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Implementation and Evaluation of an AI-Assisted Telerehabilitation System for Postdischarge Continuation of
Charmaine You Mei Tan1, Xin Yi Seah2, Sharna Si Ying Seah2
1Department of Post-Acute and Continuing Care, Outram Community Hospital, Singhealth Community Hospitals, Singapore, Singapore.
Background:
The World Health Organization (WHO) launched a Rehabilitation 2030 initiative to call for global action to scale up rehabilitation efforts. Rehabilitation needs are growing, and efforts should be made to strengthen and integrate rehabilitation into all levels of health care, including building research capacity and expanding evidence for rehabilitation. Postdischarge rehabilitation is essential for reducing hospital readmissions and maintaining patients' health within the community. However, locally, the shift toward community-based rehabilitation is often hampered by long waiting times for admission to day rehabilitation centers (DRCs), cost and logistical barriers, and the lack of a structured program to onboard patients and caregivers to the digital solutions required for rehabilitation in Singapore. Telerehabilitation systems that incorporate wearables within a structured rehabilitation program support inpatient rehabilitation and enable patients to continue with physical rehabilitation after discharge while awaiting admission to a DRC.
Objective:
The aim of this randomized controlled trial (RCT) is to investigate the clinical and cost-effectiveness of ATLAS, an AI-assisted telerehabilitation system, among patients admitted to community hospitals (CHs) for rehabilitation.
Methods:
This is a 2-arm pragmatic RCT. Participants admitted to CHs for rehabilitation for hip fracture, musculoskeletal conditions, or deconditioning will be enrolled and randomized to either the intervention or control group in a 1:1 ratio. The intervention group, in addition to usual care, will be enrolled in an ATLAS system. The ATLAS system consists of Rebee, an AI-assisted device equipped with a lightweight wireless motion sensor that patients can strap on for real-time feedback and log their exercises via the Rebee application to an electronic platform, as well as a therapist-designed, structured exercise program that patients follow during their inpatient stay and continue upon discharge. The control group will continue to receive usual rehabilitative care in the CHs and upon discharge but will not have access to the ATLAS system. Our primary outcome is functional status. Secondary outcomes include quality of life, length of stay in the CH, 30-day readmission rates, and cost-effectiveness.
Results:
A total of 407 participants were recruited between January and September 2025 across 3 CHs, with data collection completed in December 2025. Data analysis is currently underway, and the results are expected to be submitted for publication in Q4 2026.
Conclusions:
Our trial will provide valuable insights into the effectiveness and implementation of an AI-assisted telerehabilitation system for patients admitted to the CH for rehabilitation for hip fracture, musculoskeletal conditions, or deconditioning. This trial will also evaluate the sustainability and cost-effectiveness of the ATLAS system, with the potential of scaling across inpatient settings in both acute and CHs.
Trial Registration:
ClinicalTrials.gov NCT06683963; https://clinicaltrials.gov/study/NCT06683963.
International Registered Report Identifier (Irrid):
DERR1-10.2196/78400.
