Free-living Behavior Research Articles

Classifying physical activity levels using Mean Amplitude Deviation in adults using a chest worn accelerometer: validation of the Vivalink ECG Patch

BackgroundThe development of readily available wearable accelerometers has enabled clinicians to objectively monitor physical activity (PA) remotely in the community, a superior alternative to patient self-reporting measures. Critical to the value of these monitors is the ability to reliably detect when patients are undergoing ambulatory activity. Previous studies have highlighted the strength of using mean amplitude deviation (MAD) as a universal measure for analysing raw accelerometery data and defining cut-points between sedentary and ambulatory activities. Currently however there is little evidence surrounding the use of chest-worn accelerometers which can provide simultaneous monitoring of other physiological parameters such as heart rate (HR), RR intervals, and Respiratory Rate alongside accelerometery data. We aimed to calibrate the accelerometery function within the VivaLink ECG patch to determine the cut-point MAD value for differentiating sedentary and ambulatory activities.MethodsWe recruited healthy volunteers to undergo a randomised series of 9 activities that simulate typical free-living behaviours, while wearing a VivaLink ECG Patch (Campbell, California). MAD values were applied to a Generalised Linear Mixed Model to determine cut-points between sedentary and ambulatory activities. We constructed a Receiver Operating Characteristic (ROC) curve to analyse the sensitivity and specificity of the cut-off MAD value.ResultsEighteen healthy adults volunteered to the study and mean MAD values were collected for each activity. The optimal MAD cut-point between sedentary and ambulatory activities was 47.73mG. ROC curve analysis revealed an area under the curve of 0.99 (p < 0.001) for this value with a sensitivity and specificity of 98% and 100% respectively.ConclusionIn conclusion, the MAD cut-point determined in our study is very effective at categorising sedentary and ambulatory activities among healthy adults and may be of use in monitoring PA in the community with minimal burden. It will also be useful for future studies aiming to simultaneously monitor PA with other physiological parameters via chest worn accelerometers.

Feasibility of a laboratory-based protocol for measuring energy expenditure and accelerometer calibration in adults with intellectual disabilities

Adults with intellectual disabilities experience numerous health inequalities. Targeting unhealthy lifestyle behaviours, such as high levels of sedentary behaviour and overweight/obesity, is a priority area for improving the health and adults with intellectual disabilities and reducing inequalities. Energy expenditure is a fundamental component of numerous health behaviours and an essential component of various free-living behaviour measurements, e.g. accelerometry. However, little is known about energy expenditure in adults with intellectual disabilities and no population-specific accelerometer data interpretation methods have been calibrated. The limited research in this area suggests that adults with intellectual disabilities have a higher energy expenditure, which requires further exploration, and could have significant impacts of device calibration. However, due to the complex methods required for measuring energy expenditure, it is essential to first evaluate feasibility and develop an effective protocol. This study aims to test the feasibility of a laboratory-based protocol to enable the measurement of energy expenditure and accelerometer calibration in adults with intellectual disabilities.We aimed to recruit ten adults (≥ 18 years) with intellectual disabilities. The protocol involved a total of nine sedentary, stationary, and physical activities, e.g. sitting, lying down, standing, and treadmill walking. Each activity was for 5 min, with one 10 min lying down activity to measure resting energy expenditure. Breath by breath respiratory gas exchange and accelerometry (ActiGraph and ActivPAL) were measured during each activity. Feasibility was assessed descriptively using recruitment and outcome measurement completion rates, and participant/stakeholder feedback.Ten adults (N = 7 female) with intellectual disabilities participated in this study. The recruitment rate was 50% and 90% completed the protocol and all outcome measures. Therefore, the recruitment strategy and protocol are feasible.This study addresses a significant gap in our knowledge relating to exercise laboratory-based research for adults with intellectual disabilities The findings from this study provide essential data that can be used to inform the development of future protocols to measure energy expenditure and for accelerometer calibration in adults with intellectual disabilities.

Day-to-Day Variability and Year-to-Year Reproducibility of Accelerometer-Measured Free-Living Sit-to-Stand Transitions Volume and Intensity among Community-Dwelling Older Adults.

