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ASF Standards · Fitness & Wellness · Standard 11

Standard 11 — Digital Care and Artificial Intelligence Systems for Care

5 criteria · 2 core · 3 standard-level · Version 1.0 · Aligned to ISQua EEA Principle 7, 6th Edition

Criteria in this standard

11.1 — Digital Systems Are Genuinely Evaluated Before Adoption
11.2 — Digital Access Never Disadvantages a Member Who Cannot Use It
11.3 — Biometric and Health-Tracking Data Is Genuinely Protected and Appropriately Used
11.4 — AI-Driven Recommendations Are Genuinely Reviewed for Appropriateness
11.5 — Accountability for AI-Assisted Recommendations Is Explicitly Defined
11.6 — Members Are Genuinely Informed When a Recommendation Involves AI
11.1

Digital Systems Are Genuinely Evaluated Before Adoption

Standard

Before adopting any new digital system — membership management, class booking, biometric tracking — the facility genuinely evaluates its cost, benefit, and compatibility with existing systems, not adopted on vendor assurance with no real internal evaluation.

In plain terms: Before the facility adopts a new app or tracking tool, someone has actually checked it’s worth it and will genuinely work.

Facility category Crisis Transition Small Standard
Applicability Adapted Full Full Full

Why this matters

Fitness facilities are frequently offered membership apps, booking platforms, and wearable-integration tools, each promising genuine convenience — but a real evaluation is what confirms a tool actually fits this facility’s member base and existing systems rather than creating duplicate work or member confusion.

What good looks like

  • A genuine cost/benefit evaluation happens before adoption.
  • Compatibility is genuinely checked.
  • Unintended consequences are genuinely considered.

Common failure modes

  • A system is adopted on vendor demonstration alone.

Worked example

In practice
A gym considering a new booking app.
BeforeA vendor’s app looked appealing but no check was made of whether it would genuinely integrate with the existing membership database.
ActionA brief evaluation confirmed genuine integration before full rollout.
AfterThe Monitor reviewed the evaluation notes. Verified.

If you are starting from zero — do this first

  1. Build a simple evaluation checklist before adoption.
The most common mistake: Adopting an app because it looks polished, without checking actual integration with existing systems.

Self-assessment questions

1. Is a genuine cost/benefit evaluation conducted? — A real, documented evaluation.
Evidence: Evaluation checklist
2. Is compatibility genuinely checked? — Real, verified compatibility.
Evidence: Compatibility check record
3. Are unintended consequences genuinely considered? — Real, proactive consideration.
Evidence: Pre-launch risk review

Common reasons for a PARTIAL answer

  • Evaluation happens for major systems but is skipped for smaller add-ons.

Implementation plan

When What
Week 1 Build an evaluation checklist.

How the Monitor verifies this

Method What Detail
DOCUMENT Evaluation review Reviews the evaluation for a recently adopted system.

Supervisor tips

  • Ask about the most recently adopted digital tool.

Evidence base

World Health Organization. Global Strategy on Digital Health 2020-2025. Geneva: WHO; 2021.

ASF training courses on GMJ Academy →

Foundation courses A-00 to A-03 are live. Criterion-specific modules are being developed and will link here when published.

11.2

Digital Access Never Disadvantages a Member Who Cannot Use It

Core

A genuine process ensures that members who cannot use digital devices, lack internet access, or prefer not to use an app are never disadvantaged in booking classes, accessing services, or managing their membership — not digital convenience purchased at the cost of real access for members least able to adapt.

In plain terms: A member who can’t or doesn’t want to use the facility’s app still gets exactly the same real access to classes and services.

Facility category Crisis Transition Small Standard
Applicability Adapted Full Full Full

Why this matters

This is marked Core because fitness and wellness facilities genuinely serve a wide age and ability range, and an app-only booking system quietly excludes real members — often older adults, who make up a genuine and valuable part of many facilities’ membership — unless a real, equally functional alternative is maintained.

What good looks like

  • A genuine, equally functional alternative exists.
  • New services are genuinely pre-tested with affected members.
  • Real evidence shows equivalent service.

Common failure modes

  • Front-desk booking becomes genuinely slower or deprioritised once an app launches.

