Activity Intensity, Volume, and Norms: Utility and Interpretation of Accelerometer Metrics

Rowlands, Alex V.; Fairclough, Stuart J.; Yates, Thomas; Edwardson, Charlotte L.; Davies, Melanie J.; Munir, Fehmidah; Khunti, Kamlesh; Stiles, Victoria

doi:10.1249/mss.0000000000002047

Cited by 46 publications

(80 citation statements)

References 25 publications

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“…MX is a novel accelerometer metric that captures the intensity (acceleration) during a person's most active period of the day. 30 In addition, we report the intensity gradient, which reflects the distribution of physical activity intensity across the 24 hour day and has been described in detail elsewhere. 31 In brief, it describes the negative curvilinear relationship between physical activity intensity and the time accumulated at that intensity during the 24 hour day.…”

Section: Chronotypementioning

confidence: 99%

Physical behaviors and chronotype in people with type 2 diabetes

Henson

Rowlands

Baldry

et al. 2020

BMJ Open Diab Res Care

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IntroductionPrevious investigations have suggested that evening chronotypes may be more susceptible to obesity-related metabolic alterations. However, whether device-measured physical behaviors differ by chronotype in those with type 2 diabetes (T2DM) remains unknown.Research design and methodsThis analysis reports data from the ongoing Chronotype of Patients with Type 2 Diabetes and Effect on Glycaemic Control (CODEC) observational study. Eligible participants were recruited from both primary and secondary care settings in the Midlands area, UK. Participants were asked to wear an accelerometer (GENEActiv, ActivInsights, Kimbolton, UK) on their non-dominant wrist for 7 days to quantify different physical behaviors (sleep, sedentary, light, moderate-to-vigorous physical activity (MVPA), intensity gradient, average acceleration and the acceleration above which the most active continuous 2, 10, 30 and 60 min are accumulated). Chronotype preference (morning, intermediate or evening) was assessed using the Morningness-Eveningness Questionnaire. Multiple linear regression analyses assessed whether chronotype preference was associated with physical behaviors and their timing. Evening chronotypes were considered as the reference group.Results635 participants were included (age=63.8±8.4 years, 34.6% female, body mass index=30.9±5.1 kg/m2). 25% (n=159) of the cohort were morning chronotypes, 52% (n=330) intermediate and 23% (n=146) evening chronotypes. Evening chronotypes had higher sedentary time (28.7 min/day, 95% CI 8.6 to 48.3) and lower MVPA levels (–9.7 min/day, –14.9 to –4.6) compared to morning chronotypes. The intensity of the most active continuous 2-60 min of the day, average acceleration and intensity gradient were lower in evening chronotypes. The timing of physical behaviors also differed across chronotypes, with evening chronotypes displaying a later sleep onset and consistently later physical activity time.ConclusionsPeople with T2DM lead a lifestyle characterized by sedentary behaviors and insufficient MVPA. This may be exacerbated in those with a preference for ‘eveningness’ (ie, go to bed late and get up late).

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Section: Chronotypementioning

confidence: 99%

Physical behaviors and chronotype in people with type 2 diabetes

Henson

Rowlands

Baldry

et al. 2020

BMJ Open Diab Res Care

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“…[12, 13]) or intensity and volume may have a cumulative effect (e.g. [14]). Two data-driven metrics that, together, capture the volume and intensity of physical activity of the 24-h activity profile are the average acceleration (indicative of volume) and the intensity gradient (intensity distribution) [15].…”

Section: Main Textmentioning

confidence: 99%

“…Together, the average acceleration and the intensity gradient enable investigation into whether the volume and intensity of physical activity have independent, additive or interactive effects on health. Using these metrics, we have recently shown that the intensity gradient was independently associated with body fatness, metabolic risk and cardiorespiratory fitness in children [14, 16]; intensity and volume were additively associated with body fat in adults; and adult bone health was highest if activity intensity was high, somewhat irrespective of volume [14].…”

Section: Main Textmentioning

confidence: 99%

“…In the following sections, we provide examples of how the translational metrics can be used to interpret and translate results obtained from applying the analytical metrics to several diverse samples of children and adults. All samples wore GENEActiv or ActiGraph raw acceleration wrist-worn accelerometers 24 h a day for up to a week [14]; data were processed in the open-source R package, GGIR [18], as previously described [14]. In brief, the average magnitude of dynamic acceleration corrected for gravity (Euclidean Norm minus 1 g , ENMO) was averaged over 5 s epochs expressed in milli-gravitational units (m g ).…”

