Installing an outdoor fitness area is a significant public investment, generally between €20,000 and €120,000 depending on the scale of the project. Yet in the vast majority of cases, no means of measuring usage is planned at design stage. The result: three years after the opening, the operator cannot answer the simplest question an elected member or a budget officer will ask — how many people actually use this facility, and how often?
That gap is a problem on several counts: it makes it impossible to justify renewed funding, to steer future investment, or to match the maintenance programme to real use. This guide sets out the methods, tools and indicators for putting in place reliable usage monitoring, proportionate to a local authority’s means and genuinely usable for decision-making.
Key takeaways from this article:Â
- Measuring usage of a fitness area is essential for justifying investment, adapting maintenance and steering future development.
- Three indicators are enough for effective initial monitoring: volume of visits, distribution across the day, and seasonality.
- Measurement methods should be chosen according to objectives and budget: field observation, automatic sensors, surveys or mobility data analysis.
- A standardised collection protocol and a monitoring dashboard are essential for comparing data over time and supporting decisions.
- Usage of an area depends heavily on its location, the variety of equipment, its state of maintenance and the activities organised around the site.
Why measure usage of an outdoor fitness area?
Measuring usage is not an end in itself. It answers several concrete management needs that directly shape the decisions of the local authority or the operator.
Justifying the investment and making the case for renewal
A public sports facility with no usage data is a facility with no advocate when budgets are being decided. An area that demonstrably receives 150 visits a week wins a budget discussion every time against an area that is merely assumed to be used. Usage data is the main objective argument for requesting renewal or extension funding.
Matching the maintenance programme to real use
EN 16630 requires routine visual inspection at a frequency matched to how intensively the equipment is used. A heavily used area needs more frequent inspections than an under-used one. Without usage data, the maintenance programme is either inadequate (a safety risk) or oversized (a waste of budget).
Steering future investment
Usage data reveals which outdoor fitness machines are used most, when the peak hours fall, which user profiles dominate and where the gaps in the offer lie. That information is decisive for sizing a future extension project or prioritising the replacement of the most heavily used machines.
Meeting reporting and evaluation obligations
Authorities that have received co-funding (national grant schemes, EU LEADER funds, regional funds) are often required to produce a usage report at the end of a set period. Without usage data, writing that report credibly is impossible.
Worth remembering
Measuring usage of an outdoor fitness area is not a smart city exercise reserved for large cities. Simple, low-cost methods are within reach of any authority, including rural ones.
Measurement should be designed in at project stage, not bolted on six months after the opening.
The key indicators to measure
Before choosing a counting method, you need to define the relevant indicators. Not all indicators are equally useful depending on your objective.
| Indicator | What it measures | Main use | Collection difficulty |
| Weekly visit volume | Number of entries over a rolling 7 days | Budget justification, comparing sites | Low to medium |
| Distribution across the day | Peak and off-peak hours | Adapting signage and maintenance | Medium |
| Distribution by day of the week | Strong days vs weak days | Scheduling activities and staff | Low |
| Average visit duration | Time spent on the area per user | Quality of use, sizing | High |
| Socio-demographic profile | Age, gender, self-reported activity | Match between equipment and public | High (survey) |
| Usage rate per machine | How often each machine is used | Prioritising maintenance and renewal | High (observation) |
| Seasonality | Monthly or quarterly variation | Maintenance budget, seasonal communication | Low to medium |
A note on priorities: for a first measurement system, focus on three fundamental indicators — weekly volume, distribution across the day and seasonality. They deliver 80% of the decision-making value for 20% of the complexity of a full system.
Counting methods: from the simplest to the most sophisticated
Several approaches exist for measuring usage of an outdoor fitness area. The choice depends on the budget available, the level of detail required and the authority’s in-house skills.
Method 1: direct observation by a staff member
The simplest and cheapest method. A staff member visits the area at set times (three times a day, on weekdays and at the weekend, for example) and counts the users present. The data is recorded on a field sheet.
•    Cost: nil apart from staff time (15 to 30 min per visit)
•    Accuracy: low; it captures a snapshot, not total flow
•    Recommended use: rural authorities, lightly used areas, initial diagnosis before investing in an automated system
•    Main limitation: highly sensitive to the times chosen; it cannot capture peaks that fall outside those windows
Method 2: infrared or beam people counters
Automatic counting sensors, installed at the entrance to the area or along the access paths, detect and count every passage. They transmit data in real time or in batches to a visualisation platform.
