• Adapting Citizen Science to Improve Health in an Occupational Setting: Preliminary Results of a Qualitative Study.

      van den Berge, Mandy; Hulsegge, Gerben; van der Molen, Henk F; Proper, Karin I; Pasman, H Roeline W; den Broeder, Lea; Tamminga, Sietske J; Hulshof, Carel T J; van der Beek, Allard J (2020-07-08)
    • Characterizing Adult Sleep Behavior Over 20 Years-The Population-Based Doetinchem Cohort Study.

      Zomers, Margot L; Hulsegge, Gerben; van Oostrom, Sandra H; Proper, Karin I; Verschuren, W M Monique; Picavet, H Susan J (2017-07-01)
      To describe sleep duration patterns of adults over a 20-year period; to compare sociodemographic, lifestyle, and health characteristics across these patterns; and to relate the patterns to sleep quality.
    • The contribution of work and lifestyle factors to socioeconomic inequalities in self-rated health ‒ a systematic review.

      Dieker, Amy Cm; IJzelenberg, Wilhelmina; Proper, Karin I; Burdorf, Alex; Ket, Johannes Cf; van der Beek, Allard J; Hulsegge, Gerben (2019-03-01)
      Objective This study aimed to systematically review the literature on the contribution of work and lifestyle factors to socioeconomic inequalities in self-rated health among workers. Methods A search for cross-sectional and longitudinal studies assessing the contribution of work and/or lifestyle factors to socioeconomic inequalities in self-rated health among workers was performed in PubMed, PsycINFO and Web of Science in March 2017. Two independent reviewers performed eligibility and risk of bias assessment. The median change in odds ratio between models without and with adjustment for work or lifestyle factors across studies was calculated to quantify the contribution of work and lifestyle factors to health inequalities. A best-evidence synthesis was performed. Results Of those reviewed, 3 high-quality longitudinal and 17 cross-sectional studies consistently reported work factors to explain part (about one-third) of the socioeconomic health inequalities among workers (grade: strong evidence). Most studies separately investigated physical and psychosocial work factors. In contrast with the 12 cross-sectional studies, 2 longitudinal studies reported no separate contribution of physical workload and physical work environment to health inequalities. Regarding psychosocial work factors, lack of job resources (eg, less autonomy) seemed to contribute to health inequalities, whereas job demands (eg, job overload) might not. Furthermore, 2 longitudinal and 4 cross-sectional studies showed that lifestyle factors explain part (about one-fifth) of the health inequalities (grade: strong evidence). Conclusions The large contribution of work factors to socioeconomic health inequalities emphasizes the need for future longitudinal studies to assess which specific work factors contribute to health inequalities.
    • The mediating role of lifestyle in the relationship between shift work, obesity and diabetes.

      Hulsegge, Gerben; Proper, Karin I; Loef, Bette; Paagman, Heleen; Anema, Johannes R; van Mechelen, Willem (2021-03-11)
      In this cross-sectional study, 3188 shift workers and 6395 non-shift workers participated between 2013 and 2018 in periodical occupational health checks. Weight and height were objectively measured to calculate obesity (BMI ≥ 30 kg/m2). Diabetes status, physical activity, diet, smoking, and sleep quality were assessed using standardized questionnaires. Structural equation models adjusted for relevant confounders were used to analyze the mediating role of lifestyle in the relationships between shift work, and obesity and diabetes.
    • The mediating role of sleep, physical activity, and diet in the association between shift work and respiratory infections.

      Loef, Bette; van der Beek, Allard J; Hulsegge, Gerben; van Baarle, Debbie; Proper, Karin I (2020-04-07)
    • The moderating role of lifestyle, age, and years working in shifts in the relationship between shift work and being overweight.

      Hulsegge, Gerben; van Mechelen, Willem; Paagman, Heleen; Proper, Karin I; Anema, Johannes R (2020-02-10)
      Cross-sectional data were used of 2569 shift and 4848 non-shift production workers who participated between 2013 and 2018 in an occupational health check. Overweight (BMI ≥ 25 kg/m2) was calculated using measured weight and height; lifestyle was assessed by questionnaires. Multiple-adjusted logistic regression with interaction terms between shift work and potential moderators assessed multiplicative interaction; the relative excess risk due to interaction assessed additive interaction (synergism).
    • Objectively measured physical activity of hospital shift workers.

