Association of Physical Activity from Wearable Devices and Chronic Disease Risk: Insights from the All of Us Research Program

Association of Physical Activity from Wearable Devices and Chronic Disease Risk: Insights from the All of Us Research Program | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association of Physical Activity from Wearable Devices and Chronic Disease Risk: Insights from the All of Us Research Program Rui Zhang, Yu Hou, Erjia Cui, Kelvin Lim, Lisa Chow, Michael Howell, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6263507/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract ​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. Health sciences/Risk factors Health sciences/Health care/Disease prevention/Lifestyle modification Figures Figure 1 Figure 2 Introduction Chronic diseases, including cardiometabolic disorders, obesity, and major depressive disorder (MDD), are the main causes of morbidity and mortality worldwide 1 – 3 . Given the critical role of lifestyle factors in their onset and progression, physical activity has garnered increasing attention as a key modifiable determinant 4 , 5 . Wearable devices enable continuous, non-invasive monitoring of physical activity, offering novel opportunities to explore its association with chronic disease outcomes. Prior research has established links between physical activity and multiple chronic diseases, such as cardiovascular disease, type 2 diabetes, depression, Parkinson’s disease, and multiple sclerosis 4 – 8 . However, many studies rely on self-reported activity data or short-term objective assessments via accelerometers or pedometers. Self-reported data, though informative, often diverge from objectively measured activity levels 9 , 10 . The emergence of commercial wearables, such as Fitbit, offers a means to address these limitations by enabling long-term, objective activity tracking. Fitbit-based studies have validated their accuracy in measuring steps, heart rate, and energy expenditure 11 , 12 . These devices generate longitudinal data, providing novel insights into individual activity patterns and facilitating large-scale epidemiological investigations of activity-related chronic disease risk. The All of Us Research Program (AoU), a National Institutes of Health initiative, seeks to collect health data from over one million individuals across diverse U.S. populations 13 , 14 . The program integrates electronic health records (EHRs), genomic data, physical measurements, and participant surveys. As of Version 8, more than 59,000 participants have contributed Fitbit data, encompassing activity and sleep metrics. This dataset uniquely enables the first large-scale exploration of longitudinal associations between objectively measured activity behaviors and chronic disease outcomes in a diverse U.S. cohort. This study leverages extensive Fitbit activity data and longitudinal EHRs from AoU to examine associations between real-world activity patterns and chronic disease incidence. We aim to elucidate how activity intensity, duration, and regularity influence chronic disease risk, with findings informing evidence-based lifestyle recommendations for disease prevention in the general U.S. population. Results The AoU Research Program included 58,785 participants with available Fitbit activity data. Among them, 22,019 participants had both corresponding EHR data and at least six months of Fitbit monitoring (supplementary Figure. 1). The median participants age was 52 years (interquartile range: 38 to 63 years). The majority of participants were female (71.4%), followed by males (28.2%), with 0.4% having unknown sex information. Race distribution was as follows: White (77.5%), Black (6.3%), Asian (3.4%), Other (7.3%), and Unknown (5.5%). Hispanic or Latino participants comprised 7.8%, while 90.4% were non-Hispanic, and 1.9% had unknown ethnicity. The median Fitbit monitoring duration was 2.53 years (1.42, 4.32). Participants had a median daily step count of 6,166 steps (3,698, 8,600). Participants had a median daily step count of 6,166 steps (484.3, 1050.2), with a median basal metabolic rate (BMR) of 1,474.0 kcal calories (1,287.9, 1,686.9). The median total daily calories burned was 2,101.6 kcal (1,787.4, 2,507.4). Median daily elevation gain was 33.9 meters (4.3, 91.0). Daily median activity intensity metrics included sedentary time (738 min; 12.1–931.3), lightly active time (177.0 min; 118.3–227.5), fairly active time (9.6 min; 2.6–19.0), and very active time (13.0 min; 6.0–24.7) (Table 1 ). Table 1 Baseline characteristics of study participants Variable Fitbit activity participants Age 52.00 (38.00, 