Pooling data from thigh-worn accelerometers across multiple studies has great potential to advance evidence on the health benefits of physical activity. This requires harmonization of information on body postures, physical activity types, volumes and time patterns across different brands of devices. The aim of this study is to compare the physical behavior estimates provided by three different brands of thigh-worn accelerometers. Twenty participants volunteered for a 7-day free-living measurement. Three accelerometers - ActiGraph GT3X+, Axivity AX3 and ActivPAL Micro4 - were randomly placed in a vertical line on the midsection of the right thigh. Raw data from each accelerometer was processed and classified into 8 physical activities and postures using the Acti4 software. Absolute differences between estimates and the respective coefficient of variation (CV) were calculated. We observed very minor differences between physical behavior estimates from three different accelerometer brands. When averaged over 24 h (1,440 min), the absolute difference (CV) between accelerometers were: 1.2 mins (0.001) for lying/sitting, 3.4 mins (0.02) for standing, 3.5 mins (0.06) for moving, 1.9 mins (0.03) for walking, 0.1 mins (0.19) for running, 1.2 mins (0.19) for stair climbing, 1.9 mins (0.07) for cycling. Moreover, there was an average absolute difference of 282 steps (0.03) per 24 h. Physical behaviors were classified with negligible difference between the accelerometer brands. These results support harmonization of data from different thigh-worn accelerometers across multiple cohorts when analyzed in an identical manner.

Introduction The Prospective Physical Activity Sitting and Sleep consortium (ProPASS) is an international collaboration platform committed to harmonise thigh-worn accelerometry data. The aim of this paper is to (1) outline observational thigh-worn accelerometry studies and (2) summarise key strategic directions arising from the inaugural ProPASS meeting. Methods (1) We performed a systematic scoping review for observational studies of thigh-worn triaxial accelerometers in free-living adults (n≥100, 24 hours monitoring protocols). (2)Attendees of the inaugural ProPASS meeting were sent a survey focused on areas related to developing ProPASS: important terminology (Q1); accelerometry constructs (Q2); advantages and distinct contribution of the consortium (Q3); data pooling and harmonisation (Q4); data access and sharing (Q5 and Q6). Results (1) Eighty eligible articles were identified (22 primary studies; n~17 685). The accelerometers used most often were the ActivPAL3 and ActiGraph GT3X. The most commonly collected health outcomes were cardiometabolic and musculoskeletal. (2) None of the survey questions elicited the predefined 60% agreement. Survey responses recommended that ProPASS: use the term physical behaviour or movement behaviour rather than ‘physical activity’ for the data we are collecting (Q1); make only minor changes to ProPASS’s accelerometry construct (Q2); prioritise developing standardised protocols/tools (Q4); facilitate flexible methods of data sharing and access (Q5 and Q6). Conclusions Thigh-worn accelerometry is an emerging method of capturing movement and posture across the 24 hours cycle. In 2020, the literature is limited to 22 primary studies from high-income western countries. This work identified ProPASS’s strategic directions—indicating areas where ProPASS can most benefit the field of research: use of clear terminology, refinement of the measured construct, standardised protocols/tools and flexible data sharing.

Background: Researchers applying compositional data analysis to time-use data (e.g., time spent in physical behaviors) often face the problem of zeros, that is, recordings of zero time spent in any of the studied behaviors. Zeros hinder the application of compositional data analysis because the analysis is based on log-ratios. One way to overcome this challenge is to replace the zeros with sensible small values. The aim of this study was to compare the performance of three existing replacement methods used within physical behavior time-use epidemiology: simple replacement, multiplicative replacement, and log-ratio expectation-maximization (lrEM) algorithm. Moreover, we assessed the consequence of choosing replacement values higher than the lowest observed value for a given behavior.Method: Using a complete dataset based on accelerometer data from 1310 Danish adults as reference, multiple datasets were simulated across six scenarios of zeros (5–30% zeros in 5% increments). Moreover, four examples were produced based on real data, in which, 10 and 20% zeros were imposed and replaced using a replacement value of 0.5 min, 65% of the observation threshold, or an estimated value below the observation threshold. For the simulation study and the examples, the zeros were replaced using the three replacement methods and the degree of distortion introduced was assessed by comparison with the complete dataset.Results: The lrEM method outperformed the other replacement methods as it had the smallest influence on the structure of relative variation of the datasets. Both the simple and multiplicative replacements introduced higher distortion, particularly in scenarios with more than 10% zeros; although the latter, like the lrEM, does preserve the ratios between behaviors with no zeros. The examples revealed that replacing zeros with a value higher than the observation threshold severely affected the structure of relative variation.Conclusions: Given our findings, we encourage the use of replacement methods that preserve the relative structure of physical behavior data, as achieved by the multiplicative and lrEM replacements, andto avoidsimple replacement. Moreover, we do not recommend replacing zeros with values higher than the lowest observed value for a behavior.

