City For All Ages: Elderly-friendly City Services for Active and Healthy Ageing

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

RECRUITING

Total Enrollment

19 participants

Study Classification

OBSERVATIONAL

Study Start Date

2016-01-30

Study Completion Date

2026-01-03

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Remote Monitoring to Prevent Frailty Progression in the Elderly

NCT05173870

Frailty Elderly Wearable Device

RECRUITING

INNOVATEDIGNITY: Co-designing Digital Health Technologies With Older People in Homecare Settings.

NCT04884711

Dignity Old Age; Debility Healthy Aging +1 more

UNKNOWN

Safe & Easy Environment at Home for Patients Presenting a Frail Syndrome

NCT02288221

Cognitive Deterioration

TERMINATED NA

Validation of a Digital Multidimensional Assessment Tool for the Elderly in Geriatric Consultations

NCT06004557

Elderly People in a Consultation Context

UNKNOWN

Health in Smart Rurality: Impact on Coordination of Care of Telemonitoring and Data Centralization of Frailty Patients

NCT04866550

Chronic Obstructive Pulmonary Disease Heart Failure

UNKNOWN

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Baseline/Control Phase Intervention Phase

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

ECOLOGIC_OR_COMMUNITY

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Time period prior to & after the implementation of 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.

IoT sensors

Intervention Type DEVICE

The proposed assistive Activities of Daily Living (ADL) monitoring system consists of ambient infrared sensors embedded seamlessly into the living environment, and a visualization app. Multimodality sensors with wireless data transmission capability will be installed at different locations (e.g. bedroom, kitchen, toilet, bathroom, living room, etc.) to monitor and detect the activities performed by individual elderly, such as cooking, sleeping, going to the bathroom, going out of the apartment or potential wandering, bathroom falls, etc. In addition, a micro-bend fiber optic pressure sensor mat will be placed unobtrusively below the bed mattress to measure the elderly's heart and respiratory rates during sleep. This mat helps provide information on the quality of sleep and sleep-wake rhythms of the elderly with sleep disorders. The collected data will then be transferred through a secured gateway with Raspberry Pi to a dedicated server for data processing and analysis.

Traditional/Manual elderly monitoring

Intervention Type OTHER

Traditional elderly care without the use of IoT sensors.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

IoT sensors

The proposed assistive Activities of Daily Living (ADL) monitoring system consists of ambient infrared sensors embedded seamlessly into the living environment, and a visualization app. Multimodality sensors with wireless data transmission capability will be installed at different locations (e.g. bedroom, kitchen, toilet, bathroom, living room, etc.) to monitor and detect the activities performed by individual elderly, such as cooking, sleeping, going to the bathroom, going out of the apartment or potential wandering, bathroom falls, etc. In addition, a micro-bend fiber optic pressure sensor mat will be placed unobtrusively below the bed mattress to measure the elderly's heart and respiratory rates during sleep. This mat helps provide information on the quality of sleep and sleep-wake rhythms of the elderly with sleep disorders. The collected data will then be transferred through a secured gateway with Raspberry Pi to a dedicated server for data processing and analysis.

Intervention Type DEVICE

Traditional/Manual elderly monitoring

Traditional elderly care without the use of IoT sensors.

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* 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

* Have Severe disabilities
* Have reduced mobility (using wheelchair)
* Have severe dementia

Minimum Eligible Age

65 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes