Quality of Life Assessment in Diabetic Patients Using a Validated Tool in a Patient Population Visiting a Tertiary Care Center in Bhubaneswar, Odisha, India

Abstract

Odisha has 4.2 million diabetic patients against the country’s 70 million with an urban prevalence of nearly 15.4%. Diabetes is affecting younger age groups, thus having a crucial impact on quality of life of the affected. A qualitative endeavour was attempted at the diabetic clinic of a tertiary care set up in the capital city of Bhubaneswar to create a diabetic surveillance data assembly, wherein subjects above 18 years of age and newly diagnosed or on follow-up, after obtaining informed consent, were made to respond to a quality of life (QOLID) validated tool. The pretested tool has 8-domain role limitation due to physical health, physical endurance, general health, treatment satisfaction, symptom botherness, financial worries, emotional/mental health, and diet advice tolerance. The validated tool had 34 items (questions) that were selected to represent these domains on the basis of extraction communality, factor loading, and interitem and item-total correlations. The final questionnaire had an overall Cronbach’s alpha value of 0.894 (subscale: 0.55 to 0.85), showing high internal consistency in the current study population. A score for each domain was calculated by simple addition of items scores. Each individual domain score was then standardized by dividing by maximum possible domain score and multiplying by 100. All individual standardized domain scores were then added and divided by 8 (number of domain) to obtain an overall score. The data collection was done for 400 patients as an interim analysis. Univariate and subsequently multivariate analysis was performed to decide the predictors that affected quality of life. Age over 50 years (OR = 1.81, CI 1.12–2.93; p = 0.014 ), female gender (OR = 2.05, CI 1.26–3.35; p = 0.004 ), having foot complications (OR = 2.81, CI 1.73–4.55; p < 0.001 ), and having depression (OR = 1.88, CI 1.15–3.06, p = 0.011 ) emerged as predictors of poor QOLID scores. The tool can be made a subtle part of chronic case management of diabetes to ensure patient’s participation in the treatment of the disease and to create a database that can redefine diabetic care in India to suit the diverse regional settings in the country.

Self-care activities and quality of life among people with type II diabetes in rural east India: a cross-sectional study

“…13,14 Some other studies also reported similar findings, however, they used different instruments other than WHQOL BREF. 12,15 It was observed that the physical health domain was the most affected domain followed by the environmental domain, at 55.02±7.70 and 57.81±19.64 respectively, supported by a study conducted in Nepal Mishra et al reported similar findings in their studies for physical quality of life domain 50.7±11.8. 13,16 With regard to self-care activities, it was reported that even though a high percentage of individuals practiced healthy diet (65.3%), including 5 or more fruits or vegetables (67.3%) and spacing the carbohydrates well (72.6%), when it comes to physical exercise (22%), blood glucose monitoring (0.7%) and foot care (33.3%) they are falling behind.…”

Quality of Life and Its Determinants Among Diabetic Patients in A Rural Area of Gautam Buddha Nagar, Uttar Pradesh, India

“…Similarly, in a study conducted by Meher D et al in Bhubaneswar among 400 diabetic subjects, in the year 2020, it was found that the patients with age over 50 years had poor quality of life. 18 However, in a study conducted by Timar R et al in 2016 in Romania, no significant association was found between quality of life and age of diabetic subjects. 19 Overall quality of life and general health score was significantly lower in illiterate subjects (26.97) than literate subjects in the present study.…”

Effect of pandemic on Quality of life in Diabetics (QOLID) assessment

“…The initial objective of the study which is published,[ 7 ] of validating the QOLID tool in eastern India population, 10 samples per item were needed for validation of questionnaire[ 8 9 ] for 34 items in the scale, thus minimum sample of 340 subjects. For this study, to assess the effect of the pandemic, we had a ready sample size of 599 respondents, stratified for 343 respondents in pre COVID and 256 in COVID period.…”

“…All individual standardized domain scores were then added and divided by 8 (number of domain) to obtain an overall score for the study. [ 7 ]…”