Sports Apps AI Visibility Report: Who Wins When Runners Ask AI for Recommendations [2026 Data]

MARKET REPORT: Sports Apps

March 2026

Photo credits: Katarzyna Milewska

Executive Summary

The running and fitness app market is in the middle of a land grab. With the global fitness app market valued at $13.9 billion in 2026 and the running app segment alone growing at 17.2% CAGR, the question of which apps get recommended when users ask AI "What's the best running app?" carries real revenue consequences. Strava's back-to-back acquisitions of Runna and The Breakaway in 2025 signaled the scale of the prize - and the intensity of the consolidation.

This report analyzes how major sports and running apps perform in AI-generated recommendations. Using GetMentioned data across thousands of prompts related to running plans, race preparation, and sports app recommendations, we found that the AI visibility landscape looks nothing like what app store rankings or download numbers would suggest.

Key Findings:

• Runna leads overall AI visibility at 50.9% - appearing in more than half of all AI responses about sports and running apps, despite being a fraction of the size of competitors like Nike Run Club and Strava
• Strava holds second position at 43.9% but trails Runna by 7 percentage points - a notable gap given Strava's 180 million registered users vs. Runna's 2 million
• Nike Run Club, with 100+ million users, ranks third at just 32.5% - AI visibility does not correlate with user base size
• TrainingPeaks (14.1%) and Garmin Coach (10.1%) have minimal AI presence despite strong positions among serious endurance athletes
• Runna dominates 4 out of 5 topic categories - including all distance-specific training plan queries (5K, 10K, half marathon, marathon)
• runna.com is the single most-cited source (32.6%), outranking even Reddit (25.8%) - a rare case of a brand's own domain leading AI citations

Industry AI Visibility Rankings

GetMentioned analyzed AI responses across thousands of prompts related to sports apps, running training, and fitness app recommendations. The results reveal a clear hierarchy.

Overall Leaderboard

Key Insights

Runna's AI dominance defies its market size. Runna has approximately 2 million monthly users. Strava has 180 million registered users. Nike Run Club has over 100 million. Yet Runna leads AI visibility by 7 percentage points over Strava and 18.4 points over Nike Run Club. This is one of the most striking examples in our data of AI visibility diverging from traditional market metrics.

The top 3 capture the vast majority of AI attention. Runna (50.9%), Strava (43.9%), and Nike Run Club (32.5%) account for the overwhelming share of AI mentions. TrainingPeaks and Garmin Coach - both strong products with loyal user bases - are essentially invisible in AI recommendations, at 14.1% and 10.1% respectively.

There's a 40.8 percentage point gap between #1 and #5. Runna appears in AI recommendations 5x more often than Garmin Coach. For a runner asking ChatGPT "What running app should I use?", Garmin Coach barely exists in the AI's consideration set.

The Visibility-vs-Users Paradox

This report reveals what may be the starkest visibility-vs-user-base paradox in any category we've analyzed:

Runna achieves 100x more AI visibility per user than Strava. This isn't random - it reflects specific content and positioning strategies that align with how AI models select and cite sources. More on this in the Source Analysis section below.

Topic Analysis Overview

GetMentioned tracks AI responses across specific topic categories. In the sports apps category, we analyzed five key topics that represent the most common queries runners bring to AI assistants.

Topic Leaders at a Glance

Key Insights

Runna owns the training plan conversation. Across all four distance-specific queries - 5K, 10K, half marathon, and marathon - Runna is the AI-recommended leader. This is a category sweep. When a runner asks AI for help training for any race distance, Runna is the app AI mentions first.

Strava wins the community angle. The one topic where Strava leads - Race Discovery - is inherently social. Finding races, connecting with local running communities, and discovering events is where Strava's 180-million-user network gives it a natural advantage. AI models correctly associate Strava with community features rather than structured training plans.

Nike Run Club is absent from topic leadership. Despite being the third-most-visible app overall, Nike Run Club doesn't lead any of the five topic categories. Its visibility is broad but shallow - it appears in AI responses across topics but isn't the #1 recommendation for any specific use case.

The "training plan" query is the highest-value moment. When a runner asks AI "How should I train for a marathon?", they're making a commitment decision - a 16-20 week training cycle that typically involves a paid subscription. Owning this moment is arguably worth more than leading any other topic. Runna's dominance here explains why Strava paid to acquire it.

Topic Deep Dive: Training Plans (5K through Marathon)

Running training plans are the core use case driving the sports app market. Our data reveals consistent AI behavior across all distances.

