Abstract
This study investigated association of Perceived Organization Support (POS) with diabetes treatment among workers. This prospective cohort study was conducted online, and parts of nations wide study stratified similarly with workers’ characteristic in Japan. Samples were screened to those who had diabetes in the baseline years. Binary regression analysis and p for trend were used for statistical analysis. There were 1,203 participants with diabetes followed up regarding their treatment behavior. Higher POS were likely to seek appropriate diabetes treatment after adjustment with personal and occupational factors (p=0.032) but became marginally significant when adjusted with night shift status (p=0.051). Further analysis found that POS was only associated with diabetes treatment among workers with night shift. Higher POS was likely associated with proper diabetes treatment specifically among workers with night shift.
Keywords: Diabetes, Perceived organization support (POS), Workers, Night shift, Treatment
Introduction
Diabetes continues to be a significant health concern globally. Between 2010 and 2030, there is an expected 69% increase in numbers of adults with diabetes in developing countries and a 20% increase in developed countries1). Diabetes has also been identified as a major contributor to conditions such as blindness, kidney failure, heart attacks, stroke, and lower limb amputation2). The impact of this problem extends to the workforce as well. A 2014 report indicated that diabetes affected one in 13 male workers and one in 30 female workers in Japan3). A similar male domination has been observed among workers in Sweden. Moreover, drivers and manufacturing workers have been identified as the occupations most commonly associated with diabetes4).
Diabetes is linked with several risk factors including genetic, environmental, and metabolic elements5). Among the environmental factors, several are workplace related. Sedentary work and shift work are known to be risk factors of diabetes arising from workplace6, 7). Additionally, inadequate treatment of diabetes can lead to negative outcomes, including impaired psychological health and induced productivity loss8,9,10). Workers face various workplace factors that impact their diabetes treatment. Weijman et al. suggested that a high workload, while Adi et al. identified shift work, contribute to difficulties in managing diabetes11, 12).
The treatment of diabetes relies on self-care management and multidisciplinary approach by health professionals13, 14). Since diabetes are also suffered by workers and considering some challenges available in the workplace, it is crucial for employers to promote effective diabetes treatment for the employees. Employer support has been directly linked to successful chronic disease self-care and medication adherence15). Additionally, organizational support has been shown in qualitative study to play a role in the education of diabetes patients16). However, none of these studies have specifically focused on groups of workers with diabetes and incorporated longitudinal observation.
According to organizational support theory, employee perceptions regarding how much the organization values their contribution and cares about their well-being are called perceived organizational support (POS)17). A high level of POS is likely to be associated with positive outcomes, such as improved well-being, which encompasses health. This concept should be applicable to diabetes management among workers. Moreover, supported workers are more likely exhibit positive behavior change18). This could extend behaviors related to diabetes treatment. However, to date, there is no empirical evidence linking POS with the success of diabetes treatment among workers.
Therefore, this study aims to investigate how POS might be associated with the treatment behavior of diabetes in a longitudinal framework, specifically among workers with diabetes. We hypothesize that POS will encourage proper diabetes treatment-seeking behavior among workers. Additionally, we aim to explore whether the presence or absence of night shifts, a known workplace factor affecting diabetes treatment, modifies the effect of POS.
Subjects and Methods
This study was designed as a prospective cohort encompassing all regions of Japan, conducted online in collaboration with a private data collection company, which limited participation to individuals registered with the company. To mirror the demographic characteristics of the Japanese workforce, the sample was stratified by workers’ status, gender, age, and region. A total of 16,629 participants were selected for a one-year prospective cohort observation. Screening was further refined to include only those identified as having diabetes at baseline. Screening involved a single question regarding their diabetes status and/or treatment related to it. For those who answered, “I don’t have the disease” will be excluded from data analysis. Through this screening, 1,203 participants with diabetes were identified in the baseline year, in February 2022. These participants were then monitored in the following year, March 2023, and followed up regarding their treatment behavior. Details of the participants screening were shown in Fig. 1. The study protocol has been detailed in a previous publication19). The study received approval from the Ethics Committee of the University of Occupational and Environmental Health, Japan (R3-076) and complied with the Checklist for Reporting Results of Internet E-Surveys (CHERRIES)20) and adhered to STROBE checklist as provided in the supplementary.
