Criterion Validity of Competing Accelerometry-Based Activity Monitoring Devices

Criterion validity of wrist accelerometry for assessing energy intake via the intake-balance technique

BACKGROUND

Intake-balance assessments measure energy intake (EI) by summing energy expenditure (EE) with concurrent change in energy storage (ΔES). Prior work has not examined the validity of such calculations when EE is estimated via open-source techniques for research-grade accelerometry devices. The purpose of this study was to test the criterion validity of accelerometry-based intake-balance methods for a wrist-worn ActiGraph device.

METHODS

Healthy adults (n = 24) completed two 14-day measurement periods while wearing an ActiGraph accelerometer on the non-dominant wrist. During each period, criterion values of EI were determined based on ΔES measured by dual X-ray absorptiometry and EE measured by doubly labeled water. A total of 11 prediction methods were tested, 8 derived from the accelerometer and 3 from non-accelerometry methods (e.g., diet recall; included for comparison). Group-level validity was assessed through mean bias, while individual-level validity was assessed through mean absolute error, mean absolute percentage error, and Bland-Altman analysis.

RESULTS

Mean bias for the three best accelerometry-based methods ranged from -167 to 124 kcal/day, versus -104 to 134 kcal/day for the non-accelerometry-based methods. The same three accelerometry-based methods had mean absolute error of 323-362 kcal/day and mean absolute percentage error of 18.1-19.3%, versus 353-464 kcal/day and 19.5-24.4% for the non-accelerometry-based methods. All 11 methods demonstrated systematic bias in the Bland-Altman analysis.

CONCLUSIONS

Accelerometry-based intake-balance methods have promise for advancing EI assessment, but ongoing refinement is necessary. We provide an R package to facilitate implementation and refinement of accelerometry-based methods in future research (see paulhibbing.com/IntakeBalance).

Validity of the activPAL monitor to measure stepping activity and activity intensity: A systematic review

BACKGROUND

Accumulating step counts and engaging in moderate-to-vigorous intensity physical activity is positively associated with numerous health benefits. The activPAL is a thigh-worn monitor that is frequently used to measure physical activity.

RESEARCH QUESTION

Can the activPAL accurately measure stepping activity and identify physical activity intensity?

METHODS

We systematically reviewed validation studies examining the accuracy of activPAL physical activity outcomes relative to a criterion measure in adults (>18 years). Citations were not restricted to language or date of publication. Sources were searched up to May 16, 2021 and included Scopus, EMBASE, MEDLINE, CINAHL, and Academic Search Premier. The study was pre-registered in Prospero (ID# CRD42021248240). Study quality was determined using a modified Hagströmer Bowles checklist.

RESULTS

Thirty-nine studies (20 laboratory arms, 17 semi-structured arms, 11 uncontrolled protocol arms; 1272 total participants) met the inclusion criteria. Most studies demonstrated a high validity of the activPAL to measure steps across laboratory (12/15 arms), semi-structured (10/13 arms) and uncontrolled conditions (5/7 arms). Studies that demonstrated low validity were generally conducted in unhealthy populations, included slower walking speeds, and/or short walking distances. Few studies indicated that the activPAL accurately measured physical activity intensity across laboratory (0/6 arms), semi-structured (0/5 arms) and uncontrolled conditions (2/5 arms). Using the default settings, the activPAL overestimates light-intensity activity but underestimates moderate-to-vigorous intensity activity. The overall study quality was 11.5 ± 2.0 out of 19.

CONCLUSION

Despite heterogeneous methodological and statistical approaches, the included studies generally provide supporting evidence that the activPAL can accurately detect stepping activity but not physical activity intensity. Strategies that use alternative data processing methods have been developed to better characterize physical activity intensity, but all methods still underestimate vigorous-intensity activity.

Establishing Accelerometer Cut-Points to Classify Walking Speed in People Post Stroke

While accelerometers could be used to monitor important domains of walking in daily living (e.g., walking speed), the interpretation of accelerometer data often relies on validation studies performed with healthy participants. The aim of this study was to develop cut-points for waist- and ankle-worn accelerometers to differentiate non-ambulation from walking and different walking speeds in people post stroke. Forty-two post-stroke persons wore waist and ankle accelerometers (ActiGraph GT3x+, AG) while performing three non-ambulation activities (i.e., sitting, setting the table and washing dishes) and while walking in self-selected and brisk speeds. Receiver operating characteristic (ROC) curve analysis was used to define AG cut-points for non-ambulation and different walking speeds (0.41−0.8 m/s, 0.81−1.2 m/s and >1.2 m/s) by considering sensor placement, axis, filter setting and epoch length. Optimal data input and sensor placements for measuring walking were a vector magnitude at 15 s epochs for waist- and ankle-worn AG accelerometers, respectively. Across all speed categories, cut-point classification accuracy was good-to-excellent for the ankle-worn AG accelerometer and fair-to-excellent for the waist-worn AG accelerometer, except for between 0.81 and 1.2 m/s. These cut-points can be used for investigating the link between walking and health outcomes in people post stroke.

