Connectedness: How Technology and Social Networks Are Advancing Diabetes Nutrition Care

Boucher, Jackie L.

doi:10.2337/ds18-0079

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Literature review indicates that mobile apps and related technology can positively affect health aspects, in particular weight loss, as well as diabetes prevention and treatment [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Similarly, an initial literature review indicates a significant number of publications on the use of Artificial Intelligence and Machine Learning techniques for diabetes prediction, prevention, and treatment [ 28 , 29 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ].…”

Section: Methodology and Contextual Approachmentioning

confidence: 99%

Capacity Strengthening Undertaking—Farm Organized Response of Workers against Risk for Diabetes: (C.S.U.—F.O.R.W.A.R.D. with Cal Poly)—A Concept Approach to Tackling Diabetes in Vulnerable and Underserved Farmworkers in California

Sikalidis

Kristo

Reaves

et al. 2022

Sensors

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In our project herein, we use the case of farmworkers, an underserved and understudied population at high risk for Type-2 Diabetes Mellitus (T2DM), as a paradigm of an integrated action-oriented research, education and extension approach involving the development of long-term equitable strategies providing empowerment and tailored-made solutions that support practical decision-making aiming to reduce risk of T2DM and ensuing cardiovascular disease (CVD). A Technology-based Empowerment Didactic module (TEDm) and an Informed Decision-Making enhancer (IDMe) coupled in a smart application (app) for farmworkers aiming to teach, set goals, monitor, and support in terms of nutrition, hydration, physical activity, sleep, and circadian rhythm towards lowering T2DM risk, is to be developed and implemented considering the particular characteristics of the population and setting. In parallel, anthropometric, biochemical, and clinical assessments will be utilized to monitor risk parameters for T2DM and compliance to dietary and wellness plans. The app incorporating anthropometric/clinical/biochemical parameters, dietary/lifestyle behavior, and extent of goal achievement can be continuously refined and improved through machine learning and re-programming. The app can function as a programmable tool constantly learning, adapting, and tailoring its services to user needs helping optimization of practical informed decision-making towards mitigating disease symptoms and associated risk factors. This work can benefit apart from the direct beneficiaries being farmworkers, the stakeholders who will be gaining a healthier, more vibrant workforce, and in turn the local communities.

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