      Loef, Bette; van der Beek, Allard J; Holtermann, Andreas; Hulsegge, Gerben; van Baarle, Debbie; Proper, Karin I (2018-01-22)
      Objectives Shift work may alter workers' leisure-time and occupational physical activity (PA) levels, which might be one of the potential underlying mechanisms of the negative health effects of shift work. Therefore, we compared objectively measured PA levels between hospital shift and non-shift workers. Methods Data were used from Klokwerk+, a cohort study examining the health effects of shift work among healthcare workers employed in hospitals. In total, 401 shift workers and 78 non-shift workers were included, all of whom wore Actigraph GT3X accelerometers for up to seven days. Time spent sedentary, standing, walking, running, stairclimbing, and cycling during leisure time and at work was estimated using Acti4 software. Linear regression was used to compare proportions of time spent in these activities between hospital shift and non-shift workers. Results Average accelerometer wear-time was 105.9 [standard deviation (SD) 14.0] waking hours over an average of 6.9 (SD 0.6) days. No differences between hospital shift and non-shift workers were found in leisure-time PA (P>0.05). At work, shift workers were less sedentary [B=-10.6% (95% CI -14.3- -6.8)] and spent larger proportions of time standing [B=9.5% (95% CI 6.4-12.6)] and walking [B=1.2% (95% CI 0.1-2.2)] than non-shift workers. However, these differences in occupational PA became smaller when the number of night shifts during accelerometer wear-time increased. Conclusions Leisure-time PA levels of hospital shift workers were similar to those of non-shift workers, but shift workers were less sedentary and more physically active (ie, standing/walking) at work. Future research to the role of occupational activities in the health effects of shift work is recommended.
    • Shift work and its relation with meal and snack patterns among healthcare workers.

      Hulsegge, Gerben; Loef, Bette; Benda, Tessa; van der Beek, Allard J; Proper, Karin I (2019-05-02)
    • Shift work is associated with reduced heart rate variability among men but not women.

      Hulsegge, Gerben; Gupta, Nidhi; Proper, Karin I; van Lobenstein, Natasja; IJzelenberg, Wilhelmina; Hallman, David M; Holtermann, Andreas; van der Beek, Allard J (2018-02-09)
      Imbalance in the autonomic nervous system due to a disrupted circadian rhythm may be a cause of shift work-related cardiovascular diseases.
    • Shift work, and burnout and distress among 7798 blue-collar workers.

      Hulsegge, Gerben; van Mechelen, Willem; Proper, Karin I; Paagman, Heleen; Anema, Johannes R (2020-04-30)
    • Shift work, chronotype and the risk of cardiometabolic risk factors.

      Hulsegge, Gerben; Picavet, H Susan J; van der Beek, Allard J; Verschuren, W M Monique; Twisk, Jos W; Proper, Karin I (2019-02-01)
      The relation between shift work and a large variety of cardiometabolic risk factors is unclear. Also, the role of chronotype is understudied. We examined relations between shift work and cardiometabolic risk factors, and explored these relations in different chronotypes. Cardiometabolic risk factors (anthropometry, blood pressure, lipids, diabetes, γ-glutamyltransferase, C-reactive protein, uric acid and estimated glomerular filtration rate) were assessed among 1334 adults in 1987-91, with repeated measurements every 5 years. Using shift work history data collected in 2013-15, we identified shift work status 1 year prior to all six waves. Linear mixed models and logistic generalized estimating equations were used to estimate the longitudinal relations between shift work and risk factors 1 year later. Shift work was not significantly related with cardiometabolic risk factors (P ≥ 0.05), except for overweight/body mass index. Shift workers had more often overweight (OR: 1.44, 95% CI 1.06-1.95) and a higher body mass index (BMI) (β: 0.56 kg m-2, 95% CI 0.10-1.03) than day workers. A significant difference in BMI between day and shift workers was observed among evening chronotypes (β: 0.97 kg m-2, 95% CI 0.21-1.73), but not among morning chronotypes (β: 0.04 kg m-2, 95% CI -0.85 to 0.93). No differences by frequency of night shifts and duration of shift work were observed. Shift workers did not have an increased risk of cardiometabolic risk factors compared with day workers, but, in particular shift working evening chronotypes, had an increased risk of overweight. More research is needed to verify our results, and establish whether tailored interventions by chronotype are wanted.
    • Shift work, sleep disturbances and social jetlag in healthcare workers.