(1) Background: The purpose of this study was to evaluate the day-to-day variability and year-to-year reproducibility of an accelerometer-based algorithm for sit-to-stand (STS) transitions in a free-living environment among community-dwelling older adults. (2) Methods: Free-living thigh-worn accelerometry was recorded for three to seven days in 86 (women n = 55) community-dwelling older adults, on two occasions separated by one year, to evaluate the long-term consistency of free-living behavior. (3) Results: Year-to-year intraclass correlation coefficients (ICC) for the number of STS transitions were 0.79 (95% confidence interval, 0.70–0.86, p < 0.001), for mean angular velocity—0.81 (95% ci, 0.72–0.87, p < 0.001), and maximal angular velocity—0.73 (95% ci, 0.61–0.82, p < 0.001), respectively. Day-to-day ICCs were 0.63–0.72 for number of STS transitions (95% ci, 0.49–0.81, p < 0.001) and for mean angular velocity—0.75–0.80 (95% ci, 0.64–0.87, p < 0.001). Minimum detectable change (MDC) was 20.1 transitions/day for volume, 9.7°/s for mean intensity, and 31.7°/s for maximal intensity. (4) Conclusions: The volume and intensity of STS transitions monitored by a thigh-worn accelerometer and a sit-to-stand transitions algorithm are reproducible from day to day and year to year. The accelerometer can be used to reliably study STS transitions in free-living environments, which could add value to identifying individuals at increased risk for functional disability.

Free-Living Motor Activity Monitoring in Ataxia-Telangiectasia.

With disease-modifying approaches under evaluation in ataxia-telangiectasia and other ataxias, there is a need for objective and reliable biomarkers of free-living motor function. In this study, we test the hypothesis that metrics derived from a single wrist sensor worn at home provide accurate, reliable, and interpretable information about neurological disease severity in children with A-T.A total of 15 children with A-T and 15 age- and sex-matched controls wore a sensor with a triaxial accelerometer on their dominant wrist for 1 week at home. Activity intensity measures, derived from the sensor data, were compared with in-person neurological evaluation on the Brief Ataxia Rating Scale (BARS) and performance on a validated computer mouse task.Children with A-T were inactive the same proportion of each day as controls but produced more low intensity movements (p < 0.01; Cohen’s d = 1.48) and fewer high intensity movements (p < 0.001; Cohen’s d = 1.71). The range of activity intensities was markedly reduced in A-T compared to controls (p < 0.0001; Cohen’s d = 2.72). The activity metrics correlated strongly with arm, gait, and total clinical severity (r: 0.71–0.87; p < 0.0001), correlated with specific computer task motor features (r: 0.67–0.92; p < 0.01), demonstrated high reliability (r: 0.86–0.93; p < 0.00001), and were not significantly influenced by age in the healthy control group.Motor activity metrics from a single, inexpensive wrist sensor during free-living behavior provide accurate and reliable information about diagnosis, neurological disease severity, and motor performance. These low-burden measurements are applicable independent of ambulatory status and are potential digital behavioral biomarkers in A-T.

Competitive interactions in plant-parasitic nematode communities affecting organic vegetable cropping systems

Plant-parasitic nematodes (PPN) are detected everywhere as mixed-species communities. Most non-chemical control strategies of PPN only target some species, thus raising questions about the consequences that this specificity may have on the residual community. In this respect, the long-term ecological sustainability of such strategies is challenged. In order to evaluate the impacts of agronomical practices on PPN communities, two four-year experiments that differed by the presence or absence of root-knot nematodes (RKN - Meloidogyne spp.) were carried out under cold shelters in the south of France, under native field conditions of vegetable cropping systems that included a nematicidal sorghum green manure and a pepper variety carrying a RKN resistance gene. At the site with RKN, RKN populations developed on susceptible vegetables. They were controlled by the green manure but not by the R-pepper, and were also vulnerable to low soil temperatures. At the site without RKN, Paratylenchidae populations developed on susceptible vegetables, but were controlled by both the green manure and the R-pepper, and not by low temperatures. At each site, populations of Telotylenchidae exhibited dynamics suggesting competition with RKN or Paratylenchidae. Hypotheses about competition models are discussed according to the specific life traits of the PPN involved, including ecto- vs. endoparasitism and sedentary vs. free-living behaviour, and to the antagonist mechanisms of the cover and resistant crops that must be introduced in vegetable cropping systems.