Fig. 1.
Participants screening.
Diabetes treatment
Diabetes treatment was assessed with a single question in the following year: “Are you currently receiving outpatient care or treatment for diabetes?” Participants were required to choose from the following option: 1. I don’t have the disease, 2. Currently going to hospital or undergoing treatment (using prescription drugs), 3. Currently going to hospital or undergoing treatment (not using prescription drugs), 4. I used to go to the hospital or receive treatment, but now I am self-suspending, 5. You have been told that you need tests or treatment, but you have not visited a medical institution. Participants answering options number 1,2, or 3 were categorized as properly seeking treatment or cured, while those answering 4 or 5 were categorized as not properly seeking treatment.
Perceived organization support (POS)
We defined POS similarly to Mori et al.21) using the eight-items of the Survey of Perceived Organizational Support (SPOS) translated into Japanese22, 23). The eight items were: “The organization strongly considers my goals and values”, “Help is available from the organization when I have a problem”, “The organization really cares about my well-being”, “The organization would forgive an honest mistake on my part”, “The organization is willing to help me when I need a special favor”, “If given the opportunity, the organization would take advantage of me”, “The organization shows very little concern for me”, and “The organization cares about my opinions”. Responses were rated on a 7-point Likert-type scale from 0 (strongly disagree) to 6 (strongly agree). Scores were totaled, with 0 being the lowest score and 48 the highest. The Cronbach’s α coefficient for the total score was 0.71. POS data were collected in the baseline year of the survey.
Covariates
The covariates were divided into personal and occupational data. Personal data included age (numerical, based on data from the online questionnaire), sex (self-reported and categorized as men or women), and education level (categorized as low for junior high school and high school, medium for vocational school and junior college/technical college and high for university and graduate school). Occupational data covered medical insurance provider, working system and night shift status. Medical insurance provider will likely affect participants treatment behavior. Participants were asked to select one of the most appropriate medical insurances providers those participants registered. There were nine answered options based on the medical insurance providers in Japan. The working system was categorized based on the respondents’ selection from fixed/regular working system, flexible working system (allowing to adjust the start and end of working time), shift working system, variable working system (temporary different work arrangement/schedule), discretionary system (sales, telecommuting, with specific schedule system), no specific work arrangement. Night shift status was determined by asking: “Do you have night shift work (working from 10.00 pm to 05.00 am, at least four times in a month)” and participants should be responded by “yes” or “no” reflecting their most appropriate condition.
Statistical analysis
The distribution of POS score and covariates was examined by POS score category. POS scores were categorized into three groups based on 30th and 60th percentiles. Binary logistic regression analysis was employed to explore the association between POS score category and diabetes treatment. Adjusted odds ratio and confidence interval was measured along with p value. Model 1 adjusted for age, sex, education, medical insurance provider, and working system. Model 2 included adjustment from model one plus night shift status. Model 3 further included the interaction between POS score and night shift status. Additional analysis explored the association between POS score category and diabetes treatment status within group defined by night shift status. Associations were defined by p for trend value of POS score. All the statistical analyses used Statistical Program for Social Science, Faculty Packs version 29 (IBM, Armonk, NY, USA).
Results
Men are more populous than women identified to have diabetes in the baseline year. Most of the participants were likely originated from older age, higher education, and had fixed/regular working system. The mean age was higher among participants in the higher POS score category. The proportion of participants not working night shift was greater than those working night shifts. Details are shown in Table 1.
Table 1. Distribution of covariates among perceived organization support (POS) scores’ category.
