iFAST Motion Age
How old is your movement — and what does that predict?
Motion Age is the digital transformation of an analogue insight: that movement quality reflects systemic health. Classic sit-to-rise tests link poor movement to cardiovascular risk and mortality, but they are subjective and impractical at scale.
MAI Motion analyses sit-to-stand videos from a mobile or laptop camera, reconstructing 3D movement from 150,000 biomechanical markers into a single score. This programme asks whether that score can predict multimorbidity risk when benchmarked against established frailty indices.
ΔMA
Motion Age Minus Actual Age
3,000
Existing Patient Records
CFS
Benchmarked Against Frailty
The Health Problem
Inactivity does not stay in the joints.
Musculoskeletal disorders are responsible for 30 million lost working days and around 30% of GP consultations annually in the UK. The inactivity they drive sets off a cascade of decline that ends in multi-morbidity.
1.7bn
People affected by musculoskeletal disorders globally
~20m
People affected in the UK — over a third of the population, and the leading cause of disability
£5bn+
NHS spending on musculoskeletal disorders each year, set to rise as the over-65 population reaches 15.3 million by 2030
The cascade of decline
The gap
Despite the known link between musculoskeletal disorders and long-term decline, no validated digital biomarker exists for early biomechanical deterioration in the community. Standard tools such as the Clinical Frailty Scale are subjective and clinician-dependent, limiting remote scalability. Motion Age is an alternative — but its correlation with multimorbidity remains unproven.
Scope
Six places a single score could sit
Motion Age is calculated without demographic or diagnostic input, which is what allows it to work as a standalone, objective indicator of movement efficiency.
Prevention of multimorbidity
A pre-disease risk trajectory marker that identifies early biomechanical decline before deteriorating mobility leads to complex conditions.
Reducing workforce demand
Assessments are fully remote and automated, reducing burden on physiotherapists, GPs and specialists.
Early detection
Subtle changes in movement are detected long before clinical diagnosis thresholds are reached.
Remote monitoring
Assessments performed on smartphone or laptop cameras at home, processed remotely and shared asynchronously with clinicians.
Patient self-management
A simple score and trend-line over time, empowering people to track movement health and act on lifestyle adjustments.
Integrated care
Results shared across primary care, physiotherapy, rehabilitation and social care teams to support multidisciplinary planning.
The Research Question
Can Motion Age predict elevated multimorbidity risk?
Can Motion Age accurately predict elevated risk of multimorbidity in adults with musculoskeletal conditions, when benchmarked against established frailty indices?
A 12-month study establishing a statistical correlation, using an existing dataset of 3,000 patients with Motion Age and Clinical Frailty Scale scores drawn from routine clinics, incorporating demographic variables.
75%
Classification agreement
Between Motion Age and standard multimorbidity indices.
~80%
User comprehension
Of users find Motion Age understandable and useful for self-monitoring.
>10yrs
Elevated-risk threshold
Motion Age more than ten years above actual age, as a clear risk signal.
WP1
M1–M12 · £17,953
Project coordination
MSK Doctors
Operational delivery, inter-site coordination, PPIE workstreams and risk monitoring.
Deliverable — Quarterly reports.
WP2
M1–M10 · £48,342.21
Clinical validation and threshold calibration
MSK Doctors & University of Exeter
Apply the existing Motion Age dataset alongside CFS scores; model associations using ΔMA adjusted for demographic variables; test generalisability across subgroups and apply interpretability tools such as SHAP and partial dependence plots.
Deliverable — Validated ΔMA–frailty index associations.
WP3
M9–M12 · £33,137.51
Generalisability modelling & PPIE
MSK Doctors
Finalise the algorithm; build a config/API block with threshold logic; PPIE-driven user testing to refine threshold framing and score explanation.
Deliverable — Report on system robustness and exploratory analysis.
Project Team
Existing datasets, validated tools, established partnerships.
The project is structured around work packages that draw on pre-existing data and partnerships, so no new infrastructure is required — keeping timelines and delivery realistic.
MSK Doctors & Associates
Prof. Paul Lee, orthopaedic surgeon and founder; Dr Yan Wen, PhD in computer vision and AI; Dr Chengke Sun, software engineer, PhD in robotics.
University of Exeter
Professor Xujiong Ye, AI and computer vision in healthcare, with prior Innovate UK-funded collaborations with MSK Doctors; Dr Lei Zhang, Assistant Professor in Computer Science.
Patient & public involvement
Dr Tanvi Verma leads user engagement with the Bionic Joint Community. An advisory board of eight supports study design, with two co-design workshops refining language and accessibility for diverse and low-tech users.
Patient & public involvement
Early input from the Bionic Joint Group (MSK Regen Charity) shaped the preferred “motion age” concept because of its easy-to-understand single score. Ongoing work with a cross-demographic group — older adults, ethnically diverse populations and people with chronic conditions — will co-design score explanations and thresholds, visual and language framing, and access pathways for low-tech users.
Plan-Stage Evidence Boundary
What the plan does — and does not — establish.
This page describes a project plan and its intended outcomes. It does not confirm that the programme was funded, completed, or that its success criteria were met.
Supported by the plan
- A 12-month, £99,432.71 project plan across three work packages, using an existing dataset of 3,000 patients.
- MAI Motion is MHRA/UKCA registered (Ref# 32940) and in active use by clinicians in the UK, Turkey, Italy and Romania — stated as TRL 5.
- Registered trade mark UK00004072701; patents filed (PCT/GB2025/050722 and UK 2404991.8).
- Three peer-reviewed publications support Motion Age’s clinical applicability as a potential screening tool.
Not established by the plan
- That Motion Age has been validated as a predictor of multimorbidity — the correlation with multimorbidity is described in the plan as unproven.
- That the 75% agreement, 80% comprehension and >10-year threshold targets have been achieved; they are the study’s success criteria.
- That the programme was funded, commenced or completed.
- That NHS adoption, ICS integration or health-economic savings have been demonstrated.
Source provenance
- Funder
- NIHR — iFAST
- Lead organisation
- MSK Doctors & Associates
- Academic partner
- University of Exeter
- Proposed duration
- 12 months
- Proposed value
- £99,432.71
- Stated maturity
- TRL 5 — MHRA/UKCA registered, in clinical use
A number people can act on
The long-term goal is to establish Motion Age as a scalable, validated, remotely-monitored marker for multimorbidity detection — providing the clinical, technical and patient-facing evidence required for implementation readiness.
NHS East Midlands ICS are engaged to explore integration into primary care pathways. Next steps set out in the plan include a larger implementation trial in an NHS setting, evaluating longitudinal predictive power, and expanding Motion Age to other risk domains such as cardiometabolic disease.
Academic & Clinical Collaboration
We welcome ICS digital leads, integrated musculoskeletal services and academic partners interested in digital biomarkers for early risk stratification.