Measurement of sedentary time and physical activity in rheumatoid arthritis: an ActiGraph and activPAL™ validation study

Accurate measurement of sedentary time and physical activity (PA) is essential to establish their relationships with rheumatoid arthritis (RA) outcomes. Study objectives were to: (1) validate the GT3X+ and activPAL3^μ™, and develop RA-specific accelerometer (count-based) cut-points for measuring sedentary time, light-intensity PA and moderate-intensity PA (laboratory-validation); (2) determine the accuracy of the RA-specific (vs. non-RA) cut-points, for estimating free-living sedentary time in RA (field-validation). Laboratory-validation: RA patients (n = 22) were fitted with a GT3X+, activPAL3^μ™ and indirect calorimeter. Whilst being video-recorded, participants undertook 11 activities, comprising sedentary, light-intensity and moderate-intensity behaviours. Criterion standards for devices were indirect calorimetry (GT3X+) and direct observation (activPAL3^μ™). Field-validation: RA patients (n = 100) wore a GT3X+ and activPAL3^μ™ for 7 days. The criterion standard for sedentary time cut-points (RA-specific vs. non-RA) was the activPAL3^μ™. Results of the laboratory-validation: GT3X-receiver operating characteristic curves generated RA-specific cut-points (counts/min) for: sedentary time = ≤ 244; light-intensity PA = 245-2501; moderate-intensity PA ≥ 2502 (all sensitivity ≥ 0.87 and 1-specificity ≤ 0.11). ActivPAL3^μ™-Bland-Altman 95% limits of agreement (lower-upper [min]) were: sedentary = (- 0.1 to 0.2); standing = (- 0.7 to 1.1); stepping = (- 1.2 to 0.6). Results of the field-validation: compared to the activPAL3^μ™, Bland-Altman 95% limits of agreement (lower-upper) for sedentary time (min/day) estimated by the RA-specific cut-point = (- 42.6 to 318.0) vs. the non-RA cut-point = (- 19.6 to 432.0). In conclusion, the activPAL3^μ™ accurately quantifies sedentary, standing and stepping time in RA. The RA-specific cut-points offer a validated measure of sedentary time, light-intensity PA and moderate-intensity PA in these patients, and demonstrated superior accuracy for estimating free-living sedentary time, compared to non-RA cut-points.

Real World, Real People: Can We Assess Walking on a Treadmill to Establish Step Count Recommendations in Adolescents?

BACKGROUND

Currently, it is not known how much walking should be advocated for good health in an adolescent population. Step count recommendations for minimum time in moderate-intensity activity have been translated predominantly from treadmill walking.

PURPOSE

To compare the energy cost of walking on a treadmill with overground walking in adolescent girls.

METHODS

A total of 26 adolescent girls undertook resting metabolic measurements for individual determination of 1 metabolic equivalent using indirect calorimetry. Energy expenditure was subsequently assessed during treadmill and overground walking at slow, moderate, and fast walking speeds for 4 to 6 minutes. Treadmill step rates were matched overground using a metronome.

RESULTS

The energy cost of treadmill walking was found to be significantly greater than and not equivalent to overground walking at 133 steps per minute; (equivalent to the fast walking pace): V˙O2 3.90 (2.78-5.01), P < .001, mean absolute percentage error (MAPE) = 18.18%, and metabolic equivalent 0.77 (0.54-1.00), P < .001, MAPE = 18.16%. The oxygen cost per step (V˙O2 mL·step-1) was significantly greater and not equivalent on the treadmill at 120 and 133 steps per minute: 0.43 (0.12-0.56), P < .05, MAPE = 10.12% versus 1.40 (1.01-1.76), P < .001, MAPE = 17.64%, respectively.

CONCLUSION

The results suggest that there is a difference in energy cost per step of walking on a treadmill and overground at the same step rate. This should be considered when utilizing the treadmill in energy expenditure studies. Studies which aim to provide step recommendations should focus on overground walking where most walking activity is adopted.

Providing a Basis for Harmonization of Accelerometer-Assessed Physical Activity Outcomes Across Epidemiological Datasets

