GATEKEEPER: Lifestyle-related Early Detection and Intervention for Older Adults & Elderly at Risk for Metabolic Syndrome
Study Details
Study Description
Brief 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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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N/A |
Detailed 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
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Active Comparator: Control group (Standard care) Participants in the control group will receive only the 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 for 3 months. |
Behavioral: Standard care
Participants will receive only the 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.
|
| Experimental: Intervention group 1 (Application) Participants will will be additionally provided with a health-promotion application for self-management for 3 months. |
Behavioral: Health-promotion application for self-management
Participants will be provided with a health-promotion application for self-management for 3 months, additionally to the standard care.
|
| Experimental: Intervention group 2 (Devices) Participants will be additionally provided with wearables and devices for 3 months including: A weighing scale (assessing also body composition) device A smartwatch/wristband to assess physical activity but also sleep pattern. |
Device: Wearables and devices
Participants will be provided with wearables and devices, including a weighing scale (assessing also body composition) device and a smartwatch/wristband to assess physical activity but also sleep pattern, for 3 months, additionally to the standard care and the Health-promotion application.
|
Outcome Measures
Primary Outcome Measures
- Change in Waist circumference (cm) from baseline to 3 months [baseline and monthly until 3-month follow-up]
Participants' waist circumference will be measured in triplicates (in cm) at baseline and at the 3rd month follow-up visit.
Secondary Outcome Measures
- Changes in body mass index (kg/m2) from baseline to 3 months [baseline and monthly until 3-month follow-up]
Participants' BMI will be measured in triplicates (in kg/m2) at baseline and at the 3rd month follow-up visit.
- Changes in percentage of body fat from baseline to 3 months [baseline and monthly until 3-month follow-up]
Participants' percentage of body fat will be measured at baseline and at the 3rd month follow-up visit.
- Patient-reported outcome measures (PROMs) [baseline and 3 months]
Patient-reported outcome measures (PROMs), including satisfaction of the intervention at baseline and at the 3rd month follow-up visit. Satisfaction of the intervention will be measured via the UTAUT questionnaire [Liu D et al. 2019] or other validated questionnaire.
- Changes in Diet quality on FFQ and healthy diet score from baseline to 3 months [baseline and 3 months]
Diet quality will be assessed via the food-frequency questionnaire (FFQ) and the healthy diet score developed and validated in the EU-funded Feel4Diabetes-study (www.feel4diabetes-study.eu) or other validated questionnaires at baseline and at the 3rd month follow-up visit.
- Changes in Quality of life on EQ5D (Generic HRQL) from baseline to 3 months [baseline and 3 months]
Quality of life will be assessed via the EQ5D (Generic HRQL) [Konerding U et al. 2014] at baseline and at the 3rd month follow-up visit.
- Changes in Physical activity on validated questionnaires from baseline to 3 months [baseline and 3 months]
Physical activity will be assessed via validated questionnaires at baseline and at the 3rd month follow-up visit.
- Changes in sedentary time on validated questionnaires from baseline to 3 months [baseline and 3 months]
Sedentary time will be assessed via validated questionnaires at baseline and at the 3rd month follow-up visit.
- Changes in Sleep duration/quality on Athens Insomnia Scale questionnaire from baseline to 3 months [baseline and 3 months]
Sleep duration and sleep quality will be assessed via the Athens Insomnia Scale questionnaire [Soldatos CR et al. 2000] or other validated questionnaires at baseline and at the 3rd month follow-up visit.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Males and females aged ≥55 years old
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Having any of the following risk factors for MetS:
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waist circumference >94 cm for men and >80 cm for women
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Triglycerides (TG) ≥150 mg/dL
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High-density lipoprotein cholesterol (HDL-C) <40 mg/dL for men and <50 mg/dL for women
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Fasting glucose ≥100 mg/dL
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Blood pressure ≥130 /≥85 mm Hg
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Living at home (either alone or with relatives)
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Informed consent form provided
Exclusion Criteria:
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Having severe hearing or vision problems or any other acute or chronic condition that would limit the ability of the user to participate in the study
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Having dementia or cognitive impairment
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Being institutionalised
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Participation in another research project
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Harokopio University of Athens | Kallithea | Attica | Greece | 17671 |
| 2 | University of Thessaly | Trikala | Greece | 42132 |
Sponsors and Collaborators
- Harokopio University
- CitiesNet
- University of Thessaly
- University of Patras
- BioAssist
- University of Ioannina
- Centre for Research & Technology Hellas (CERTH)
Investigators
- Principal Investigator: Odysseas Androutsos, PhD, University of Thessaly
Study Documents (Full-Text)
None provided.More Information
Publications
- Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SC Jr; International Diabetes Federation Task Force on Epidemiology and Prevention; Hational Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; International Association for the Study of Obesity. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009 Oct 20;120(16):1640-5. doi: 10.1161/CIRCULATIONAHA.109.192644. Epub 2009 Oct 5.
