Ivo Fridolin

HTTK toob kokku juhtivad psüühika, keha, sotsiaalse konteksti ja ruumilise konteksti uurijad, et luua distsipliinide ülene arusaam komplekssüsteemidest, mis mõjutavad heaolu: elu kvaliteeti erinevates valdkondades objektiivses ja eriti subjektiivses mõttes. Me käsitleme 4 uurimisvaldkonda. 1) KORRELAADID: Millised bio-psühholoogilised ja sotsiaal-ruumilised omadused on seotud heaolu püsivamate komponentidega nagu eluga rahulolu? 2) MEHHANISMID: Kuidas rulluvad inimestes lahti heaolu dünaamilised komponendid, näiteks emotsioonid? 3) ENESEHOOL: Kuidas inimesed ise oma heaolu enesehoole ökosüsteemides mõistavad ja juhivad? 4) SEKKUMISED: Kuidas heaolu isikustatud ja kohandatud sekkumistega edendada? HTTK rahastab interdistsiplinaarseid ametikohti; registriandmetega lõimitud longituud-uuring; doktorikooli; tippsündmusi; ja rändluse ja koostöö toetusmeedet. HTTK tõstab osalevate rühmade, asutuste ja Eesti heaoluteaduste tulemuslikkust ja mõjukust.

Life-sustaining artificial kidney treatment or haemodialysis (HD) is needed for end-stage renal disease and critical care patients. High quality HD must ensure effective and personalized blood purification from harmful uremic toxins (UT), among inflammation-cardiovascular disease related middle size (MM)-UT. This, urged by a need for HD surveillance in crisis (coronavirus, war, energy), has created a demand for on-line, bloodless, and non-infectious tools for UT removal monitoring. Optical monitoring can provide a feasible tool for this. However, to date no reliable monitoring technology of MM-UTs is available. This project aims to fill this knowledge gap. Optical spectral signature identification combined with chromatographic and biochemical analyses to reveal the main optical MM markers in biofluids, selection of best signal processing algorithms, and a proof-of-concept in-vivo clinical study is planned to develop a novel optical technology for on-line MM-UT removal monitoring in HD.

The overall goal of the project is to increase the number of healthy life years of the population. Currently, Estonia has recorded one of the lowest number of healthy life years at birth in the EU. To achieve this goal, three closely related areas of digital health are researched, developed and piloted. We use the standardized data exchange environment and digital data of the Estonian health information system (EHIS) to develop applications that increase the use of data collected by the person for health promotion, prevention and control of chronic conditions. Second, we focus on sensors and digital applications supported by artificial intelligence (AI) to allow a person to collect both biosignals and textual data in machine-readable form. With this, we speed up the detection of health risks and reduce the healthcare workload. Thirdly, we develop various AI methods by combining the data in EHIS and the Health Insurance Fund's database, as well as the data collected by the person.