Introduction: To capitalize on the increasing availability of accelerometry data for epidemiological research it is desirable to compare and/or pool data from surveys worldwide. This study aimed to establish whether free-living physical activity outcomes can be considered equivalent between three research-grade accelerometer brands worn on the dominant and non-dominant wrist. Of prime interest were the average acceleration (ACC) and the intensity gradient (IG). These two metrics describe the volume and intensity of the complete activity profile; further, they are comparable across populations making them ideal for comparing and/or pooling activity data. Methods: Forty-eight adults wore a GENEActiv, Axivity, and ActiGraph on both wrists for up to 7-days. Data were processed using open-source software (GGIR) to generate physical activity outcomes, including ACC and IG. Agreement was assessed using pairwise 95% equivalence tests (±10% equivalence zone) and intra-class correlation coefficients (ICC). Results: ACC was equivalent between brands when measured at the non-dominant wrist (ICC ≥ 0.93), but approximately 10% higher when measured at the dominant wrist (GENEActiv and Axivity only, ICC ≥ 0.83). The IG was equivalent irrespective of monitor brand or wrist (ICC ≥ 0.88). After adjusting ACC measured at the dominant wrist by −10% (GENEActiv and Axivity only), ACC was also within (or marginally outside) the 10% equivalence zone for all monitor pairings. Conclusion: If average acceleration is decreased by 10% for studies deploying monitors on the dominant wrist (GENEActiv and Axivity only), ACC and IG may be suitable for comparing and/or collating physical activity outcomes across accelerometer datasets, regardless of monitor brand and wrist.

Objective measurement of sedentary time and physical activity in people with rheumatoid arthritis: protocol for an accelerometer and activPALTM validation study

BACKGROUND

The accurate measurement of sedentary time and physical activity in Rheumatoid Arthritis (RA) is critical to identify important health consequences and determinants of these behaviours in this patient group. However, objective methods have not been well-validated for measurement of sedentary time and physical activity in RA.

AIMS

Specific objectives are to: 1) validate the ActiGraph GT3X+ accelerometer and activPAL3μTM against indirect calorimetry and direct observation respectively, and define RA-specific accelerometer cut-points, for measurement of sedentary time and physical activity in RA; 2) validate the RA-specific sedentary time accelerometer cut-points against the activPAL3μTM; 3) compare sedentary time and physical activity estimates in RA, using RA-specific vs. widely-used non-RA accelerometer cut-points.

METHODS

Objective 1: People with RA will wear an ActiGraph GT3X+, activPAL3μTM, heart rate monitor and indirect calorimeter, whilst being video-recorded undertaking 11 activities representative of sedentary behaviour, and light and moderate intensity physical activity. Objectives 2 and 3: People with RA will wear an ActiGraph GT3X+ and activPAL3μTM for 7 days to measure free-living sedentary time and physical activity.

DISCUSSION

This will be the first study to define RA-specific accelerometer cut-points, and represents the first validation of the ActiGraph accelerometer and activPALTM, for measurement of sedentary time and physical activity in RA. Findings will inform future RA studies employing these devices, ensuring more valid assessment of sedentary time and physical activity in this patient group.

Concurrent validity of the Fitbit for assessing sedentary behavior and moderate-to-vigorous physical activity

Background

Recent advances in sensor technologies have promoted the use of consumer-based accelerometers such as Fitbit Flex in epidemiological and clinical research; however, the validity of the Fitbit Flex in measuring sedentary behavior (SED) and physical activity (PA) has not been fully determined against previously validated research-grade accelerometers such as ActiGraph GT3X+. Therefore, the purpose of this study was to examine the concurrent validity of the Fitbit Flex against ActiGraph GT3X+ in a free-living condition.

Methods

A total of 65 participants (age: M = 42, SD = 14 years, female: 72%) each wore a Fitbit Flex and GT3X+ for seven consecutive days. After excluding sleep and non-wear time, time spent (min/day) in SED and moderate-to-vigorous PA (MVPA) were estimated using various cut-points for GT3X+ and brand-specific algorithms for Fitbit, respectively. Repeated measures one-way ANOVA and mean absolute percent errors (MAPE) served to examine differences and measurement errors in SED and MVPA estimates between Fitbit Flex and GT3X+, respectively. Pearson and Spearman correlations and Bland-Altman (BA) plots were used to evaluate the association and potential systematic bias between Fitbit Flex and GT3X+. PROC MIXED procedure in SAS was used to examine the equivalence (i.e., the 90% confidence interval with ±10% equivalence zone) between the devices.

Results

Fitbit Flex produced similar SED and low MAPE (mean difference [MD] = 37 min/day, P = .21, MAPE = 6.8%), but significantly higher MVPA and relatively large MAPE (MD = 59–77 min/day, P < .0001, MAPE = 56.6–74.3%) compared with the estimates from GT3X+ using three different cut-points. The correlations between Fitbit Flex and GT3X+ were consistently higher for SED (r = 0.90, ρ = 0.86, P < .01), but weaker for MVPA (r = 0.65–0.76, ρ = 0.69–0.79, P < .01). BA plots revealed that there is no apparent bias in estimating SED.

Conclusion

In comparison with the GT3X+ accelerometer, the Fitbit Flex provided comparatively accurate estimates of SED, but the Fitbit Flex overestimated MVPA under free-living conditions. Future investigations using the Fitbit Flex should be aware of present findings.

A Primer on the Use of Equivalence Testing for Evaluating Measurement Agreement

PURPOSE

Statistical equivalence testing is more appropriate than conventional tests of difference to assess the validity of physical activity (PA) measures. This article presents the underlying principles of equivalence testing and gives three examples from PA and fitness assessment research.

