Can the bias of self-reported sitting time be corrected? A statistical model validation study based on data from 23 993 adults in the Norwegian HUNT study

BACKGROUND

Despite apparent shortcomings such as measurement error and low precision, self-reported sedentary time is still widely used in surveillance and research. The aim of this study was threefold; (i) to examine the agreement between self-reported and device-measured sitting time in a general adult population; (ii), to examine to what extent demographics, lifestyle factors, long-term health conditions, physical work demands, and educational level is associated with measurement bias; and (iii), to explore whether correcting for factors associated with bias improves the prediction of device-measured sitting time based on self-reported sitting time.

METHODS

A statistical validation model study based on data from 23 993 adults in the Trøndelag Health Study (HUNT4), Norway. Participants reported usual sitting time on weekdays using a single-item questionnaire and wore two AX3 tri-axial accelerometers on the thigh and low back for an average of 3.8 (standard deviation [SD] 0.7, range 1-5) weekdays to determine their sitting time. Statistical validation was performed by iteratively adding all possible combinations of factors associated with bias between self-reported and device-measured sitting time in a multivariate linear regression. We randomly selected 2/3 of the data (n = 15 995) for model development and used the remaining 1/3 (n = 7 998) to evaluate the model.

RESULTS

Mean (SD) self-reported and device-measured sitting time were 6.8 (2.9) h/day and 8.6 (2.2) h/day, respectively, corresponding to a mean difference of 1.8 (3.1) h/day. Limits of agreement ranged from - 8.0 h/day to 4.4 h/day. The discrepancy between the measurements was characterized by a proportional bias with participants device-measured to sit less overestimating their sitting time and participants device-measured to sit more underestimating their sitting time. The crude explained variance of device-measured sitting time based on self-reported sitting time was 10%. This improved to 24% when adding age, body mass index and physical work demands to the model. Adding sex, lifestyle factors, educational level, and long-term health conditions to the model did not improve the explained variance.

CONCLUSIONS

Self-reported sitting time had low validity and including a range of factors associated with bias in self-reported sitting time only marginally improved the prediction of device-measured sitting time.

Do Sit-Stand Tables Affect Physical Behavior and Body Composition Similarly in Normal-Weight and Overweight Office Workers? A Pilot Study

OCCUPATIONAL APPLICATIONSSedentary behavior is a significant health concern among office workers. We completed the same 6-month sit-stand table intervention at work for groups of normal-weight and overweight workers, and compared it to not having sit-stand tables. The intervention caused the intended decrease in sitting time in both groups and a corresponding increase in standing. We did not find compensation effects on physical behavior outside of work. Furthermore, the intervention did not change the composition of fat, lean, and bone mass in either group. Thus, strategies including initiatives to increase physical activity are likely needed to have effects on body composition; and an intervention needs to be sustained for longer than six months for any changes in body composition to be observed.

Prediction of Patient Satisfaction after Treatment of Chronic Neck Pain with Mulligan's Mobilization

Chronic neck pain is among the most common types of musculoskeletal pain. Manual therapy has been shown to have positive effects on this type of pain, but there are not yet many predictive models for determining how best to apply manual therapy to the different subtypes of neck pain. The aim of this study is to develop a predictive learning approach to determine which basal outcome could give a prognostic value (Global Rating of Change, GRoC scale) for Mulligan's mobilization technique and to identify the most important predictive factors for recovery in chronic neck pain subjects in four key areas: the number of treatments, time of treatment, reduction of pain, and range of motion (ROM) increase. A prospective cohort dataset of 80 participants with chronic neck pain diagnosed by their family doctor was analyzed. Logistic regression and machine learning modeling techniques (Generalized Boosted Models, Support Vector Machine, Kernel, Classsification and Decision Trees, Random Forest and Neural Networks) were each used to form a prognostic model for each of the nine outcomes obtained before and after intervention: disability-neck disability index (NDI), patient satisfaction (GRoC), quality of life (12-Item Short Form Survey, SF-12), State-Trait Anxiety Inventory (STAI), Beck Depression Inventory (BDI II), pain catastrophizing scale (ECD), kinesiophobia-Tampa scale of kinesiophobia (TSK-11), Pain Intensity Visual Analogue Scale (VAS), and cervical ROM. Pain descriptions from the subjects and pain body diagrams guided the physical examination. The most important predictive factors for recovery in chronic neck pain patients indicated that the more anxiety and the lower the ROM of lateroflexion, the higher the probability of success with the Mulligan concept treatment.

