Zhang Rui, Hou Yu, Cui Erjia, Lim Kelvin, Chow Lisa, Howell Michael, Ikramuddin Sayeed
Res Sq. 2025 May 7:rs.3.rs-6263507. doi: 10.21203/rs.3.rs-6263507/v1.
Physical activity is a modifiable factor influencing chronic disease risk. Previous studies often relied on self-reported activity measures or short-term assessments, limiting their accuracy. Leveraging Fitbit-derived data from the All of Us Research Program, we investigated associations between long-term physical activity patterns and chronic disease incidence in a diverse cohort. The study included 22,019 participants with at least six months of Fitbit monitoring and linked electronic health records. Key activity metrics included daily step count, activity calories, elevation gain, and activity duration at different intensities. Higher physical activity levels were associated with a lower risk of multiple chronic diseases. A 2,000-step increase in daily step count was linked to a reduced risk of obesity (hazard ratio [HR] = 0.85, 95% confidence interval [CI]: 0.80-0.90), type 2 diabetes (HR = 0.78, CI: 0.72-0.84), and major depressive disorder (HR = 0.83, CI: 0.77-0.90). Elevation gain was inversely associated with obesity (HR = 0.86, CI: 0.78-0.95) and type 2 diabetes (HR = 0.65, CI: 0.53-0.80). Increased time spent in very active intensity correlated with a lower risk of multiple conditions, including obstructive sleep apnea and morbid obesity. Conversely, prolonged sedentary time was associated with an increased risk of cardiometabolic diseases, including obesity (HR = 1.08, CI: 1.06-1.10) and essential hypertension (HR = 1.05, CI: 1.04-1.07). A sensitivity analysis using BMI-defined obesity instead of EHR-based diagnoses confirmed the robustness of these associations. These findings underscore the protective role of increased physical activity and reduced sedentary time in mitigating chronic disease risk. They support the development of personalized physical activity recommendations and targeted public health interventions aimed at improving long-term health outcomes. Future research integrating machine learning approaches could further refine activity-based disease prevention strategies.
身体活动是影响慢性病风险的一个可改变因素。以往的研究通常依赖自我报告的活动量测量方法或短期评估，这限制了其准确性。利用来自“我们所有人”研究计划中Fitbit获取的数据，我们在一个多样化的队列中研究了长期身体活动模式与慢性病发病率之间的关联。该研究纳入了22019名参与者，他们至少有六个月的Fitbit监测数据以及相关联的电子健康记录。关键的活动指标包括每日步数、活动消耗卡路里、海拔增益以及不同强度下的活动时长。较高的身体活动水平与多种慢性病风险较低相关。每日步数增加2000步与肥胖风险降低相关（风险比[HR]=0.85，95%置信区间[CI]：0.80 - 0.90）、2型糖尿病风险降低相关（HR = 0.78，CI：0.72 - 0.84）以及重度抑郁症风险降低相关（HR = 0.83，CI：0.77 - 0.90）。海拔增益与肥胖（HR = 0.86，CI：0.78 - 0.95）和2型糖尿病（HR = 0.65，CI：0.53 - 0.80）呈负相关。在非常活跃强度下花费的时间增加与多种疾病风险降低相关，包括阻塞性睡眠呼吸暂停和病态肥胖。相反，久坐时间延长与心血管代谢疾病风险增加相关，包括肥胖（HR = 1.08，CI：1.06 - 1.10）和原发性高血压（HR = 1.05，CI：1.04 - 1.07）。使用基于BMI定义的肥胖而非基于电子健康记录的诊断进行的敏感性分析证实了这些关联的稳健性。这些发现强调了增加身体活动和减少久坐时间在降低慢性病风险方面的保护作用。它们支持制定个性化的身体活动建议以及旨在改善长期健康结果的针对性公共卫生干预措施。未来整合机器学习方法的研究可以进一步完善基于活动的疾病预防策略。
