Validity of an Integrative Method for Processing Physical Activity Data

Dynamics of gas exchange and heart rate signal entropy in standard cardiopulmonary exercise testing during critical periods of growth and development

Standard cardiopulmonary exercise testing (CPET) produces a rich dataset but its current analysis is often limited to a few derived variables such as maximal or peak oxygen uptake (V̇O2). We tested whether breath-by-breath CPET data could be used to determine sample entropy (SampEn) in 81 healthy children and adolescents (age 7-18 years old, equal sex distribution). To overcome challenges of the relatively small time-series CPET data size and its nonstationarity, we developed a Python algorithm for short-duration physiological signals. Comparing pre- and post-ventilatory threshold (VT1) CPET phases, we found: (1) SampEn decreased by 9.46% for V̇O2 and 5.01% for V̇CO2 (p < 0.05), in the younger, early-pubertal participants; and (2) HR SampEn fell substantially by 70.8% in the younger and 77.5% in the older participants (p < 0.001). Across all ages, females exhibited greater HR SampEn than males during both pre- and post VT1 CPET phases by 14.10% and 23.79%, respectively, p < 0.01. In females, late-pubertal had 17.6% lower HR SampEn compared to early-pubertal participants (p < 0.05). Breath-by-breath gas exchange and HR data from CPET are amenable to SampEn analysis that leads to novel insight into physiological responses to work intensity, and sex and maturational effects.

Accelerometer techniques for capturing human movement validated against direct observation: a scoping review

Objective.Accelerometers are devices commonly used to measure human physical activity and sedentary time. Accelerometer capabilities and analytical techniques have evolved rapidly, making it difficult for researchers to keep track of advances and best practices for data processing and analysis. The objective of this scoping review is to determine the existing methods for analyzing accelerometer data for capturing human movement which have been validated against the criterion measure of direct observation.Approach.This scoping review searched 14 academic and 5 grey databases. Two independent raters screened by title and abstract, then full text. Data were extracted using Microsoft Excel and checked by an independent reviewer.Mainresults.The search yielded 1039 papers and the final analysis included 115 papers. A total of 71 unique accelerometer models were used across a total of 4217 participants. While all studies underwent validation from direct observation, most direct observation occurred live (55%) or using recordings (42%). Analysis techniques included machine learning (ML) approaches (22%), the use of existing cut-points (18%), receiver operating characteristic curves to determine cut-points (14%), and other strategies including regressions and non-ML algorithms (8%).Significance.ML techniques are becoming more prevalent and are often used for activity identification. Cut-point methods are still frequently used. Activity intensity is the most assessed activity outcome; however, both the analyses and outcomes assessed vary by wear location. This scoping review provides a comprehensive overview of accelerometer analysis and validation techniques using direct observation and is a useful tool for researchers using accelerometers.

Intervening to reduce sedentary behavior among African American elders: the "Stand Up and Move More" intervention

Background

Reducing sedentary behavior is a promising intervention target for improving health for older adults; however, few interventions include African American communities. The purpose of this research was to extend the reach of an effective sedentary behavior intervention to African American elders.

Methods

Two pilot studies assessed the feasibility (retention, adherence, and safety) and acceptability (participant and leader perspectives) of a 4-wk "Stand Up and Move More" (SUMM) intervention. Sedentary behavior (self-reported and monitor-derived), function (short physical performance battery), and quality of life (SF-36) were measured at baseline (wk0), postintervention (wk4), and follow up (wk12; study 1) to examine preliminary effectiveness of the intervention. Participants (N=26) attended SUMM or an attention-matched stress management intervention (study 2). The magnitude of treatment effects were determined using Hedge's g effect size calculations [small (g=0.20 to 0.49), moderate (g=0.50 to 0.79), large (g>0.80)].

Results

Retention and adherence rates ranged from 50%-100% and 80%-100%, respectively. There were no adverse events. Participants expressed high satisfaction, and the leader of the SUMM intervention indicated that the intervention content was beneficial. Hedges' g revealed negligible to small changes in sedentary behavior (g<0.50) following SUMM. There were moderate to large improvements in function (g=0.51-0.82) and quality of life (g=0.54-1.07) from wk0 to wk4 in study 1; and moderate to large improvements in function (g=0.51-0.88) from wk0 to wk4 in study 2. There was a moderate improvement in quality of life (SF-36 emotional role limitations g=0.54) in the SUMM group only.

