Enhancing Chronic-Disease Education through Integrated Medical and Social Care: Exploring the Beneficial Role of a Community Teaching Kitchen in Oregon.
Jacob P TanumihardjoHeidi DavisMengqi ZhuHelen OnKayla K GuilloryJill ChristensenPublished in: Nutrients (2023)
Teaching kitchens (TKs) are rapidly being utilized as models to integrate culinary education and chronic-disease education into healthcare settings. Our observational study details the structure and organizational processes (e.g., referral, services, medical and social care integration) of the Community TK at Providence Milwaukie Hospital in Portland, OR. We utilize electronic medical-record data from engaged TK participants ( n = 3077) to evaluate between the association of engagement and clinical outcomes (e.g., HbA1c, blood pressure, weight and cholesterol). Mean baseline HbA1c of Highly Engaged TK patients with diabetes ( n = 88) reduced from 9.8% to 8.6% at 6 months ( p < 0.0001) and sustained significant reductions at 12, 18, 24, 30, and 36 months ( p < 0.05). Highly Engaged patients with hypertension ( n = 152) had significant, sustained reductions in blood pressure ( p < 0.0001). Engaged patients in the same high-risk groups also had significant improvements in HbA1c and blood pressure. Both engagement subgroups had moderate improvements in weight change and cholesterol. This study shows promising associations of TK services that promote chronic-disease self-management with improved clinical outcomes among higher risk patients (e.g., high blood pressure, high HbA1c, high low-density lipoprotein) with different medical issues (e.g., diabetes, obesity) and social barriers (e.g., food insecurity).
Keyphrases
- healthcare
- blood pressure
- low density lipoprotein
- end stage renal disease
- mental health
- newly diagnosed
- hypertensive patients
- type diabetes
- ejection fraction
- weight loss
- chronic kidney disease
- heart rate
- cardiovascular disease
- body mass index
- metabolic syndrome
- insulin resistance
- social media
- physical activity
- emergency department
- deep learning
- health information
- machine learning
- medical students
- adipose tissue
- skeletal muscle
- pain management