Moving Forward With Accelerometer-Assessed Physical Activity: Two Strategies to Ensure Meaningful, Interpretable, and Comparable Measures

Significant advances have been made in the measurement of physical activity in youth over the past decade. Monitors and protocols promote very high compliance, both night and day, and raw measures are available rather than "black box" counts. Consequently, many surveys and studies worldwide now assess children's physical behaviors (physical activity, sedentary behavior, and sleep) objectively 24 hours a day, 7 days a week using accelerometers. The availability of raw acceleration data in many of these studies is both an opportunity and a challenge. The richness of the data lends itself to the continued development of innovative metrics, whereas the removal of proprietary outcomes offers considerable potential for comparability between data sets and harmonizing data. Using comparable physical activity outcomes could lead to improved precision and generalizability of recommendations for children's present and future health. The author will discuss 2 strategies that he believes may help ensure comparability between studies and maximize the potential for data harmonization, thereby helping to capitalize on the growing body of accelerometer data describing children's physical behaviors

Associations of Physical Behaviours and Behavioural Reallocations with Markers of Metabolic Health: A Compositional Data Analysis.

Standard statistical modelling has shown that the reallocation of sitting time to either standing or stepping may be beneficial for metabolic health. However, this overlooks the inherent dependency of time spent in all behaviours. The aim is to examine the associations between physical behaviours and markers of metabolic health (fasting glucose, fasting insulin, 2-h glucose, 2-h insulin, Homeostasis Model Assessment of Insulin Sensitivity (HOMA-IS), Matsuda Insulin Sensitivity Index (Matsuda-ISI) while quantifying the associations of reallocating time from one physical behaviour to another using compositional analysis. Objectively measured physical behaviour data were analysed (n = 435) using compositional analysis and compositional isotemporal substitutions to estimate the association of reallocating time from one behaviour to another in a population at high risk of type 2 diabetes mellitus (T2DM). Stepping time was associated with all markers of metabolic health relative to all other behaviours. Reallocating 30 min from sleep, sitting, or standing to stepping was associated with 5⁻6 fold lower 2-h glucose, 15⁻17 fold lower 2-h insulin, and higher insulin sensitivity (10⁻11 fold via HOMA-IS, 12⁻15 fold via Matsuda-ISI). Associations of reallocating time from any behaviour to stepping were maintained for 2-h glucose, 2-h insulin, and Matsuda-ISI after further adjusting for body mass index (BMI). Relocating time from stepping into sleep, sitting, or standing was associated with lower insulin sensitivity. Stepping time may be the most important behavioural composition when promoting improved metabolic health in adults at risk of T2DM

Twenty-four-hour physical behaviour profiles across type 2 diabetes mellitus subtypes

Aim: To investigate how 24-h physical behaviours differ across type 2 diabetes (T2DM) subtypes. Materials and Methods: We included participants living with T2DM, enrolled as part of an ongoing observational study. Participants wore an accelerometer for 7 days to quantify physical behaviours across 24 h. We used routinely collected clinical data (age at onset of diabetes, glycated haemoglobin level, homeostatic model assessment index of beta-cell function, homeostatic model assessment index of insulin resistance, body mass index) to replicate four previously identified subtypes (insulin-deficient diabetes [INS-D], insulin-resistant diabetes [INS-R], obesity-related diabetes [OB] and age-related diabetes [AGE]), via k-means clustering. Differences in physical behaviours across the diabetes subtypes were assessed using generalized linear models, with the AGE cluster as the reference. Results: A total of 564 participants were included in this analysis (mean age 63.6 ± 8.4 years, 37.6% female, mean age at diagnosis 53.1 ± 10.0 years). The proportions in each cluster were as follows: INS-D: n = 35, 6.2%; INS-R: n = 88, 15.6%; OB: n = 166, 29.4%; and AGE: n = 275, 48.8%. Compared to the AGE cluster, the OB cluster had a shorter sleep duration (−0.3 h; 95% confidence interval [CI] −0.5, −0.1), lower sleep efficiency (−2%; 95% CI −3, −1), lower total physical activity (−2.9 mg; 95% CI −4.3, −1.6) and less time in moderate-to-vigorous physical activity (−6.6 min; 95% CI −11.4, −1.7), alongside greater sleep variability (17.9 min; 95% CI 8.2, 27.7) and longer sedentary time (31.9 min; 95% CI 10.5, 53.2). Movement intensity during the most active continuous 10 and 30 min of the day was also lower in the OB cluster. Conclusions: In individuals living with T2DM, the OB subtype had the lowest levels of physical activity and least favourable sleep profiles. Such behaviours may be suitable targets for personalized therapeutic lifestyle interventions

Self-reported walking pace: A simple screening tool with lowest risk of all-cause mortality in those that ‘walk the talk’