Choice of Processing Method for Wrist-Worn Accelerometers Influences Interpretation of Free-Living Physical Activity Data in a Clinical Sample

Wrist-worn accelerometers are increasingly used to assess free-living physical activity (PA), but the implications of different processing methods are not well characterized. To advance research in this area it is important to better understand how choice of processing method influences estimates of free-living PA behavior. This study compared PA profiles resulting from processing wrist-worn data collected under free-living conditions using four different methods in a clinical sample of 160 women with chronic pain, a condition for which PA serves as a treatment. Participants wore monitors on their non-dominant wrist for 7 days and completed a self-report PA measure. Processing methods were Hildebrand linear, a modified nonlinear Hildebrand, Staudenmayer linear, and Staudenmayer random forest. Using each method, minutes per day in sedentary, light, and moderate-to-vigorous PA (MVPA) were estimated and individuals were classified as meeting PA guidelines based on their accumulated MVPA. Comparisons of outcomes among processing methods and with self-reported PA were made using repeated measures ANOVA, correlations, and kappa statistics. With few exceptions, estimated time at each intensity differed significantly across processing methods and with self-report (p &lt; .001). Correlations between methods ranged widely (ρrange = 0.09 to 1.00) and showed inconsistent agreement for classifying individuals as meeting PA guidelines (κrange: −0.02 to 0.90). Thus, choice of processing method significantly influenced conclusions regarding free-living PA. Researchers and clinicians should exercise caution when interpreting accelerometer activity data and comparing across existing studies using different processing methods when examining how PA influences clinical conditions.

Normative Peak 30-Min Cadence (Steps per Minute) Values for Older Adults: NHANES 2005-2006.

Walking cadence (steps per minute) is associated with the intensity of ambulatory behavior. This analysis provides normative values for peak 30-min cadence, an indicator of "natural best effort" during free-living behavior. A sample of 1,196 older adults (aged from 60 to 85+) with accelerometer data from the National Health and Nutrition Examination Survey 2005-2006 was used. Peak 30-min cadence was calculated for each individual. Quintile-defined values were computed, stratified by sex and age groups. Smoothed sex-specific centile curves across the age span were fitted using the LMS method. Peak 30-min cadence generally trended lower as age increased. The uppermost quintile value was >85steps/min (men: 60-64years), and the lowermost quintile value was <22steps/min (women: 85+). The highest 95th centile value was 103steps/min (men: 64-70years), and the lowest 5th centile value was 15steps/min (women: 85+). These normative values may be useful for evaluating older adults' "natural best effort" during free-living ambulatory behavior.

Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants

Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Hidden Markov Models, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 159,504 minutes of recorded free-living behaviours from 132 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,220 participants. For example, we find that men spend more time in both low- and high- intensity behaviours, while women spend more time in mixed behaviours. Walking time is highest in spring and sleep time lowest during the summer. This work opens the possibility of future public health guidelines informed by the health consequences associated with specific, objectively measured, physical activity and sleep behaviours.

Free-living Walking Behavior in Persons with Multiple Sclerosis at Increased and Normal Fall Risk

Free-living Walking Behavior in Persons with Multiple Sclerosis at Increased and Normal Fall Risk

Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification

Accelerometers are a valuable tool for objective measurement of physical activity (PA). Wrist-worn devices may improve compliance over standard hip placement, but more research is needed to evaluate their validity for measuring PA in free-living settings. Traditional cut-point methods for accelerometers can be inaccurate and need testing in free living with wrist-worn devices. In this study, we developed and tested the performance of machine learning (ML) algorithms for classifying PA types from both hip and wrist accelerometer data. Forty overweight or obese women (mean age = 55.2 ± 15.3 yr; BMI = 32.0 ± 3.7) wore two ActiGraph GT3X+ accelerometers (right hip, nondominant wrist; ActiGraph, Pensacola, FL) for seven free-living days. Wearable cameras captured ground truth activity labels. A classifier consisting of a random forest and hidden Markov model classified the accelerometer data into four activities (sitting, standing, walking/running, and riding in a vehicle). Free-living wrist and hip ML classifiers were compared with each other, with traditional accelerometer cut points, and with an algorithm developed in a laboratory setting. The ML classifier obtained average values of 89.4% and 84.6% balanced accuracy over the four activities using the hip and wrist accelerometer, respectively. In our data set with average values of 28.4 min of walking or running per day, the ML classifier predicted average values of 28.5 and 24.5 min of walking or running using the hip and wrist accelerometer, respectively. Intensity-based cut points and the laboratory algorithm significantly underestimated walking minutes. Our results demonstrate the superior performance of our PA-type classification algorithm, particularly in comparison with traditional cut points. Although the hip algorithm performed better, additional compliance achieved with wrist devices might justify using a slightly lower performing algorithm.