METHODS

The three examples illustrate different uses of equivalence tests. Example 1 uses PA data to evaluate an activity monitor's equivalence to a known criterion. Example 2 illustrates the equivalence of two field-based measures of physical fitness with no known reference method. Example 3 uses regression to evaluate an activity monitor's equivalence across a suite of 23 activities.

RESULTS

The examples illustrate the appropriate reporting and interpretation of results from equivalence tests. In the first example, the mean criterion measure is significantly within ±15% of the mean PA monitor. The mean difference is 0.18 METs and the 90% confidence interval of -0.15 to 0.52 is inside the equivalence region of -0.65 to 0.65. In the second example, we chose to define equivalence for these two measures as a ratio of mean values between 0.98 and 1.02. The estimated ratio of mean V˙O2 values is 0.99, which is significantly (P = 0.007) inside the equivalence region. In the third example, the PA monitor is not equivalent to the criterion across the suite of activities. The estimated regression intercept and slope are -1.23 and 1.06. Neither confidence interval is within the suggested regression equivalence regions.

CONCLUSIONS

When the study goal is to show similarity between methods, equivalence testing is more appropriate than traditional statistical tests of differences (e.g., ANOVA and t-tests).

Effect of different operationalizations of sedentary behavior in people with chronic stroke

Purpose: Sedentary behavior is common in people with stroke and has devastating impact on their health. Quantifying it is important to provide people with stroke with adequate physical behavior recommendations. Sedentary behavior can be quantified in terms of posture (sitting) or intensity (low energy expenditure). We compared the effect of different operationalizations of sedentary behavior on sedentary behavior outcomes (total time; way of accumulation) in people with stroke.Methods: Sedentary behavior was analyzed in 44 people with chronic stroke with an activity monitor that measured both body postures and movement intensity. It was operationalized as: (1) combining postural and intensity data; (2) using only postural data; (3) using only intensity data. For each operationalization, we quantified a set of outcomes. Repeated measures ANOVA and Bland-Altman plots were used to compare the operationalizations.Results: All sedentary behavior outcomes differed significantly between all operationalizations (p < 0.01). Bland-Altman plots showed large limits of agreement for all outcomes, showing large individual differences between operationalizations.Conclusions: Although it was neither possible nor our aim to investigate the validity of the two-component definition of sedentary behavior, our study shows that the type of operationalization of sedentary behavior significantly influences sedentary behavior outcomes in people with stroke.Implications for RehabilitationReliable assessment of sedentary behavior after stroke is important in order to provide adequate physical behavior recommendations for people with stroke.Sedentary behavior can be operationalized in terms of body posture (sitting time) or in terms of movement intensity (time <1.5 MET) or as a combination of both criteria; this study reveals that the type of operationalization affects the different outcome measures used to quantify sedentary behavior.Comparing sedentary behavior outcomes requires caution and should only be done when sedentary behavior is operationalized in the same way.

Accelerometer-assessed Physical Activity in Epidemiology: Are Monitors Equivalent?

PURPOSE

Accelerometers are increasingly being used to assess physical activity in large-scale surveys. Establishing whether key physical activity outcomes can be considered equivalent between three widely used accelerometer brands would be a significant step toward capitalizing on the increasing availability of accelerometry data for epidemiological research.

METHODS

Twenty participants wore a GENEActiv, an Axivity AX3, and an ActiGraph GT9X on their nondominant wrist and were observed for 2 h in a simulated living space. Participants undertook a series of seated and upright light/active behaviors at their own pace. All accelerometer data were processed identically using open-source software (GGIR) to generate physical activity outcomes (including average dynamic acceleration (ACC) and time within intensity cut points). Data were analyzed using pairwise 95% equivalence tests (±10% equivalence zone), intraclass correlation coefficients (ICC) and limits of agreement.

RESULTS

The GENEActiv and Axivity could be considered equivalent for ACC (ICC = 0.95, 95% confidence interval (CI), 0.87-0.98), but ACC measured by the ActiGraph was approximately 10% lower (GENEActiv/ActiGraph: ICC = 0.86; 95% CI, 0.56-0.95; Axivity/ActiGraph: ICC = 0.82; 95% CI, 0.50-0.94). For time spent within intensity cut points, all three accelerometers could be considered equivalent to each other for more than 85% of outcomes (ICC ≥0.69, lower 95% CI ≥0.36), with the GENEActiv and Axivity equivalent for 100% of outcomes (ICC ≥0.95, lower 95% CI ≥0.86).

CONCLUSIONS

GENEActiv and Axivity data processed in GGIR are largely equivalent. If GENEActiv or Axivity is compared with the ActiGraph, time spent within intensity cut points has good agreement. These findings can be used to inform selection of appropriate outcomes if outputs from these accelerometer brands are compared.