Section: Main Textmentioning

confidence: 99%

“…Figure 1 shows the MX metrics for three groups differing in both average acceleration and intensity gradient: highest: adolescent girls ( N = 1669, age (mean ± SD) = 12.8 ± 0.8 years), mid: adult office workers ( N = 114, age = 41.2 ± 10. 9 years), lowest: adults with type 2 diabetes ( N = 475, age = 64.2 ± 8.7 years) [14]. The MX metrics are plotted on the radii of the plot, one radius for each metric.…”

Section: Main Textmentioning

confidence: 99%

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Enhancing the value of accelerometer-assessed physical activity: meaningful visual comparisons of data-driven translational accelerometer metrics

et al. 2019

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The lack of consensus on meaningful and interpretable physical activity outcomes from accelerometer data hampers comparison across studies. Cut-point analyses are simple to apply and easy to interpret but can lead to results that are not comparable. We propose that the optimal accelerometer metrics for data analysis are not the same as the optimal metrics for translation. Ideally, analytical metrics are precise continuous variables that cover the intensity spectrum, while translational metrics facilitate meaningful, public-health messages and can be described in terms of activities (e.g. brisk walking) or intensity (e.g. moderate-to-vigorous physical activity). Two analytical metrics that capture the volume and intensity of the 24-h activity profile are average acceleration (volume) and intensity gradient (intensity distribution). These allow investigation of independent, additive and interactive associations of volume and intensity of activity with health; however, they are not immediately interpretable. The MX metrics, the acceleration above which the most active X minutes are accumulated, are translational metrics that can be interpreted in terms of indicative activities. Using a range of MX metrics illustrates the intensity gradient and average acceleration (i.e. 24-h activity profile). The M120, M60, M30, M15 and M5 illustrate the most active accumulated minutes of the day, the M1/3DAY the most active accumulated 8 h of the day. We demonstrate how radar plots of MX metrics can be used to interpret and translate results from between- and within-group comparisons, provide information on meeting guidelines, assess individual activity profiles relative to percentiles and compare activity profiles between domains and/or time periods.

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Improving the identification of bone‐specific physical activity using wrist‐worn accelerometry: A cross‐sectional study in 11–12‐year‐old Australian children

Brailey,

Metcalf,

Price

et al. 2024

European Journal of Sport Science

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Physical activity (PA) during childhood and adolescence is important for the accrual of maximal peak bone mass. The precise dose that benefits bone remains unclear as methods commonly used to analyze PA data are unsuitable for measuring bone‐relevant PA. Using improved accelerometry methods, this study identified the amount and intensity of PA most strongly associated with bone outcomes in 11–12‐year‐olds. Participants (n = 770; 382 boys) underwent tibial peripheral quantitative computed tomography to assess trabecular and cortical density, endosteal and periosteal circumference and polar stress‐strain index. Seven‐day wrist‐worn raw acceleration data averaged over 1‐s epochs was used to estimate time accumulated above incremental PA intensities (50 milli‐gravitational unit (mg) increments from 200 to 3000 mg). Associations between time spent above each 50 mg increment and bone outcomes were assessed using multiple linear regression, adjusted for age, sex, height, weight, maturity, socioeconomic position, muscle cross‐sectional area and PA below the intensity of interest. There was a gradual increase in mean R2 change across all bone‐related outcomes as the intensity increased in 50 mg increments from >200 to >700 mg. All outcomes became significant at >700 mg (R2 change = 0.6%–1.3% and p = 0.001–0.02). Any further increases in intensity led to a reduction in mean R2 change and associations became non‐significant for all outcomes >1500 mg. Using more appropriate accelerometry methods (1‐s epochs; no a priori application of traditional cut‐points) enabled us to identify that ∼10 min/day of PA >700 mg (equivalent to running ∼10 km/h) was positively associated with pQCT‐derived measures of bone density, geometry and strength in 11–12‐year‐olds.

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Activity Intensity, Volume, and Norms: Utility and Interpretation of Accelerometer Metrics

Cited by 46 publications

References 25 publications

Physical behaviors and chronotype in people with type 2 diabetes

Physical behaviors and chronotype in people with type 2 diabetes

Enhancing the value of accelerometer-assessed physical activity: meaningful visual comparisons of data-driven translational accelerometer metrics

Improving the identification of bone‐specific physical activity using wrist‐worn accelerometry: A cross‐sectional study in 11–12‐year‐old Australian children

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