New insights: animal-borne cameras and accelerometers reveal the secret lives of cryptic species.

Logging cameras and accelerometers have opened our eyes to the secret lives of many enigmatic species. Here some of the new opportunities provided by this technology are reviewed. Recent discoveries are highlighted including the observation of selective feeding on energy-rich parts of prey. As such, biologging cameras provide new opportunities for consideration of selective feeding within the same sort of theoretical framework (marginal value theory/optimal foraging) that exploitation of prey patches has been examined. A recent study with the world's largest bony fish, the ocean sunfish (Mola mola), is highlighted where animal-borne cameras allowed the ground-truthing of data sets collected with depth recorders and accelerometers. This synergistic use of a range of biologging approaches will help drive an holistic understanding of the free-living behaviour of a range of species.

Response

Dear Editor-in-Chief: We thank Dr. Thompson et al. (7) for their thoughtful and complimentary comments regarding our recent review. We certainly agree with their proposal that to fully appreciate the impact of exercise on total daily energy expenditure (TDEE), one must consider the activity or activities that are being replaced. Indeed, recent data from one of the coauthors of our review indicate that sedentary time did not change when prescribed exercise was performed by previously sedentary individuals, suggesting that some form of substitution of occurred (5). From the perspective of energy balance, however, the impact will be the same; that is, whether prescribed exercise causes a reduction in nonexercise physical activity or if it simply replaces nonexercise physical activity, the increase in TDEE will be less than would be expected based on simple addition (or in cases of complete compensation or equal substitution, no increase would occur). However, given the emerging data clearly indicating that sedentary behavior confers health risks independent of physical activity behavior (4), we agree that it is an area that warrants attention in future investigations. An important consideration will be that the degrees of and variability in compensation and substitution will likely differ between free-living and laboratory-based investigations. Evolving technologies (6) provide an opportunity to more fully characterize the impact of prescribed exercise on multiple components of physical activity and sedentary behavior in free-living humans, and more studies using these technologies are needed. We also agree with Thompson et al. that prescribing exercise for individuals who already have high levels of nonexercise physical activity will have minimal impact on TDEE if exercise replaces some of the nonexercise physical activity; this is rather intuitive. However, the focus of our review was on prescribing exercise for weight loss in obese individuals. Because high levels of PA are associated with reduced risk of obesity (1) and prospective weight gain (2,3), it is not likely that many individuals seeking to lose weight through exercise are achieving high levels of PA in their normal lives. In our mind, the optimal exercise intervention is one that increases physical activity while at the same time reducing sedentary behavior. That is, the prescribed exercise replaces the sedentary behavior, not any existing habitually physical activity. However, it is a safe assumption that for interventions in extremely sedentary individuals, there is a “ceiling” to the amount of sedentary behavior. Therefore, any increments in physically activity will inevitably lead to a net increase in EE. We feel it is critical to recognize, as highlighted in our review, that some individuals may adopt compensatory behaviors (increased energy intake and/or reduced activity) that limit weight loss. Studies that utilize technologies to monitor free-living behavior may help identify susceptible individuals, and researchers can then develop strategies to minimize the impact of this substitution/compensation through targeted intervention. These studies will inform practitioners on how to prescribe behavioral change to induce weight loss and achieve successful weight loss maintenance. Edward Melanson, PhD University of Colorado Anschutz Medical Campus Aurora, CO Sarah Kozy Keadle, PhD National Cancer Institute Bethesda, MD Joseph E. Donnelly, PhD University of Kansas Lawrence, KS Barry Braun, PhD University of Massachusetts Amherst, MA Neal A. King, PhD Queensland University of Technology Brisbane, Australia

Event-based analysis of free-living behaviour

Introduction: The quantification of free-living physical activities (PA) is important in understanding how physical activity and sedentary behaviour impact on health and how interventions might modify free-living behaviour to enhance health. Quantification, and the terminology used, has often been determined by the choice of measurement technique. Many systems use cumulated acceleration over fixed epochs resulting in outcomes of counts which do not have any real physical units and can be difficult to interpret. The aims of this paper are to describe a systematic approach for the analysis of PA.

