TY - JOUR
AU1 - , De Cocker, Katrien
AU2 - Ketels,, Margo
AU3 - Bennie, Jason, A
AU4 - Clays,, Els
AB - Abstract Background There is increasing interest in the association between psychological distress and time spent in sedentary behaviour (e.g. sitting), a highly prevalent behaviour in modern society. The limited evidence is mixed and mainly based on studies using self-reported sedentary time. Few studies have investigated device-based total sedentary time in its association with distress. None, however, have examined device-based domain-specific sedentary time in relation to psychological distress. The aim of this study was to investigate whether device-based total and domain-specific sedentary behaviour were associated with psychological distress. Methods Flemish employees (n = 401; 20–64 years; 42.6% male; 83.6% had a ‘physically active occupation’) of seven organizations in service and production sectors participated. Sedentary behaviour (exposure) was assessed by two Axivity AX3 accelerometers (one placed on the thigh and one placed between the shoulders) for two to four consecutive working days. Based on diary completion, domain-specific sedentary behaviour (leisure vs. work) was assessed. The 12-item General Health Questionnaire was used to assess psychological distress (outcome). Adjusted hierarchical multiple regression models were conducted to report on the associations between total and domain-specific sedentary behaviour and psychological distress. Results About 35% of the sample had high levels of distress and average total sedentary time was 7.2 h/day. Device-based total sedentary behaviour [B = −0.009, 95% confidence interval (CI), −0.087 to 0.068], leisure-time (B = 0.001, 95% CI, −0.017 to 0.018) and work-related (B = 0.004, 95% CI, −0.006 to 0.015) sedentary behaviour were not significantly associated with psychological distress. Conclusion This cross-sectional study examining the association between device-based total and domain-specific sedentary behaviour and psychological distress among employees showed a lack of significant findings. Introduction Mental health is an important global indicator for health and well-being. Mental health disorders affect approximately one in five individuals across all regions of the world,1 resulting in a global burden of disability.2 Psychological distress, also called ‘stress’ and ‘emotional distress’ and referring to symptoms of depression and anxiety, is related to increased risks of chronic diseases and mortality.3–6 In Belgium, it is estimated that 32% of the adult population suffer from psychological distress.7 To prevent and manage psychological distress in the population, there is a need to better understand modifiable lifestyle factors related to poor mental health outcomes. In the last decade, public health interest in sedentary behaviours (i.e. waking behaviour spend in a sitting, reclining or lying posture with a low energy expenditure8) as an important modifiable lifestyle behaviour in the prevention and treatment of poor mental health has grown.9–11 Systematic reviews indicate that prolonged sitting is associated with depression10 and anxiety.11 However, the evidence on the association between sedentary behaviour and psychological distress is limited and rather mixed.12–20 To advance the knowledge in this topic, existing gaps in the literature need to be addressed. First, in the existing studies, sedentary behaviour was mainly measured through self-reports,20 which are likely to be prone to recall bias, inaccuracies and socially desirable responding, representing a clear limitation of the current literature on the association between sedentary behaviour and psychological distress. Device-based sitting time estimates using accelerometery and inclinometery have shown to produce more accurate assessments of sedentary behaviour.21 However, at present, few studies used accelerometer-based total sitting time20 and the evidence of its association with psychological distress is currently conflicting. This suggests that more research using more objective measures is warranted to increase our understanding of the association between device-based sedentary behaviour and psychological distress. Second, some literature suggests that not all forms of sedentary behaviours may be associated with psychological distress.12,15,19 The way in which sedentary behaviour occurs (i.e. the type, context or domain of the behaviour, and not so much the sitting posture itself) may