Factors associated with sleep quality among operating engineers

Exploring workability in an older working population: associations with cognitive functioning, sleep quality, and technostress

Objective

This observational study investigates workability and its associations with cognitive functioning, sleep quality and technostress among an older working population, also shedding light on potential differences between two occupational categories with different work schedules.

Methods

Workers aged over 50, employed in different working sectors (banking/finance, chemical and metal-mechanic industry) were administered a self- report questionnaire including Work Ability Index (WAI), cognitive tests (Stroop Color Task, Corsi Blocks, Digit Span), sleep quality questionnaires (Pittsburgh Sleep Quality Index-PSQI; Insomnia Severity Index-ISI; Ford Insomnia Response to Stress Test-FIRST) and technostress scale. Linear regression models evaluated associations among variables, interaction effects investigated potential moderators.

Results

A total of 468 aged workers categorized as white (WCWs; N = 289, 62%) or blue collars (BCWs; N = 179, 38%) were enrolled; most BCWs (N = 104; 58%) were night shift workers. WCWs reported higher workability, cognitive functioning, sleep quality and lower technostress (except for invasion and privacy subscales) than BCWs. Associations between cognitive functioning and workability were statistically significant only for BCWs [slopes equal to 0.2 (0.33), 0.8 (0.34), -0.02 (0.001) for Memory Span Corsi, Block Span Digit and Interference Speed respectively]; additionally, sleep quality significantly moderated this association (p = 0.007). Higher levels of technostress were associated with lower workability, and this relationship was stronger for BCWs.

Conclusion

The aging of the workforce has important implications for occupational health and safety. Our findings suggest potential interventions and protective measures to promote older workers' wellbeing; blue-collar workers particularly should benefit from tailored intervention to sustain workability and prevent technostress, considering the role of healthy sleep habits promotion.

Prevalence and associated factors of poor sleep quality among industrial workers in Addis Ababa, Ethiopia: findings from a cross-sectional study

OBJECTIVE

This study aimed to investigate the prevalence and associated factors of poor sleep quality among Bole Lemi Industrial Park workers in Addis Ababa, the capital of Ethiopia.

DESIGN

An institution-based cross-sectional study was conducted from July to August 2022. The data were collected using a validated and standardised Pittsburgh Sleep Quality Index Questionnaire. The collected data were entered into EpiData V.4.6 and analysed using Stata V.14. A multivariable binary logistic regression analysis was conducted to identify factors associated with poor sleep quality. The association was determined using an adjusted OR (AOR) with a 95% CI at a p value of <0.05.

SETTING

The study was conducted in Bole Lemi Industrial Park in Addis Ababa.

PARTICIPANTS

A total of 370 workers participated in this study.

OUTCOME MEASURES

The primary outcome of the study was the prevalence of poor sleep quality.

RESULTS

The total response rate was 87.7%. The majority (260, 70.3%) of the participants were female. The mean (±SD) age of the participants was 30.5 (±10.6) years. The prevalence of poor sleep quality during the last months was found to be 75.4% (n=279) (95% CI: 70.7% to 79.7%). Undiversified dietary intake (AOR: 1.63; 95% CI (1.09 to 2.78)), caffeine consumption more than 250 mg per day (AOR: 2.57; 95% CI (1.12 to 6.05)) and night shift work (AOR: 2.36; 95% CI (1.27 to 4.41)) were risk factors for poor sleep quality among sampled workers.

CONCLUSION

This study detected that poor sleep quality is highly prevalent among garment and textile industry workers, indicating that it is a significant public health issue that requires immediate attention. Therefore, modifications of the work environment involving night shift work and the improvement of workers' personal lifestyles involving dietary diversification and caffeine consumption are needed to minimise the burden of poor sleep quality among workers.

Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.

Sleep, Physical Activity, and Diet of Adults during the Second Lockdown of the COVID-19 Pandemic in Greece

The aim of the present study was to evaluate the possible correlations between sleep, physical activity, and diet in the general population of Greece during the second lockdown due to COVID-19 pandemic. A web-based questionnaire including 28 items was completed by 494 adults (age 31.5 ± 12.4 years). Half of the participants (49.8%) reported good, 44.1% moderate, and 6.1% bad quality of sleep, whereas 17.2% answered that the quality of sleep improved, 56.5% did not change, and 26.3% worsened compared to a normal week. Compared to normal, sleep duration in lockdown increased by 0.24 h (95% CI; 0.13, 0.35; p < 0.001, d = 0.198). More under-weight (32.4%) and obese (34.1%) respondents reported worsened quality of sleep in lockdown compared with normal (25.6%) and overweight participants (22.7%) (p = 0.006, Cramer's φ = 0.191). A small effect for BMI group on sleep duration was observed (p = 0.011, η2 = 0.023), where overweight and obese slept less (-0.44 h and -0.66 h, respectively) than normal weight participants. Subjects with the highest percentage of increased food consumption reported decreased sleep duration (p = 0.012) and worsened sleep quality (p = 0.003). Compared with a normal week, physical activity of a high and moderate intensity decreased for 43.0% and 37.0% of participants, did not change in 32.9% and 36.1% of participants, and increased in 24.1% 26.9%, respectively, whereas walking time decreased in 31.3%, did not change in 27.3%, and increased in 41.5% of participants. Increased high and moderate intensity physical activity was related with an improvement in sleep quality (p < 0.001). Those with decreasing walking time reported the highest percentage of decreased sleep quality (p = 0.006) and worsened sleep quality (p = 0.016). In conclusion, both quality and quantity of sleep were impaired during the second lockdown and the observed changes were associated with diet and physical activity.

