Principal Investigator: Mr Salvatore Tedesco
University College Cork, IrelandTags: 47845, Accelerometry, activity trackers, ageing, grip strength, multi-morbidities, wrist-worn devices
Wrist-bands are massively diffused nowadays, especially in young and sport-oriented cohorts, but limited adoption is observed in older adults. The main limitations are related to the lack of medical insights that current mainstream wristbands may provide to older subjects. One of the most important aspects investigated by researchers is the possibility to extrapolate clinical information from these wearable devices.
Our research question is to understand if raw accelerometry data collected for 7-days using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, may be suitable to predict hand grip strength in a number of cohorts: older subjects (> 65 years) with single-morbidities, healthy older adults, and in older adults with multi-morbidities. Morbidities considered in this study are chronic heart failure (CHF), coronary heart disease (CHD), diabetes, and chronic obstructive pulmonary disease (COPD).
The study is not only an ICT/health-related research, but will pave the way to the adoption of wearable devices as an efficient tool for clinical assessment in elderlies with multimorbities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases.
This study is supported by EU H2020 funded project ProACT under grant agreement No. 689996. Aspects of this work have been supported in part by a research grant from Science Foundation Ireland (SFI) and is co-funded under the European Regional Development Fund under Grant Number 13/RC/2077. Aspects of this work have been supported in part by INTERREG NPA funded project SenDOC. We foresee that the project will be completed by September 2020.