Worked example

In practice
A gym that moved class booking to an app.
BeforeAfter the app launched, front-desk staff began directing members to “just use the app,” with in-person booking becoming genuinely slower and less prioritized.
ActionFront-desk booking was reaffirmed as an equally supported option, with staff specifically trained not to discourage its use.
AfterThe Monitor tested in-person booking directly and found it genuinely as fast and welcoming as the app. Verified.

If you are starting from zero — do this first

  1. Test your own non-digital booking channel directly.
The most common mistake: Staff informally steering members toward the app, quietly degrading the in-person alternative.

Self-assessment questions

1. Is there a genuine, equally functional alternative? — A real, equivalent alternative.
Evidence: Documented alternative channel
2. Was a new digital service genuinely pre-tested with affected members? — Real, prior testing.
Evidence: Pre-launch testing record
3. Is there real evidence the alternative delivers equivalent service? — A real, demonstrated instance.
Evidence: Direct test

Common reasons for a PARTIAL answer

  • An alternative exists but staff informally discourage its use.

Implementation plan

When What
Week 1 Test the non-digital channel directly and brief staff.

How the Monitor verifies this

Method What Detail
ASK Direct test Tests the non-digital alternative directly.

Supervisor tips

  • Book a class in person and see how staff actually respond.

Evidence base

World Health Organization. Ethics and Governance of Artificial Intelligence for Health. Geneva: WHO; 2021.

ASF training courses on GMJ Academy →

Foundation courses A-00 to A-03 are live. Criterion-specific modules are being developed and will link here when published.

11.3

Biometric and Health-Tracking Data Is Genuinely Protected and Appropriately Used

Core

Any biometric or health-tracking data collected through digital fitness tools — heart rate, body composition, activity tracking — is genuinely protected with real data security measures and used only for its stated purpose, with the member’s genuine, informed consent, not collected broadly with no real limit on its use.

In plain terms: A member’s heart rate, body composition, and activity data is actually kept secure, and only used for what they genuinely agreed to.

Facility category Crisis Transition Small Standard
Applicability Adapted Full Full Full

Why this matters

This is marked Core because biometric and health-tracking data is genuinely sensitive personal information, and fitness facilities increasingly collect it through wearables, body composition scanners, and app integrations — often through third-party platforms whose own data practices the facility may not have genuinely scrutinized. A member sharing this data deserves real, specific assurance about how it’s protected and used, not a generic platform privacy policy nobody has actually reviewed.

What good looks like

  • Genuine, specific security measures protect member data.
  • Data is used only for its genuinely stated purpose, with real consent.
  • Members can genuinely access or request deletion of their data.

Common failure modes

  • A third-party platform’s data practices were never actually reviewed.

Worked example

In practice
A gym offering a body composition scanner through a third-party vendor.
BeforeThe scanner’s data was uploaded to the vendor’s cloud platform, with nobody at the gym having actually reviewed the vendor’s data security or retention practices.
ActionThe vendor’s data practices were reviewed and documented, and a clear member consent form was introduced specifically covering this data use.
AfterThe Monitor reviewed the vendor assessment and the consent form. Verified.

If you are starting from zero — do this first

  1. Review the data practices of any third-party biometric tool used.
  2. Introduce a clear, specific member consent process.
The most common mistake: Assuming a vendor’s platform handles data security adequately without ever actually reviewing it.

Self-assessment questions

1. Is member biometric data genuinely protected with specific security measures? — Real, documented protection.
Evidence: Data security review
2. Is data used only for its genuinely stated purpose, with real consent? — A real, specific limit on use.
Evidence: Member consent form
3. Can a member genuinely access, correct, or request deletion of their data? — A real, functioning process.
Evidence: Data-rights process documentation

Common reasons for a PARTIAL answer

  • A consent form exists but the underlying vendor’s data practices were never checked.

Implementation plan

When What
Week 1-2 Review third-party vendor data practices and introduce consent process.

How the Monitor verifies this

Method What Detail
DOCUMENT Data practice review Reviews the vendor assessment and consent documentation.

Supervisor tips

  • Ask whether anyone has actually read the third-party vendor’s data policy.