To determine whether the association between self-reported walking pace and all-cause mortality (ACM) persists across categories of accelerometer-assessed physical activity status. Data from 93,709 UK Biobank participants were included. Physical activity was assessed using wrist-worn accelerometers for 7-days. Participants accumulating <150 min/week moderate-to-vigorous- activity were classed as “inactive”, ≥150 min/week moderate (≥3 METs) activity as “somewhat active” excluding those with ≥150 min/week upper-moderate-to-vigorous activity (≥4.3 METs), who were classed as “high-active”. Over a 6.3 y (median) follow-up, 2,173 deaths occurred. More than half of slow walkers were “inactive”, but only 26% of steady and 12% of brisk walkers. Associations between walking pace and ACM were consistent with those for activity. “High active” brisk walkers had the lowest risk of ACM (Hazard Ratio (HR) 0.22; 95% CI: 0.17,0.28), relative to “inactive” slow walkers. Within those classed as “inactive”, steady (HR 0.54; 0.46,0.64) and brisk walkers (HR 0.42; 0.34,0.52) had lower risk than slow walkers. In conclusion, self-reported walking pace was associated with accelerometer-assessed physical activity with both exposures having similar associations with ACM. “inactive”, steady, and brisk walkers had lower ACM risk than slow walkers. The pattern was similar for “High active” participants. Overall, “High active” brisk walkers had lowest risk.</p

High intensity interval training does not result in short- or long-term dietary compensation in cardiac rehabilitation: Results from the FITR heart study

The aim of this study was to investigate short- and long-term compensatory effects on dietary intake following high intensity interval training (HIIT) compared with usual care moderate intensity continuous training (MICT) during and following a cardiac rehabilitation program. This study investigates secondary outcomes of a clinical trial. Ninety-three participants with coronary artery disease enrolled in a 4-week cardiac rehabilitation program, were randomised to 1) 4x4-minute HIIT; or 2) 40-min of MICT (usual care). Patients were instructed to complete 3 weekly sessions (2 supervised, 1 home-based) for 4-weeks, and 3 weekly home-based sessions thereafter for another 48-weeks. Dietary intake was measured by telephone-based 24-h recall over 2 day at baseline, 4-weeks, 3-months, 6-months, and 12-months. Three-Factor Eating Questionnaire was used to measure dietary behaviour and Leeds Food Preference Questionnaire used to measure food preferences. Appetite was assessed by a visual analogue scale and appetite-regulating hormones. There was no change over the study period or differences between groups for daily energy intake at 4-weeks or 12-months. There were also no group differences for any other measures of dietary intake, fasting hunger or appetite-related hormones, dietary behaviour, or food preferences. These findings suggest that compared to moderate intensity exercise, HIIT does not result in compensatory increases of energy intake or indicators of poor diet quality. This finding appears to be the same for patients with normal weight and obesity. HIIT can therefore be included in cardiac rehabilitation programs as an adjunct or alterative to MICT, without concern for any undesirable dietary compensation

Can activity monitors predict outcomes in patients with heart failure? A systematic review

Actigraphy is increasingly incorporated into clinical practice to monitor intervention effectiveness and patient health in congestive heart failure (CHF). We explored the prognostic impact of actigraphy-quantified physical activity (AQPA) on CHF outcomes. PubMed and Medline databases were systematically searched for cross-sectional studies, cohort studies or randomised controlled trials from January 2007 to December 2017. We included studies that used validated actigraphs to predict outcomes in adult HF patients. Study selection and data extraction were performed by two independent reviewers. A total of 17 studies (15 cohort, 1 cross-sectional, 1 randomised controlled trial) were included, reporting on 2,759 CHF patients (22-89 years, 27.7% female). Overall, AQPA showed a strong inverse relationship with mortality and predictive utility when combined with established risk scores, and prognostic roles in morbidity, predicting cognitive function, New York Heart Association functional class and intercurrent events (e.g. hospitalisation), but weak relationships with health-related quality of life scores. Studies lacked consensus regarding device choice, time points and thresholds of PA measurement, which rendered quantitative comparisons between studies difficult. AQPA has a strong prognostic role in CHF. Multiple sampling time points would allow calculation of AQPA changes for incorporation into risk models. Consensus is needed regarding device choice and AQPA thresholds, while data management strategies are required to fully utilise generated data. Big data and machine learning strategies will potentially yield better predictive value of AQPA in CHF patients

The impact of COVID-19 restrictions on accelerometer-assessed physical activity and sleep in individuals with type 2 diabetes

AimsRestrictions during the COVID‐19 crisis will have impacted on opportunities to be active. We aimed to (a) quantify the impact of COVID‐19 restrictions on accelerometer‐assessed physical activity and sleep in people with type 2 diabetes and (b) identify predictors of physical activity during COVID‐19 restrictions.MethodsParticipants were from the UK Chronotype of Patients with type 2 diabetes and Effect on Glycaemic Control (CODEC) observational study. Participants wore an accelerometer on their wrist for 8 days before and during COVID‐19 restrictions. Accelerometer outcomes included the following: overall physical activity, moderate‐to‐vigorous physical activity (MVPA), time spent inactive, days/week with ≥30‐minute continuous MVPA and sleep. Predictors of change in physical activity taken pre‐COVID included the following: age, sex, ethnicity, body mass index (BMI), socio‐economic status and medical history.ResultsIn all, 165 participants (age (mean±S.D = 64.2 ± 8.3 years, BMI=31.4 ± 5.4 kg/m2, 45% women) were included. During restrictions, overall physical activity was lower by 1.7 mg (~800 steps/day) and inactive time 21.9 minutes/day higher, but time in MVPA and sleep did not statistically significantly change. In contrast, the percentage of people with ≥1 day/week with ≥30‐minute continuous MVPA was higher (34% cf. 24%). Consistent predictors of lower physical activity and/or higher inactive time were higher BMI and/or being a woman. Being older and/or from ethnic minorities groups was associated with higher inactive time.ConclusionsOverall physical activity, but not MVPA, was lower in adults with type 2 diabetes during COVID‐19 restrictions. Women and individuals who were heavier, older, inactive and/or from ethnic minority groups were most at risk of lower physical activity during restrictions.</div