Sedentary behaviour, physical activity, and sleep among office workers during the COVID-19 pandemic: a comparison of Brazil and Sweden

Background

The COVID-19 pandemic has affected the physical behaviours of office workers worldwide, but studies comparing physical behaviours between countries with similar restrictions policies are rare. This study aimed to document and compare the 24-hour time-use compositions of physical behaviours among Brazilian and Swedish office workers on working and non-working days during the pandemic.

Methods

Physical behaviours were monitored over 7 days using thigh-worn accelerometers in 73 Brazilian and 202 Swedish workers. Daily time-use compositions were exhaustively described in terms of sedentary behaviour (SED) in short (< 30 min) and long (≥30 min) bouts, light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and time-in-bed. We examined differences between countries using MANOVA on data processed according to compositional data analysis. As Swedish workers had the possibility to do hybrid work, we conducted a set of sensitivity analyses including only data from days when Swedish workers worked from home.

Results

During working days, Brazilian office workers spent more time SED in short (294 min) and long (478 min) bouts and less time in LPA (156 min) and MVPA (50 min) than Swedish workers (274, 367, 256 and 85 min, respectively). Time spent in bed was similar in both groups. Similar differences between Brazilians and Swedes were observed on non-working days, while workers were, in general, less sedentary, more active and spent more time-in-bed than during working days. The MANOVA showed that Brazilians and Swedes differed significantly in behaviours during working (p <  0.001, ηp2 = 0.36) and non-working days (p <  0.001, ηp2 = 0.20). Brazilian workers spent significantly more time in SED relative to being active, less time in short relative to long bouts in SED, and more time in LPA relative to MVPA, both during workdays and non-workdays. Sensitivity analyses only on data from days when participants worked from home showed similar results.

Conclusions

During the COVID-19 pandemic Brazilian office workers were more sedentary and less active than Swedish workers, both during working and non-working days. Whether this relates to the perception or interpretation of restrictions being different or to differences present even before the pandemic is not clear, and we encourage further research to resolve this important issue.

Use of wearable sensors to assess patterns of trunk flexion in young and old workers in the Metalworking Industry

Workers exposed to repeated trunk flexions are at risk of onset of low-back disorders and in individuals aged over 50 this issue is exacerbated by the physiologic decline of the musculoskeletal system and longer lifetime occupational exposure. In this study, we investigated the existence of possible age-related differences in patterns of trunk flexion of workers in the metalworking industry. Thirty-three subjects were monitored during an actual shift using a wearable Inertial Measurement Unit (IMU) to assess trunk flexion angles (i.e. between 30° and 60°, 60°-90° and > 90°). Results show that older workers spent less time with their trunk flexed, regardless of the class of flexion considered, with respect to their younger colleagues. Although further studies are necessary to clarify the existence of strategies aimed at optimising trunk movements during ageing, the IMU-based approach appears useful in highlighting potentially harmful conditions, especially in workers with marked signs of decline in their physical capacities. Practitioner summary: Wearable sensors, which are well tolerated and minimally intrusive, represent a valid option to continuously monitor trunk posture in workers employed in metalworking industry. The results of this study show that they provide valuable information about the patterns of flexion of young and old individuals engaged in physically demanding tasks.