Validity of Two Wearable Monitors to Estimate Breaks from Sedentary Time

Investigations using wearable monitors have begun to examine how sedentary time behaviors influence health. The objective of this study is to demonstrate the use of a measure of sedentary behavior and to validate the activPAL (PAL Technologies Ltd., Glasgow, Scotland) and ActiGraph GT3X (Actigraph, Pensacola, FL) for estimating measures of sedentary behavior: absolute number of breaks and break rate. Thirteen participants completed two 10-h conditions. During the baseline condition, participants performed normal daily activity, and during the treatment condition, participants were asked to reduce and break up their sedentary time. In each condition, participants wore two ActiGraph GT3X monitors and one activPAL. The ActiGraph was tested using the low-frequency extension filter (AG-LFE) and the normal filter (AG-Norm). For both ActiGraph monitors, two count cut points to estimate sedentary time were examined: 100 and 150 counts per minute. Direct observation served as the criterion measure of total sedentary time, absolute number of breaks from sedentary time, and break rate (number of breaks per sedentary hour (brk·sed-h)). Break rate was the only metric sensitive to changes in behavior between baseline (5.1 [3.3-6.8] brk·sed-h) and treatment conditions (7.3 [4.7-9.8] brk·sed-h) (mean (95% confidence interval)). The activPAL produced valid estimates of all sedentary behavior measures and was sensitive to changes in break rate between conditions (baseline, 5.1 [2.8-7.1] brk·sed-h; treatment, 8.0 [5.8-10.2] brk·sed-h). In general, the AG-LFE and AG-Norm were not accurate in estimating break rate or the absolute number of breaks and were not sensitive to changes between conditions. This study demonstrates the use of expressing breaks from sedentary time as a rate per sedentary hour, a metric specifically relevant to free-living behavior, and provides further evidence that the activPAL is a valid tool to measure components of sedentary behavior in free-living environments.

Use of wearable cameras to assess population physical activity behaviours: an observational study

BackgroundInsufficient levels of physical activity are associated with increased morbidity and mortality for many non-communicable diseases, as identified by the Chief Medical Officer reports and WHO. However, many physical-activity-related health associations have been based on self-report measurement, which is limited by recall, comprehension, and social desirability bias. Current objective measures, such as the accelerometer, can identify episodes of physical activity. Accelerometers are unable to identify detailed type and context behavioural information. First-person point-of-view images can capture health-related behaviours, and the context in which they occur. Historically, wearable cameras were bespoke devices, with poor battery life, constructed in individual life-logging research groups. However, the SenseCam is wearable camera, able to record a full day's worth of behaviours, which other researchers now have access to. This study investigates the feasibility of the SenseCam wearable camera to objectively categorise the type and context of participants' accelerometer-identified episodes of activity. MethodsA convenience sample of 52 university workers was recruited for this study from the USA (n=37) and New Zealand (n=15). Adults were given an Actical hip-mounted accelerometer (Mini-Mitter, Respironics, Bend, OR, USA) and a SenseCam (Vicon Oxford Metrics Group, UK). The device is a lightweight camera worn via a laynard around the neck. The camera has several sensors including: tri-axial accelerometer, magnetometer, ambient temperature, light level, and passive infrared. Images are captured about once every 20 s, and are based on a change in the aforementioned sensor values. The onboard clocks on the SenseCam and Actical devices were time-synchronised. Participants engaged in free-living activities for 3 days. Accelerometer data were cleaned and episodes of sedentary, lifestyle-light, lifestyle-moderate, and moderate-to-vigorous physical activity (MVPA) were identified with standard algorithms. Accelerometer episodes were manually categorised according to their context and physical-activity-compendium code as identified from time-matched SenseCam images. Findings212 days of footage from 49 participants was analysed. SenseCam images, type and context attributes were coded for 386 randomly selected episodes, taking 63 s (95% CI 41–86) to manually code each episode. 12 categories, and 114 subcategory types, which aligned with the physical activity compendium, were identified. About a fifth of episodes could not be classified, mostly because of participant compliance issues. 311 (81%) episodes could be categorised. Of the coded data, 183 (59%) were outdoors versus 120 (39%) indoors; 104 (33%) episodes were recorded as leisure time activities, 103 (33%) transport, 57 (18%) domestic, and 47 (15%) occupational. 104 (33%) of the exemplar episodes contained direct social interaction and 67 (22%) were in social situations in which the participant was not involved in direct engagement. InterpretationWearable cameras can provide data for objective categorisation of accelerometer-defined episodes of activity in free-living situations. However, these devices need further validation against other measures of behaviour such as direct observation. Future studies should investigate state-of-art computer vision techniques to automatically identify behaviours from SenseCam images. National surveillance systems such as Health Survey for England, Biobank, and National Health and Nutrition Examination Survey could use wearable cameras in a subset of participants to develop more appropriate accelerometer classification algorithms of free-living behaviours. FundingBritish Heart Foundation (grant 021/P&C/Core/2010/HPRG), Irish Health Research Board (MCPD/2010/12), and Microsoft Research PhD Scholarship Programme.