Conclusion

Given its feasibility, safety, and acceptability, SUMM may be a promising intervention to improve functioning and well-being among African American elders.

Why machine learning (ML) has failed physical activity research and how we can improve

Measuring physical activity is a critical issue for our understanding of the health benefits of human movement. Machine learning (ML), using accelerometer data, has become a common way to measure physical activity. ML has failed physical activity measurement research in four important ways. First, as a field, physical activity researchers have not adopted and used principles from computer science. Benchmark datasets are common in computer science and allow the direct comparison of different ML approaches. Access to and development of benchmark datasets are critical components in advancing ML for physical activity. Second, the priority of methods development focused on ML has created blind spots in physical activity measurement. Methods, other than cut-point approaches, may be sufficient or superior to ML but these are not prioritised in our research. Third, while ML methods are common in published papers, their integration with software is rare. Physical activity researchers must continue developing and integrating ML methods into software to be fully adopted by applied researchers in the discipline. Finally, training continues to limit the uptake of ML in applied physical activity research. We must improve the development, integration and use of software that allows for ML methods’ broad training and application in the field.

How Do We Assess Energy Availability and RED-S Risk Factors in Para Athletes?

Low energy availability (LEA) is considered to be the underlying cause of a number of maladaptations in athletes, including impaired physiological function, low bone mineral density (BMD), and hormonal dysfunction. This is collectively referred to as 'Relative Energy Deficiency in Sport' (RED-S). LEA is calculated through assessment of dietary energy intake (EI), exercise energy expenditure (EEE) and fat-free mass (FFM). The incidence of LEA in Paralympic athletes is relatively unknown; however, there are legitimate concerns that Para athletes may be at even higher risk of LEA than able-bodied athletes. Unfortunately, there are numerous issues with the application of LEA assessment tools and the criterion for diagnosis within the context of a Para population. The calculation of EEE, in particular, is limited by a distinct lack of published data that cover a range of impairments and activities. In addition, for several RED-S-related factors, it is difficult to distinguish whether they are truly related to LEA or a consequence of the athlete's impairment and medical history. This narrative review outlines deficits and complexities when assessing RED-S and LEA in Para athletes, presents the information that we do have, and provides suggestions for future progress in this important area of sports nutrition.

Modeling Energy Expenditure Estimation in Occupational Context by Actigraphy: A Multi Regression Mixed-Effects Model

The accurate prediction of energy requirements for healthy individuals has many useful applications. The occupational perspective has also been proven to be of great utility for improving workers' ergonomics, safety, and health. This work proposes a statistical regression model based on actigraphy and personal characteristics to estimate energy expenditure and cross-validate the results with reference standardized methods. The model was developed by hierarchical mixed-effects regression modeling based on the multitask protocol data. Measurements combined actigraphy, indirect calorimetry, and other personal and lifestyle information from healthy individuals (n = 50) within the age of 29.8 ± 5 years old. Results showed a significant influence of the variables related to movements, heart rate and anthropometric variables of body composition for energy expenditure estimation. Overall, the proposed model showed good agreement with energy expenditure measured by indirect calorimetry and evidenced a better performance than the methods presented in the international guidelines for metabolic rate assessment proving to be a reliable alternative to normative guidelines. Furthermore, a statistically significant relationship was found between daily activity and energy expenditure, which raised the possibility of further studies including other variables, namely those related to the subject's lifestyle.

Safety and efficacy of short-term structured resistance exercise in Gulf War Veterans with chronic unexplained muscle pain: A randomized controlled trial

AIMS

Chronic widespread musculoskeletal pain (CMP) is a primary condition of Veterans suffering from Gulf War illness. This study evaluated the influence of resistance exercise training (RET) on symptoms, mood, perception of improvement, fitness, and total physical activity in Gulf War Veterans (GWV) with CMP.

MAIN METHODS

Fifty-four GWV with CMP were randomly assigned to 16 weeks of RET (n = 28) or wait-list control (n = 26). Supervised exercise was performed twice weekly starting at a low intensity. Outcomes, assessed at baseline, 6, 11 and 17 weeks and 6- and 12-months post-intervention, were: pain, fatigue, mood, sleep quality, perception of improvement, and physical activity via self-report and accelerometry. Muscular strength was assessed at baseline, 8 and 16 weeks. Accelerometer data yielded estimates of time spent in sedentary, light, and moderate-to-vigorous physical activities. Analyses used separate linear mixed models with group and time point as fixed effects. All models, except for perceived improvement, included baseline values as a covariate.