•    Cost: €300 to €1,500 per sensor depending on the technology (passive infrared, Doppler radar, lidar), excluding installation and platform subscription
•    Accuracy: high for visit volume, limited for user profiles
•    Recommended use: urban areas with medium to high usage, authorities with an IT department
•    Main limitation: it counts passages, not users; the same person entering and leaving twice in one visit is counted twice
Method 3: presence and usage sensors on the equipment
Dedicated sensors fitted to the machines detect the vibrations or load variations that signal active use. They measure not only overall usage but also the usage rate of each machine.
•    Cost: €500 to €2,000 per machine fitted, depending on the technology and manufacturer
•    Accuracy: very high for per-machine usage
•    Recommended use: multi-machine parks where knowing usage per machine is strategic for maintenance and renewal
•    Main limitation: expensive to fit across a whole estate, and under some contracts the data remains the property of the supplier’s platform
Method 4: field surveys and questionnaires
Staff or volunteers interview users directly following a structured protocol, at regular intervals (for example a two-week campaign each quarter). Surveys collect qualitative and socio-demographic data that automatic counting cannot capture.
•    Cost: staff time or an outsourced survey (between €2,000 and €8,000 for a full external survey)
•    Accuracy: depends on the response rate and the representativeness of the sample
•    Recommended use: in-depth post-installation diagnosis, assessing an extension project, reporting to funders
•    Main limitation: a one-off snapshot rather than continuous data, with self-reporting bias on how often people use the area
Method 5: mobility data from mobile network big data
Specialist operators offer mobility flow analyses based on mobile phone signals. These analyses estimate the number of visitors to a space, where they come from and how long they stay, with no physical equipment on the area at all.
•    Cost: €3,000 to €15,000 for a one-off study depending on the provider and the analysis period
•    Accuracy: statistically solid on volumes and origins, limited on individual profiles
•    Recommended use: overall assessment of a facility as part of a development review or a public policy evaluation
•    Main limitation: costly for recurring use, and the aggregated data does not allow detailed per-machine analysis
| Method | Initial cost | Recurring cost | Volume accuracy | Profile accuracy | Recommended for |
| Direct staff observation | Nil | Staff time | Low | Low | Small areas, zero budget |
| Infrared/radar counter | €300 – €1,500 | Subscription €200–600/year | High | None | Urban areas, continuous monitoring |
| Sensors on equipment | €500 – €2,000/machine | Platform included | Very high | None | Multi-machine parks |
| Field survey | €2,000 – €8,000 | Per campaign | Medium | High | Reviews, qualitative studies |
| Mobile network big data | €3,000 – €15,000 | Per study | High | Medium | Public policy evaluations |
Setting up a measurement system: the practical steps
Setting up usage monitoring follows a four-phase project logic that works whatever the size of the authority.
Phase 1: define the questions the monitoring must answer
A measurement system with no guiding question produces data with no decision-making value. Before choosing a technology, state explicitly the questions you want answered: is the area used enough to justify extending it? Which machines are used most? Does the user profile match the target public defined at design stage?
Phase 2: choose the method that fits the budget and the questions
Based on the questions set out in phase 1, select the most relevant method or combination of methods. The general rule: start with the simplest method that answers your questions. Direct observation by staff covers most of what a small authority needs. Automatic counters become necessary as soon as monitoring has to be continuous and independent of staff time.
Phase 3: define the collection protocol
Whichever method you choose, a standardised collection protocol is essential to make the data comparable over time. The protocol sets out: the times and days of measurement, the length of the counting periods, the exact definition of what is being counted (does one passage equal one distinct user, or every passage including going back and forth?), and how the data is recorded.
Phase 4: build the monitoring dashboard
The data you collect only has value if it is centralised, visualised and commented on in a monitoring document shared with the stakeholders (the elected member responsible for sport, the technical services department, funders). A simple dashboard, even in a spreadsheet, is enough in most cases. It should show the key indicators as they evolve month by month or quarter by quarter.