High physical work demands have worse consequences for older workers: prospective study of long-term sickness absence among 69 117 employees

Objective

This study investigates the role of age for the prospective association between physical work demands and long-term sickness absence (LTSA).

Methods

We followed 69 117 employees of the general working population (Work Environment and Health in Denmark study 2012–2018), without LTSA during the past 52 weeks preceding initial interview, for up to 2 years in the Danish Register for Evaluation of Marginalisation. Self-reported physical work demands were based on a combined ergonomic index including seven different types of exposure during the working day. Using weighted Cox regression analyses controlling for years of age, gender, survey year, education, lifestyle, depressive symptoms and psychosocial work factors, we determined the interaction of age with physical work demands for the risk of LTSA.

Results

During follow-up, 8.4% of the participants developed LTSA. Age and physical work demands interacted (p<0.01). In the fully adjusted model, very high physical work demands were associated with LTSA with HRs of 1.18 (95% CI 0.93 to 1.50), 1.57 (95% CI 1.41 to 1.75) and 2.09 (95% CI 1.81 to 2.41) for 20, 40 and 60 years old (point estimates), respectively. Results remained robust in subgroup analyses including only skilled and unskilled workers and stratified for gender.

Conclusion

The health consequences of high physical work demands increase with age. Workplaces should consider adapting physical work demands to the capacity of workers in different age groups.

Time-Based Data in Occupational Studies: The Whys, the Hows, and Some Remaining Challenges in Compositional Data Analysis (CoDA)

Data on the use of time in different exposures, behaviors, and work tasks are common in occupational research. Such data are most often expressed in hours, minutes, or percentage of work time. Thus, they are constrained or ‘compositional’, in that they add up to a finite sum (e.g. 8 h of work or 100% work time). Due to their properties, compositional data need to be processed and analyzed using specifically adapted methods. Compositional data analysis (CoDA) has become a particularly established framework to handle such data in various scientific fields such as nutritional epidemiology, geology, and chemistry, but has only recently gained attention in public and occupational health sciences. In this paper, we introduce the reader to CoDA by explaining why CoDA should be used when dealing with compositional time-use data, showing how to perform CoDA, including a worked example, and pointing at some remaining challenges in CoDA. The paper concludes by emphasizing that CoDA in occupational research is still in its infancy, and stresses the need for further development and experience in the use of CoDA for time-based occupational exposures. We hope that the paper will encourage researchers to adopt and apply CoDA in studies of work exposures and health.

Work-Time Compositions of Physical Behaviors and Trajectories of Sick Leave Due to Musculoskeletal Pain

We aimed to investigate the association between work-time compositions of physical behavior and sick leave trajectories due to musculoskeletal pain over one year. We conducted a secondary analysis using the data of 981 workers in a Danish prospective cohort (DPHACTO 2012–2014). At baseline, we assessed physical behaviors (sitting, standing, light physical activity (LIPA), and moderate-to-vigorous physical activity (MVPA)) at work and during leisure, using accelerometers. Over 1 year follow-up, workers reported sick-leave days due to musculoskeletal pain at 4-week intervals. Four distinct trajectories of sick leave were previously identified in this cohort (“no sick leave”, “few days—increasing trajectory”, “some days—decreasing trajectory”, “some days—increasing trajectory”), and used as an outcome in multinomial regression models with work-time compositions as predictors, adjusted for compositions of behavior during leisure, age, sex, body mass index, and smoking habits. More time spent sitting relative to the other behaviors was negatively associated with the trajectory of few days—increasing sick leave (p = 0.004), while time in LIPA was positively associated with the trajectory of some days—increasing sick leave (p = 0.009). Standing and MVPA were not significantly associated with sick leave trajectories. In conclusion, work-time compositions with more sitting relative to the other behaviors had lower risk for an increasing trajectory of sick leave due to pain, while compositions with more LIPA had higher risk. This may have implications for prevention of pain-related sick leave in blue-collar workers.