KEY FINDINGS

Participants assigned to RET completed 87% of training sessions and exhibited strength increases between 16 and 34% for eight lifts tested (Hedges' g range: 0.47-0.78). The treatment by time interaction for perceived improvement (F_1,163 = 16.94, p < 0.001) was characterized by greater perceived improvement since baseline for RET at each time point, until the 12-month follow-up. Effects were not significant for other outcomes (p > 0.05). RET caused no adverse events.

SIGNIFICANCE

After 16 weeks of RET, GWV with CMP reported improvements in their condition and exhibited increases in muscular strength, without symptom exacerbation or reductions in total physical activity.

The Feasibility and Effectiveness of a Community-Based Intervention to Reduce Sedentary Behavior in Older Adults

The purpose of this study was to examine the effectiveness and feasibility of translating a 4-week "Stand Up and Move More" (SUMM) intervention by state aging units to older adults (N = 56, M age = 74 years). A randomized controlled trial assessed sedentary behavior, physical function, and health-related quality of life (HRQoL) before and after the intervention. Participants included healthy community-dwelling, sedentary (sit > 6 hr/day) and aged ≥ 55 years adults. For the primary outcome, the SUMM group (n = 31) significantly (p < .05) reduced total sedentary time post-intervention by 68 min/day on average (Cohen's d = -0.56) compared with no change in the wait-list control group (n = 25, Cohen's d = 0.12). HRQoL and function also improved (p < .05) in the SUMM group post-intervention. Workshop facilitators indicated the intervention was easy to implement, and participants expressed high satisfaction. The SUMM intervention reduced sedentary time, improved physical function and HRQoL, and was feasible to implement in community settings.

Is Sitting Always Inactive and Standing Always Active? A Simultaneous Free-Living activPal and ActiGraph Analysis

Sedentary Behavior (SB), defined as sitting with minimal physical activity, is an emergent public health topic. However, the measurement of SB considers either posture (e.g., activPal) or physical activity (e.g., ActiGraph), and thus neglects either active sitting or inactive standing. The aim of this study was to determine the true amount of active sitting and inactive standing in daily life, and to analyze by how much these behaviors falsify the single sensors' sedentary estimates. Sedentary time of 100 office workers estimated with activPal and ActiGraph was therefore compared with Bland-Altman statistics to a combined sensor analysis, the posture and physical activity index (POPAI). POPAI classified each activPal sitting and standing event into inactive or active using the ActiGraph counts. Participants spent 45.0% [32.2%-59.1%] of the waking hours inactive sitting (equal to SB), 13.7% [7.8%-21.6%] active sitting, and 12.0% [5.7%-24.1%] inactive standing (mean [5th-95th percentile]). The activPal overestimated sedentary time by 30.3% [12.3%-48.4%] and the ActiGraph by 22.5% [3.2%-41.8%] (bias [95% limit-of-agreement]). The results showed that sitting is not always inactive, and standing is not always active. Caution should therefore be paid when interpreting the activPal (ignoring active sitting) and ActiGraph (ignoring inactive standing) measured time as SB.

Evaluating the Performance of Sensor-based Bout Detection Algorithms: The Transition Pairing Method

Bout detection algorithms are used to segment data from wearable sensors, but it is challenging to assess segmentation correctness.

Purpose

To present and demonstrate the Transition Pairing Method (TPM), a new method for evaluating the performance of bout detection algorithms.

Methods

The TPM compares predicted transitions to a criterion measure in terms of number and timing. A true positive is defined as a predicted transition that corresponds with one criterion transition in a mutually exclusive pair. The pairs are established using an extended Gale-Shapley algorithm, and the user specifies a maximum allowable within-pair time lag, above which pairs cannot be formed. Unpaired predictions and criteria are false positives and false negatives, respectively. The demonstration used raw acceleration data from 88 youth who wore ActiGraph GT9X monitors (right hip and non-dominant wrist) during simulated free-living. Youth Sojourn bout detection algorithms were applied (one for each attachment site), and the TPM was used to compare predicted bout transitions to the criterion measure (direct observation). Performance metrics were calculated for each participant, and hip-versus-wrist means were compared using paired T-tests (α = 0.05).