Evidence base

European Union. General Data Protection Regulation (GDPR), Article 9: Special Categories of Data. Brussels: EU; 2016.

ASF training courses on GMJ Academy →

Foundation courses A-00 to A-03 are live. Criterion-specific modules are being developed and will link here when published.

11.4

AI-Driven Recommendations Are Genuinely Reviewed for Appropriateness

Standard

Any AI-driven fitness or nutrition recommendation tool is genuinely reviewed for appropriateness and safety before being offered to members, with genuine awareness that automated recommendations can be inappropriate or unsafe for a member’s actual health status — not assumed inherently safe because it’s automated.

In plain terms: Before an AI tool suggests workouts or nutrition plans to members, someone has actually checked it won’t recommend something unsafe for a real person’s situation.

Facility category Crisis Transition Small Standard
Applicability Adapted Full Full Full

Why this matters

AI-driven fitness and nutrition tools genuinely cannot account for every member’s real medical history, injuries, or physical limitations, and an automated recommendation that looks reasonable in general can be genuinely inappropriate or unsafe for a specific individual. A real review process before offering such a tool to members is what catches this gap.

What good looks like

  • A tool is genuinely reviewed before being offered to members.
  • Members have a genuine, clear way to flag an inappropriate recommendation.
  • A documented instance shows a flagged issue leading to genuine review.

Common failure modes

  • A tool is offered with no review, assumed safe because it’s automated.

Worked example

In practice
A gym offering an AI-generated workout plan tool.
BeforeThe tool was activated with no review of whether it accounted for common injury limitations or flagged when a member should consult a trainer first.
ActionA qualified trainer reviewed sample outputs and confirmed the tool included an appropriate disclaimer and injury-flagging prompt, with a clear member feedback channel added.
AfterThe Monitor reviewed the trainer’s assessment and the feedback channel. Verified.

If you are starting from zero — do this first

  1. Have a qualified staff member review sample tool outputs before launch.
The most common mistake: Assuming an AI recommendation tool is inherently safe because it’s automated and widely used elsewhere.

Self-assessment questions

1. Is the tool genuinely reviewed before being offered to members? — A real, documented review.
Evidence: Review record
2. Do members have a genuine way to flag an inappropriate recommendation? — A real, accessible channel.
Evidence: Feedback channel
3. Is there a documented instance of a flagged recommendation leading to genuine review? — A real, concrete example.
Evidence: Issue response record

Common reasons for a PARTIAL answer

  • A review happened at launch but hasn’t been repeated as the tool updates.

Implementation plan

When What
Week 1-2 Review tool outputs and introduce a member feedback channel.

How the Monitor verifies this

Method What Detail
DOCUMENT Review record check Reviews the documented appropriateness review.

Supervisor tips

  • Ask a qualified trainer whether they’ve personally reviewed the tool’s outputs.

Evidence base

American College of Sports Medicine. ACSM’s Guidelines for Exercise Testing and Prescription. 11th ed. Philadelphia: Wolters Kluwer; 2021.

ASF training courses on GMJ Academy →

Foundation courses A-00 to A-03 are live. Criterion-specific modules are being developed and will link here when published.

11.5

Accountability for AI-Assisted Recommendations Is Explicitly Defined

Standard

The facility has genuinely considered and documented who is responsible when an AI-driven fitness or nutrition recommendation causes harm or proves inappropriate — not leaving this as an unexamined question until it actually matters.

In plain terms: Everyone knows, in advance, who’s actually responsible if an AI-generated fitness plan turns out to be wrong for someone.

Facility category Crisis Transition Small Standard
Applicability Adapted Full Full Full

Why this matters

Without clear accountability, a member injured following an AI-generated recommendation faces a genuinely confusing situation about who is actually responsible — the facility, the software vendor, or themselves for following an automated suggestion without professional review.

What good looks like

  • Accountability is explicitly documented.
  • Staff genuinely understand their own role.
  • A defined process exists for reviewing an incident.

Common failure modes

  • Accountability has never been explicitly addressed.

Worked example

In practice
A gym offering an AI nutrition recommendation tool.
BeforeStaff were unclear whether responsibility for an inappropriate recommendation sat with the facility, the vendor, or the member for following it unreviewed.
ActionA clear policy was documented: recommendations are suggestions only, reviewed by staff before being presented as personalized advice, with staff accountable for that review step.
AfterThe Monitor interviewed a staff member who could clearly describe this accountability. Verified.