Comparing 24 h physical activity profiles: Office workers, women with a history of gestational diabetes and people with chronic disease condition(s)

This study demonstrates a novel data-driven method of summarising accelerometer data to profile physical activity in three diverse groups, compared with cut-point determined moderate-to-vigorous physical activity (MVPA). GGIR was used to generate average daily acceleration, intensity gradient, time in MVPA and MX metrics (acceleration above which the most active X-minutes accumulate) from wrist-worn accelerometer data from three datasets: office-workers (OW, N = 697), women with a history of post-gestational diabetes (PGD, N = 267) and adults with ≥1 chronic disease (CD, N = 1,325). Average acceleration and MVPA were lower in CD, but not PGD, relative to OW (−5.2 mg and −30.7 minutes, respectively, P < 0.001). Both PGD and CD had poorer intensity distributions than OW (P < 0.001). Application of a cut-point to the M30 showed 7%, 17% and 28%, of OW, PGD and CD, respectively, accumulated 30 minutes of brisk walking per day. Radar plots showed OW had higher overall activity than CD. The relatively poor intensity distribution of PGD, despite similar overall activity to OW, was due to accumulation of more light and less higher intensity activity. These data-driven methods identify aspects of activity that differ between groups, which may be missed by cut-point methods alone

Moderate-intensity stepping in older adults: insights from treadmill walking and daily living

Background: A step cadence of 100 steps/minute is widely used to define moderate-intensity walking. However, the generalizability of this threshold to different populations needs further research. We investigate moderate-intensity step cadence values during treadmill walking and daily living in older adults. Methods: Older adults (≥ 60 years) were recruited from urban community venues. Data collection included 7 days of physical activity measured by an activPAL3™ thigh worn device, followed by a laboratory visit involving a 60-min assessment of resting metabolic rate, then a treadmill assessment with expired gas measured using a breath-by-breath analyser and steps measured by an activPAL3™. Treadmill stages were undertaken in a random order and lasted 5 min each at speeds of 1, 2, 3, 4 and 5 km/h. Metabolic equivalent values were determined for each stage as standardised values (METSstandard) and as multiples of resting metabolic rate (METSrelative). A value of 3 METSstandard defined moderate-intensity stepping. Segmented generalised estimating equations modelled the association between step cadence and MET values. Results: The study included 53 participants (median age = 75, years, BMI = 28.0 kg/m2, 45.3% women). At 2 km/h, the median METSstandard and METSrelative values were above 3 with a median cadence of 81.00 (IQR 72.00, 88.67) steps/minute. The predicted cadence at 3 METSstandard was 70.3 (95% CI 61.4, 75.8) steps/minute. During free-living, participants undertook median (IQR) of 6988 (5933, 9211) steps/day, of which 2554 (1297, 4456) steps/day were undertaken in continuous stepping bouts lasting ≥ 1 min. For bouted daily steps, 96.4% (90.7%, 98.9%) were undertaken at ≥ 70 steps/minute. Conclusion: A threshold as low as 70 steps/minute may be reflective of moderate-intensity stepping in older adults, with the vast majority of all bouted free-living stepping occurring above this threshold

Association between Chronotype and Physical Behaviours in Adolescent Girls

The aim of this study was to (1) describe accelerometer-assessed physical behaviours by chronotype, and (2) examine the association between chronotype and accelerometer-assessed physical behaviours in a cohort of adolescent girls. Chronotype (single question) and physical behaviours (GENEActiv accelerometer on the non-dominant wrist) were assessed in 965 adolescent girls (13.9 ± 0.8 years). Linear mixed-effects models examined the relationships among chronotype and physical behaviours (time in bed, total sleep time, sleep efficiency, sedentary time, overall, light and moderate-to-vigorous physical activity) on weekdays and weekend days. Over the 24 h day, participants spent 46% sedentary, 20% in light activity, 3% in moderate-to-vigorous physical activity, and 31% in ‘time in bed’. Seventy percent of participants identified as ‘evening’ chronotypes. Compared to evening chronotypes, morning chronotypes engaged in less sedentary time (10 min/day) and had higher overall physical activity (1.3 mg/day, ~30 min of slow walking) on weekdays. Most girls identified as evening chronotypes with a large proportion of their day spent sedentary and a small amount in physical activities which may be exacerbated in evening chronotypes on weekdays. The results maybe be important for programmes aiming to promote physical activity in adolescent girls