A Combined Multidisciplinary Intervention for Health Promotion in the Workplace: A Pilot Study

Self Cite

Background: Due to the SARS-CoV-2 pandemic, human lifestyles and occupational settings have changed in the workplace. This survey explores associations of home working employment and related physical activity (PA–MET min/week). Methods: A longitudinal cohort study was conducted between March 2020 and March 2021. A standardized method for assessing PA and sedentary time, the Italian version of the International Physical Activity Questionnaire-Short Form (IPAQ-SF), was used through the Microsoft Forms® platform for self-administering the questionnaire. Baseline data were collected, and four follow-ups were performed; a full calendar year was observed. Results: In total, 310 home workers were recruited in this investigation. The average body mass index (BMI- kg/m2) was 21.4 ± 4.2 at baseline. The value increased at the first follow-up and fluctuated in the other recalls. The t-test of MET values of the four activities (Total PA, Vigorous-intensity activity, Moderate-intensity activity, Walking) show similar results; the total PA, at baseline 275.7 ± 138.6, decreased statistically significantly at the first (198.5 ± 84.6), third (174.9 ± 98.4), and fourth (188.7 ± 78.5) follow-ups, while it increased statistically significantly at the second follow-up (307.1 ± 106.1) compared to the baseline. Sedentary time was constant until the second follow-up, while it increased statistically significantly at the 3rd and 4th follow-up. Conclusion: workers involved reduced and reorganized their PA during this pandemic year. Each business company should intervene to improve the PA levels of workers and reduce sedentary behavior in the workplace.

Section: Discussionmentioning

confidence: 62%

Home Working and Physical Activity during SARS-CoV-2 Pandemic: A Longitudinal Cohort Study

Rapisarda

Loreto

Angelis

et al. 2021

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“…Planning such interventions may be difficult, given the heavy workloads of healthcare workers. Effective interventions should also address work organization (e.g., the distribution of night shifts and the rotation in multiple settings with different workloads) and include occupational physicians’ role in promoting healthy habits, which are effective in preserving good health status and work ability [ 49 ]. Furthermore, other needed interventions are those which can improve relationships between colleagues.…”

Section: Discussionmentioning

confidence: 99%

Return to Work of Healthcare Workers after SARS-CoV-2 Infection: Determinants of Physical and Mental Health

Grazzini

Lulli

Mucci

et al. 2022

Introduction. The SARS-CoV-2 pandemic has involved healthcare workers (HCWs) both as caregivers and as patients. This study is a retrospective cross-sectional analysis of the HCWs working in a third-level hospital in Central Italy who were infected with COVID-19 from March 2020 to April 2021. This research aims at identifying the physical and mental health outcomes of HCWs infected with COVID-19 who returned to work after the infection, the determinants of those outcomes, such as age and sex, and the identification of possible vulnerable professional groups. Methods. A questionnaire about the acute illness, the experience of returning to work, and health perceptions after the disease was administered to 427 healthcare workers 3 months after recovering from the SARS-CoV-2 infection. Results. The majority interviewed (84.5%) reported symptoms at the time of the positive test, with no significant differences regarding age or sex, while a significant difference in the mean age was found regarding hospitalization (p < 0.001). At 3 months after the infection, females (p = 0.001), older workers (p < 0.001), and healthcare assistants (p < 0.001) were more likely to report persistent symptoms. Sex (p = 0.02) and age (p = 0.006) influenced the quality of sleep after the infection. At work, the nurses group reported increase in workload (p = 0.03) and worse relationships (p = 0.028). At 3 months after the infection, female workers perceived worse physical (p = 0.002) and mental (p < 0.001) health status according to the SF-12. A negative correlation was found between age and PCS score (p < 0.001) but not MCS score (p = 0.86). A significant difference in PCS score was found between nurses and physicians (p = 0.04) and between residents and all other groups (p < 0.001). Finally, the group of workers reporting sleep alterations showed lower PCS and MCS scores (p < 0.001) and working relationships had an impact on MCS scores (p < 0.001). Conclusions. Age, sex, and type of job had an impact on physical and mental outcomes. Organizing specific interventions, also tailored to professional sub-groups, should be a target for healthcare systems to protect and boost the physical and mental health of their workers.

“…Conscious actions and engagement of the employee towards the prevention of involutionary processes combined with the employer’s policy in that respect may effectively lead to a change of lifestyle into a healthier one and to the improvement of work efficiency [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Non-Exercise Model for Predicting Cardiovascular Risks among Apparently Healthy Male Office Workers—Cross-Sectional Analysis: A Pilot Study

Zadarko

Zadarko-Domaradzka

Barabasz

et al. 2022

The health condition of working-age males in Poland remains largely associated with long-lasting sick leaves, one of the main reasons of which being cardiovascular diseases (CVD). The aim of this work was to develop a prediction model for FIT Treadmill Score (‘FIT’ refers to Henry Ford ExercIse Testing (FIT) Project) that only depends on easily accessible somatic data and smoking without the need to perform the exercise test anymore. The study comprised 146 men with a negative cardiological history, aged 26–60, with desk-jobs. By means of regression analysis it was tested to what degree obesity-related indices as well as smoking cigarettes allow for determining the measure level of mortality risk, without the necessity of performing an exercise test. The following independent variables were entered into the linear regression model: age, BMI, Fat%, waist circumference (WC), waist to height ratio (WHtR) as well as smoking. Statistically significant factors were singled out from among them. The obtained model accounts for a significant part (over 87%) of the variability of the mortality risk measure among the tested population. Based on the value of the standardised regression coefficient β, it can be stated that age is the factor that mostly determines the mortality risk measure, followed by the WHtR and smoking. The simplicity of the worked-out model and, resulting from it, the possibility of its common application should enable better health monitoring of working-age men with regard to cardiovascular disease occurrence and, related to it, mortality risk, thereby improving the quality of public health management.