      Hulsegge, Gerben; Loef, Bette; van Kerkhof, Linda W; Roenneberg, Till; van der Beek, Allard J; Proper, Karin I (2018-12-05)
      The aim of this study was to compare chronotype- and age-dependent sleep disturbances and social jetlag between rotating shift workers and non-shift workers, and between different types of shifts. In the Klokwerk+ cohort study, we included 120 rotating shift workers and 74 non-shift workers who were recruited from six Dutch hospitals. Participants wore Actigraph GT3X accelerometers for 24 hr for 7 days. From the Actigraph data, we predicted the sleep duration and social jetlag (measure of circadian misalignment). Mixed models and generalized estimation equations were used to compare the sleep parameters between shift and non-shift workers. Within shift workers, sleep on different shifts was compared with sleep on work-free days. Differences by chronotype and age were investigated using interaction terms. On workdays, shift workers had 3.5 times (95% confidence interval: 2.2-5.4) more often a short (< 7 hr per day) and 4.1 times (95% confidence interval: 2.5-6.8) more often a long (≥ 9 hr per day) sleep duration compared with non-shift workers. This increased odds ratio was present in morning chronotypes, but not in evening chronotypes (interaction p-value < .05). Older shift workers (≥ 50 years) had 7.3 times (95% confidence interval: 2.5-21.8) more often shorter sleep duration between night shifts compared with work-free days, while this was not the case in younger shift workers (< 50 years). Social jetlag due to night shifts increased with increasing age (interaction p-value < .05), but did not differ by chronotype (interaction p-value ≥ .05). In conclusion, shift workers, in particular older workers and morning chronotypes, experienced more sleep disturbances than non-shift workers. Future research should elucidate whether these sleep disturbances contribute to shift work-related health problems.
    • Time-restricted feeding improves adaptation to chronically alternating light-dark cycles.

      Schilperoort, Maaike; van den Berg, Rosa; Dollé, Martijn E T; van Oostrom, Conny T M; Wagner, Karina; Tambyrajah, Lauren L; Wackers, Paul; Deboer, Tom; Hulsegge, Gerben; Proper, Karin I; et al. (2019-05-27)
    • Trajectories of (Bio)markers During the Development of Cognitive Frailty in the Doetinchem Cohort Study.

      Rietman, M Liset; Hulsegge, Gerben; Nooyens, Astrid C J; Dollé, Martijn E T; Picavet, H Susan J; Bakker, Stephan J L; Gansevoort, Ron T; Spijkerman, Annemieke M W; Verschuren, W M Monique (2019-01-01)
      Background: Long-term changes in (bio)markers for cognitive frailty are not well characterized. Therefore, our aim is to explore (bio)marker trajectories in adults who became cognitively frail compared to age- and sex-matched controls who did not become cognitively frail over a 15 year follow-up. We hypothesize that those who become cognitively frail have more unfavorable trajectories of (bio)markers compared to controls. Methods: The Doetinchem Cohort Study is a longitudinal population-based study that started in 1987-1991 in men and women aged 20-59 years, with follow-up examinations every 5 years. For the current analyses, we used data of 17 potentially relevant (bio)markers (e.g., body mass index (BMI), urea) from rounds 2 to 5 (1993-2012). A global cognitive functioning score (based on memory, speed, and flexibility) was calculated for each round and transformed into education and examination round-adjusted z-scores. The z-score that corresponded to the 10th percentile in round 5 (z-score = -0.77) was applied as cut-off point for incident cognitive frailty in rounds 2-5. In total, 455 incident cognitively frail cases were identified retrospectively and were compared with 910 age- and sex-matched controls. Trajectories up to 15 years before and 10 years after incident cognitive frailty were analyzed using generalized estimating equations with stratification for sex and adjustment for age and, if appropriate, medication use. Results were further adjusted for level of education, depressive symptoms, BMI, and lifestyle factors. Results: In men, (bio)marker trajectories did not differ as they ran parallel and the difference in levels was not statistically significant between those who became cognitively frail compared to controls. In women, total cholesterol trajectories first increased and thereafter decreased in cognitively frail women and steadily increased in controls, gamma-glutamyltransferase trajectories were more or less stable in cognitively frail women and increased in controls, and urea trajectories increased in cognitively frail women and remained more or less stable in controls. Results were similar after additional adjustment for potential confounders. Conclusions: Out of the 17 (bio)markers included in this explorative study, differential trajectories for three biomarkers were observed in women. We do not yet consider any of the studied (bio)markers as promising biomarkers for cognitive frailty.