Fitbit 的 AI Health Coach 属于 Fitbit Premium 的 10 美元/月服务，在三周试用中呈现出一系列可量化的限制与作用。其依赖的 Zone 2 心率训练区间通常为最大心率的 60–70%，但对心率较易飙升的用户并不稳定；当用户报告“生病”后，系统持续下调训练至 1.5–2 英里等低量级跑步，即便症状恢复仍无法自动纠正，必须在 Coach Notes 中手动删除相关记录。多项功能在公测版中缺失，包括月经记录、血糖记录、Cardio Fitness 分数及 Pixel Watch 3/4 的高级跑步指标，使整体追踪能力不完整。
Coach 能识别用户每周模式（如周日瑜伽、周三攀岩）并调整训练，并提供对跑者有效的力量训练（如壶铃摆动、臀桥），显示其信息源较可靠。前置条件包括：Android 11+、Fitbit Premium、美国地区、英语环境。设备端表现包含同步手表记录、无需实时监测即可整合训练结果。然而，模型在记忆保持与情境判断上仍不稳定，Fitbit 官方承认公测阶段的“记忆过期”会导致训练计划异常调整。
文章指出另一组对比性“数据”：真实人际互动对训练成效的提升。跑步社群经验显示，与更快跑者一起训练可在一个月左右明显提升速度；朋友能即时判断用户是否生病、是否处于可对话配速、或是否“累到不行”，这些均是模型无法提供的实时反馈。最终趋势显示，AI 教练可在繁忙日程中提供结构化建议，但其缺乏社交与情境敏感性，使其无法替代现实社交网络在坚持运动与维持健康行为中的关键作用。
Fitbit’s AI Health Coach, part of the $10-per-month Premium service, showed measurable limits and benefits across a three-week trial. Its Zone 2–based running plans target 60–70% of max heart rate but behave inconsistently for users whose heart rates spike easily; once the user reported being sick, the system repeatedly downgraded workouts to 1.5–2-mile sessions and did not self-correct, requiring manual deletion of entries in Coach Notes. Several features are missing in the public preview—menstrual logging, glucose logging, Cardio Fitness scores, and advanced Pixel Watch 3/4 running metrics—reducing overall tracking completeness.
Coach learned weekly behavior patterns (such as Sunday yoga and Wednesday climbing) and integrated them into plans, while recommending strength exercises relevant for runners (like kettlebell swings and glute bridges), indicating reasonable information grounding. Requirements include Android 11+, Fitbit Premium, US region, and English settings. Users can sync watch-logged workouts without live tracking, but memory-persistence issues—acknowledged by Fitbit—can lead to unexpected program adjustments.
The narrative contrasts AI assistance with quantifiable social effects in real training. Running experts note that training with faster partners can reliably improve pace within about a month; human partners can detect illness, conversational-pace effort, or exhaustion—real-time assessments that large language models cannot match. The overall trend suggests AI coaches provide structure for busy users but lack the social and situational sensitivity that real people contribute to exercise adherence, motivation, and performance.