A Novel Approach for Fully Automated, Personalized Health Coaching for Adults with Prediabetes: Pilot Clinical Trial

Everett, Estelle; Kane, Brian; Yoo, Ashley; Dobs, Adrian S.; Mathioudakis, Nestoras

doi:10.2196/jmir.9723

Cited by 70 publications

(65 citation statements)

References 25 publications

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“…Recently, Everett et al presented a DSS using machine learning to promote adherence to physical activity and weight reduction [ 130 ]. Authors validated the system with 55 patients with prediabetes.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Contreras¹,

Vehı́²

2018

J Med Internet Res

364

207

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BackgroundArtificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.ObjectiveThe objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.MethodsA review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review.ResultsWe propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results.ConclusionsWe obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Contreras¹,

Vehı́²

2018

J Med Internet Res

364

207

View full text Add to dashboard Cite

show abstract

“…Of the included studies, five were RCTs [ 19 , 23 - 26 ] and four were single-arm pre-post studies [ 20 - 22 , 27 ]. Studies were published between 2013 and 2018, and conducted in four different countries (two in Canada [ 26 , 27 ], one in Germany [ 23 ], three in the Republic of Korea [ 21 , 22 , 25 ], and three in the United States [ 19 , 20 , 24 ]).…”

Section: Resultsmentioning

confidence: 99%

Effectiveness of Mobile Health Interventions Promoting Physical Activity and Lifestyle Interventions to Reduce Cardiovascular Risk Among Individuals With Metabolic Syndrome: Systematic Review and Meta-Analysis

et al. 2020

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Background Physical activity and lifestyle interventions, such as a healthy diet, have been proven to be effective approaches to manage metabolic syndrome. However, these interventions require great commitment from patients and clinicians owing to their economic costs, time consumption, and lack of immediate results. Objective The aim of this systematic review and meta-analysis was to analyze the effect of mobile-based health interventions for reducing cardiometabolic risk through the promotion of physical activity and healthy lifestyle behaviors. Methods PubMed, Scopus, Web of Science, Cochrane Central Register of Controlled Trials, and SPORTdiscus databases were searched for experimental studies evaluating cardiometabolic risk indicators among individuals with metabolic syndrome who were included in technology-assisted physical activity and lifestyle interventions. Effect sizes, pooled mean changes, and their respective 95% CIs were calculated using the DerSimonian and Laird method. Outcomes included the following clinical and biochemical parameters: body composition (waist circumference [WC] and BMI), blood pressure (systolic blood pressure [SBP] and diastolic blood pressure [DBP]), glucose tolerance (fasting plasma glucose [FPG] and glycated hemoglobin A1c [HbA1c]), and lipid profile (total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol [HDL-C], and triglycerides). Results A total of nine studies were included in the meta-analysis. Owing to the scarcity of studies, only pooled mean pre-post changes in the intervention groups were estimated. Significant mean changes were observed for BMI (−1.70 kg/m2, 95% CI −3.20 to −0.20; effect size: −0.46; P=.03), WC (−5.77 cm, 95% CI −9.76 to −1.77; effect size: −0.54; P=.005), SBP (−7.33 mmHg, 95% CI −13.25 to −1.42; effect size: −0.43; P=.02), DBP (−3.90 mmHg, 95% CI −7.70 to −0.11; effect size: −0.44; P=.04), FPG (−3.65 mg/dL, 95% CI −4.79 to −2.51; effect size: −0.39; P<.001), and HDL-C (4.19 mg/dL, 95% CI 2.43-5.95; effect size: 0.23; P<.001). Conclusions Overall, mobile-based health interventions aimed at promoting physical activity and healthy lifestyle changes had a strong positive effect on cardiometabolic risk indicators among individuals with metabolic syndrome. Nevertheless, further research is required to compare this approach with usual care in order to support the incorporation of these technologies in health systems. Trial Registration PROSPERO CRD42019125461; https://tinyurl.com/y3t4wog4.

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“…The usefulness of these results is clear; either the drug is safe and efficient to use, or not. However, intervention studies for chronic illnesses that utilized modern technologies often conclude that their results require further testing [ 2 – 4 ]. This is, in part, due to the complicated nature of chronic illness self-management, requiring individuals’ to make daily health decisions in response to biological changes, such as their blood glucose in the case of diabetes.…”

Section: Introductionmentioning

confidence: 99%

Analysing mHealth usage logs in RCTs: Explaining participants’ interactions with type 2 diabetes self-management tools

et al. 2018

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BackgroundThe Introduction of mobile health (mHealth) devices to health intervention studies challenges us as researchers to adapt how we analyse the impact of these technologies. For interventions involving chronic illness self-management, we must consider changes in behaviour in addition to changes in health. Fortunately, these mHealth technologies can record participants’ interactions via usage-logs during research interventions.ObjectiveThe objective of this paper is to demonstrate the potential of analysing mHealth usage-logs by presenting an in-depth analysis as a preliminary study for using behavioural theories to contextualize the user-recorded results of mHealth intervention studies. We use the logs collected by persons with type 2 diabetes during a randomized controlled trial (RCT) as a use-case.MethodsThe Few Touch Application was tested in a year-long intervention, which allowed participants to register and review their blood glucose, diet and physical activity, goals, and access general disease information. Usage-logs, i.e. logged interactions with the mHealth devices, were collected from participants (n = 101) in the intervention groups. HbA1c was collected (baseline, 4- and 12-months). Usage logs were categorized into registrations or navigations.ResultsThere were n = 29 non-mHealth users, n = 11 short-term users and n = 61 long-term users. Non-mHealth users increased (+0.33%) while Long-term users reduced their HbA1c (-0.86%), which was significantly different (P = .021). Long-term users significantly decreased their usage over the year (P < .001). K-means clustering revealed two clusters: one dominated by diet/exercise interactions (n = 16), and one dominated by BG interactions and navigations in general (n = 40). The only significant difference between these two clusters was that the first cluster spent more time on the goals functionalities than the second (P < .001).ConclusionBy comparing participants based upon their usage-logs, we were able to discern differences in HbA1c as well as usage patterns. This approach demonstrates the potential of analysing usage-logs to better understand how participants engage during mHealth intervention studies.

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A Novel Approach for Fully Automated, Personalized Health Coaching for Adults with Prediabetes: Pilot Clinical Trial

Cited by 70 publications

References 25 publications

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Effectiveness of Mobile Health Interventions Promoting Physical Activity and Lifestyle Interventions to Reduce Cardiovascular Risk Among Individuals With Metabolic Syndrome: Systematic Review and Meta-Analysis

Analysing mHealth usage logs in RCTs: Explaining participants’ interactions with type 2 diabetes self-management tools

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