and fasting BG by 7.26 mg/dL (p=0.54), though neither between effect was statistically significant. The within-individual effects of daily steps on post-meal and fasting BG were very small and non-significant (b=-1.78; p=0.59; b=0.72; p=0.30, respectively). A 1-minute increase in mean MVPA was associated with a slight increase in mean post-meal BG by 1.53 mg/dL (p=0.07). The within-individual effect of daily MVPA on daily post-meal BG was negligible and non-significant (b=-0.39, p=0.51). Between-individual effects showed SED had small, positive, non-significant associations with post-meal BG. Specifically, per 60-minute mean SED increase, mean post-meal BG increased by 1.02 mg/dL (p=0.44). Within-individual daily SED increases of 60 minutes were associated with increases of 1.87 mg/dL (p=0.63) in daily post-meal BG. MVPA and SED were not associated with fasting BG. Conclusions: Greater mean steps were linked to lower post-meal and fasting BG while greater SED and MVPA were linked to greater post-meal BG. However, within individual daily increases in MVPA and decreases in SED, were protective for post-meal BG, while controlling for individual mean MVPA and SED. Most effect sizes were small and results were not statistically significant in part due to the small sample size. Participants generally had well-controlled post-meal and fasting BGs, so results may not be generalizable to larger populations.