AI-Fit-Scan: A Labeled Dataset of AI-Powered Fitness Applications on Google Play

Summary

AI-Fit-Scan is a manually annotated dataset of 161 fitness-related mobile applications scraped from Google Play, with 61 apps claiming AI capabilities receiving fine-grained human annotation for AI authenticity. We introduce a three-tier labeling framework (True AI / Quasi-AI / Fake AI) and achieve inter-annotator agreement of κ = 0.856 (Cohen's Kappa), indicating almost perfect agreement.

Key finding: Among 61 apps claiming to be "AI-powered fitness" applications, 36.1% (22) are fake AI — their core functionality does not use AI/ML, and only 57.4% (35) are confirmed true AI.

Why This Dataset Matters

1. AI-Washing quantification: First systematic measurement of AI claim inflation in the fitness app market
2. Reusable annotation framework: Three-tier (T/Q/F) labeling methodology with proven reliability
3. AI function taxonomy: Six-category classification of AI capabilities in fitness apps
4. Academic utility: Benchmark for studying AI claim verification, consumer deception, and health app regulation

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Dataset Structure

Files

File	Description	Rows
ai_fit_scan_full.csv	Complete 161-app dataset with L1 metadata labels	161
ai_fit_scan_annotated.csv	61 AI-claiming apps with L2 human annotation	61

Columns

Both files:

• name — App name
• category — Google Play category
• installs — Download count
• rating — User rating (0-5)
• reviews — Number of user reviews
• description — App description from Google Play
• l1_label — L1 automatic classification (AI_FITNESS / FITNESS_NO_AI / EXCLUDE_GENERIC / AI_NOT_FITNESS / UNRELATED)

Annotated file only:

• l2_label — L2 human annotation (T / Q / F)
• ai_function — AI capability category (see taxonomy below)
• annotator_a — Annotator A label
• annotator_b — Annotator B label
• l2_evidence — Evidence for L2 label (source URL or rationale)

Label Definitions

L1: Metadata-Based Classification

Label	Definition	Count
AI_FITNESS	Health/Fitness category + AI keywords in description	61
FITNESS_NO_AI	Health/Fitness category, no AI keywords	50
EXCLUDE_GENERIC	Generic AI tools (chatbots, translators, etc.)	25
AI_NOT_FITNESS	AI keywords but non-fitness category	13
UNRELATED	Neither fitness nor AI	12

L2: Human Annotation of AI Authenticity

Label	Definition	Count	%
T (True AI)	AI/ML is the core driver of the app's primary functionality. Removing AI would fundamentally change the product.	35	57.4%
Q (Quasi-AI)	App claims AI but evidence is inconclusive. May use rule-based algorithms or simple heuristics marketed as AI.	4	6.6%
F (Fake AI)	AI label is marketing only. Core functionality works without AI, or "AI" refers to basic automation/features unrelated to ML.	22	36.1%

Annotation criteria:

• T: Description explicitly describes AI/ML-driven personalization, adaptive planning, computer vision, or LLM-based coaching as a core feature
• Q: App name or description claims "AI" but lacks specific mechanism description; could be rule-based
• F: "Smart"/"AI" used as marketing buzzword; core is workout logging, pre-set routines, or timer functionality; AI only used for peripheral features (e.g., food photo recognition in a calorie counter)

AI Function Taxonomy

Category	Description	Count
Plan Generation	AI generates/adapts personalized workout plans based on user data	28
LLM Chat Coach	LLM-powered conversational coaching interface	8
Nutrition AI	AI-driven diet and nutrition recommendations	6
Pose/Motion Detection	Computer vision for real-time form checking and rep counting	5
Wearable Integration	AI adjusts plans based on biometric data (HRV, sleep, etc.)	4
Voice Coach	Real-time AI voice guidance during workouts	3

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Data Collection

Methodology

1. Search strategy: 10 keyword queries on Google Play ("AI fitness", "AI workout", "AI personal trainer", "AI gym", "AI exercise", "AI coach", "AI training plan", "AI running", "smart fitness", "AI health")
2. Deduplication: Removed duplicate entries by package name
3. Collection date: May 2026
4. Data extracted: App name, category, installs, rating, reviews, description, developer, price, version history

Annotation Process

1. L1 automatic classification: Keyword-based filtering using AI-related terms and category matching
2. L2 human annotation: Two annotators independently labeled all 61 AI_FITNESS apps
   • Annotator A: AI research assistant (automated labeling + L2 web search verification)
   • Annotator B: Domain expert (CSCS-certified, HCI researcher)
3. L2 evidence collection: For ambiguous cases (initially labeled Q), web search was conducted to verify AI technology claims against official websites, technical documentation, and third-party reviews
4. Disagreement resolution: 5 disagreements (all Q vs T/F) resolved by adopting Annotator B's judgment after reviewing app screenshots and user reviews

Inter-Annotator Agreement

Metric	Value
Raw agreement	56/61 = 91.8%
Cohen's Kappa	0.856
Interpretation	Almost Perfect (Landis & Koch, 1977)

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Key Findings

1. AI-Washing is Rampant

36.1% of apps claiming "AI" in the fitness space have no meaningful AI in their core functionality. The most common fake-AI patterns:

• "Smart" = basic algorithm/timer (e.g., Smart Workout Counter = interval timer)
• "AI-Powered" added to existing traditional apps (e.g., MyFitnessPal adding AI food recognition)
• "Personal Trainer" marketing without any ML (e.g., Home Workout - No Equipment)

2. Fake AI Apps Have Higher Ratings

Group	Mean Rating	Median Rating
True AI (T)	4.28	4.50
Fake AI (F)	4.41	4.54

High-rating apps sell outcome promises; low-rating apps sell AI technology.

3. Long Tail of Irrelevance

• 28 of 61 "AI fitness" apps have <50K downloads
• 15 apps have zero or missing ratings — likely inactive or abandoned
• The market has not consolidated; room for genuine AI entrants

4. AI Function Distribution

Plan generation dominates (28/35 true-AI apps), while pose detection (5) and voice coaching (3) remain underdeveloped despite high user value potential.

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Limitations

1. Single platform: Google Play only; Apple App Store data not included
2. Single time point: Data collected May 2026; AI claims may change with updates
3. Description-dependent: L2 annotation relies primarily on app descriptions and public documentation, not source code inspection
4. Binary framing: The T/Q/F taxonomy simplifies a spectrum; some "Quasi-AI" apps may use sophisticated rule engines that approach ML-level personalization
5. Keyword bias: Search results favor apps with "AI" in their name/description; genuine AI apps using different marketing language may be missed

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Ethical Considerations

• App names and descriptions are publicly available Google Play metadata
• Developer contact and revenue data are not included
• We do not accuse any app of fraud; "Fake AI" refers to absence of ML in core functionality, not malicious intent
• Apps labeled F may use AI in peripheral features (e.g., food recognition, recommendation systems)

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Citation

@dataset{guo2026aifitscan,
  title={AI-Fit-Scan: A Labeled Dataset of AI-Powered Fitness Applications on Google Play},
  author={Guo, Baixin (Max) and MaxCoze},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/MaxGuo/ai-fit-scan}
}

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Acknowledgments

• Cal Dietz Triphasic Training system — conceptual influence on training periodization analysis
• Google Play Store — public data source