If you are starting from zero — do this first

  1. Explicitly document accountability for each AI-driven tool offered to members.
The most common mistake: Leaving accountability genuinely ambiguous until an actual incident forces clarity.

Self-assessment questions

1. Is accountability explicitly documented? — A real, clear answer.
Evidence: Documented accountability policy
2. Do staff genuinely understand their own role in reviewing recommendations? — Real, demonstrated understanding.
Evidence: Staff interview
3. Is there a defined process for reviewing accountability if harm occurs? — A real, usable process.
Evidence: Incident review protocol

Common reasons for a PARTIAL answer

  • A policy exists but hasn’t been communicated to frontline staff.

Implementation plan

When What
Week 1 Document accountability for each AI tool offered.

How the Monitor verifies this

Method What Detail
ASK Staff interview Asks a staff member to describe their accountability.

Supervisor tips

  • Ask a staff member directly: if a recommendation causes injury, who is responsible?

Evidence base

European Commission. Ethics Guidelines for Trustworthy Artificial Intelligence. Brussels: European Commission; 2019.

ASF training courses on GMJ Academy →

Foundation courses A-00 to A-03 are live. Criterion-specific modules are being developed and will link here when published.

11.6

Members Are Genuinely Informed When a Recommendation Involves AI

Standard

A member whose fitness recommendation involves AI-driven input is genuinely informed of this — not left to assume every recommendation came from a human instructor alone, with disclosure treated as optional rather than a genuine, standard part of informed membership.

In plain terms: If an AI tool actually helped shape a workout or wellness recommendation, the member genuinely knows this — not left to assume a human instructor made it alone.

Facility category Crisis Transition Small Standard
Applicability Adapted Full Full Full

Why this matters

A member’s genuine right to understand their own fitness and wellness recommendations includes knowing, in plain terms, when an AI-driven tool actually shaped a suggestion rather than a human instructor’s judgement alone. This matters especially where an app-generated recommendation reaches a member with no genuine human review — the member deserves to know the real source.

What good looks like

  • Members are genuinely informed when AI contributed to a recommendation.
  • Disclosure is genuinely understandable, in plain language.
  • A member can genuinely confirm they were told.

Common failure modes

  • AI involvement is buried in app terms and conditions, technically present but never genuinely read or understood.

Worked example

In practice
A facility using an AI-generated workout plan feature in its member app.
BeforeAI’s role was mentioned only in the app’s general terms and conditions, which members accepted without genuine awareness that their specific plan had actually been AI-generated.
ActionA brief label was added directly on the plan itself: “This plan was generated with AI assistance and reviewed by a certified instructor.”
AfterThe Monitor interviewed a member who could genuinely confirm they understood AI had been involved. Verified.

If you are starting from zero — do this first

  1. Add a brief, plain-language label directly on any AI-generated recommendation a member receives.
The most common mistake: Treating a line buried in app terms and conditions as genuine disclosure, when the member never actually registers it.

Self-assessment questions

1. Is a member genuinely informed when AI contributed to a recommendation? — Real, standard disclosure.
Evidence: Disclosure labelling
2. Is this disclosure genuinely understandable, not buried in app terms? — A real, plain-language explanation.
Evidence: Labelling sample
3. Can a member asked directly confirm they were genuinely told? — A real, concrete confirmation.
Evidence: Member interview

Common reasons for a PARTIAL answer

  • Disclosure exists in app terms but members genuinely cannot recall or explain it when asked directly.

Implementation plan

When What
Week 1-2 Add a plain-language AI-disclosure label to any AI-generated recommendation.

How the Monitor verifies this

Method What Detail
ASK Member interview Asks a member whether they were genuinely told AI was involved in their recommendation.

Supervisor tips

  • Ask a member directly rather than relying on the app terms’ existence alone.

Evidence base

World Health Organization. Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models. Geneva: WHO; 2024.

ASF training courses on GMJ Academy →

Foundation courses A-00 to A-03 are live. Criterion-specific modules are being developed and will link here when published.

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