be a key factor in the relationship with mental health outcomes.10,11,20 While leisure time sedentary behaviour, e.g. reading, might be a strategy to de-stress; alternatively, screen-based sedentary behaviours could lead to more feelings of distress when they displace time spent in, e.g. other mental health-related behaviours like physical activity, or when they negatively impact social isolation, mood state, or sleep.20,22,23 Furthermore, some research suggests that occupational sedentary behaviour could be associated with more stress.20 Current studies examining the type (different activities done during sitting, i.e. PC use, TV viewing, reading) or the domain (setting in which the behaviour occurs, i.e. leisure-time vs. workplace) of sedentary behaviour in relation to psychological distress are however scarce and show conflicting results.12,15,19,20 In addition, assessments of type- and domain-specific sedentary behaviours are mainly based on self-reports,20 which might be prone to the above-mentioned measurement biases. Hence, in their review study, Teychenne et al.20 concluded that more research using device-based measures of sedentary behaviour which capture type, context or domain of the behaviour is required when examining associations with stress-related outcomes. To date, no studies have used objective measures of different domains of sedentary behaviours in its relation with psychological distress.20 Hence, it is necessary to look further into this matter using more device-based exposure data. Finally, few studies examining the association between sedentary behaviour and psychological distress were conducted among working adults,20 a group in which distress is becoming more prevalent globally.24,25 Existing studies among employees were only based on self-reported sedentary behaviour data and had no information on both work- and leisure time-related sedentary behaviour.12,14 In addition, research on this association is missing among employees from manufacturing and service sectors. Workers in these sectors are however at risk of an imbalance in work–life, leading to health problems including stress.26 Moreover, these workers mainly have high levels of occupational physical activity, which is often compensated with high amounts of sedentary behaviour during leisure, that in turn could impact their psychological distress level.27 Considering the above, there is a lack of research on the association between device-based domain-specific sedentary behaviour and psychological distress among working adults. Therefore, the aim of the present study was to investigate the associations between device-based total and domain-specific (leisure vs. workplace) sedentary behaviour and psychological distress in a large sample of Flemish employees in the manufacturing and service sector. Given the current inconsistent evidence on this association, no hypotheses were formulated. Research in this area is important for the future development of mental health prevention and promotion initiatives for employees. Methods Study design, recruitment and study population This cross-sectional Flemish Employees’ Physical Activity (FEPA) study was approved by the Research Ethical Committee of Ghent University Hospital (number 2017/0129). Between February 2017 and June 2018, data were collected via convenience sampling in seven different Flemish organizations within the secondary (finished goods) sector (n = 4, i.e. a manufacturer of luggage, food products, fences and plastic materials) and tertiary (service) sector (n = 3, i.e. a logistics and courier company, hospital and medical centre), mainly employing adults in occupations involving some level of physical work. Participants were included when meeting the following criteria: (i) being Dutch-speaking, (ii) not pregnant, (iii) employed for at least 50% and (iv) doing no exclusive night shifts. A total of 1135 eligible employees were contacted and invited to participate. Of this sample, 430 (participation rate: 38%) signed an informed consent form and agreed to participate voluntarily. Further details of the protocol have been published elsewhere.28 Procedures All participating employees were asked to complete baseline measurements, including a self-administered questionnaire (paper or online version) and ambulatory device-based measurements of their daily activities. A trained researcher took anthropometrics measures at the workplace during working hours and attached two measurement