Idea of a common standard for physical-activity data originally the brain-child of imin, a private company (2014)
Funded by EU (ONDINE) and UK govt grants (Sport England) to determine whether use of open data and open data standards could help get inactive people more active
Originally existed as a small consortium of private companies; taken up by the ODI in 2016
Opportunity modelling specification first released 2017
Open Booking API specification first released 2019
Steady growth in publication since 2016. Pandemic oscillations make figures unreliable, but:
46 datasets (many from large regional or national operators)
100 000+ exercise opportunities per fortnight
Number of commercial organisations involved in using data has remained roughly static since 2016: a small handful, clustered around imin
See further the
Guide to the Standards
Below are links to our standards, along with a brief summary and salient points related to social prescribing.
Modelling Opportunity Data
‘Opportunities’ are the fundamental data item in the OpenActive ecosystem: events, classes, or facility slots occurring at a given location, time, and price in which individuals may engage. Examples of opportunities include a yoga class held at a community centre, a time-slot during which a football pitch can be booked or, as a result of COVID-related rules, a time during which use of a gym is bookable for an individual.
One question that has arisen in relation to social prescribing and service directories is whether the level of granularity in the data is right for this use-case: are link workers going to consume detailed scheduling information, or is a general awareness of the existence and availability of a service sufficient?
There have also been continual tussles over the notion of a ‘difficulty rating’ for opportunities. Would this provide helpful guidance, or is the concept too subjective to be useful?
RPDE (Realtime Paged Data Exchange)
This is a technical specification governing the ordering and updating of opportunities in a data feed.
The feed model has provoked some consternation for some parties. Many developers are accustomed to a more full-featured API, and harvesting all the data feeds in the ecosystem can be an onerous operation. The question of data-granularity is relevant here as well: a gym open from 9.00 AM - 5:00 PM and available in hour-long slots will typically be represented in a data feed by 8 separate items. Is this efficient for the social-prescribing use-case?
One practical difficulty with the feed model is that first-time harvesters need to ingest the entirety of the feed to be sure all their data is synchronised. Some feeds, however, run to over a million items, imposing significant overheads (particularly if data-cleaning and normalising is also taking place).
This is still very much under development. However, it is the product of research and consultation with significant stakeholders (the Activity Alliance; ukactive) and an independent consultant, so the general approach has survived informed scrutiny.
Application to the social-prescribing sphere, however, might involve considerable expansion, and/or a reassessment of the Participant Condition Supported taxonomy.
Open Booking API Standard
This standard provides a common interface for making opportunities bookable.
This capacity is crucial to our commercial partners. How relevant this is for link workers and their clients is an open question.
A standard for describing recreational paths and trails, typically self-guided. Although plenty of research has been done on the mental-health benefits of outdoor activity of this type and walking is a low-bar activity for the sedentary (as emphasised in London Sport’s initiatives), it is not clear how relevant this standard is to social prescribers.
A taxonomy of physical activities. Mostly unproblematic, as taxonomies go.
Public Infrastructure Supporting Activity (PISA) Standard
This is an identified need rather than a standard in development. Throughout the various lockdowns there has been an increased emphasis on self-motivated activities in urban areas and on active travel. There then emerges a need to find publicly-available facilities supporting this - e.g. public tennis courts, exercise machines in parks, secure bike-parking facilities, and so forth.
Work has not proceeded further than identifying the need and a few sources for datasets. Relevance to social prescribing unclear.