Event-based analysis of free-living behaviour

The quantification of free-living physical activities is important in understanding how physical activity and sedentary behaviour impact on health and also on how interventions might modify free-living behaviour to enhance health. Quantification, and the terminology used, has in many ways been determined by the choice of measurement technique. The inter-related issues around measurement devices and terminology used are explored. This paper proposes a terminology and a systematic approach for the analysis of free-living activity information using event-based activity data. The event-based approach uses a flexible hierarchical classification of events and, dependent on the research question, analysis can then be undertaken on a selection of these events. The quantification of free-living behaviour is therefore the result of the analysis on the patterns of these chosen events. The application of this approach is illustrated with results from a range of published studies by our group showing how event-based analysis provides a flexible yet robust method of addressing the research question(s) and provides a deeper insight into free-living behaviour. It is proposed that it is through event-based analysis we can more clearly understand how behaviour is related to health and also how we can produce more relevant outcome measures.

How many steps/day are enough? for adults

Physical activity guidelines from around the world are typically expressed in terms of frequency, duration, and intensity parameters. Objective monitoring using pedometers and accelerometers offers a new opportunity to measure and communicate physical activity in terms of steps/day. Various step-based versions or translations of physical activity guidelines are emerging, reflecting public interest in such guidance. However, there appears to be a wide discrepancy in the exact values that are being communicated. It makes sense that step-based recommendations should be harmonious with existing evidence-based public health guidelines that recognize that "some physical activity is better than none" while maintaining a focus on time spent in moderate-to-vigorous physical activity (MVPA). Thus, the purpose of this review was to update our existing knowledge of "How many steps/day are enough?", and to inform step-based recommendations consistent with current physical activity guidelines. Normative data indicate that healthy adults typically take between 4,000 and 18,000 steps/day, and that 10,000 steps/day is reasonable for this population, although there are notable "low active populations." Interventions demonstrate incremental increases on the order of 2,000-2,500 steps/day. The results of seven different controlled studies demonstrate that there is a strong relationship between cadence and intensity. Further, despite some inter-individual variation, 100 steps/minute represents a reasonable floor value indicative of moderate intensity walking. Multiplying this cadence by 30 minutes (i.e., typical of a daily recommendation) produces a minimum of 3,000 steps that is best used as a heuristic (i.e., guiding) value, but these steps must be taken over and above habitual activity levels to be a true expression of free-living steps/day that also includes recommendations for minimal amounts of time in MVPA. Computed steps/day translations of time in MVPA that also include estimates of habitual activity levels equate to 7,100 to 11,000 steps/day. A direct estimate of minimal amounts of MVPA accumulated in the course of objectively monitored free-living behaviour is 7,000-8,000 steps/day. A scale that spans a wide range of incremental increases in steps/day and is congruent with public health recognition that "some physical activity is better than none," yet still incorporates step-based translations of recommended amounts of time in MVPA may be useful in research and practice. The full range of users (researchers to practitioners to the general public) of objective monitoring instruments that provide step-based outputs require good reference data and evidence-based recommendations to be able to design effective health messages congruent with public health physical activity guidelines, guide behaviour change, and ultimately measure, track, and interpret steps/day.