Challenges in Diabetes Care: Can Digital Health Help Address Them?

Iyengar, Varun; Wolf, Alexander T.; Brown, Adam S.; Close, Kelly L.

doi:10.2337/diaclin.34.3.133

Cited by 66 publications

(53 citation statements)

References 22 publications

(29 reference statements)

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“…Treatment of chronic conditions relies on people’s self-management, which requires their ability to perform health-related behaviors, such as proper use of medication, physical activity, and following dietary recommendations, by themselves in the context of their daily lives 7 . Chronic diseases are a great burden on primary healthcare systems, 8 as average clinic visit times is only 10.3 minutes, leaving insufficient time for professional guidance to affected people for optimal self-management of their care 9 …”

Section: Background and Significancementioning

confidence: 99%

An expandable approach for design and personalization of digital, just-in-time adaptive interventions

Gönül

Namlı

Huisman

et al. 2018

Journal of the American Medical Informatics Association

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ObjectiveWe aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people’s individual needs, momentary contexts, and psychosocial variables.Materials and MethodsWe propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions.ResultsWe evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns.ConclusionWhile the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.

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Section: Background and Significancementioning

confidence: 99%

An expandable approach for design and personalization of digital, just-in-time adaptive interventions

Gönül

Namlı

Huisman

et al. 2018

Journal of the American Medical Informatics Association

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“…When compared to standard or even a non-mHealth approach, technology based interventions can impact positively glycemic control among patients with diabetes. mHealth shows potential as a diabetes selfmanagement tool that will aid clinical decision making and improve patient outcomes [40][41]. As such, these technologies are increasingly becoming standard in clinical practice.…”

Section: Identifying and Assessing Risk For Culturally Appropriate Inmentioning

confidence: 99%

A Decade of Diabetes Hospitalizations: Meaningful Information for Community-Based Health Services Administrators for Identifying and Assessing Risk

Shepherd¹,

Goodman²,

Sheth-Chandra³

2017

J Pat Care

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The American Diabetes Association has established that the largest contributor of expenditures related to the cost of care for diabetes is inpatient hospital care. Research has shown that when multiple hospitalizations have been examined, patients diagnosed with diabetes have higher same year readmission rates. Medicare-enrolled patients with coronary artery disease and diabetes who participated in a diabetes management intervention that included self-care behavior instructions and nurse management had fewer emergency room visits and hospitalizations for diabetes related care. In the US, an aging population and expected changes in the ethnic composition prompts an alert to actively address the need for prevention, early detection, reduction of associated complications experienced, and exploration of a cure of the chronic condition of diabetes. This study's purpose was twofold: to examine the variability in hospitalization rates of diabetes by geographical location and age groups from 2000-2011 to examine any statistically significant relationships. The study further proposes the exploration of low-cost technology mechanisms to reduce diabetes related hospitalizations through the use of mHealth. Practical interventions using mHealth technologies are feasible solutions to addressing virtual prevention efforts and improving the outcomes of care among patients diagnosed with diabetes.

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“…The US Food and Drug Administration has compiled a list of software programs with medical device functions (apps for smartphones and personal computers with diagnostic, monitoring, or therapeutic objectives), advanced business intelligence data analysis tools, artificial intelligence (AI), cloud, cyber security, and innovative health technologies [ 4 ]. These tools belong to an evolving reality; they are still unclear but they are potentially leading to challenging and promising scenarios [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists

Musacchio¹,

Giancaterini²,

Guaita³

et al. 2020

J Med Internet Res

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Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. Machine learning software programs that disclose the reasoning behind a prediction allow for “what-if” models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that “affect” the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.

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Challenges in Diabetes Care: Can Digital Health Help Address Them?

Cited by 66 publications

References 22 publications

An expandable approach for design and personalization of digital, just-in-time adaptive interventions

An expandable approach for design and personalization of digital, just-in-time adaptive interventions

A Decade of Diabetes Hospitalizations: Meaningful Information for Community-Based Health Services Administrators for Identifying and Assessing Risk

Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists

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