A new study shows that early detection of age related diseases could be done from the comfort of your own home.
The researchers hope that their results could help in the future to help with longevity and quality of life for seniors, as well as elevate increasing pressure on the healthcare system.
This study was conducted by a team at Bern University hospital, but using sensors capable of recording movement patterns.
These sensors could help to detect health problems in the elderly, including fall risk, cognitive impairment or old-age depression at an early stage.
Specific changes in movement patterns can be indicators of numerous health problems, including: mild cognitive impairment, depression, respiratory problems and cardiac arrhythmias.
For elderly individuals, systematic detection of such changes could help identify chronic diseases such as dementia, heart disease or Parkinson’s disease at an early stage.
Age related health issues are often discovered late, and their progression is usually difficult to assess objectively.
The study displays how large scale, sensor-based health monitoring could help to tackle these problems.
Researchers combined a variety of everyday activity and behaviour patterns, which they monitored with sensors installed in the homes of elderly study participants, which helps to create a summary picture.
Study first author Dr Narayan Schütz says: “We used non-contact sensors at home to create an extensive collection of digital measures that capture broad parts of daily life, behaviour and physiology, in order to identify health risks of older people at an early stage.”
This has the possibility to benefit early detection as well as foster development of personalised treatments and research into new drugs and therapeutic approaches.
To start, the researchers collected 1,268 health parameters by the use of non-interaction sensors particularly tailored to the senior demographic.
The system they deployed consists of simple, contactless motion sensor in each room, a bed sensor under the mattress, and door sensors on the front door and refrigerator.
These sensors are all connected to a central hub, which analyses the registered motion signals and inform relatives or an alarm centre in the event of emergencies.
These emergencies can be detected by such events as the individual not returning to bed at night.
The research team evaluated the collected data by using machine learning approaches.
Co-last author of the study, Tobias Nef, says: “We were able to show that such a systems approach – in contrast to the common use of a few health metrics – allows to detect age-relevant health problems such as cognitive impairment, fall risk or frailty surprisingly well.”
The study finds that participants perceived the idea of these home monitoring sensors over wearable devices, due to some health issues, such as cognitive issues.
The evaluation of the data collected on everyday behaviour also has the potential to identify possible new ageing relevant biomarkers.
Nef says: “For example, we found indications that fall risk could significantly depend on certain sleep parameters.”
Professor Hugo Saner, co-last author of the study highlights the importance of clinical relevance: “Such a system marks a milestone in early detection of worsening health for seniors living alone into old age.
“We assume that it can make a significant contribution to enabling older people to live at home for as long as possible by delaying hospital admissions and transfers to nursing institutions or, in the best case, even avoiding them.”
According to the research team, the better early detection and personalised treatment of typical diseases of old age, could not only help older people achieve better health, but also reduce healthcare costs.