Results

When the maximum allowable lag was 1-s, both algorithms had recall <20% (2.4% difference from one another, p<0.01) and precision <10% (1.4% difference from one another, p<0.001). That is, >80% of criterion transitions were undetected, and >90% of predicted transitions were false positives.

Conclusion

The TPM improves on conventional analyses by providing specific information about bout detection in a standardized way that applies to any bout detection algorithm.

Sedentary Behavior and Quality of Life in Individuals With Parkinson's Disease

Background. Sedentary behavior is a growing public health concern and may have particular relevance for the Parkinson disease (PD) population. However, the influence of sedentary time on factors associated with quality of life (QOL) in PD is unknown. The primary purpose of this study was to examine relationships between sedentary behaviors and markers of PD-specific QOL. A secondary purpose was to examine relationships between physical activity behaviors and QOL. Methods. We assessed sedentary and active behaviors using objective and interview measures and examined relationships between these behaviors and a measure of PD-specific QOL in individuals with PD. Results. Results demonstrated that sedentary time was significantly related to several aspects of QOL, including perceived deficits in the domains of mobility, cognitive processing, and communication. Additionally, results showed that time spent watching television was more strongly associated with lower levels of QOL than other more engaging sedentary activities. For physical activity, relationships between objective measures and QOL were weaker and only significantly associated with mobility. Time spent doing housework was associated with lower levels of QOL, whereas time spent in recreational activity was associated with lower levels of discomfort. Discussion. These results suggest that targeting decreases in sedentary behaviors (eg, reducing time spent watching television, breaking up prolonged bouts of sedentary time) may be effective for improving QOL in individuals with PD.

Evaluating and Enhancing the Generalization Performance of Machine Learning Models for Physical Activity Intensity Prediction From Raw Acceleration Data

PURPOSE

To evaluate and enhance the generalization performance of machine learning physical activity intensity prediction models developed with raw acceleration data on populations monitored by different activity monitors.

METHOD

Five datasets from four studies, each containing only hip- or wrist-based raw acceleration data (two hip- and three wrist-based) were extracted. The five datasets were then used to develop and validate artificial neural networks (ANN) in three setups to classify activity intensity categories (sedentary behavior, light, and moderate-to-vigorous). To examine generalizability, the ANN models were developed using within dataset (leave-one-subject-out) cross validation, and then cross tested to other datasets with different accelerometers. To enhance the models' generalizability, a combination of four of the five datasets was used for training and the fifth dataset for validation. Finally, all the five datasets were merged to develop a single model that is generalizable across the datasets (50% of the subjects from each dataset for training, the remaining for validation).

RESULTS

The datasets showed high performance in within dataset cross validation (accuracy 71.9-95.4%, Kappa K = 0.63-0.94). The performance of the within dataset validated models decreased when applied to datasets with different accelerometers (41.2-59.9%, K = 0.21-0.48). The trained models on merged datasets consisting hip and wrist data predicted the left-out dataset with acceptable performance (65.9-83.7%, K = 0.61-0.79). The model trained with all five datasets performed with acceptable performance across the datasets (80.4-90.7%, K = 0.68-0.89).

CONCLUSIONS

Integrating heterogeneous datasets in training sets seems a viable approach for enhancing the generalization performance of the models. Instead, within dataset validation is not sufficient to understand the models' performance on other populations with different accelerometers.

Evaluating Motivational Interviewing and Habit Formation to Enhance the Effect of Activity Trackers on Healthy Adults' Activity Levels: Randomized Intervention

Background

While widely used and endorsed, there is limited evidence supporting the benefits of activity trackers for increasing physical activity; these devices may be more effective when combined with additional strategies that promote sustained behavior change like motivational interviewing (MI) and habit development.

Objective

This study aims to determine the utility of wearable activity trackers alone or in combination with these behavior change strategies for promoting improvements in active and sedentary behaviors.

Methods

A sample of 91 adults (48/91 female, 53%) was randomized to receive a Fitbit Charge alone or in combination with MI and habit education for 12 weeks. Active and sedentary behaviors were assessed pre and post using research-grade activity monitors (ActiGraph and activPAL), and the development of habits surrounding the use of the trackers was assessed postintervention with the Self-Reported Habit Index. During the intervention, Fitbit wear time and activity levels were monitored with the activity trackers. Linear regression analyses were used to determine the influence of the trial on outcomes of physical activity and sedentary time. The influence of habits was examined using correlation coefficients relating habits of tracker use (wearing the tracker and checking data on the tracker and associated app) to Fitbit wear time and activity levels during the intervention and at follow-up.

