Wrist-worn Accelerometry for Runners: Objective Quantification of Training Load.

PURPOSE: This study aimed to apply open-source analysis code to raw habitual physical activity data from wrist-worn monitors to: 1) objectively, unobtrusively, and accurately discriminate between "running" and "nonrunning" days; and 2) develop and compare simple accelerometer-derived metrics of external training load with existing self-report measures. METHODS: Seven-day wrist-worn accelerometer (GENEActiv; Activinsights Ltd, Kimbolton, UK) data obtained from 35 experienced runners (age, 41.9 ± 11.4 yr; height, 1.72 ± 0.08 m; mass, 68.5 ± 9.7 kg; body mass index, 23.2 ± 2.2 kg·m; 19 [54%] women) every other week over 9 to 18 wk were date-matched with self-reported training log data. Receiver operating characteristic analyses were applied to accelerometer metrics ("Average Acceleration," "Most Active-30mins," "Mins≥400 mg") to discriminate between "running" and "nonrunning" days and cross-validated (leave one out cross-validation). Variance explained in training log criterion metrics (miles, duration, training load) by accelerometer metrics (Mins≥400 mg, "workload (WL) 400-4000 mg") was examined using linear regression with leave one out cross-validation. RESULTS: Most Active-30mins and Mins≥400 mg had >94% accuracy for correctly classifying "running" and "nonrunning" days, with validation indicating robustness. Variance explained in miles, duration, and training load by Mins≥400 mg (67%-76%) and WL400-4000 mg (55%-69%) was high, with validation indicating robustness. CONCLUSIONS: Wrist-worn accelerometer metrics can be used to objectively, unobtrusively, and accurately identify running training days in runners, reducing the need for training logs or user input in future prospective research or commercial activity tracking. The high percentage of variance explained in existing self-reported measures of training load by simple, accelerometer-derived metrics of external training load supports the future use of accelerometry for prospective, preventative, and prescriptive monitoring purposes in runners

A comparison of analytical approaches to investigate associations for accelerometry-derived physical activity spectra with health and developmental outcomes in children

Under embargo until: 2021-09-20The use of high-resolution physical activity intensity spectra obtained from accelerometry can improve knowledge of associations with health and development beyond the use of traditional summary measures of intensity. The aim of the present study was to compare three different approaches for determining associations for spectrum descriptors of physical activity (the intensity gradient, principal component analysis, and multivariate pattern analysis) with relevant outcomes in children. We used two datasets including physical activity spectrum data (ActiGraph GT3X+) and 1) a cardiometabolic health outcome in 841 schoolchildren and 2) a motor skill outcome in 1081 preschool children. We compared variance explained (R2) and associations with the outcomes for the intensity gradient (slope) across the physical activity spectra, a two-component principal component model describing the physical activity variables, and multivariate pattern analysis using the intensity spectra as the explanatory data matrices. Results were broadly similar for all analytical approaches. Multivariate pattern analysis explained the most variance in both datasets, likely resulting from use of more of the information available from the intensity spectra. Yet, volume and intensity dimensions of physical activity are not easily disentangled and their relative importance may be interpreted differently using different methodology.acceptedVersio

Introducing novel approaches for examining the variability of individuals' physical activity

Tudor-Locke and colleagues previously assessed steps/day for 1 year. The aim of this study was to use this data set to introduce a novel approach for the investigation of whether individual's physical activity exhibits periodicity fluctuating round a mean and, if so, the degree of fluctuation and whether the mean changes over time. Twenty-three participants wore a pedometer for 365 days, recorded steps/day and whether the day was a workday. Fourier transform of each participant's daily steps data showed the physical activity had a periodicity of 7 days in half of the participants, matching the periodicity of the workday pattern. Activity level remained stable in half of the participants, decreased in ten participants and increased in two. In conclusion, the 7-day periodicity of activity in half of the participants and correspondence with the workday pattern suggest a social or environmental influence. The novel analytical approach introduced herein allows the determination of the periodicity of activity, the degree of variability in activity that is tolerated during day-to-day life and whether the activity level is stable. Results from the use of these methodologies in larger data sets may enable a more focused approach to the design of interventions that aim to increase activity

Maturational timing, physical self-perceptions and physical activity in UK adolescent females: Investigation of a mediated effects model

