Accuracy

We work with top academic institutions to ensure that Quealth accurately assesses disease risk, has evidence-based health content and provides effective health coaching; all based on the latest research and evidence.

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AUC 0.77

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AUC 0.67

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AUC 0.78

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AUC 0.80

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AUC 0.72

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The predictive accuracy of Quealth's algorithms is measured using the AUC (c-statistic) value – the higher the better, with anything over 0.7 indicating good predictive strength. This level of accuracy is directly comparable to – and in many cases higher than – other internationally recognised and respected disease risk algorithms.

 

Quealth has the added advantage of acquiring a much richer data set which drives both greater sensitivity to change as well as enabling highly detailed and personalised health coaching.

– Paul Nash, Head of Clinical Governance

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Based on Evidence

Our team's combined expertise helps ensure everything Quealth says or advises is always based on the latest evidence and research.

Evidence
Design

Intuitive Design

Quealth's user experience is continuously refined and improved using the latest in app design and innovation alongside expert partnerships.

Healthier. Together.

It's what we're in this for: to help you make the best decisions about your health and lifestyle and, ultimately, to reduce the prevalence of the biggest non-communicable diseases in the world.

Healthier. Together.

Our Health Experts

Making Quealth a world-class, market-leading health improvement programme requires an ongoing programme of clinical and academic validation, research and development.

That's where our expert health team comes in. We're proud and priviledged to work with a fantastic group of clinical, academic and editorial leads who provide expert insight into the ongoing development of Quealth.

Paul Nash
Dr Stephen Weng
Dr Luis Vaz
Dr Graham Cope
Dr Dale Esliger
Dr Jonathan Ward
Leo Pemberton
Claire Read
Chris Ritchie