Simultaneous Validation of Seven Physical Activity Questionnaires Used in Japanese Cohorts for Estimating Energy Expenditure: A Doubly Labeled Water Study

BACKGROUND

Physical activity questionnaires (PAQs) used in large-scale Japanese cohorts have rarely been simultaneously validated against the gold standard doubly labeled water (DLW) method. This study examined the validity of seven PAQs used in Japan for estimating energy expenditure against the DLW method.

METHODS

Twenty healthy Japanese adults (9 men; mean age, 32.4 [standard deviation {SD}, 9.4] years, mainly researchers and students) participated in this study. Fifteen-day daily total energy expenditure (TEE) and basal metabolic rate (BMR) were measured using the DLW method and a metabolic chamber, respectively. Activity energy expenditure (AEE) was calculated as TEE - BMR - 0.1 × TEE. Seven PAQs were self-administered to estimate TEE and AEE.

RESULTS

The mean measured values of TEE and AEE were 2,294 (SD, 318) kcal/day and 721 (SD, 161) kcal/day, respectively. All of the PAQs indicated moderate-to-strong correlations with the DLW method in TEE (rho = 0.57-0.84). Two PAQs (Japan Public Health Center Study [JPHC]-PAQ Short and JPHC-PAQ Long) showed significant equivalence in TEE and moderate intra-class correlation coefficients (ICC). None of the PAQs showed significantly equivalent AEE estimates, with differences ranging from -547 to 77 kcal/day. Correlations and ICCs in AEE were mostly weak or fair (rho = 0.02-0.54, and ICC = 0.00-0.44). Only JPHC-PAQ Short provided significant and fair agreement with the DLW method.

CONCLUSIONS

TEE estimated by the PAQs showed moderate or strong correlations with the results of DLW. Two PAQs showed equivalent TEE and moderate agreement. None of the PAQs showed equivalent AEE estimation to the gold standard, with weak-to-fair correlations and agreements. Further studies with larger sample sizes are needed to confirm these findings.

Routine Assessment and Promotion of Physical Activity in Healthcare Settings: A Scientific Statement From the American Heart Association

Physical inactivity is one of the most prevalent major health risk factors, with 8 in 10 US adults not meeting aerobic and muscle-strengthening guidelines, and is associated with a high burden of cardiovascular disease. Improving and maintaining recommended levels of physical activity leads to reductions in metabolic, hemodynamic, functional, body composition, and epigenetic risk factors for noncommunicable chronic diseases. Physical activity also has a significant role, in many cases comparable or superior to drug interventions, in the prevention and management of >40 conditions such as diabetes mellitus, cancer, cardiovascular disease, obesity, depression, Alzheimer disease, and arthritis. Whereas most of the modifiable cardiovascular disease risk factors included in the American Heart Association’s My Life Check - Life’s Simple 7 are evaluated routinely in clinical practice (glucose and lipid profiles, blood pressure, obesity, and smoking), physical activity is typically not assessed. The purpose of this statement is to provide a comprehensive review of the evidence on the feasibility, validity, and effectiveness of assessing and promoting physical activity in healthcare settings for adult patients. It also adds concrete recommendations for healthcare systems, clinical and community care providers, fitness professionals, the technology industry, and other stakeholders in order to catalyze increased adoption of physical activity assessment and promotion in healthcare settings and to contribute to meeting the American Heart Association’s 2020 Impact Goals.

Comparability of children's sedentary time estimates derived from wrist worn GENEActiv and hip worn ActiGraph accelerometer thresholds

OBJECTIVES

To examine the comparability of children's free-living sedentary time (ST) derived from raw acceleration thresholds for wrist mounted GENEActiv accelerometer data, with ST estimated using the waist mounted ActiGraph 100count·min^-1 threshold.

DESIGN

Secondary data analysis.

METHOD

108 10-11-year-old children (n=43 boys) from Liverpool, UK wore one ActiGraph GT3X+ and one GENEActiv accelerometer on their right hip and left wrist, respectively for seven days. Signal vector magnitude (SVM; mg) was calculated using the ENMO approach for GENEActiv data. ST was estimated from hip-worn ActiGraph data, applying the widely used 100count·min^-1 threshold. ROC analysis using 10-fold hold-out cross-validation was conducted to establish a wrist-worn GENEActiv threshold comparable to the hip ActiGraph 100count·min^-1 threshold. GENEActiv data were also classified using three empirical wrist thresholds and equivalence testing was completed.

RESULTS

Analysis indicated that a GENEActiv SVM value of 51mg demonstrated fair to moderate agreement (Kappa: 0.32-0.41) with the 100count·min^-1 threshold. However, the generated and empirical thresholds for GENEActiv devices were not significantly equivalent to ActiGraph 100count·min^-1. GENEActiv data classified using the 35.6mg threshold intended for ActiGraph devices generated significantly equivalent ST estimates as the ActiGraph 100count·min^-1.