devices (see measurement instruments; Axivity AX3 accelerometer) on the skin of the participants. The devices had to be worn continuously (24 h/day) for two to four consecutive workdays. During this recording period, participants kept a paper diary to describe their daily routines of sleep (going to sleep and getting out of bed), working hours (hours spent at the primary occupation), leisure time (remaining waking hours), and non-wear time of the devices. When these ambulatory assessments were completed, the devices and diary were returned to the researcher. Measurement instruments Twelve-item General Health Questionnaire: psychological distress The 12-item version of the General Health Questionnaire (GHQ-12) is a widely used instrument to screen mental health in general settings.29 The questionnaire is considered to be valid and reliable (reliability coefficients 0.78–0.95)30,31 to screen symptoms of depression, anxiety and somatic symptoms. The GHQ-12 comprises 12 items describing particular symptoms, mood states or behaviours, six of which were positively phrased (feeling useful, being capable of making decisions, enjoying normal activities, being able to face up to problems, being able to concentrate and feeling reasonably happy) and six negatively phrased (losing much sleep, being under stress, being unable to overcome difficulties, feeling unhappy and depressed, losing confidence and feeling worthless). The items, rated on a four-point Likert scale, were summed according to standardized scoring methods for the GHQ-1231,32, to get a total score (ranging from 0 to 12), with higher scores indicating more psychological distress. A score of minimum 2 was used to detect high levels of psychological distress.29 Cronbach’s alpha for the 12 items was 0.76, suggesting appropriate internal consistency of the items. Axivity AX3 accelerometer: sedentary behaviour Two Axivity AX3 accelerometers (© Axivity Ltd., UK) were worn on the skin using Opsite Flexifit wound foil to make it waterproof. Using standardized protocols, one accelerometer was placed in the middle of the back (at the level of T1–T2 on the processus spinosus) and one in the middle of the right thigh (at the front, midway between the iliac crest and the upper border of the patella), enabling to assess different free living activities and postures.33 Consistent with previous studies, both accelerometers were orientated with the x-axis and the USB port pointing down, y-axis horizontally to the left and z-axis horizontally forward. Self-administered questionnaire and anthropometric measures: covariates Socio-demographics and smoking status (never smoked, used to smoke, occasional smoker, daily smoker) were assessed in a questionnaire. The socio-demographic items included age, sex, education level [grouped in low (primary school, lower secondary school), medium (higher secondary school, 1–2 years of specialization) and high (college or university) education], job status (white-collar vs. blue-collar), job roster (shift work vs. fixed day shift), employment equivalent (100% vs. 80–90% vs. 50–80%) and type of job (mainly sedentary job vs. physically active job). Height and weight were measured by the researcher using a Seca 704 column scale (SECA Medical Measuring Systems and Scales, Birmingham, UK; scales 701/704). The corresponding body mass index (BMI) was calculated as weight (kg) divided by the square of height (m). Data processing and reduction The Axivity Software (AX3-GUI, Omgui Software) was used to initialize recording, to synchronize the two accelerometers and to download the data. The custom-made MATLAB Acti4 Software (The National Research Centre for the Working Environment, Copenhagen, Denmark and Federal Institute for Occupational Safety and Health, Berlin, Germany) was used to analyze the Axivity data.33,34 Intervals were identified as non-wear time if (i) the Acti4 software detected no movement for >90 minutes, (ii) the participants reported the interval as ‘non-wear time’ in the diary or (iii) artefacts or missing data were detected by the Acti4 software. To divide the measurement intervals into leisure-time and work-related time, in order to obtain domain-specific behaviours, information from the diaries (before working hours, during working hours, after working hours and sleep after working hours) was used. Only participants with measurements of both work and leisure time for at least one valid day were included for further analysis. A valid day was