| POS score category* | ||||
|---|---|---|---|---|
| Low | Medium | High | ||
| N | 397 | 436 | 370 | |
| Sex, n (%) | ||||
| Men | 329 (82.9 %) | 334 (76.6 %) | 298 (80.5 %) | |
| Women | 68 (17.1 %) | 102 (23.4 %) | 72 (19.5 %) | |
| Age, mean (SD) | 53.4 (12.0) | 54.2 (13.3) | 59.9 (10.3) | |
| Education, n (%) | ||||
| Low | 114 (28.8 %) | 135 (31 %) | 92 (24.9 %) | |
| Medium | 83 (20.9 %) | 85 (19.5 %) | 62 (16.8 %) | |
| High | 200 (50.3 %) | 216 (49.5 %) | 216 (58.3 %) | |
| Medical insurance providers | ||||
| Japan Health Insurance Association | 150 (37.8 %) | 146 (33.5 %) | 137 (37.0 %) | |
| Association-managed Health Insurance | 100 (25.2 %) | 108 (24.8 %) | 84 (22.7 %) | |
| Public Service Personnel Mutual Aid Associations | 25 (6.3 %) | 33 (7.6 %) | 23 (6.2%) | |
| Private School Personnel Mutual Aid Associations | 8 (2.0 %) | 6 (1.4 %) | 6 (1.6 %) | |
| National Health Insurance | 82 (20.7 %) | 97 (22.2 %) | 95 (25.7 %) | |
| Advanced Elderly Medical Service | 4 (1.0 %) | 8 (1.8 %) | 9 (2.4 %) | |
| Others or don’t know | 28 (7.1 %) | 38 (8.7 %) | 16 (4.3 %) | |
| Working system | ||||
| Fixed/regular working system | 225 (56.7 %) | 253 (58.0 %) | 181 (48.9 %) | |
| Flexible working system | 54 (13.6 %) | 81 (18.6 %) | 66 (17.8 %) | |
| Shift working system | 33 (8.3 %) | 26 (6.0 %) | 27 (7.3 %) | |
| Variable working system | 17 (4.3 %) | 17 (3.9 %) | 19 (5.1 %) | |
| Discretionary system | 15 (3.8 %) | 11 (2.5 %) | 16 (4.3 %) | |
| No specific arrangement | 53 (13.4 %) | 48 (11.0 %) | 61 (16.5 %) | |
| Night shift status | ||||
| Yes | 96 (24.2 %) | 80 (18.3 %) | 47 (12.7 %) | |
*Based on 30th and 60th percentile.
The POS scores’ category was found to be associated with diabetes treatment-seeking behavior in the subsequent year, indicating that higher POS scores were likely to seek appropriate treatment. This analysis revealed a significant trend after adjustment for sex, age, education, medical insurance provider and working system (Model one, p=0.032). The significance became marginal when further adjusted with night shift status (Model two, p=0.051). When the model was further adjusted for interaction between POS score and night shift status, the interaction was observed to be significant (Model three, p=0.016). Details are provided in Table 2.
Table 2. Binary regression analysis of perceived organization support (POS) scores’ category toward diabetes treatment.
| Variables | Model 1 | Model 2 | Model 3 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| aOR | 95% CI | p-value | aOR | 95% CI | p-value | aOR | 95% CI | p-value | ||
| Constant | 4.639 | 0.004 | 6.94 | 0.001 | 7.387 | 0.001 | 0.758 | |||
| POS score category | ||||||||||
| Low | Ref | Ref | Ref | |||||||
| Medium | 1.365 | 0.866–2.150 | 0.18 | 1.335 | 0.846–2.108 | 0.213 | 1.124 | 0.692–1.827 | 0.636 | |
| High | 1.68 | 0.999–2.824 | 0.05 | 1.593 | 0.946–2.683 | 0.08 | 1.246 | 0.708–2.189 | 0.446 | |
| Night shift status (reference: no shift work) | 0.536 | 0.331–0.866 | 0.011 | 0.122 | 0.035–0.429 | 0.001 | ||||
| POS score × Night shift status | 1.067 | 1.012–1.126 | 0.016 | |||||||
| p for trend of POS score | 0.032 | 0.051 | 0.569 | |||||||
Model 1: adjusted for age, sex, education, medical insurance provider, working system.
Model 2: adjusted for model 1 and night shift status.
Model 3: adjusted for model 2 and interaction between POS score and night shift status.
Ref: reference; CI: confidence interval; aOR: adjusted odds ratio.
We further analyzed the association of POS scores category and diabetes treatment-seeking behavior in groups with and without night shift status separately. The analysis revealed that the association between POS score category and diabetes treatment-seeking behavior was significant only in the group of workers with night status (p=0.007) notably in categorical analysis, among high POS score category (aOR=5.494 (1.184−25.484)) compared to low score category. Details are provided in Table 3.