Why Runna Dominates Training Plan Queries

The data points to several factors:

1. Content specificity. Runna's entire product and content strategy is built around structured training plans. Every page on runna.com speaks directly to the query "How do I train for [distance]?" AI models favor content that precisely matches user intent.

2. Front-loaded information. GetMentioned research across nearly 1 million prompts shows that 44.2% of AI citations come from the first 30% of a page's content. Runna's pages lead with the direct answer - "Here's your 10K plan" - rather than burying it under brand messaging.

3. Data-rich content. Content with original data and statistics has 30-40% higher visibility in AI responses. Runna publishes specific data on completion rates, average pacing improvements, and DNF reduction - their clinical trial showed 38% lower marathon DNF rates with Runna plans. AI models weight this kind of evidence heavily.

4. Cross-source verification. AI models look for confirmation across multiple independent sources. Runna's training plans are discussed on Reddit (where 25.8% of citations originate), reviewed in Runner's World (15.5% of citations), and featured on Tom's Guide (10.0%). This multi-source presence creates the cross-verification signal that AI needs to recommend with confidence.

The Marathon Query: Highest Stakes, Clearest Winner

Marathon training is the highest-commitment decision in recreational running - 16 to 20 weeks, 4-6 days per week, often involving a paid subscription. When AI recommends a marathon training app, it's influencing months of engagement and likely a paid conversion.

Runna leads this category as Topic Leader. This aligns with Runna's core positioning as a "personalized marathon coach" and reflects the AI's understanding that marathon training requires the kind of adaptive, AI-driven plans that Runna specializes in - versus Strava's social tracking or Nike Run Club's more general fitness approach.

Topic Deep Dive: Race Discovery

Race discovery is the one category where Strava breaks Runna's dominance.

Why Strava Leads Here

Community scale. With 180 million registered users and 1 million clubs (a 4x increase in 2025 alone), Strava is where runners connect. Running clubs on Strava grew 3.5x in 2025. AI models correctly associate Strava with community, events, and social running.

Activity data. Over 51 million activities are uploaded to Strava per week - more than 2.2 billion per year. This activity data feeds into route discovery, popular race routes, and event recommendations. AI models recognize Strava as the definitive source for "where people are actually running."

Event integration. Strava's partnerships with race organizers and its segment/challenge features create a natural connection between the platform and race discovery. When a user asks AI "How do I find a 10K near me?", Strava's event ecosystem is the most logical answer.

The Strategic Implication

Strava leads in race discovery (community) while Runna leads in training plans (coaching). Post-acquisition, this creates a powerful one-two punch: discover your race on Strava, train for it with Runna. AI models already seem to understand this division of roles.

Source Analysis: Where AI Gets Its Sports App Recommendations

Understanding which sources AI models cite when recommending sports apps reveals where influence actually lives - and where brands should focus their content strategies.

Top Sources Cited by AI

Key Insights

Runna.com as the #1 cited source is exceptional. Across all GetMentioned market reports, it is extremely rare for a brand's own domain to be the single most-cited source in AI recommendations. In our Streaming Services report, the top sources were tech publications (PCMag at 26.9%, Tom's Guide at 21.1%) - not Netflix.com or any streaming brand's own site.

Runna achieving 32.6% citation share with its own domain suggests that runna.com's content is structured in a way that AI models treat it as an authoritative, information-rich resource - not just a product page. This is a textbook example of effective GEO (Generative Engine Optimization).

Reddit holds strong influence at 25.8%. This mirrors the pattern we see across categories. In our Streaming Services report, Reddit accounted for 22.8% of citations. For sports apps, Reddit's influence is even higher (25.8%), reflecting the running community's active presence on subreddits like r/running, r/marathontraining, and r/Strava. AI models heavily weight authentic user discussions when making app recommendations.

The App Store as a citation source (19.1%) is unique to this category. apps.apple.com doesn't appear as a top source in most other categories we've analyzed. For sports apps, AI models cite App Store listings - including ratings, reviews, and app descriptions - as supporting evidence. This means your App Store listing isn't just for App Store search optimization; it directly influences AI recommendations.

Hal Higdon's outsized influence (17.5%). halhigdon.com - the personal site of legendary running coach Hal Higdon - ranks #4 with 17.5% citation share. Hal Higdon's training plans have been the default recommendation for new runners for decades. AI models carry this legacy authority forward. This is a powerful example of how individual thought leadership translates to AI citation authority.

Runner's World (15.5%) validates the niche publication pattern. Similar to how PCMag and Tom's Guide dominate streaming recommendations, Runner's World - the authoritative running publication - carries significant AI influence. Niche, topically-focused publications consistently outperform general media in AI citations.