defined as having at least 4 hours of work and at least 4 hours of leisure time, or 75% of the average reported work and leisure time. The data on a daily basis were only included in the data pool if a minimum of 10 hours of data were available.33,35 This Acti4 software determined the type and duration of the different activities (lying, sitting, standing, walking, running, walking on stairs, rowing and cycling) based on the G-values and the angle of the accelerometer.33,35 Sleep/bedtime periods and non-wear time periods were excluded in this analysis. Lying was identified by the accelerometer on the back (inclination of the x-axis above 65°), sitting by the accelerometer on the thigh (inclination of the longitudinal axis above 45°). Sedentary time was calculated by combining the time spent lying and sitting during waking hours. In the present study, average daily time spent sedentary during leisure, at work and total sedentary behaviour (summation of average daily time spent sedentary during leisure and during work during waking hours) were used. In the analyses testing the association between domain-specific sedentary time and psychological distress, standardized measures, i.e. percentage variables (proportion of time spent sedentary within, respectively, the working hours and leisure hours) were used. Domain-specific moderate-to-vigorous intensity physical activity (MVPA) (daily time) was calculated by summing the time in walking fast, running, walking on stairs, rowing and cycling during leisure and work, respectively.33 The standardized domain-specific measures, i.e. percentage variables (proportion of time) were used as covariates in the analyses. Statistical analyses To determine the association between device-based total sedentary behaviour (exposure) and psychological distress (outcome), we used a hierarchical multiple linear regression model. In this stepwise model, the following covariates were added to adjust the model: age, gender, education, and type of job (block 2), BMI and smoking status (block 3), and leisure and work-related MVPA (block 4). A second hierarchical multiple linear regression model tested domain-specific sedentary behaviour. Prior to this analysis, the exposure variables (leisure sedentary behaviour and work sedentary behaviour) were centred by subtracting the population mean value from each participant. To avoid multicollinearity, these variables were used to calculate the interaction term, classified as leisure × work. In the first block, the independent variables were the leisure and work-related sedentary behaviour variables. In a second block, the interaction term (leisure × work) was additionally included, while in the other blocks, the analyses were additionally adjusted for age, gender, education, and type of job (block 3), BMI and smoking status (block 4), and MVPA (block 5). All analyses were conducted in SPSS version 24.0 (IBM Corporation, Armonk, NY, USA) and significance was set at P < 0.05. Results Study sample A total of 401 employed adults had complete valid data and were included in the current study. Participants’ demographics, employment information, health behaviours and psychological distress are presented in table 1. The present sample had a mean age of 38.3 years, about 42% was male, more than half were highly educated, the majority was full-time employed, about 57% had a white-collar job, about 58% were shift-workers and about 84% had a ‘physically active occupation’ (see table 1). Participants spent on average 7.2 h/day sedentary and had low psychological distress scores (see table 1). Psychological distress did not significantly differ for or correlate with the companies or any of the covariates (data not shown). Table 1 Participant characteristics Characteristic . . Gender: men, n (%) 167/401 (41.6) Age  Mean (SD) years 38.3 (11.1)  Range: min–max years 19.8–63.8 Education, n (%)  Low 62/401 (15.5)  Medium 127/401 (31.7)  High 212/401 (52.9) Job status, n (%)  White-collar 227/396 (57.3)  Blue-collar 169/396 (42.7) Job roster, n (%)  Shift work 232/399 (58.1)  Fixed day shifts 167/399 (41.9) Employment equivalent, n (%)  100% 301/398 (75.6)  80–90% 63/398 (15.8)  50–80% 34/398 (8.5) Type of job: mainly sedentary job, n (%) 65/396 (16.4) Body mass index: mean (SD) kg/m2 24.7 (4.1) Currently smoking, n (%) 88/398 (22.1) Device-based behaviours  Total sedentary behaviour   Mean (SD) h/day 7.2 (2.6)  Leisure-time sedentary behavioura   Mean (SD) h/day 4.6 (1.4)   Mean (SD) proportion of leisure-time (%) 60.2 (14.5)  Work-related sedentary behavioura   Mean (SD) h/day 2.8 (1.9)   Mean (SD) proportion of work time (%) 35.5 (23.8)  Total MVPA   Mean (SD) h/day 1.8 (0.7)  Leisure-time MVPAa   Mean (SD) h/day 0.7 (0.4)   Mean (SD) proportion of leisure-time (%) 9.3 (4.8)  Work-related MVPAa   Mean (SD) h/day 1.1 (0.6)   Mean (SD) proportion of work time (%) 13.5 (7.2) Psychological distress  Mean (SD) score 1.34 (1.79)  High level (≥2/12), n (%) 139/398 (34.9) Characteristic . . Gender: men, n (%) 167/401 (41.6) Age  Mean (SD) years 38.3 (11.1)  Range: min–max years 19.8–63.8 Education, n (%)  Low 62/401 (15.5)  Medium 127/401 (31.7)  High 212/401 (52.9) Job status, n (%)  White-collar 227/396 (57.3)  Blue-collar 169/396 (42.7) Job roster, n (%)  Shift work 232/399 (58.1)  Fixed day shifts 167/399 (41.9) Employment equivalent, n (%)  100% 301/398 (75.6)  80–90% 63/398 (15.8)  50–80% 34/398 (8.5) Type of job: mainly sedentary job, n (%) 65/396 (16.4) Body mass index: mean (SD) kg/m2 24.7 (4.1) Currently smoking, n (%) 88/398 (22.1) Device-based behaviours  Total sedentary behaviour   Mean (SD) h/day 7.2 (2.6)  Leisure-time sedentary behavioura   Mean (SD) h/day 4.6 (1.4)   Mean (SD) proportion of leisure-time (%) 60.2 (14.5)  Work-related sedentary behavioura   Mean (SD) h/day 2.8 (1.9)   Mean (SD) proportion of work time (%) 35.5 (23.8)  Total MVPA   Mean (SD) h/day 1.8 (0.7)  Leisure-time MVPAa   Mean (SD) h/day 0.7 (0.4)   Mean (SD) proportion of leisure-time (%) 9.3 (4.8)  Work-related MVPAa   Mean (SD) h/day 1.1 (0.6)   Mean (SD) proportion of work time (%) 13.5 (7.2) Psychological distress  Mean (SD) score 1.34 (1.79)  High level (≥2/12), n (%) 139/398 (34.9) SD, standard deviation; MVPA, moderate-to-vigorous intensity physical activity. a Normalized data. Open in new tab Table 1 Participant characteristics Characteristic . . Gender: men, n (%) 167/401 (41.6) Age  Mean (SD) years 38.3 (11.1)  Range: min–max years 19.8–63.8 Education, n (%)  Low 62/401 (15.5)  Medium 127/401 (31.7)  High 212/401 (52.9) Job status, n (%)  White-collar 227/396 (57.3)  Blue-collar 169/396 (42.7) Job roster, n (%)  Shift work 232/399 (58.1)  Fixed day shifts 167/399 (41.9) Employment equivalent, n (%)  100% 301/398 (75.6)  80–90% 63/398 (15.8)  50–80% 34/398 (8.5) Type of job: mainly sedentary job, n (%) 65/396 (16.4) Body mass index: mean (SD) kg/m2 24.7 (4.1) Currently smoking, n (%) 88/398 (22.1) Device-based behaviours  Total sedentary behaviour   Mean (SD) h/day 7.2 (2.6)  Leisure-time sedentary behavioura   Mean (SD) h/day 4.6 (1.4)   Mean (SD) proportion of leisure-time (%) 60.2 (14.5)  Work-related sedentary behavioura   Mean (SD) h/day 2.8 (1.9)   Mean (SD) proportion of work time (%) 35.5 (23.8)  Total MVPA   Mean (SD) h/day 1.8 (0.7)  Leisure-time MVPAa   Mean (SD) h/day 0.7 (0.4)   Mean (SD) proportion of leisure-time (%) 9.3 (4.8)  Work-related MVPAa   Mean (SD) h/day 1.1 (0.6)   Mean (SD) proportion of work time (%) 13.5 (7.2) Psychological distress  Mean (SD) score 1.34 (1.79)  High level (≥2/12), n (%) 139/398 (34.9) Characteristic . . Gender: men, n (%) 167/401 (41.6) Age  Mean (SD) years 38.3 (11.1)  Range: min–max years 19.8–63.8 Education, n (%)  Low 62/401 (15.5)  Medium 127/401 (31.7)  High 212/401 (52.9) Job status, n (%)  White-collar 227/396 (57.3)  Blue-collar 169/396 (42.7) Job roster, n (%)  Shift work 232/399 (58.1)  Fixed day shifts 167/399 (41.9) Employment equivalent, n (%)  100% 301/398 (75.6)  80–90% 63/398 (15.8)  50–80% 34/398 (8.5) Type of job: mainly sedentary job, n (%) 65/396 (16.4) Body mass index: mean (SD) kg/m2 24.7 (4.1) Currently smoking, n (%) 88/398 (22.1) Device-based behaviours  Total sedentary behaviour   Mean (SD) h/day 7.2 (2.6)  Leisure-time sedentary behavioura   Mean (SD) h/day 4.6 (1.4)   Mean (SD) proportion of leisure-time (%) 60.2 (14.5)  Work-related sedentary behavioura   Mean (SD) h/day 2.8 (1.9)   Mean (SD) proportion of work time (%) 35.5 (23.8)  Total MVPA   Mean (SD) h/day 1.8 (0.7)  Leisure-time MVPAa   Mean (SD) h/day 0.7 (0.4)   Mean (SD) proportion of leisure-time (%) 9.3 (4.8)  Work-related MVPAa   Mean (SD) h/day 1.1 (0.6)   Mean (SD) proportion of work time (%) 13.5 (7.2) Psychological distress  Mean (SD) score 1.34 (1.79)  High level (≥2/12), n (%) 139/398 (34.9) SD, standard deviation; MVPA, moderate-to-vigorous intensity physical activity. a Normalized data. Open in new tab Association between device-based sedentary behaviour