Table 3. Binary regression analysis of perceived organization support (POS) scores’ category toward diabetes treatment sorted by night shift status.
| aOR | 95% CI for aOR | p-value | ||
|---|---|---|---|---|
| Analysis among workers with night shift | ||||
| POS score category | Low | Ref | ||
| Medium | 1.921 | 0.792–4.662 | 0.149 | |
| High | 5.494 | 1.184–25.484 | 0.030 | |
| p for trend of POS score | 0.007 | |||
| Analysis among workers without night shift | ||||
| POS score category | Low | Ref | ||
| Medium | 1.149 | 0.658–2.005 | 0.625 | |
| High | 1.222 | 0.672–2.225 | 0.510 | |
| p for trend of POS score | 0.559 | |||
Model was adjusted for sex, age, education, medical insurance provider, working system.
Ref: reference; CI: confidence interval; aOR: adjusted odds ratio.
Discussion
We concluded that higher POS scores were likely associated with proper diabetes treatment-seeking behavior. This association remained robust after adjustments for personal and occupational factors. Further analysis within the night shift status group revealed that POS was significantly associated with proper diabetes treatment-seeking behavior among workers with night shift, but not significant among workers without night shift. Therefore, we accepted our hypothesis, and concluded the association specifically among night shift workers.
POS is based on the relationship between the employee and the organization from the employee’s perspective. There are three aspects constructed POS according to organizational support theory: employee attributions, social exchange, and self enhancement. These aspects are also implied in several antecedents and consequences of POS17). Among the consequences, well-being is the most closely related to self-care of health. If employees perceive that the organization provides a supportive environment (including leadership, organization, human resources practices and working conditions), they are likely to respond with good work performance and improved well-being. Moreover, related to subjective well-being, employees with high POS are likely to fulfill their emotional needs, increase the anticipation of help when needed, strengthen their self-efficacy and experienced balance between work and life17). In terms of diabetes treatment, employees with high POS are likely to be more aware of their health behavior, have greater self-efficacy, and therefore, are more likely to seek proper treatment compared with employees who have lower POS. This could be a secondary or spillover effect of high POS related to employees’ health behavior, which would lead to improvement of well-being and health24).
We also found that night shift status influenced the association between POS and diabetes treatment-seeking behaviors. POS plays a crucial role in supporting diabetes treatment among night shift workers. Scholars identified that night shift workers faced more stressful working conditions than day workers25, 26), which lead to the benefit of POS. According to the Job-Demand Resources Model, high POS may act as a resource in stressful conditions27). This underlines why POS is likely to have a positive effect on diabetes treatment-seeking behavior among night shift workers. Furthermore, Adi et al concluded that rotating night shift altered treatment-seeking behavior of diabetes patients, considering night shift work as a barrier to accessing health facilities12). They likely faced difficulties managing their schedule to meet regular activities of day workers including to meet health professionals for seeking treatment28). Our result enhanced Adi et al. suggestion that organizations should provide support to help night shift workers with diabetes manage their treatment12). We concluded that when night shift workers feel supported by the organization, they are more likely to seek proper diabetes treatment. This is a strong message for organizations employing night shift workers with diabetes. They should maintain their general support so that psychologically night shift workers feel supported and might also extend this support to diabetes treatment, such as adjusting work schedule or facilitating meeting with health professionals. These efforts, which relate to the antecedents of POS, will likely enhance their diabetes treatment outcomes and, consequently, workers’ productivity.
We measured POS using general survey, which might not directly define organizations support for disease treatment but covers general aspects of psychological supports perceived by employees and possibly not closely related with medication adherence. This could be considered our first limitation. Second, although we used longitudinal observation, we didn’t observe behavioral changes and clinical outcome of our participants. We also measured diabetes status only through screening questions which may affect objectivity of the results and did not differentiate between type 1 and 2 diabetes although it implied to different treatment. Furthermore, we realized some possible biases in our study due to sample selection and distribution, also related with healthy worker’s effect. However, our study is part of nationwide study that includes a large sample size. The questionnaires were designed for large samples to enhance the objectivity of the responses.
The results of our study are important for health promotion campaigns at the workplace. The outcomes related to POS could extend beyond work performance to support treatment among workers with diabetes, especially those working night shift. Future research agendas might include intervention studies specifically targeting night shift workers, focusing on adjustment of work schedules or support for treatment facilitation.
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