CONCLUSIONS

The newly generated and empirical GENEActiv wrist thresholds do not provide equivalent estimates of ST to the ActiGraph 100count·min^-1 approach. More investigation is required to assess the validity of applying ActiGraph cutpoints to GENEActiv data. Future studies are needed to examine the backward compatibility of ST data and to produce a robust method of classifying SVM-derived ST.

A novel integrative procedure for identifying and integrating three-dimensions of objectively measured free-living sedentary behaviour

BACKGROUND

The widely accepted definition of sedentary behaviour [SB] refers to any waking behaviour characterized by an energy expenditure ≤1.5 metabolic equivalents [METs] while in a sitting or reclining posture. At present, there is no single field-based device which objectively measures sleep, posture and activity intensity simultaneously. The aim of this study was to develop a novel integrative procedure [INT] to combine information from two validated activity monitors on sleep, activity intensity and posture, the three key dimensions of SB.

METHODS

Participants in this analysis were initially recruited from a series of three studies conducted between December 2014 and June 2016 at the University of Leeds. Sixty-three female participants aged 37.1 (13.6) years with a body mass index of 29.6 (4.7) kg/m² were continuously monitored for 5-7 days with the SenseWear Armband [SWA] (sleep and activity intensity) and the activPAL [AP] (posture). Data from both activity monitors were analysed separately and integrated resulting in three measures of sedentary time. Differences in Sedentary time between the three measurement methods were assessed as well as how well the three measures correlated.

RESULTS

The three measures of sedentary time were positively correlated, with the weakest relationship between SED^SWA (awake and <1.5 METs) and SED^AP (awake and sitting/lying posture) [r(61) = .37,p = .003], followed by SED^SWA and SED^INT (awake, <1.5 METs and sitting/lying posture) [r(61) = .58,p < .001], and the strongest relationship was between SED^AP and SED^INT [r(61) = .91,p < .001]. There was a significant difference between the three measures of sedentary time [F(1.18,73.15) = 104.70,p < .001]. Post-hoc tests revealed all three methods differed significantly from each other [p < .001]. SED^SWA resulted in the most sedentary time 11.74 (1.60) hours/day, followed by SED^AP 10.16 (1.75) hours/day, and SED^INT 9.10 (1.67) hours/day. Weekday and weekend day sedentary time did not differ for any of the measurement methods [p = .04-.25].

CONCLUSION

Information from two validated activity monitors was combined to obtain an objective measure of free-living SB based on posture and activity intensity during waking hours. The amount of sedentary time accumulated varied according to the definition of SB and its measurement. The novel data integration and processing procedures presented in this paper represents an opportunity to investigate whether different components of SB are differentially related to health end points.

Calibration of Self-Report Measures of Physical Activity and Sedentary Behavior

INTRODUCTION

Calibration equations offer potential to improve the accuracy and utility of self-report measures of physical activity (PA) and sedentary behavior (SB) by rescaling potentially biased estimates. The present study evaluates calibration models designed to estimate PA and SB in a representative sample of adults from the Physical Activity Measurement Study.

METHODS

Participants in the Physical Activity Measurement Study project completed replicate single-day trials that involved wearing a Sensewear armband (SWA) monitor for 24 h followed by a telephone administered 24-h PA recall (PAR). Comprehensive statistical model selection and validation procedures were used to develop and test separate calibration models designed to predict objectively measured SB and moderate-to-vigorous PA (MVPA) from self-reported PAR data. Equivalence testing was used to evaluate the equivalence of the model-predicted values with the objective measures in a separate holdout sample.

RESULTS

The final prediction model for both SB and MVPA included reported time spent in SB and MVPA, as well as terms capturing sex, age, education, and body mass index. Cross-validation analyses on an independent sample exhibited high correlations with observed SB (r = 0.72) and MVPA (r = 0.75). Equivalence testing demonstrated that the model-predicted values were statistically equivalent to the corresponding objective values for both SB and MVPA.

CONCLUSIONS

The results demonstrate that simple regression models can be used to statistically adjust for overestimation or underestimation in self-report measures among different segments of the population. The models produced group estimates from the PAR that were statistically equivalent to the observed time spent in SB and MVPA obtained from the objective SWA monitor; however, additional work is needed to correct for estimates of individual behavior.

Validity of an Integrative Method for Processing Physical Activity Data

Accurate assessments of both physical activity and sedentary behaviors are crucial to understand the health consequences of movement patterns and to track changes over time and in response to interventions.

PURPOSE

The study evaluates the validity of an integrative, machine learning method for processing activity monitor data in relation to a portable metabolic analyzer (Oxycon mobile [OM]) and direct observation (DO).