Results

Regression analyses revealed no significant differences by group in any of the primary outcomes (all P>.05). However, personal characteristics, including lower baseline activity levels (beta=–.49, P=.01) and lack of previous experience with pedometers (beta=–.23, P=.03) were predictive of greater improvements in moderate and vigorous physical activity. Furthermore, for individuals with higher activity levels at the baseline, MI and habit education were more effective for maintaining these activity levels when compared with receiving a Fitbit alone (eg, small increase of ~48 steps/day, d=0.01, vs large decrease of ~1830 steps/day, d=0.95). Finally, habit development was significantly related to steps/day during (r=.30, P=.004) and following the intervention (r=.27, P=.03).

Conclusions

This study suggests that activity trackers may have beneficial effects on physical activity in healthy adults, but benefits vary based on individual factors. Furthermore, this study highlights the importance of habit development surrounding the wear and use of activity trackers and the associated software to promote increases in physical activity.

Trial Registration

ClinicalTrials.gov NCT03837366; https://clinicaltrials.gov/ct2/show/NCT03837366

Calibration and validation of accelerometer-based activity monitors: A systematic review of machine-learning approaches

BACKGROUND

Objective measures using accelerometer-based activity monitors have been extensively used in physical activity (PA) and sedentary behavior (SB) research. To measure PA and SB precisely, the field is shifting towards machine learning-based (ML) approaches for calibration and validation of accelerometer-based activity monitors. Nevertheless, various parameters regarding the use and development of ML-based models, including data type (raw acceleration data versus activity counts), sampling frequency, window size, input features, ML technique, accelerometer placement, and free-living settings, affect the predictive ability of ML-based models. The effects of these parameters on ML-based models have remained elusive, and will be systematically reviewed here. The open challenges were identified and recommendations are made for future studies and directions.

METHOD

We conducted a systematic search of PubMed and Scopus databases to identify studies published before July 2017 that used ML-based techniques for calibration and validation of accelerometer-based activity monitors. Additional articles were manually identified from references in the identified articles.

RESULTS

A total of 62 studies were eligible to be included in the review, comprising 48 studies that calibrated and validated ML-based models for predicting the type and intensity of activities, and 22 studies for predicting activity energy expenditure.

CONCLUSIONS

It appears that various ML-based techniques together with raw acceleration data sampled at 20-30 Hz provide the opportunity of predicting the type and intensity of activities, as well as activity energy expenditure with comparable overall predictive accuracies regardless of accelerometer placement. However, the high predictive accuracy of laboratory-calibrated models is not reproducible in free-living settings, due to transitive and unseen activities together with differences in acceleration signals.

Intervening to reduce sedentary behavior in older adults - pilot results

Background: Older adults spend most of their day in sedentary behavior (SB) (i.e., prolonged sitting), increasing risk for negative health outcomes, functional loss, and diminished ability for activities of daily living. The purpose of this study was to develop and pilot test an intervention designed to reduce SB in older adults that could be translated to communities.

Methods: Two pilot studies implementing a 4-week SB intervention were conducted. SB,physical function, and health-related quality of life were measured via self-report and objective measures. Participants (N=21) completed assessments pre- and post-intervention (studies 1 and 2) and at follow-up (4-weeks post-intervention; study 2). Due to the pilot nature of this research, data were analyzed with Cohen’s d effect sizes to examine the magnitude of change in outcomes following the intervention.

Results: Results for study 1 indicated moderate (d=0.53) decreases in accelerometry-obtained total SB and increases (d=0.52) in light intensity physical activity post-intervention. In study 2,there was a moderate decrease (d=0.57) in SB evident at follow-up. On average SB decreased by approximately 60 min/d in both studies. Also, there were moderate-to-large improvements in vitality (d=0.74; study 1) and gait speed (d=1.15; study 2) following the intervention. Further,the intervention was found to be feasible for staff to implement in the community.

Conclusion: These pilot results informed the design of an ongoing federally funded randomized controlled trial with a larger sample of older adults from underserved communities. Effective,feasible, and readily-accessible interventions have potential to improve the health and function of older adults.

