Guest blog: Get savvy - you won't regret it

by Rachel Stokes on August 11, 2021

Dr Nikita Rowley, CPsychol, has over 12 years’ experience in exercise referral. She has a BSc Sport Psychology, MSc Health Psychology degree and completed a PhD which reviewed exercise referral data from ReferAll’s National Referral Database.

From experience, what exercise referral schemes (ERSs) achieve is nothing short of miraculous. I have witnessed schemes helping to prevent, manage and treat a multitude of health conditions, working with an individual for a set amount of time, getting to know and understand them, seeing them grow in confidence and their health and physical activity levels improve, with the aim of creating long-term behaviour change.

Yet for many ERS providers, especially post-pandemic, funding is increasingly hard to come by. From working on the ground within ERSs, I’ve always thought more support is needed for scheme co-ordinators, in particular around data collection.

Data collection is nothing short of vital for ERSs and wellbeing services. And it’s not just data collection that’s important, but good data collection.

When commissioners and stakeholders review services and potential funding streams, first and foremost they want to know whether a service has been successful. Has that service changed a specific behaviour or improved health outcomes? How many people took up a referral? How many completed the scheme? If an individual dropped out, why? Without good data collection, the data that is available may show that interventions have been ineffective or have poor completion rates, when, in reality, they have great outcomes for the individuals involved, but poor data.

So, what’s the recipe for good data collection?

• Have a set of standardised measures you use for each person who enters your service, so you collect the same data for every participant who uses the scheme
• Have a set of additional measures which are relevant to specific health conditions. If an individual is referred with high blood pressure, make sure you are measuring any change in blood pressure. Ensure data relates to the specific health condition referred for.
• Make sure all instructors understand the data being collected – if they don’t know what’s being collected and why, data loses its value
• Collect data at specified time points (pre scheme, mid and post-scheme, follow up etc)
• Do you have the option of using fitness/activity trackers? These can be a more accurate way to collect data on physical activity levels, compared to self-report questionnaires.
• Make sure data is inputted immediately after meeting with individuals to minimise missing data, plus ask for someone to check for any typos/errors.
• If an individual drops out, ensure this is followed up and properly reported, with drop out reasons recorded.
• Ensure someone is responsible for checking data to eliminate any processing errors and certify that all instructors collecting data in the correct/same way.

Just as vital as data collection is data review, as only this can support what effects your services have had on health-related outcomes. From experience of working within exercise referral, it’s clear not everyone understands how to interpret data. And why would they? Data analysis is not typically taught within exercise referral qualifications, so how can we expect ERS instructors to possess this skill? It’s a training issue that needs further exploration.

Since completing my PhD, I have been supporting ERSs to help improve data collection and analysis. I’m hoping my research will help guide future adaptations to national guidance, so schemes have better support from national policy.

I’m focused on helping those working within exercise referral to develop better schemes and collect good data, so if you’d like support with redesigning any aspects of your offer, reach out to ReferAll. We work together on research projects so if you have something in mind please get in touch. 

Warm regards,


Dr Nikita Rowley, CPsychol

Topics: Referral Schemes, Exercise Referral, Funding, Evaluation, Data collection