METHODS

Forty-nine adults (age 18-40 yr) each completed 5-min bouts of 15 activities ranging from sedentary to vigorous intensity in a laboratory setting while wearing ActiGraph (AG) on the hip, activPAL on the thigh, and OM. Estimates of energy expenditure (EE) and categorization of activity intensity were obtained from the AG processed with Lyden's sojourn (SOJ) method and from our new sojourns including posture (SIP) method, which integrates output from the AG and activPAL. Classification accuracy and estimates of EE were then compared with criterion measures (OM and DO) using confusion matrices and comparisons of the mean absolute error of log-transformed data (MAE ln Q).

RESULTS

The SIP method had a higher overall classification agreement (79%, 95% CI = 75%-82%) than the SOJ (56%, 95% CI = 52%-59%) based on DO. Compared with OM, estimates of EE from SIP had lower mean absolute error of log-transformed data than SOJ for light-intensity (0.21 vs 0.27), moderate-intensity (0.33 vs 0.42), and vigorous-intensity (0.16 vs 0.35) activities.

CONCLUSIONS

The SIP method was superior to SOJ for distinguishing between sedentary and light activities as well as estimating EE at higher intensities. Thus, SIP is recommended for research in which accuracy of measurement across the full range of activity intensities is of interest.

Lab-based validation of different data processing methods for wrist-worn ActiGraph accelerometers in young adults

The wrist is increasingly being used as the preferred site for objectively assessing physical activity but the relative accuracy of processing methods for wrist data has not been determined.

OBJECTIVE

This study evaluates the validity of four processing methods for wrist-worn ActiGraph (AG) data against energy expenditure (EE) measured using a portable metabolic analyzer (OM; Oxycon mobile) and the Compendium of physical activity.

APPROACH

Fifty-one adults (ages 18-40) completed 15 activities ranging from sedentary to vigorous in a laboratory setting while wearing an AG and the OM. Estimates of EE and categorization of activity intensity were obtained from the AG using a linear method based on Hildebrand cutpoints (HLM), a non-linear modification of this method (HNLM), and two methods developed by Staudenmayer based on a Linear Model (SLM) and using random forest (SRF). Estimated EE and classification accuracy were compared to the OM and Compendium using Bland-Altman plots, equivalence testing, mean absolute percent error (MAPE), and Kappa statistics.

MAIN RESULTS

Overall, classification agreement with the Compendium was similar across methods ranging from a Kappa of 0.46 (HLM) to 0.54 (HNLM). However, specificity and sensitivity varied by method and intensity, ranging from a sensitivity of 0% (HLM for sedentary) to a specificity of ~99% for all methods for vigorous. None of the methods was significantly equivalent to the OM (p  >  0.05).

SIGNIFICANCE

Across activities, none of the methods evaluated had a high level of agreement with criterion measures. Additional research is needed to further refine the accuracy of processing wrist-worn accelerometer data.

Breaking sitting with light activities vs structured exercise: a randomised crossover study demonstrating benefits for glycaemic control and insulin sensitivity in type 2 diabetes

AIMS/HYPOTHESIS

We aimed to examine the effects of breaking sitting with standing and light-intensity walking vs an energy-matched bout of structured exercise on 24 h glucose levels and insulin resistance in patients with type 2 diabetes.

METHODS

In a randomised crossover study, 19 patients with type 2 diabetes (13 men/6 women, 63 ± 9 years old) who were not using insulin each followed three regimens under free-living conditions, each lasting 4 days: (1) Sitting: 4415 steps/day with 14 h sitting/day; (2) Exercise: 4823 steps/day with 1.1 h/day of sitting replaced by moderate- to vigorous-intensity cycling (at an intensity of 5.9 metabolic equivalents [METs]); and (3) Sit Less: 17,502 steps/day with 4.7 h/day of sitting replaced by standing and light-intensity walking (an additional 2.5 h and 2.2 h, respectively, compared with the hours spent doing these activities in the Sitting regimen). Blocked randomisation was performed using a block size of six regimen orders using sealed, non-translucent envelopes. Individuals who assessed the outcomes were blinded to group assignment. Meals were standardised during each intervention. Physical activity and glucose levels were assessed for 24 h/day by accelerometry (activPAL) and a glucose monitor (iPro2), respectively. The incremental AUC (iAUC) for 24 h glucose (primary outcome) and insulin resistance (HOMA2-IR) were assessed on days 4 and 5, respectively.

RESULTS

The iAUC for 24 h glucose (mean ± SEM) was significantly lower during the Sit Less intervention than in Sitting (1263 ± 189 min × mmol/l vs 1974 ± 324 min × mmol/l; p = 0.002), and was similar between Sit Less and Exercise (Exercise: 1383 ± 194 min × mmol/l; p = 0.499). Exercise failed to improve HOMA2-IR compared with Sitting (2.06 ± 0.28 vs 2.16 ± 0.26; p = 0.177). In contrast, Sit Less (1.89 ± 0.26) significantly reduced HOMA2-IR compared with Exercise (p = 0.015) as well as Sitting (p = 0.001).