and psychological distress Table 2 shows the results of the hierarchical multiple regression models. The first set, having total sedentary behaviour as exposure variable, showed no significant association between sedentary behaviour and psychological distress. Likewise, the second set, including leisure-time sedentary behaviour, work-related sedentary behaviour and the interaction (leisure × work), revealed no associations between these sedentary behaviour exposure variables and psychological distress. Table 2 Associations between domain-specific sedentary behaviour and psychological distress . Block 1a . Block 2b . Block 3c . Block 4d . Block 5e . First set of hierarchical multiple regression model: B (95% CI)  SB total −0.030 (−0.100 to 0.040) / −0.018 (−0.093 to 0.057) −0.019 (−0.095 to 0.056) −0.017 (−0.094 to 0.060) Second set of hierarchical multiple regression model: B (95% CI)  SB leisure 0.004 (−0.008 to 0.017) 0.004 (−0.010 to 0.018) 0.002 (−0.012 to 0.017) 0.001 (−0.013 to 0.016) 0.001 (−0.017 to 0.018)  SB work −0.003 (−0.011 to 0.005) −0.003 (−0.011 to 0.005) 0.000 (−0.008 to 0.009) 0.001 (−0.008 to 0.010) 0.003 (−0.007 to 0.013)  SB leisure × work / 0.000 (−0.001 to 0.001) 0.000 (−0.001 to 0.001) 0.000 (−0.001 to 0.001) 0.000 (−0.001 to 0.001) . Block 1a . Block 2b . Block 3c . Block 4d . Block 5e . First set of hierarchical multiple regression model: B (95% CI)  SB total −0.030 (−0.100 to 0.040) / −0.018 (−0.093 to 0.057) −0.019 (−0.095 to 0.056) −0.017 (−0.094 to 0.060) Second set of hierarchical multiple regression model: B (95% CI)  SB leisure 0.004 (−0.008 to 0.017) 0.004 (−0.010 to 0.018) 0.002 (−0.012 to 0.017) 0.001 (−0.013 to 0.016) 0.001 (−0.017 to 0.018)  SB work −0.003 (−0.011 to 0.005) −0.003 (−0.011 to 0.005) 0.000 (−0.008 to 0.009) 0.001 (−0.008 to 0.010) 0.003 (−0.007 to 0.013)  SB leisure × work / 0.000 (−0.001 to 0.001) 0.000 (−0.001 to 0.001) 0.000 (−0.001 to 0.001) 0.000 (−0.001 to 0.001) SB, sedentary behaviour. a Unadjusted model. b Unadjusted model with interaction term included. c Model 2 adjusted for age, gender, education and type of job. d Model 2 adjusted for age, gender, education, type of job, BMI and smoking. e Model 2 adjusted for age, gender, education, type of job, BMI, smoking and moderate-to-vigorous-intensity physical activity. Open in new tab Table 2 Associations between domain-specific sedentary behaviour and psychological distress . Block 1a . Block 2b . Block 3c . Block 4d . Block 5e . First set of hierarchical multiple regression model: B (95% CI)  SB total −0.030 (−0.100 to 0.040) / −0.018 (−0.093 to 0.057) −0.019 (−0.095 to 0.056) −0.017 (−0.094 to 0.060) Second set of hierarchical multiple regression model: B (95% CI)  SB leisure 0.004 (−0.008 to 0.017) 0.004 (−0.010 to 0.018) 0.002 (−0.012 to 0.017) 0.001 (−0.013 to 0.016) 0.001 (−0.017 to 0.018)  SB work −0.003 (−0.011 to 0.005) −0.003 (−0.011 to 0.005) 0.000 (−0.008 to 0.009) 0.001 (−0.008 to 0.010) 0.003 (−0.007 to 0.013)  SB leisure × work / 0.000 (−0.001 to 0.001) 0.000 (−0.001 to 0.001) 0.000 (−0.001 to 0.001) 0.000 (−0.001 to 0.001) . Block 1a . Block 2b . Block 3c . Block 4d . Block 5e . First set of hierarchical multiple regression model: B (95% CI)  SB total −0.030 (−0.100 to 0.040) / −0.018 (−0.093 to 0.057) −0.019 (−0.095 to 0.056) −0.017 (−0.094 to 0.060) Second set of hierarchical multiple regression model: B (95% CI)  SB leisure 0.004 (−0.008 to 0.017) 0.004 (−0.010 to 0.018) 0.002 (−0.012 to 0.017) 0.001 (−0.013 to 0.016) 0.001 (−0.017 to 0.018)  SB work −0.003 (−0.011 to 0.005) −0.003 (−0.011 to 0.005) 0.000 (−0.008 to 0.009) 0.001 (−0.008 to 0.010) 0.003 (−0.007 to 0.013)  SB leisure × work / 0.000 (−0.001 to 0.001) 0.000 (−0.001 to 0.001) 0.000 (−0.001 to 0.001) 0.000 (−0.001 to 0.001) SB, sedentary behaviour. a Unadjusted model. b Unadjusted model with interaction term included. c Model 2 adjusted for age, gender, education and type of job. d Model 2 adjusted for age, gender, education, type of job, BMI and smoking. e Model 2 adjusted for age, gender, education, type of job, BMI, smoking and moderate-to-vigorous-intensity physical activity. Open in new tab Discussion This study is the first to our knowledge investigating the association between device-based total and domain-specific sedentary behaviour and psychological distress assessed with the GHQ-12. In our sample of healthy working adults, mainly employed in physically active jobs, no associations were found between accelerometer-based