Falls Prevention and Frailty Monitoring: Using Technology Safely in Ageing Well Services

February 7, 2026

Falls prevention and frailty management sit at the centre of technology, telecare and digital support for ageing well, especially where providers are supporting people living alone, living with dementia, or recovering after a health crisis. In practice, these approaches often need to align with dementia service models and care pathways, where cognition, insight and risk fluctuate and where repeated falls can trigger escalation, safeguarding concerns, or premature admission.

Technology can help—but only if it is used as part of a managed support pathway. Sensors, wearables and alerts do not replace observation, relationship-based support, and skilled decision-making. The operational question is always: what changes in daily practice when technology flags risk, and how is that change evidenced?

Where technology genuinely adds value in falls and frailty

Across ageing well services, technology tends to add the most value in three specific areas:

Early identification of deterioration (e.g., increased night-time wandering, reduced mobility, repeated near-falls, changes in routine).
Reducing response time (e.g., rapid support following a fall, proactive checks after an alert pattern shift).
Supporting consistent risk decisions (e.g., agreed escalation thresholds, shared visibility across staff teams, and structured review).

To realise those benefits, providers need clear ownership: who monitors, who responds, who reviews patterns, and who has authority to adjust care plans, telecare thresholds or staffing levels.

Operational example 1: falls detection with structured escalation

Context: An extra care and community-based ageing well service identified repeated falls among a group of residents living independently. Falls detection pendants were in place, but response was inconsistent and documentation lacked clarity on why some incidents escalated to 999 while others did not.

Support approach: The provider moved to a structured escalation pathway: all falls alerts triggered an immediate triage call, followed by a welfare check within a defined timeframe based on risk category (known frailty, anticoagulant use, prior head injury, and ability to self-mobilise). Escalation thresholds were agreed with individuals and, where appropriate, families.

Day-to-day delivery detail: Staff received short scenario-based briefings (what to ask, what to record, when to escalate). Managers ran weekly alert reviews to identify patterns (same time of day, same location, repeat triggers). A monthly thematic review fed into supervision and training, focusing on decision quality and record quality.

Evidencing effectiveness: The provider demonstrated reduced avoidable ambulance call-outs, clearer incident narratives, and evidence that risk decisions were consistent with agreed plans. Audit sampling showed improved completion of post-falls observations and escalation documentation.

Operational example 2: night-time movement monitoring to prevent escalation

Context: A domiciliary and supported housing provider supported older people experiencing night-time disorientation. Repeated night falls were occurring, alongside staff reports of “increased confusion” without objective detail.

Support approach: The provider introduced discreet bed occupancy and movement sensors for a time-limited period, used explicitly to inform a review rather than as a permanent “solution”. The purpose was to understand patterns (e.g., frequency of rising, wandering, bathroom trips) and to trial preventative adjustments.

Day-to-day delivery detail: Alerts were routed to an on-call function with clear thresholds: a single bathroom trip did not trigger a call-out; repeated movement within a short window did. Staff response prioritised reassurance, hydration prompts and environmental checks (lighting, trip hazards, accessible footwear). Weekly reviews linked sensor data to care notes and medication changes.

Evidencing effectiveness: The service evidenced fewer night falls and fewer crisis escalations. Critically, it also evidenced why: changes to evening routines, pain management review requests to primary care, and environmental adjustments informed by the data.

Operational example 3: frailty monitoring and proactive MDT escalation

Context: A provider supporting people with multiple long-term conditions saw repeated “near misses” where deterioration was noticed late—often after a fall or a hospital admission.

Support approach: The provider introduced a simple frailty monitoring model using routine observations (where appropriate and consented) alongside structured wellbeing checks and trend logging. The focus was not clinical diagnosis, but early identification of change that warranted MDT input.

Day-to-day delivery detail: Staff were trained to capture consistent indicators: reduced appetite, increased breathlessness on exertion, reduced hydration, increased fatigue, and change in mobility. Where thresholds were met, an escalation was triggered to the GP, community nursing, or falls team, with a clear summary of observed change over time.

Evidencing effectiveness: The provider evidenced earlier referrals, faster MDT responses, reduced avoidable admissions, and better continuity of decision-making. Governance was evidenced through audit trails showing escalation decisions and outcomes.

Safeguarding, capacity and least restrictive practice

Technology can introduce safeguarding risk if it is used without clear consent pathways or where it becomes a substitute for meaningful support. Providers must be explicit about:

Consent and capacity: whether the person understands what monitoring does and what happens when an alert triggers.
Least restrictive practice: whether the technology is proportionate, time-limited where appropriate, and reviewed as needs change.
Information-sharing boundaries: who can access alert data and how privacy is protected.

Where a person lacks capacity, best-interest decisions must be recorded with clear rationale, evidence of consultation, and a plan for review.

Commissioner expectation

Commissioner expectation: Commissioners expect providers to evidence that technology reduces avoidable escalation and supports independence without inflating commissioned hours or masking unmet need. They will expect measurable outcomes (e.g., reduced falls frequency, reduced admissions, improved response times) and assurance that technology is reviewed and adjusted rather than left in place indefinitely.

Regulator expectation (CQC)

Regulator / Inspector expectation (CQC): The CQC will expect evidence that falls-related technology is safe, reliably monitored, and responded to consistently. Inspectors will look for staff competence, clear escalation pathways, learning from incidents, and governance that identifies patterns and acts on them—particularly where repeated falls indicate poor risk management or inadequate review.

Quality and governance mechanisms that stand up to scrutiny

Falls and frailty technology becomes defensible when supported by routine governance:

Alert pattern review (weekly or fortnightly) with clear actions logged.
Post-incident learning that feeds into supervision and practice improvement.
Audit sampling focused on decision quality and recording quality, not just completion.
Escalation oversight to ensure referrals and MDT requests are timely and evidenced.

When these mechanisms are embedded, technology supports safer care and clearer assurance—not additional risk.