CONCLUSIONS/INTERPRETATION

Breaking sitting with standing and light-intensity walking effectively improved 24 h glucose levels and improved insulin sensitivity in individuals with type 2 diabetes to a greater extent than structured exercise. Thus, our results suggest that breaking sitting with standing and light-intensity walking may be an alternative to structured exercise to promote glycaemic control in patients type 2 diabetes.

TRIAL REGISTRATION

Clinicaltrials.gov NCT02371239 FUNDING: : The study was supported by a Kootstra grant from Maastricht University Medical Centre⁺, and the Dutch Heart Foundation. Financial support was also provided by Novo Nordisk BV, and Medtronic and Roche made the equipment available for continuous glucose monitoring.

Accuracy of inclinometer functions of the activPAL and ActiGraph GT3X+: A focus on physical activity

PURPOSE

The purpose of the study was to examine the accuracy of inclinometer functions of the ActiGraph GT3X+ (AG) (worn on the waist and wrist) and the activPAL (AP) in assessing time spent sitting, standing, and stepping.

METHODS

A total of 62 adults (age: 18-40 yrs; male:37; female:25) wore three activity monitors (AG waist, and AG wrist, and AP) while completing 15 different types of activities. The 15 activities were classified into 3 different postures (sitting, standing, and stepping) based on the directly observed behaviors. Minutes estimated from the inclinometers of the three monitors were directly compared to those from direct observation (criterion method) using mean absolute percent error (MAPE) values, effect sizes (Cohen's D), and equivalence testing.

RESULTS

The AP was more accurate than the both waist- and wrist-worn AG in both sitting and standing activities, but the AG was more accurate than the AP in stepping activity when the stepping activity was determined with 0.7 step/s threshold. Equivalence testing indicated that the time measured by the waist-, wrist-worn AG, and AP showed significant equivalence to the time in the equivalence zone (90% confidence interval: 2.7 to 3.3min) for 6, 5, and 7 activities, respectively.

CONCLUSIONS

The AP was reasonably accurate for detecting sitting, standing, and stepping, and the AG was very accurate for classifying stepping when the stepping activity was determined by the formula created by 0.7 step/s threshold. It is expected that the result of the study would contribute to performing movement pattern analyses and health promotion research for classifying activities.

Objectively measured sedentary behavior and physical activity in a sample of Finnish adults: a cross-sectional study

Background

Regular physical activity (PA) confers many positive effects on health and well-being. Sedentary behavior (SB), in turn, is a risk factor for health, regardless of the level of moderate to vigorous PA. The present study describes the levels of objectively measured SB, breaks in SB, standing still and PA among Finnish adults.

Methods

This cross-sectional analysis is based on the sub-sample of the population-based Health 2011 Study of Finnish adults. The study population consisted of 18-to-85-year old men and women who wore a waist-worn triaxial accelerometer (Hookie AM 20) for at least 4 days, for at least 10 h per day (n = 1587) during a week. PA and SB were objectively assessed from the raw accelerometric data using novel processing and analysis algorithms with mean amplitude deviation as the processing method. The data was statistically analyzed using cross-tabulations, analysis of variance and analysis of covariance.

Results

The participants were on average 52 years old, 57 % being women. Participants were sedentary 59 % of their waking wear time, mainly sitting. They spent 17 % of the time standing still, 15 % in light intensity PA, 9 % in moderate PA and less than 1 % in vigorous PA. Participants aged 30–39 years had the highest number of breaks in SB per day. Younger participants (<30 years of age) had more moderate and vigorous PA than older ones (≥60 years of age), and 30–60-year-olds had the greatest amount of light PA.

Conclusions

Participants spent nearly 60 % of their waking time sedentary, and the majority of their daily PA was light. From a public health perspective it is important to find effective ways to decrease SB as well as to increase the level of PA. Our analysis method of raw accelerometer data may allow more precise assessment of dose-response relationships between objectively measured PA and SB and various indicators of health and well-being.

The Wild Wild West: A Framework to Integrate mHealth Software Applications and Wearables to Support Physical Activity Assessment, Counseling and Interventions for Cardiovascular Disease Risk Reduction

Physical activity (PA) interventions constitute a critical component of cardiovascular disease (CVD) risk reduction programs. Objective mobile health (mHealth) software applications (apps) and wearable activity monitors (WAMs) can advance both assessment and integration of PA counseling in clinical settings and support community-based PA interventions. The use of mHealth technology for CVD risk reduction is promising, but integration into routine clinical care and population health management has proven challenging. The increasing diversity of available technologies and the lack of a comprehensive guiding framework are key barriers for standardizing data collection and integration. This paper reviews the validity, utility and feasibility of implementing mHealth technology in clinical settings and proposes an organizational framework to support PA assessment, counseling and referrals to community resources for CVD risk reduction interventions. This integration framework can be adapted to different clinical population needs. It should also be refined as technologies and regulations advance under an evolving health care system landscape in the United States and globally.