Background: Advanced (early) biological maturation may be a risk factor for inactivity among adolescent girls. The aim of the present paper was to test the mediational effects of body attractiveness and physical self-worth on the relationship between biological maturity and accelerometer assessed moderate-to-vigorous physical activity (MVPA) in a large multi-ethnic sample of girls from the Midlands area in the UK (11-14 years). Methods: Biological maturity (predicting age at peak height velocity (APHV)); self-perceptions of body attractiveness, physical self-worth, and minutes spent in MVPA were assessed in 1062 females aged 11 to 14 years. Results: Structural equation modeling using maximum likelihood estimation and boot- strapping procedures supported the hypothesized model. Later maturation predicted higher perceptions of body attractiveness (β=.25, p<.001) which, in turn, predicted higher perceptions of physical self-worth (β=.91, p<.001) and, significantly higher MVPA (β=.22, p<.001). Examination of the bootstrap-generated bias-corrected confidence intervals suggested that perceptions of body attractiveness and physical self-worth partially mediated a positive association between predicted APHV and MVPA (β=.05, p Conclusions: Greater biological maturity (i.e. early maturity) in adolescent girls is associated with less involvement in MVPA and appears to be partly explained by lower perceptions of body attractiveness and physical self-worth. Physical activity interventions should consider girls perceptions of their pubertal related physiological changes during adolescence, particularly among early maturing girls. </p

A data-driven, meaningful, easy to interpret, standardised accelerometer outcome variable for global surveillance

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Objectives: Our aim is to demonstrate how a data-driven accelerometer metric, the acceleration above which a person’s most active minutes are accumulated, can a) quantify the prevalence of meeting current physical activity guidelines for global surveillance and b) moving forward, could inform accelerometer-driven physical activity guidelines. Unlike cut-point methods, the metric is population-independent (e.g. age) and potentially comparable across datasets. Design: Cross-sectional, secondary data analysis. Methods: Analyses were carried out on five datasets using wrist-worn accelerometers: children (N=145), adolescent girls (N=1669), office workers (N=114), pre- (N=1218) and post- (N=1316) menopausal women, and adults with type 2 diabetes (N=475). Open-source software (GGIR) was used to generate the magnitude of acceleration above which a person’s most active 60, 30 and 2 minutes are accumulated: M60ACC; M30ACC and M2ACC, respectively. Results: The proportion of participants with M60ACC (children) and M30ACC (adults) values higher than accelerations representative of brisk walking (i.e., moderate-to-vigorous physical activity) ranged from 17-68% in children and 15%-81% in adults, tending to decline with age. The proportion of pre-and postmenopausal women with M2ACC values meeting thresholds for bone health ranged from 6-13%. Conclusions: These metrics can be used for global surveillance of physical activity, including assessing prevalence of meeting current physical activity guidelines. As accelerometer and corresponding health data accumulate it will be possible to interpret the metrics relative to age- and sex- specific norms and derive evidence-based physical activity guidelines directly from accelerometer data for use in future global surveillance. This is where the potential advantages of these metrics lie

Wear Compliance and Activity in Children Wearing Wrist and Hip-Mounted Accelerometers.

PURPOSE: This study aimed to (i) explore children's compliance to wearing wrist and hip-mounted accelerometers, (ii) compare children's physical activity (PA) derived from wrist and hip raw accelerations, and (iii) examine differences in raw and counts PA measured by hip-worn accelerometry. METHODS: One hundred and twenty nine 9-10 y old children wore a wrist-mounted GENEActiv accelerometer (GAwrist) and a hip-mounted ActiGraph GT3X+ accelerometer (AGhip) for 7 d. Both devices measured raw accelerations and the AGhip also provided counts-based data. RESULTS: More children wore the GAwrist than the AGhip regardless of wear time criteria applied (p<.001 - .035). Raw data signal vector magnitude (SVM; r = .68), moderate PA (MPA; r = .81), vigorous PA (VPA; r = .85), and moderate-to-vigorous PA (MVPA; r = .83) were strongly associated between devices (p<.001). GAwrist SVM (p = .001), MPA (p = .037), VPA (p = .002), and MVPA (p = .016) were significantly greater than AGhip. According to GAwrist raw data, 86.9% of children engaged in at least 60 min MVPA[BULLET OPERATOR]d, compared to 19% for AGhip. ActiGraph MPA (raw) was 42.00 ± 1.61 min[BULLET OPERATOR]d compared to 35.05 ± 0.99 min[BULLET OPERATOR]d (counts) (p=.02). Actigraph VPA was 7.59 ± 0.46 min[BULLET OPERATOR]d (raw) and 37.06 ± 1.85 min[BULLET OPERATOR]d (counts; p=.19). CONCLUSION: In children accelerometer wrist placement promotes superior compliance than the hip. Raw accelerations were significantly higher for GAwrist compared to AGhip, possibly due to placement location and technical differences between devices. AGhip PA calculated from raw accelerations and counts differed substantially, demonstrating that PA outcomes derived from cutpoints for raw output and counts cannot be directly compared

