Case Study – Good Boost

Organisation name: Good Boost

Contact name: Ben Wilkins

Role / job title: CEO

Partnership / collaborators involved: Leisure Operators/Local Authorities Leisure Teams (multiple), Swim England, ukactive, NHS, University of Exeter (evaluation)

Synopsis:

Good Boost integrates clinically certified AI technology into community venues: gyms, pools, studios and libraries, delivering personalised therapeutic exercise, rehabilitation and education programmes for people with long-term health conditions.

In partnership with local authorities and leisure operators, Good Boost removes traditional barriers of accessibility, clinical gatekeeping and condition adaptability, to deliver validated self-management services at scale across 350+ venues nationwide for diverse populations, with large proportions facing highest risk of inequalities.

Five peer-reviewed publications demonstrate measurable improvements in health outcomes and strong social return on investment. Certified by NHS DTAC, ORCHA and recommended by NICE, Good Boost represents a scalable, cost-effective model supporting the shift from hospital to community-based condition management.

The Story:

The challenge

More than 2 in 5 people in the UK live with a long-term health condition. MSK conditions alone account for 30% of GP appointments and are the second largest contributor to long-term sickness absence. Movement and exercise are among the most clinically effective interventions available, yet significant barriers prevent uptake. Individuals face pain, fear of injury, and uncertainty about what is safe for their condition. Leisure venues lack condition-appropriate programmes and trained staff. The health system has limited visibility of, and trust in, community exercise options. The result: a large proportion of people with health conditions remain physically inactive, while community venues sit as an underutilised asset.

What Was Done

Good Boost has developed and scaled a technology-enabled solution that embeds clinical governance directly into community venues. A tablet-based system guides users through personalised exercise sessions, adapted in real time to their condition, progress, and self-reported wellbeing, without requiring a therapist to be present. Key elements include AI-driven intake assessment and adaptive exercise prescription; programmes co-developed with rehabilitation clinicians; inclusive, multi-language media designed with diverse populations; subsidised access models for low-income participants; and staff training to support onboarding and engagement. The system holds NHS DTAC certification, ORCHA Medical App approval, MHRA Class 1 medical device status, and a NICE Early Use Assessment recommendation for hip and knee osteoarthritis.

Impact and Outcomes

Now delivered across 350+ community venues, Good Boost delivers 13k-15k sessions per month. A peer-reviewed cost-consequence analysis (Wilkins et al., 2025) across 4,429 participants demonstrates 38% pain reduction and 47% functional improvement. Further evaluations demonstrates £16.51 of social value for every £1 invested. Attendance skews towards populations at higher risk of health inequality, including ethnically diverse and lower-income groups. The model is financially sustainable: venues benefit from increased utilisation; the NHS benefits from reduced primary and secondary care demand; and participants gain an affordable, long-term exercise pathway.

Learning and Relevance

Good Boost demonstrates that it is possible to bring clinically validated, personalised therapeutic exercise into the heart of community leisure infrastructure, without requiring specialist staff at every session, or being a prohibitive cost to participants.

For others working in active wellbeing, the key transferable lessons are:

Community assets can be clinical assets. Swimming pools and studios already have the physical infrastructure and regular footfall. The gap is the clinical layer, which technology can provide at scale.
AI can democratise access to personalisation. The personalisation that was previously only possible in a one-to-one clinical consultation can now be delivered digitally, enabling consistent quality at scale.
Self-management is a system outcome. By supporting people to manage their own conditions through structured exercise, education and peer-support, the model reduces dependency on clinical contact and builds long-term resilience.
Partnership is the delivery model. No single organisation delivers this alone, it requires leisure operators, clinical expertise, and technology working in concert. The commercial model aligns incentives across all partners.
Group delivery and peer-support: a critical factor for building the confidence and long-term motivation to keep moving

What would you do differently next time?

Our greatest early learning was underestimating the importance of venue onboarding and staff culture. Technology alone does not create engagement, the attitude and confidence of staff in championing the programme to participants is as important as the platform itself. We have since invested significantly in structured venue support, and data shows that venues receiving regular operational support grow sessions by around 50% compared to those receiving less. We would embed this support model from day one in future rollouts (It’s not just the programme or innovation, it’s the implementation for adoption which is critical)

We would also prioritise earlier engagement with ICBs and primary care networks. The clinical evidence base is now robust, but building NHS referral pathways takes time and relationship capital that we are only now developing systematically. Starting those conversations earlier would have accelerated impact at scale.