A smartphone-based solution to monitor daily physical activity in a care home
| Autor: | Joren Buekers, Deborah Piette, Patrick De Boever, Pieter Joosen, Joachim Taelman, Daniel Berckmans |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Male
Gerontology Population ageing 020205 medical informatics Physical activity Monitoring Ambulatory physical activity Health Informatics 02 engineering and technology Telehealth 030204 cardiovascular system & hematology elderly 03 medical and health sciences 0302 clinical medicine Heart Rate 0202 electrical engineering electronic engineering information engineering Humans Medicine Exercise mHealth Aged Aged 80 and over business.industry Telemedicine Chronic disease Physical Fitness Female Disease prevention Smartphone business |
| Zdroj: | Journal of Telemedicine and Telecare. 25:611-622 |
| ISSN: | 1758-1109 1357-633X |
| Popis: | IntroductionIn an ageing population, increasing chronic disease prevalence puts a high economic burden on society. Physical activity plays an important role in disease prevention and should therefore be promoted in the elderly.MethodsIn this study, a mobile health (mHealth) system was implemented in a care home setting to monitor and promote elderly peoples’ daily activity. The physical activity of 20 elderly people (8 female and 12 male, aged 81 ± 9 years old) was monitored over 10 weeks using the mHealth system, consisting of a smartphone and heart rate belt. Feedback on physical activity was provided weekly. A reference performance test battery derived from the Senior Fitness Test determined the participants’ physical fitness.ResultsActivity levels increased from week 1 onwards, peaking at week 5, and decreasing slightly until week 10. This illustrates that the use of mHealth and feedback on physical activity can motivate the elderly to become more active, but that the effect is transient without other incentives. Bio-data from the mHealth system were translated into a fitness score explaining 65% of the test battery’s variance. After separating the elderly into three groups depending on physical fitness determined from the test battery, classification based on the fitness score resulted in a correct classification rate of 67.3%.DiscussionThis study demonstrates that an mHealth system can be implemented in a care home setting to motivate activity of the elderly, and that the bio-data can be translated in a fitness score predicting the outcome of labour-intensive tests. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|