Tom's Guide appears in both categories. Tom's Guide ranks #6 at 10.0% for sports apps and #2 at 21.1% for streaming services. Tech review publications have cross-category AI influence, suggesting they're treated as reliable recommendation sources regardless of vertical.

What's Driving Runna's AI Dominance? A GEO Case Study

Runna's #1 AI visibility position - despite having ~90x fewer users than Strava and ~50x fewer than Nike Run Club - deserves deeper analysis. It represents one of the clearest case studies of effective AI visibility strategy (GEO) in our dataset.

Factor 1: Topic-specific content depth

GetMentioned data shows AI models cite topic-specific sources 92% of the time (ChatGPT and Perplexity) to 99% of the time (Gemini). Runna's entire domain is topic-specific to running training. Every page on runna.com is directly relevant to queries about running plans, marathon training, and race preparation. Compare this to Strava, which covers 50+ activity types, or Nike Run Club, which sits within Nike's broader brand ecosystem.

Factor 2: The brand-as-source strategy

Most brands try to get mentioned BY other sources. Runna appears to have optimized its own site to BE a source. Their training plan pages, race prep guides, and running data resources are structured as informational content - not just product pages. The result: AI models treat runna.com as both a recommendation AND a citation source.

Factor 3: Multi-platform presence

AI cross-verifies across sources. Runna is discussed on Reddit (25.8% of citations), reviewed by Runner's World (15.5%) and Tom's Guide (10.0%), listed on apps.apple.com (19.1%), and comprehensive on its own domain (32.6%). This breadth of presence across different source types creates the cross-verification signal that AI models need.

Factor 4: AI-native product positioning

Runna was Apple App of the Year finalist in 2024. Its product is explicitly built around AI-personalized training plans. When AI models describe Runna, they can accurately say "AI-powered running coach" - which is inherently more aligned with AI-generated recommendations than "social fitness network" (Strava) or "Nike's running app" (NRC).

Lessons for Other Brands

Runna's AI visibility success offers a replicable playbook:

1. Make your domain a citation-worthy resource, not just a product page
2. Be deeply specific to the queries users are asking AI
3. Front-load key information - 44.2% of citations come from the first 30% of content
4. Build presence across the sources AI cites - Reddit, niche publications, app stores, tech reviews
5. Publish original data - Runna's clinical trial data (38% DNF reduction) is exactly the kind of evidence AI models weight heavily

Competitive Implications

For Strava

Strava's 43.9% AI visibility trails Runna by 7 points despite having 90x the user base. The Runna acquisition (January 2025) strategically addresses this gap - by owning Runna, Strava effectively controls both the #1 and #2 AI visibility positions in sports apps. The combined entity dominates across training plans (Runna) and community (Strava), covering the full runner journey from race discovery to training to social sharing.

Strategic recommendation: Strava should leverage Runna's content strategy across its broader platform. The runna.com playbook - deep, topic-specific, data-rich content - could be applied to cycling, hiking, and other activities where Strava currently has lower AI visibility.

For Nike Run Club

At 32.5% visibility, Nike Run Club punches below its weight. With 100+ million users and the Nike brand behind it, NRC has the brand recognition and user data to lead AI recommendations. But AI models appear to associate NRC more with "Nike's ecosystem" than with "the best running training tool."

Strategic recommendation: Nike Run Club needs to build its identity as an independent running authority - not just a Nike product feature. Publishing original training data, creating depth-specific content for race distances, and building content that AI can cite as a source (not just a recommendation) would address the 18.4-point gap to Runna.

For TrainingPeaks and Garmin Coach

At 14.1% and 10.1% respectively, these platforms are functionally invisible in AI recommendations despite strong products. TrainingPeaks is widely regarded as the gold standard for serious endurance athletes. Garmin Coach has the advantage of being built into millions of Garmin watches. Neither is translating their product strength into AI visibility.

Strategic recommendation: Both platforms need a dedicated content and GEO strategy. Their current minimal AI presence suggests limited investment in the content types that AI cites - training plan guides, data-driven comparisons, and presence on Reddit and niche publications. This is an open lane for either platform to significantly increase AI-driven discovery.

Methodology

This report uses GetMentioned data tracking AI responses across thousands of prompts related to sports apps, running training plans, race preparation, and fitness app recommendations. AI responses were analyzed across ChatGPT, Perplexity, Gemini, and other major AI platforms.

Visibility scores represent the percentage of AI responses in which a brand is mentioned for prompts within the sports apps category. Topic Leaders are the brands with the highest visibility for each specific topic cluster. Source citations represent the percentage of AI responses that reference each domain.

Market data and user statistics are sourced from company announcements, press releases, and third-party research as cited throughout the report.

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