Modelling the Reallocation of Time Spent Sitting into Physical Activity: Isotemporal Substitution vs. Compositional Isotemporal Substitution.

Isotemporal substitution modelling (ISM) and compositional isotemporal modelling (CISM) are statistical approaches used in epidemiology to model the associations of replacing time in one physical behaviour with time in another. This study's aim was to use both ISM and CISM to examine and compare associations of reallocating 60 min of sitting into standing or stepping with markers of cardiometabolic health. Cross-sectional data collected during three randomised control trials (RCTs) were utilised. All participants (n = 1554) were identified as being at high risk of developing type 2 diabetes. Reallocating 60 min from sitting to standing and to stepping was associated with a lower BMI, waist circumference, and triglycerides and higher high-density lipoprotein cholesterol using both ISM and CISM (p < 0.05). The direction and magnitude of significant associations were consistent across methods. No associations were observed for hemoglobin A1c, total cholesterol, or low-density lipoprotein cholesterol for either method. Results of both ISM and CISM were broadly similar, allowing for the interpretation of previous research, and should enable future research in order to make informed methodological, data-driven decisions

Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K.

Funder: National Institute for Health Research; Grant(s): Biomedical Research Centre Cambridge: Nutrition, Diet, and Lifestyle Research Theme (IS-BRC-1215-20014), NIHR Applied Research Collaboration East Midlands (ARC EM), NIHR Leicester Biomedical Research CentreBackgroundPre-existing comorbidities have been linked to SARS-CoV-2 infection but evidence is sparse on the importance and pattern of multimorbidity (2 or more conditions) and severity of infection indicated by hospitalisation or mortality. We aimed to use a multimorbidity index developed specifically for COVID-19 to investigate the association between multimorbidity and risk of severe SARS-CoV-2 infection.MethodsWe used data from the UK Biobank linked to laboratory confirmed test results for SARS-CoV-2 infection and mortality data from Public Health England between March 16 and July 26, 2020. By reviewing the current literature on COVID-19 we derived a multimorbidity index including: (1) angina; (2) asthma; (3) atrial fibrillation; (4) cancer; (5) chronic kidney disease; (6) chronic obstructive pulmonary disease; (7) diabetes mellitus; (8) heart failure; (9) hypertension; (10) myocardial infarction; (11) peripheral vascular disease; (12) stroke. Adjusted logistic regression models were used to assess the association between multimorbidity and risk of severe SARS-CoV-2 infection (hospitalisation/death). Potential effect modifiers of the association were assessed: age, sex, ethnicity, deprivation, smoking status, body mass index, air pollution, 25-hydroxyvitamin D, cardiorespiratory fitness, high sensitivity C-reactive protein.ResultsAmong 360,283 participants, the median age was 68 [range 48-85] years, most were White (94.5%), and 1706 had severe SARS-CoV-2 infection. The prevalence of multimorbidity was more than double in those with severe SARS-CoV-2 infection (25%) compared to those without (11%), and clusters of several multimorbidities were more common in those with severe SARS-CoV-2 infection. The most common clusters with severe SARS-CoV-2 infection were stroke with hypertension (79% of those with stroke had hypertension); diabetes and hypertension (72%); and chronic kidney disease and hypertension (68%). Multimorbidity was independently associated with a greater risk of severe SARS-CoV-2 infection (adjusted odds ratio 1.91 [95% confidence interval 1.70, 2.15] compared to no multimorbidity). The risk remained consistent across potential effect modifiers, except for greater risk among older age. The highest risk of severe infection was strongly evidenced in those with CKD and diabetes (4.93 [95% CI 3.36, 7.22]).ConclusionThe multimorbidity index may help identify individuals at higher risk for severe COVID-19 outcomes and provide guidance for tailoring effective treatment