Study Design. A cross-sectional survey. Objective. The aim of this study was to compare patients’ and physiotherapists’ views on triggers for low back pain (LBP) and to identify any novel factors not previously reported. Summary of Background Data. Most research on risk factors for LBP is guided by the views of clinicians and researchers, not patients. Consequently, potentially valuable information about risk factors for LBP is not available from those suffering the condition. This study aimed to compare patients’ and physiotherapists’ views on triggers for LBP and to identify any novel factors not previously reported. Methods. One hundred two physiotherapists and 999 patients with a sudden, acute episode of LBP participated in this study. Participating physiotherapists were asked to nominate the most likely short-term risk factors to trigger a LBP episode. Similarly, patients were asked what they thought had triggered their onset of LBP. Responses were coded into risk factor categories and subcategories by 2 independent researchers. Endorsement of each category was compared using the Pearson Chi-square statistic. Results. Both patients and physiotherapists endorsed biomechanical risk factors as the most important risk factor category (87.7% and 89.4%, respectively) and had similar levels of endorsement for 3 of the top 5 subcategories (lifting, bending, and prolonged sitting). There were significant differences in endorsement of awkward postures (13.4% vs 1.2%; P < 0.001) sports injuries (15.9% vs 4.7%; P < 0.001), physical trauma (3.4% vs 9.2%; P < 0.001), and unaccustomed activity (2.3% vs 7.3%; P < 0.001) by patients and physiotherapists, respectively. Conclusion. Overall, patients’ and physiotherapists’ views were remarkably similar. Both patients and physiotherapists endorsed lifting as the most important trigger for LBP and agreed on 3 of the top 5 (lifting, bending, and prolonged sitting). No new risk factors were suggested by patients. Level of Evidence: 2

Previous research has shown strong associations between occupational physical activity (OPA) and need for recovery (NFR). However this research has only utilized self-reported measures of OPA which may be biased. Thus, there is a need for investigating if the previously documented association between self-reported OPA and NFR can be found when using technical measures of OPA. There is also the need to investigate whether older workers are particularly susceptible to increased NFR, since age-related declines in physical capacity mean that it is likely these workers will have a higher NFR for a given physical activity. The aim of this study was to investigate the association between technically measured OPA and NFR, and whether this relationship is modified by age. This study utilized data from the Danish Physical Activity Cohort with Objective Measurements cohort—comprising Danish workers (n = 840) from the cleaning, manufacturing, and transportation sectors. OPA was measured by accelerometers attached to the thigh and upper back for at least one work day and classified into four physical behaviour categories (sedentary, standing, light, or moderate/vigorous). NFR was measured using a shortened version of the Danish NFR scale. Analysis was conducted using linear regression and isotemporal substitution analyses for compositional data. The overall association between OPA and NFR was statistically significant in the unadjusted model (P < 0.001), but not when adjusted for age, sex, occupation, and shift work (P = 0.166). Isotemporal substitution showed small but significant reductions in NFR when increasing sedentary time relative to other behaviours (adjusted: ΔNFR = −0.010 [−0.019; −0.001]). There were no significant interactions between age and OPA (P = 0.409). This study found significant associations between OPA and NFR, but the effect sizes were small. Reallocating 30 min to sedentary behaviours from other behaviours was associated with a reduced NFR, but the effect size may not be practically relevant. Moreover, no clear modifying effects of age were identified.