sedentary behaviour and self-reported psychological distress. This seems to be in line with findings from a recent review study concluding there is strong evidence of no association between (total) sedentary behaviour and stress when using ‘objective’ measures of both variables.20 Given the broad evidence on the relation between sedentary behaviour and general mental health outcomes,10,11 this finding may be surprising. Insufficient statistical power cannot clarify the present non-associations. Post hoc power analyses showed adequate power (>81% for the fully adjusted models; data not shown) suggesting we can accept the absence of an association between accelerometer-based total and domain-specific sedentary behaviour and psychological distress. A potential explanation for the lack of associations here may be the outcome measure used. While the GHQ-12 assesses present psychological state and minor disorders, this instrument may be limited in assessing long-term specific major chronic psychological conditions, such as depression or anxiety.18 Another explanation, but only valid for the lack of associations between work-related sedentary behaviour and psychological distress, could be the ‘social withdrawal’ hypothesis,36 stating that removal from social interaction, e.g. during TV viewing, may affect mental health negatively. As most jobs take place within a social context, sedentary time in this domain may not be linked to more distress. Conversely, leisure-time sedentary behaviour was also not associated with distress, which does not seem to support the ‘social withdrawal’ hypothesis. Still, we have no information on the social context in which sedentary time during leisure was performed. The lack of associations observed in the present study suggests that more high-quality research (i.e. sufficiently powered studies using validated and reliable measures, preferably device-based for sedentary behaviour) is needed to confirm the present results and to understand potential mechanisms. Few studies12,14 investigated the association between sedentary behaviour and psychological distress in employed adults and findings were in contrast to our results. However, Atkin et al. only examined self-reported non-occupational sitting (average of 5.0 h/day vs. 4.6 h/day of device-based leisure-time sedentary behaviour here) and found a positive association with psychological distress (average of 1.8 ± 2.6 vs. 1.3 ± 1.8 here) in English employees (computer use in men; TV viewing, computer use and total non-occupational sitting in women).12 In Australian state government employees (average of 4.5 h/day of self-reported occupational sitting time vs. 2.8 h/day of device-based work-related sedentary behaviour here), those with high levels of self-reported sitting at work (>6 h/day) had increased prevalence of psychological distress (measured via the Kessler-10 scale), compared with those with low levels (<3 h/day).14 No studies did investigate the association between domain-specific sedentary behaviour and distress among workers employed in active jobs who might be at risk of higher levels of both distress26 and leisure-time sedentary behaviours.27 It should be noted that comparing our findings to those of other studies examining the association between sedentary behaviour and psychological distress is problematic and we urge caution, due to the following. First, as described above, study participants across studies are not comparable. Our sample consisted of employees mainly working in physically active jobs within the service and production sector. Other studies investigating the association between sedentary behaviour and psychological distress were done in samples of Singaporean,13,18 Belgian17 and English15 adults, and Japanese16 and Spanish19 older adults. In some of these samples,13,15,17,19 sedentary behaviour was positively associated with psychological distress, in others16,18 no associations were found, which is similar to our study. Second, many of the studies used self-reported measure to assess sedentary behaviour, while we obtained device-based exposure data. Research suggests there is insufficient evidence for an association between overall sedentary behaviour and stress when using self-reported measures, while there is strong evidence of no association when using ‘objective’ measures.20 Only two other studies examined the association between objective total sedentary time and psychological distress using the GHQ-12 and