City For All Ages: Elderly-friendly City Services for Active and Healthy Ageing
- Conditions
- Baseline/Control PhaseIntervention Phase
- Interventions
- Device: IoT sensorsOther: Traditional/Manual elderly monitoring
- Registration Number
- NCT06486935
- Lead Sponsor
- National University of Singapore
- Brief Summary
Many city-dwelling elderly people can be greatly affected after a minor change in their living or health conditions. Mild Cognitive Impairment (MCI), early dementia and frailty are among the most common risks with deep consequences on elderly's and caregivers' quality of life. Through the new wave of Information and Communication Technologies (ICT), Internet of Things (IoT) and smart city system, it is now possible to help individuals capture and make use of their personal data in a way that will help them maintain their independence for longer. The City for all Ages project will create an innovative service based on:
* ICT-enhanced early detection of risk related to frailty
* ICT-enhanced interventions that can help the elderly population to improve their daily life and also promote positive behaviour change
Through real-life pilot sites in Singapore in collaboration with TOUCH Senior Activity Centre (SAC) and the Housing Development Board (HDB), this project explores how data on individual behaviours captured through indoor and outdoor sensors could be used for the observation and detection of the following parameters:
* Activity of Daily Living (ADL): nutrition, hygiene, sleep activity
* Mobility: physical activity, going-out frequency and length
* Cognition: forgetfulness, early signs of mental decline
* Socialization: senior activity centre visits, activities attended, other places of interests visits
This 2-year project comprises of 3 phrases involving 10 healthy elderly living in HDB home in phases 1 and 2 and 100 elderly in phase 3. Our focus is to use sensing technologies installed in the elderly's home to monitor and detect their activities of daily living. Sensor data that is collected will then be analyzed to identify relevant behaviours of individuals, and to detect behavioral changes that can be correlated with risks of MCI/frailty. The appropriate ICT based interventions (e.g. data visualization and alerts to caregivers) will then be applied to mitigate these risks. Additionally, psychosocial data related to the elderly's quality of life, social activity participation and activities of daily living will also be collected via interviews and activity logs to evaluate the outcomes of our technology intervention.
- Detailed Description
Like many developed countries, Singapore faces the challenges of an ageing population. The number of Singaporeans aged 65 and above is increasing rapidly as population growth slows. The number of seniors has doubled from 220,000 in 2000 to 440,000 in 2015, and is expected to increase to 900,000 by 2030. Amongst the elderly people, close to 10% are living alone (from 35,000 in 2012 to 83,000 by 2030). The changing demographic not only increases healthcare costs but also the demand on healthcare services and care provision.
Preventing frailty and MCI is key for the elderly to maintain their day-to-day activities and remain healthy and independent at home. Prior research has shown that frailty, like disability, is a dynamic process with older individuals moving back and forth between different frailty states. Transition to frailty is a gradual progression that occurs over the course of several months or years, and there are surprisingly high rates of recovery. However, it is important to intervene within the right time window before a person goes into full blown frailty. Hence it is important to detect the onset and progression of frailty and to identify the factors that may facilitate transitions to less frail states. This can inform the development of interventions to manage elderly at risk for fraility.
City for All Ages project seeks to demonstrate that smart cities can play a pivotal role in "prevention" (i.e. the early detection and consequent intervention) of MCI and frailty-related risks. The core idea is that "smart cities", enabled by the deployment of sensor technologies and analytics can collect data about individuals: a) to identify segments of population potentially at risk, in order to start more stringent monitoring; b) to closely monitor selected individuals, in order to start a proactive intervention. In both cases adverse changes of behaviors that are identified through a set of indicators can prompt preventive actions. The aim is to advance the research on healthcare towards a proactive rather than reactive system.
The research team will leverage the existing experimentations and pilot sites that have focused on detection of elderly risky behaviors both in France and Singapore. Lessons learnt from dealing with challenges either in terms of understanding the data (such as false positives, meaningful information, etc.) or providing the appropriate and timely intervention (such as difficulty in identifying and organizing the intervention effectively, large panel of stakeholders, excessive solicitation of caregivers, etc.) would be useful for this project.
Our goal is to use sensing technologies installed in the elderly's home to monitor and detect their activities such as cooking, sleeping, going to the bathroom, going out of the apartment or potential wandering, bathroom falls. Sensor data will be collected unobtrusively and managed using a privacy-aware linked open data paradigm. Basic reasoning and learning algorithms will be applied to the data to identify relevant behaviours of individuals, and to detect behavioral changes that can be correlated with risks of MCI/frailty. The appropriate ICT based interventions (e.g. data visualization and alerts to caregivers) will then be applied to mitigate these risks.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 19
- Cognitively abled elderly people-as determined by the senior activity center staff during the identification of potential participants.
- Living alone or with no more than 2 flatmates
- Resident of an HDB apartment
- Preferably, apartments with WIFI internet connection
- English or Chinese speaker-in phase 1, only mandarin speakers will be included in the study. Depending on the number of dialect speaking elderly in the residences, in subsequence phases, dialect speaking subjects will be included.
- Member of the Senior Activity Centre
- Agree to share their activity of daily living (ADL) data with the research team, the Senior Activity Centre team and (if applicable) their designated relatives
- Those who don't meet the aforementioned inclusion criteria
- Have Severe disabilities
- Have reduced mobility (using wheelchair)
- Have severe dementia
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Time period prior to & after the implementation of IoT sensors Traditional/Manual elderly monitoring Since our study involves a single group of 19 participants, we will evaluate their quality of life at two different times: before and after the introduction of IoT sensors. Time period prior to & after the implementation of IoT sensors IoT sensors Since our study involves a single group of 19 participants, we will evaluate their quality of life at two different times: before and after the introduction of IoT sensors.
- Primary Outcome Measures
Name Time Method Change from Baseline in the Elderly's Perceived Quality of Life at 6 Months 6 months The older adults' perceived quality of life reported during semi-structured interviews
- Secondary Outcome Measures
Name Time Method Change from Baseline in the Caregiver's Perceived Fatigue at 6 Months 6 months Caregivers' perceived fatigue levels reported during semi-structured interviews
Trial Locations
- Locations (2)
Institut Mines Télécom (IMT)
🇫🇷Paris, France
National University of Singapore
🇸🇬Singapore, Singapore
Institut Mines Télécom (IMT)🇫🇷Paris, France