Diabetic Foot Assessment and Care: Barriers and Facilitators in a Cross-Sectional Study in Bangalore, India.
Sudha B GUmadevi VJoshi Manisha ShivaramPavan BelehalliShekar M AChaluvanarayana H CMohamed Yacin SikkandarMarcos Leal BrioschiPublished in: International journal of environmental research and public health (2023)
(1) Background: This cross-sectional study aims to highlight the assessment and foot care practices in an advanced clinical setting, the clinical characteristics of the patients, and to understand the barriers and facilitators for effective foot care from the perspectives of healthcare practices, resources, and patients' socioeconomic and cultural practices, and other aspects in terms of new technologies for effective foot care such as infrared thermography. (2) Methods: Clinical test data from 158 diabetic patients and a questionnaire to assess the foot care education retention rate were collected at the Karnataka Institute of Endocrinology and Research (KIER) facility. (3) Results: Diabetic foot ulcers (DFUs) were found in 6% of the examined individuals. Male patients were more likely to have diabetes complications, with an odds ratio (OR) of 1.18 (CI = 0.49-2.84). Other diabetes problems raised the likelihood of DFUs by OR 5 (CI = 1.40-17.77). The constraints include socioeconomic position, employment conditions, religious customs, time and cost, and medication non-adherence. The attitude of podiatrists and nurses, diabetic foot education, and awareness protocols and amenities at the facility were all facilitators. (4) Conclusions: Most diabetic foot complications might be avoided with foot care education, regular foot assessments as the standard of treatment, and self-care as a preventive/therapeutic strategy.
Keyphrases
- healthcare
- end stage renal disease
- quality improvement
- palliative care
- ejection fraction
- type diabetes
- newly diagnosed
- primary care
- cardiovascular disease
- peritoneal dialysis
- mental health
- risk factors
- pain management
- adipose tissue
- affordable care act
- machine learning
- deep learning
- cross sectional
- long term care
- health information
- skeletal muscle
- glycemic control
- replacement therapy
- wound healing
- adverse drug