Objective and subjective measurement of sedentary behavior in human adults: A toolkit

OBJECTIVES

Objectives: Human biologists are increasingly interested in measuring and comparing physical activities in different societies. Sedentary behavior, which refers to time spent sitting or lying down while awake, is a large component of daily 24 hours movement patterns in humans and has been linked to poor health outcomes such as risk of all-cause and cardiovascular mortality, independently of physical activity. As such, it is important for researchers, with the aim of measuring human movement patterns, to most effectively use resources available to them to capture sedentary behavior.

METHODS

This toolkit outlines objective (device-based) and subjective (self-report) methods for measuring sedentary behavior in free-living contexts, the benefits and drawbacks to each, as well as novel options for combined use to maximize scientific rigor. Throughout this toolkit, emphasis is placed on considerations for the use of these methods in various field conditions and in varying cultural contexts.

RESULTS

Objective measures such as inclinometers are the gold-standard for measuring total sedentary time but they typically cannot capture contextual information or determine which specific behaviors are taking place. Subjective measures such as questionnaires and 24 hours-recall methods can provide measurements of time spent in specific sedentary behaviors but are subject to measurement error and response bias.

CONCLUSIONS

We recommend that researchers use the method(s) that suit the research question; inclinometers are recommended for the measurement of total sedentary time, while self-report methods are recommended for measuring time spent in particular contexts of sedentary behavior.

The Relation between Domain-Specific Physical Behaviour and Cardiorespiratory Fitness: A Cross-Sectional Compositional Data Analysis on the Physical Activity Health Paradox Using Accelerometer-Assessed Data

In contrast to leisure time physical activity (LTPA), occupational physical activity (OPA) does not have similar beneficial health effects. These differential health effects might be explained by dissimilar effects of LTPA and OPA on cardiorespiratory fitness (CRF). This study investigated cross-sectional associations between different physical behaviours during both work and leisure time and CRF by using a Compositional Data Analysis approach. Physical behaviours were assessed by two accelerometers among 309 workers with various manual jobs. During work time, more sedentary behaviour (SB) was associated with higher CRF when compared relatively to time spent on other work behaviours, while more SB during leisure time was associated with lower CRF when compared to other leisure time behaviours. Reallocating more time to moderate-to-vigorous physical activity (MVPA) from the other behaviours within leisure time was positively associated with CRF, which was not the case for MVPA during work. The results of our study are in line with the physical activity health paradox and we call for further study on the interaction between LTPA and OPA by implementing device-worn measures in a longitudinal design. Our results highlight the need for recommendations to take into account the different effects of OPA and LTPA on CRF.

Calibration of Self-Reported Time Spent Sitting, Standing and Walking among Office Workers: A Compositional Data Analysis

We developed and evaluated calibration models predicting objectively measured sitting, standing and walking time from self-reported data using a compositional data analysis (CoDA) approach. A total of 98 office workers (48 women) at the Swedish Transport Administration participated. At baseline and three-months follow-up, time spent sitting, standing and walking at work was assessed for five working days using a thigh-worn accelerometer (Actigraph), as well as by self-report (IPAQ). Individual compositions of time spent in the three behaviors were expressed by isometric log-ratios (ILR). Calibration models predicting objectively measured ILRs from self-reported ILRs were constructed using baseline data, and then validated using follow-up data. Un-calibrated self-reports were inaccurate; root-mean-square (RMS) errors of ILRs for sitting, standing and walking were 1.21, 1.24 and 1.03, respectively. Calibration reduced these errors to 36% (sitting), 40% (standing), and 24% (walking) of those prior to calibration. Calibration models remained effective for follow-up data, reducing RMS errors to 33% (sitting), 51% (standing), and 31% (walking). Thus, compositional calibration models were effective in reducing errors in self-reported physical behaviors during office work. Calibration of self-reports may present a cost-effective method for obtaining physical behavior data with satisfying accuracy in large-scale cohort and intervention studies.