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Clinical Trial Summary

In GATEKEEPER intervention, Big Data Analytics techniques will be exploited to address risk stratification and early detection, based on lifestyles analysis including: pattern recognition for the improvement of public health surveillance and for the early detection of chronic conditions; data mining for inductive reasoning and exploratory data analysis; Cluster Analysis for identifying high-risk groups among elder citizens. In the above cases timely intervention is provided by through AI-based, digital coaches, structured conversations, consultation and education. The main target group (N=960) is older adults and elderly citizens with risk factors for MetS and their carers. Therefore, the GATEKEEPER intervention aims at primary (avoid occurrence of disease) and secondary (early detection and management) prevention of the ageing population at risk for MetS.


Clinical Trial Description

Over 1.5 billion people worldwide are affected by Metabolic Syndrome (MetS) - a cluster of conditions reflecting behavioural risk factors typical of modern lifestyle (excessive food intake, low physical activity, etc) - with a huge socioeconomic impact and a total estimated cost of trillions of euros. Early prevention measures especially for elderly at high risk of chronic conditions, such as prediabetics or obese, include structured lifestyle change programs that help people achieve and sustain changes in dietary and physical activity habits. It is well established that MetS prevalence, as well as its individual components (high blood pressure, high glucose, central adiposity) increase with age. Notably, MetS percentages in the age group 50-55 years old and older is almost 2-3 times higher than in the younger age groups, probably due to a life time accumulation of adversities including overnutrition, a sedentary lifestyle, obesity and dyslipidemia, changes in the hormones, untreated hypertension, changes of the functioning of beta cells and other environmental and physiological factors. Therefore, it is important to target not only elderly citizens, but rather older adults aged ≥55 years old as the optimum target group for a MetS prevention intervention. In GATEKEEPER intervention, Big Data Analytics techniques will be exploited to address risk stratification and early detection, based on lifestyles analysis including: pattern recognition for the improvement of public health surveillance and for the early detection of chronic conditions; data mining for inductive reasoning and exploratory data analysis; Cluster Analysis for identifying high-risk groups among elder citizens. In the above cases timely intervention is provided by through AI-based, digital coaches, structured conversations, consultation and education. The main target group (N=960) is older adults and elderly citizens with risk factors for MetS and their carers. Therefore, the GATEKEEPER intervention aims at primary (avoid occurrence of disease) and secondary (early detection and management) prevention of the ageing population at risk for MetS. 960 older adults and elderly citizens (aged >=55 years old) with risk factors for MetS as well as their carers (n=40) will be recruited and will be randomized to either: i) the intervention group 1 (n=320), who will be provided with the standard care plus a lifestyle application to promote self-management, increase health literacy and awareness through a digital coach, ii) the intervention group 2 (n=320), who will be provided with the standard care, the lifestyle application and additionally digital tools and wearables, such as a smart tracker and weight scale, or iii) the control group (n=320), who will only receive standard care, as provided by the local and national healthcare system as well as one face-to-face counselling session for lifestyle modification to improve their risk factors. The participants will be followed up for a total duration of 3 months, when they will be re-evaluated to assess whether their risk factors were improved through the lifestyle intervention. The users will be recruited at local community centres, such as the "Open Day Elderly Centres", health centres, private offices of health care professionals, hospitals etc. upon written informed consent form. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT05031299
Study type Interventional
Source Harokopio University
Contact Yannis Manios, PhD
Phone +30 210 9549156
Email [email protected]
Status Recruiting
Phase N/A
Start date March 1, 2021
Completion date December 2022

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