Overview Leaderboard Methodology
Industry Report

Fitness Bands

Slim wearable trackers focused on activity, sleep, and basic health metrics, often with long battery life and affordable pricing.

Brands tracked: 31
Brands analyzed: 31
Last updated: 2026-04-23
Model: OpenAI GPT-5
Prompts: 251
Total responses: 1,260
Top Brand Overall?
Fitbit
95/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Apple
87

Highest score on the LBA metric in this industry.

Authority Leader?
Fitbit
100

Highest score on the Authority metric in this industry.

TOM Leader?
Fitbit
100

Highest score on the TOM metric in this industry.

Fitbit is the default answer in AI responses for Fitness Bands

When users ask ChatGPT, Claude, or Gemini about Fitness Bands, Fitbit is the brand that surfaces first - unprompted, consistently, and usually at the top of any list the model generates. Garmin is a close second, but the gap between them is meaningful. If you're competing in this space and you're not in the top handful, you're effectively invisible to AI-driven discovery.

Brand Leaderboard All 31 Fitness Bands brands ranked

Ranked by overall AI Visibility Score (smoothed geometric mean of LBA, Authority, and TOM with an LBA-based floor on Authority and TOM, see methodology). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
Fitbit
fitbit.com
 86
100
100
95
2
Garmin
garmin.com
 82
68
87
79
3
Amazfit
amazfit.com
 70
65
72
69
4
Xiaomi
xiaomi.com
 75
40
82
63
5
Samsung
samsung.com
 81
34
54
54
6
Whoop
whoop.com
 77
14
45
38
7
Infinix
infinixmobility.com
 32
34
38
35
8
Apple
apple.com
 87
17
9
25
9
Huawei
huawei.com
 66
7
17
21
10
Polar
polar.com
 76
8
8
18
11
Withings
withings.com
 73
7
7
18
12
boAt
boat-lifestyle.com
 68
7
7
17
13
Noise
gonoise.com
 69
7
7
17
14
OnePlus
oneplus.com
 68
7
7
17
15
realme
realme.com
 69
7
7
17
16
Redmi
mi.com
 70
7
7
17
17
HONOR
honor.com
 67
7
7
16
18
Fastrack
fastrack.in
 60
6
6
15
19
GOQii
goqii.com
 59
6
6
15
20
Misfit
misfit.com
 62
6
6
15
21
Bellabeat
bellabeat.com
 57
6
6
14
22
Jawbone
jawbone.com
 58
6
6
14
23
Moov
moov.cc
 55
6
6
14
24
TomTom
tomtom.com
 57
6
6
14
25
OPPO
oppo.com
 53
5
5
13
26
Mibro
mibrofit.com
 33
3
3
9
27
Actofit
actofit.com
 30
3
3
8
28
Letsfit
letsfit.com
 29
3
3
8
29
C
Cubitt
cubitt.com
 25
2
2
7
30
Wyze
wyze.com
 27
3
3
7
31
iWOWN
iwown.com
 20
2
2
6

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 251 shared Fitness Bands prompts. The same prompts, same model, same iterations. So differences in scores reflect actual differences in AI visibility, not differences in measurement.

Overall AI Visibility Score
Smoothed geometric mean of LBA, Authority and TOM. Authority and TOM are floored at LBA × 0.1 before the geometric mean (the same floor used in the per-metric cards above, so brand cards and the composite tell the same story). Formula: composite = ((LBA + 5)(Authority + 5)(TOM + 5))^(1/3) - 5. The floor keeps brands the model clearly recognises but doesn't yet recommend from collapsing to zero, while a single genuinely weak metric still pulls the composite down. Full methodology.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 251 category prompts (e.g. "best SEO tools for agencies"). This makes brand-to-brand comparisons valid - everyone faces identical inputs. LBA prompts are per-brand because they ask brand-specific questions.
Latent Brand Association (LBA)
5 brand probes + 1 control prompt, each run 5 times in recall mode (no web search). LBA = quality × meta × stability × share × recognition × 100. Read the full LBA methodology →
LLM Authority
50 organic category prompts (discovery, comparison, problem and transactional intents), each run once in recall mode and once in retrieval mode. Score = frequency × log-decayed prominence × intent weight, then 50/50 averaged across the two modes. Read the full Authority methodology →
Top of Mind (TOM)
15 high-volume discovery prompts (sourced from Keywords Everywhere search-volume data), each run 5 times in pure recall mode (no web). Score = frequency × (0.5 + 0.5 × log-prominence), volume-weighted. Read the full TOM methodology →