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

NCT06486935 | Recruiting

• 1 other identifier: SACIoTStudy
• Study Type: observational
• Target: 19
• Locations: 2 countries
• Sites: 2

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.

Conditions

Baseline/Control Phase; Intervention Phase

Outcome Measures

Primary Outcomes (1)

• Change from Baseline in the Elderly's Perceived Quality of Life at 6 Months

  The older adults' perceived quality of life reported during semi-structured interviews

  6 months

Secondary Outcomes (1)

• Change from Baseline in the Caregiver's Perceived Fatigue at 6 Months

  6 months

Study Arms (1)

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.

Device: IoT sensors; Other: Traditional/Manual elderly monitoring

Interventions

IoT sensors 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.

Time period prior to & after the implementation of IoT sensors

Traditional/Manual elderly monitoring OTHER

Traditional elderly care without the use of IoT sensors.

Time period prior to & after the implementation of IoT sensors

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Below are the inclusion criteria for elderly participants: * Cognitively abled elderly people-as determined by the caregivers. * 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 * English or Chinese speaker * Have a smartphone, tablet or a computer

You may qualify if:

• 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

You may not qualify if:

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

Sponsors & Collaborators

• National University of Singapore: lead
• University of Sfax: collaborator

Study Sites (2)

Institut Mines Télécom (IMT)

Paris, France

RECRUITING

National University of Singapore

Singapore, Singapore

RECRUITING

Related Publications (1)

• Ntsweng O, Kodys M, Ong ZQ, Zhou F, Marasse-Enouf A, Sadek I, Aloulou H, Tan SS, Mokhtari M. Lessons Learned From the Integration of Ambient Assisted Living Technologies in Older Adults' Care: Longitudinal Mixed Methods Study. JMIR Rehabil Assist Technol. 2025 Jun 11;12:e57989. doi: 10.2196/57989.

  PMID: 40369868 DERIVED

Study Design

• Study Type: observational
• Observational Model: ECOLOGIC OR COMMUNITY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Assistant Professor

Study Record Dates

• First Submitted: February 27, 2024
• First Posted: July 5, 2024
• Study Start: January 30, 2016
• Primary Completion: December 31, 2025
• Study Completion: January 3, 2026
• Last Updated: July 5, 2024

Record last verified: 2024-06

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1); SG (1)