JMIR Publications

ABSTRACT

Background:

Restrictive practices, including restraint and seclusion, are widely used across aged care, mental health, and disability support settings to manage behaviors of concern, despite well-documented ethical, psychological, and physical harms. Digital health technologies, particularly real-time monitoring systems and artificial intelligence-enabled predictive analytics, are increasingly deployed to detect early behavioral escalation. However, detection or prediction alone does not ensure reductions in restrictive practices, and it remains unclear how these technologies are translated into care actions, decision-making processes, and outcomes in real-world settings.

Objective:

This scoping review aims to identify, map, and synthesize evidence on the use of real-time monitoring and predictive analytics to reduce or prevent restrictive practices in health and social care. A specific focus is placed on the operational processes and decision-making actions that follow detection or prediction, and the implementation factors that influence whether these technologies are associated with reduced use of restraint or seclusion.

Methods:

The review will be conducted in accordance with JBI methodology for scoping reviews and reported using the PRISMA-ScR guidelines. Initial searches were undertaken in MEDLINE, CINAHL, PsycINFO, Scopus, and IEEE Xplore, and will be updated prior to synthesis, with reference list screening and grey literature searches used to supplement database searches. Two reviewers will independently screen eligible sources and chart data using a structured charting framework informed by the CeHRes Roadmap and the Implementation Research Logic Model. Extracted data will include technology characteristics, care settings and populations, post-detection workflows and interventions, implementation determinants and strategies. Outcomes of interest include changes in the use of restrictive practices (eg, frequency or duration of restraint or seclusion), as well as implementation‑related and ethical impacts such as workflow changes, decision‑making processes, and governance considerations.

Results:

Preliminary searches have been completed. Title and abstract screening has not yet commenced. The review is expected to be completed by 2026.

Conclusions:

By clarifying how digital detection and prediction technologies are operationalized into care actions and under what conditions they are associated with reduced restrictive practices, this review will support safer, more ethical, and more implementable digital health approaches to restrictive practice reduction and inform future research, service design, and policy development.