Model indicators for a monthly dashboard
Total visits this month / total visits last month (% change)
Visits by time slot (morning 6am–12pm / afternoon 12pm–6pm / evening 6pm–10pm)
Strong days vs weak days (ranking of the 7 days of the week)
Comparison with the same month last year (seasonality)
Faults reported during the period (damage, machines out of service)
Usage benchmarks: what to expect
Usage data for outdoor fitness areas is rarely published. The figures below are orders of magnitude drawn from feedback from local authorities and managers of public sports spaces. Use them as relative reference points, not absolute standards.
| Type of area and context | Estimated weekly usage | Estimated annual usage |
| Small neighbourhood park, town < 5,000 inhabitants | 30 – 80 visits/week | 1,500 – 4,000 visits/year |
| Urban park, town 5,000 – 30,000 inhabitants | 80 – 250 visits/week | 4,000 – 13,000 visits/year |
| Central urban park, city > 30,000 inhabitants | 200 – 600 visits/week | 10,000 – 30,000 visits/year |
| Area in a tourist zone (coast, mountains) | Variable: summer peak 3 to 8× off-season | Depends on local seasonality |
| Care home / senior residence (residents only) | 10 – 40 visits/week | 500 – 2,000 visits/year |
A word on interpretation: a lightly used area is not necessarily a failure. A senior fitness area regularly used by 25 residents a week in a care home can deliver greater public health impact than an urban area with 300 very short visits. Usage must always be read against the project’s original objectives.
Factors that influence usage: the levers you can pull
Measuring usage only makes sense if it lets you identify the factors driving it and act on them. Several variables are documented as having a significant impact on how attractive an outdoor fitness area is.
Location factors
•    Visibility from a pedestrian or cycle route: areas visible from existing paths consistently generate more spontaneous visits than enclosed or set-back areas
•    Proximity to a footfall generator (school, market, public transport, sports pitch): increases usage by tapping into existing flows
•    Accessibility and the quality of the route from the road: hard-to-reach areas lose a significant share of their potential public
Equipment factors
•    Variety of machines: an area offering equipment for several muscle groups and several levels attracts a wider public than a single-purpose one
•    State of maintenance: a machine out of service with no notice reduces user confidence and can discourage people from coming back
•    Presence of cardiovascular equipment: a rower, cross trainer or outdoor elliptical bike is consistently among the most used machines and the most attractive to new users
Environmental and programming factors
•    Shade in summer: areas with no natural or artificial shade see usage drop sharply between June and August in sunny regions
•    Activities and sports outreach: taster or demonstration sessions organised at the opening and repeated periodically raise baseline usage lastingly
•    Local communication: directional signage from the road, communication through municipal channels (website, newsletter, social media) — facilities that are not signposted stay under-used
Frequently asked questions about measuring usage
Is it legal to install counting sensors on a public area without telling users?
Counting sensors that collect no identifying personal data (infrared people counters, presence sensors on equipment) are not subject to GDPR obligations and require no specific notice to users. Any technology that collects data allowing indirect identification (facial recognition, Wi-Fi or Bluetooth tracking of mobile devices), by contrast, is subject to GDPR: it requires clear information, a record of processing activities and potentially a data protection impact assessment (DPIA). National data protection authorities have published specific recommendations on counting people in public spaces.
What is the minimum measurement period for representative data?
To cover seasonal variation, a minimum of 12 consecutive months of data is needed to establish a representative baseline. If that is not possible, an approach based on two-week campaigns each quarter (8 weeks of measurement over 12 months) gives an acceptable approximation of seasonality, provided the weeks chosen avoid unusual periods (atypical school holidays, local events).
How do you compare usage of two areas with different characteristics?
Comparing raw volumes directly between two areas of different sizes and locations tells you little. The most useful comparison indicator is visits per machine per week, which normalises volume by the number of machines available. An area with 6 machines taking 120 visits a week (20 visits per machine per week) is performing better than an area with 12 machines taking 180 visits (15 visits per machine per week).
Can usage of an outdoor fitness area decline over time?
Yes, and it often does. The novelty effect produces a usage peak in the first 6 to 18 months after opening, followed by stabilisation at a lower level. A gradual decline beyond that plateau usually signals a problem with attractiveness — equipment ill-suited to the real public, deteriorating maintenance, no activities — rather than natural saturation of the local population. Analysing the usage curve over time is a diagnostic tool for the quality of the area’s management.
Can usage be estimated after the fact with no counting system?
Indirect methods allow a rough estimate: analysing mobile phone data through a specialist provider, going through the reports and complaints received by the technical department (which correlate with usage), or a recall survey among local residents. These methods produce orders of magnitude useful for a retrospective review, but they cannot replace methodical counting over time.