Sleep quality among industrial workers: related factors and impact

Purpose

The purpose of this paper is to explore the prevalence and determinants of poor sleep quality among industrial workers in Thailand. Additionally, the authors assess the risk of work-related injuries associated with poor sleep quality.

Design/methodology/approach

A descriptive correlational research was implemented. A total of 472 workers from the northern region of Thailand contributed to this study. Sleep quality was assessed by the Thai version of the Pittsburgh Sleep Quality Index (Thai-PSQI). The score of more than five indicated poor sleep quality. Participants completed self-administered demographic, work characteristics and work-related injury questionnaires. Data were analyzed by applying descriptive and logistic regression statistical techniques.

Findings

More than one-third of the workers had reported poor sleep quality. Results from multivariable logistic regression analysis yielded male gender (OR = 2.74, 95% CI 1.46–5.17), alcohol drinking (OR=2.1, 95% CI 1.24–3.35), pain (OR=2.05, 95% CI 1.32–3.17) and rotating shift work (OR=1.94, 95% CI 1.23–3.05) increased the risk of poor sleep quality. Furthermore, poor sleep quality was statistically significantly associated with the risk of work-related injuries (OR = 3.98, 95% CI = 2.39–6.66).

Originality/value

Findings of this study indicate that the prevalence of poor sleep quality is high among industrial workers. Work characteristics and health behaviors were associated with poor sleep quality which increases the risk of work-related injuries. Modification of work environment and personal life style choices can improve quality of sleep among workers and consequently lower incidence of work-related injuries.

Blue-collar work and women's health: A systematic review of the evidence from 1990 to 2015

Despite the implications of gender and sex differences for health risks associated with blue-collar work, adverse health outcomes among blue-collar workers has been most frequently studied among men. The present study provides a "state-of-the-field" systematic review of the empiric evidence published on blue-collar women's health. We systematically reviewed literature related to the health of blue-collar women published between January 1, 1990 and December 31, 2015. We limited our review to peer-reviewed studies published in the English language on the health or health behaviors of women who were presently working or had previously worked in a blue-collar job. Studies were eligible for inclusion regardless of the number, age, or geographic region of blue-collar women in the study sample. We retained 177 studies that considered a wide range of health outcomes in study populations from 40 different countries. Overall, these studies suggested inferior health among female blue-collar workers as compared with either blue-collar males or other women. However, we noted several methodological limitations in addition to heterogeneity in study context and design, which inhibited comparison of results across publications. Methodological limitations of the extant literature, alongside the rapidly changing nature of women in the workplace, motivate further study on the health of blue-collar women. Efforts to identify specific mechanisms by which blue-collar work predisposes women to adverse health may be particularly valuable in informing future workplace-based and policy-level interventions.

[Factors associated with depressive symptoms in blue-collar and white-collar male workers]

OBJECTIVES

Mental disorders are increasing and their influence on productivity is a concern in the workplace. However, few studies have investigated depression among blue-collar and white-collar workers in the manufacturing industry. The purpose of this study was to clarify the factors associated with depressive symptoms, focusing on lifestyles and insomnia.

METHODS

A self-administered questionnaire survey was conducted of 1,963 workers at an annual health checkup in a manufacturing company. Of the 1,712 respondents (response rate: 87%), 1,258 male worker subjects (blue-collar 674; white-collar 584) were analyzed after excluding those with mental diseases. The questionnaire included items on basic attributes and lifestyle. The Athens Insomnia Scale (AIS) and The Center for Epidemiologic Studies for Depression Scale (CES-D) were used to evaluate insomnia and depressive symptoms.

RESULTS

The incidence of depressive symptoms with CES-D scores of ≥16 was 15.1% in both the blue-collar and the white-collar workers. Insomnia with AIS scores of ≥6 were encountered in 18.8% of the blue-collar workers and 18.3% of the white-collar workers. Multiple logistic regression analyses showed that for the blue-collar workers, depressive symptoms were associated with "AIS scores ≥6" (Odds ratio (OR): 10.93; 95% confidence interval (CI): 6.12-19.15), "not get rid of fatigue with sleep" (OR: 3.36; 95%CI: 1.85-6.09), "skip breakfast over 3 times a week" (OR: 3.10; 95%CI:1.42-6.76), "no family living together" (OR: 2.08; 95%CI: 1.05-4.12), and "commuting time" (OR: 1.01; 95%CI: 1.00-1.02). For the white-collar workers, depressive symptoms were related to "AIS scores ≥6" (OR: 14.91; 95%CI: 7.54-29.49), and "no family living together" (OR: 2.54; 95%CI: 1.27-5.09). Sleep time was not associated with depression in both blue- and white-collar workers. Depressive symptoms were found in 51.6% of the blue-collar workers with insomnia with AIS scores ≥6 and 53.8% of white-collar workers.

CONCLUSIONS

Depressive symptoms were found at the same prevalence rate in both blue-collar and white-collar workers, which suggests that health measures for depression are necessary for both types of worker. Depressive symptoms were significantly associated with insomnia. Health advice focusing on insomnia as well as lifestyle may be important for workers.