findings were mixed.15,18 These studies were conducted in community-dwelling English15 and Singaporean18 adults with comparable levels of psychological distress (GHQ-12 ≥ 2: 24.9%18 vs. 34.9% here; GHQ-12 ≥ 4: 12.7%15 vs. 13.3% here) and somewhat higher levels of total sedentary behaviour (9.6 h/day15 vs. 7.2 h/day here; Tertiles 1–3: 7.3–8.9 h/day18 vs. 6.0 − 8.6 h/day here). However, waist-worn ActiGraph accelerometers were used to assess sedentary time15,18 and a limitation of these is that accelerometers do not measure postural allocation. Sedentary time is typically inferred from time spent below 100 counts per minute, and this is likely to include standing and hence potentially overestimate total sedentary time. In the present study, inclination, based on the position of two accelerometers, was taken into account to make a distinction between sitting down and standing still. In addition, no information was available on the domain in which the device-based sedentary behaviour occurred in the previous studies using accelerometer data.15,18 Other studies examining device-based domain-specific sedentary behaviour and the association with distress are lacking. Third, the way of assessing the dependent variable differs from study to study. For example, in some studies,14,16 other psychological distress scales, e.g. the K6 or K10 scale, were used. Finally, when using the same psychological distress scale (i.e. GHQ-12), only a few studies12,17 used the continuous total score of this scale. In the majority of the studies, one of two established cut-points29 was used to define high levels of psychological distress, i.e. GHQ-12 score ≥213,18 or GHQ-12 score ≥4.12,15 For that reason, logistic regression analyses were conducted, while we employed linear regression models. However, conducting additional logistic regression analyses with these GHQ-12 cut-points did not influence any of the present findings (data not shown). Key strengths of the present study are the large sample and the use of valid and reliable accelerometers assessing sedentary behaviour based on posture in a more objective way. In addition, by using diary data, we were able to specify the domain in which the sedentary behaviour took place, which was to our knowledge a novel approach in examining the association between domain-specific sedentary time and mental health outcomes. The main limitation is the cross-sectional study design, making it impossible to make assumptions on causality. Prospective and experimental studies examining the association between device-based sedentary behaviour and psychological distress are now needed. Further, the study sample is a group of Flemish employees mainly working in physically active jobs, which may limit the generalisability of the findings. However, the proportion of the sample experiencing high levels of psychological distress (35%) is comparable to the Belgian population.7,17 Conclusion There is a growing body of evidence on the potential adverse impact of too much sitting on mental health outcomes. Our findings add to and expand this literature as this study investigated device-based total and domain-specific sedentary time and its association with psychological distress. In this cross-sectional study in Flemish working adults, mainly employed in physically active jobs in the secondary and tertiary sector, accelerometer-based total, leisure and work-related sedentary behaviour were not significantly associated with psychological distress. Since this is one of the first studies looking at this association using device-based total and domain-specific sedentary behaviour measures in employed adults, more research using a similar approach is needed to confirm the present findings. Funding The FEPA study is funded by BOF (Bijzonder Onderzoeksfonds, Special Research Fund) of the Ghent University. The funding agency had no influence on the design of the study and the collection, analysis and interpretation of the data, nor in writing the article. Conflicts of interest: None declared. 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TI - Is device-based total and domain-specific sedentary behaviour associated with psychological distress in Flemish workers?
JF - The European Journal of Public Health
DO - 10.1093/eurpub/ckaa144
DA - 2021-02-01
UR - https://www.deepdyve.com/lp/oxford-university-press/is-device-based-total-and-domain-specific-sedentary-behaviour-g0b4OrS0VL
SP - 151
EP - 156
VL - 31
IS - 1
DP - DeepDyve
ER -