Adapted Sojourn Models to Estimate Activity Intensity in Youth: A Suite of Tools

The challenges of using physical activity data from accelerometers have been compounded with the recent focus on wrist-worn monitors and raw acceleration (as opposed to activity counts).

PURPOSE

This study developed and systematically evaluated a suite of new accelerometer processing models for youth.

METHODS

Four adaptations of the Sojourn method were developed using data from a laboratory-based experiment in which youth (N = 54) performed structured activity routines. The adaptations corresponded to all possible pairings of hip or wrist attachment with activity counts (AC) or raw acceleration (RA), and they estimated time in sedentary behavior, light activity, and moderate-to-vigorous physical activity. Criterion validity was assessed using direct observation in an independent free-living sample (N = 27). Monitors were worn on both wrists to evaluate the effect of handedness on accuracy, and status quo methods for each configuration were also evaluated as benchmarks for comparison. Tests of classification accuracy (percent accuracy, κ statistics, and sensitivity and specificity) were used to summarize utility.

RESULTS

In the development sample, percent accuracy ranged from 68.5% (wrist-worn AC, κ = 0.42) to 71.6% (hip-worn RA, κ = 0.50). Accuracy was lower in the free-living evaluation, with values ranging from 49.3% (hip-worn RA, κ = 0.25) to 56.7% (hip-worn AC, κ = 0.36). Collectively, the suite predicted moderate-to-vigorous physical activity well, with the models averaging 96.5% sensitivity and 67.5% specificity. However, in terms of overall accuracy, the new models performed similarly to the status quo methods. There were no meaningful differences in performance at either wrist.

CONCLUSIONS

The new models offered minimal improvements over existing methods, but a major advantage is that further tuning of the models is possible with continued research.

Measuring Sedentary Behavior by Means of Muscular Activity and Accelerometry

Sedentary Behavior (SB) is among the most frequent human behaviors and is associated with a plethora of serious chronic lifestyle diseases as well as premature death. Office workers in particular are at an increased risk due to their extensive amounts of occupational SB. However, we still lack an objective method to measure SB consistent with its definition. We have therefore developed a new measurement system based on muscular activity and accelerometry. The primary aim of the present study was to calibrate the new-developed 8-CH-EMG+ for measuring occupational SB against an indirect calorimeter during typical desk-based office work activities. In total, 25 volunteers performed nine office tasks at three typical workplaces. Minute-by-minute posture and activity classification was performed using subsequent decision trees developed with artificial intelligence data processing techniques. The 8-CH-EMG+ successfully identified all sitting episodes (AUC = 1.0). Furthermore, depending on the number of electromyography channels included, the device has a sensitivity of 83–98% and 74–98% to detect SB and active sitting (AUC = 0.85–0.91). The 8-CH-EMG+ advances the field of objective SB measurements by combining accelerometry with muscular activity. Future field studies should consider the use of EMG sensors to record SB in line with its definition.

Methods for Activity Monitor Validation Studies: An Example With the Fitbit Charge

Purpose: To advance research practices with consumer monitors, standard validation methods are needed. This study provides an example of best practices through systematically evaluating the validity of the Fitbit Charge (FBC) under free-living conditions using a strong reference measure and robust measurement agreement methods. Methods: 94 healthy participants (Mage 41.8 ±9.3 yrs) wore a FBC and two research grade accelerometers (Actigraph GT3X and activPAL) as they went about normal activities for a week. Estimated daily minutes of moderate to vigorous physical activity (MVPA) from the FBC were compared against reference estimates obtained from the Sojourns Including Posture (SIP) methodology, while daily step counts were compared against the activPAL. Results: Correlations with reference indicators were high for average daily MVPA (r = 0.8; p < .0001) and steps (r = 0.76; p < .0001), but the FBC overestimated time spent in MVPA by 56% and steps by 15%. The mean absolute percent errors of MVPA and steps estimated by FBC were 71.5% and 30.0%, respectively. Neither of the MVPA and step estimates from the FBC fell into the ±10% equivalence zone set by the criterion. The Kappa statistics of the classification agreement between the two MVPA assessment methods was 0.32 with a low sensitivity of 30.1% but a high specificity of 96.7%. Conclusion: The FBC overestimated minutes of MVPA and steps when compared to both reference assessments in free-living conditions. Standardized reporting in future studies will facilitate comparisons with other monitors and with future versions of the FBC.

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.

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.

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.