- Athyros VG, Ganotakis ES, Bathianaki M, Monedas I, Goudevenos IA, Papageorgiou AA, Papathanasiou A, Kakafika AI, Mikhailidis DP, Elisaf M; MetS-Greece Collaborative Group. Awareness, treatment and control of the metabolic syndrome and its components: a multicentre Greek study. Hellenic J Cardiol. 2005 Nov-Dec;46(6):380-6.
- Devers MC, Campbell S, Simmons D. Influence of age on the prevalence and components of the metabolic syndrome and the association with cardiovascular disease. BMJ Open Diabetes Res Care. 2016 Apr 25;4(1):e000195. doi: 10.1136/bmjdrc-2016-000195. eCollection 2016.
- Kokaliari ED, Roy AW. Validation of the Greek translation of the multicultural quality of life index (MQLI-gr). Health Qual Life Outcomes. 2020 Jun 15;18(1):183. doi: 10.1186/s12955-020-01426-9.
- Konerding U, Elkhuizen SG, Faubel R, Forte P, Malmström T, Pavi E, Janssen MF. The validity of the EQ-5D-3L items: an investigation with type 2 diabetes patients from six European countries. Health Qual Life Outcomes. 2014 Dec 5;12:181. doi: 10.1186/s12955-014-0181-5.
- Kraja AT, Borecki IB, North K, Tang W, Myers RH, Hopkins PN, Arnett D, Corbett J, Adelman A, Province MA. Longitudinal and age trends of metabolic syndrome and its risk factors: the Family Heart Study. Nutr Metab (Lond). 2006 Dec 5;3:41.
- Lesjak V, Stanojević-Jerković O. Physical Activity, Sedentary Behavior and Substance Use among Adolescents in Slovenian Urban Area. Zdr Varst. 2015 Jun 9;54(3):168-74. doi: 10.1515/sjph-2015-0024. eCollection 2015 Sep.
- Liu D, Maimaitijiang R, Gu J, Zhong S, Zhou M, Wu Z, Luo A, Lu C, Hao Y. Using the Unified Theory of Acceptance and Use of Technology (UTAUT) to Investigate the Intention to Use Physical Activity Apps: Cross-Sectional Survey. JMIR Mhealth Uhealth. 2019 Aug 22;7(9):e13127. doi: 10.2196/13127.
- Martín-Timón I, Sevillano-Collantes C, Segura-Galindo A, Del Cañizo-Gómez FJ. Type 2 diabetes and cardiovascular disease: Have all risk factors the same strength? World J Diabetes. 2014 Aug 15;5(4):444-70. doi: 10.4239/wjd.v5.i4.444. Review.
- Park MJ, Kim HS. Evaluation of mobile phone and Internet intervention on waist circumference and blood pressure in post-menopausal women with abdominal obesity. Int J Med Inform. 2012 Jun;81(6):388-94. doi: 10.1016/j.ijmedinf.2011.12.011. Epub 2012 Jan 21.
- Saklayen MG. The Global Epidemic of the Metabolic Syndrome. Curr Hypertens Rep. 2018 Feb 26;20(2):12. doi: 10.1007/s11906-018-0812-z. Review.
- Scuteri A, Laurent S, Cucca F, Cockcroft J, Cunha PG, Mañas LR, Mattace Raso FU, Muiesan ML, Ryliškytė L, Rietzschel E, Strait J, Vlachopoulos C, Völzke H, Lakatta EG, Nilsson PM; Metabolic Syndrome and Arteries Research (MARE) Consortium. Metabolic syndrome across Europe: different clusters of risk factors. Eur J Prev Cardiol. 2015 Apr;22(4):486-91. doi: 10.1177/2047487314525529. Epub 2014 Mar 19.
- Soldatos CR, Dikeos DG, Paparrigopoulos TJ. Athens Insomnia Scale: validation of an instrument based on ICD-10 criteria. J Psychosom Res. 2000 Jun;48(6):555-60.
- Zimmet P, M M Alberti KG, Serrano Ríos M. [A new international diabetes federation worldwide definition of the metabolic syndrome: the rationale and the results]. Rev Esp Cardiol. 2005 Dec;58(12):1371-6. Spanish. Erratum in: Rev Esp Cardiol. 2006 Feb;59(2):185.
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