63.00) Sex Female 15,724 (71.41%) Male 6,214 (28.22%) Race White 17,060 (77.48%) Black 1,382 (6.28%) Asian 743 (3.37%) Others 1,615 (7.33%) Unknown 1,219 (5.54%) Ethnicity Hispanic or Latino 1,716 (7.79%) Non-Hispanic or Latino 19,896 (90.36%) Unknown 407 (1.85%) Fitbit monitoring length 2.53 (1.42, 4.32) years Daily steps 6,166 (3,698, 8,600) Daily elevation 33.92 (4.30, 91.00) meters Activity intensity (minutes) Lightly active 177.02 (118.28, 227.45) Very active 12.96 (6.03, 24.74) Fairly active 9.61 (2.62, 18.97) Sedentary 737.98 (12.05, 931.29) Calories Daily activity calories a 765.68 (484.26, 1,123.62) Daily BMR calories b 1,474.02 (1,287.86, 1,686.94) Daily calories burned c 2,101.64 (1,787.42, 2,507.41) a The number of calories burned for the day during periods the user was active above the sedentary level. This includes both activities burned calories and BMR. b Total BMR calories burned for the day. c Total calories burned for the day Associations between physical activity patterns and chronic disease incidence After Bonferroni correction, 43 significant associations were identified between physical activity metrics and health outcomes. Daily step count was significantly associated with 27 diseases, including major depressive disorder and obstructive sleep apnea, while activity calories correlated with seven diseases, including sleep apnea and chronic fatigue syndrome. Morbid obesity and obesity were significantly associated with BMR-calories burned, with additional associations observed for total daily energy expenditure. Elevation gain was significantly linked to 11 diseases, including obesity and obstructive sleep apnea. Lightly active minutes were significantly associated with torsion dystonia and inflammatory neuropathy, fairly active minutes with three diseases (including joint pain), and very active minutes with 16 diseases (including type 2 diabetes and obesity) (Fig. 1 ). Figure 2 illustrates the associations between physical activity metrics and chronic disease incidence. An increase in daily step count was significantly associated with a lower risk of type 2 diabetes with neurological manifestations (odds ratio (OR) = 0.25; 95% confidence interval (CI) = 0.14–0.44; p-value = 0.01), major depressive disorder (OR = 0.63; 0.54–0.73; p < 0.01), sleep apnea (OR = 0.49; 0.37–0.64; p < 0.01), chronic fatigue syndrome (OR = 0.46; 0.35–0.59; p < 0.01), chronic pain syndrome (OR = 0.20; 0.12–0.35; p < 0.01), and obstructive sleep apnea (OSA) (OR = 0.57; 0.47–0.69; p < 0.01). Neurological disorders (OR = 0.35; 0.25–0.51; p < 0.01) and shortness of breath (OR = 0.49; 0.37–0.64; p < 0.01) were inversely associated with step count. Increased step count was also significantly associated with a lower risk of tachycardia NOS (OR = 0.45; 0.33–0.60; p < 0.01) and acute renal failure (OR = 0.27; 0.18–0.42; p < 0.01). Higher BMR calories were significantly associated with an increased risk of obesity (OR = 1.62; 1.44–1.82; p < 0.01) and morbid obesity (OR = 2.22; 1.90–2.59; p < 0.01). Higher activity calories were associated with a reduced risk of sleep apnea (OR = 0.76; 0.68–0.85; p = 0.02), OSA (OR = 0.83; 0.77–0.90; p = 0.03), and chronic fatigue syndrome (OR = 0.70; 0.61–0.81; p < 0.01). Greater daily elevation gain was significantly associated with a reduced risk of multiple conditions. Higher elevation gain was linked to lower risk of abdominal pain (OR = 0.83; 0.77–0.90; p = 0.01), urinary tract infection (OR = 0.65; 0.55–0.75; p < 0.01), gastroesophageal reflux disease (GERD) (OR = 0.81; 0.75–0.88; p < 0.01), and OSA (OR = 0.79; 0.72–0.87; p < 0.01). Elevation gain was also inversely associated with obesity risk (OR = 0.78; 0.71–0.86; p < 0.01). Major depressive disorder was also significantly associated with increased elevation gain (OR = 0.80; 0.73–0.88; p = 0.02), as were sleep apnea (OR = 0.68; 0.59–0.79; p < 0.01). Other conditions significantly associated with increased elevation gain included other dyspnea (OR = 0.81; 0.74–0.88; p < 0.01), and hypovolemia (OR = 0.52; 0.40–0.66; p < 0.01). Greater time spent in very active minutes was significantly associated with a lower risk of OSA (OR = 0.79; 0.73–0.86; p < 0.01), obesity (OR = 0.79; 0.73–0.87; p < 0.01), and dyspnea (OR = 0.84; 0.77–0.90; p = 0.02). Fairly active minutes were significantly associated with an increased risk of bursitis (OR = 1.31; 1.19–1.45; p < 0.01), joint pain (OR = 1.13; 1.08–1.19; p < 0.01), and osteoarthrosis (OR = 1.14; 1.08–1.20; p = 0.02). Lightly active minutes were inversely associated with torsion dystonia risk (OR = 0.53; 0.40–0.69; p = 0.02). Cox Model Results for Physical Activity and Chronic Diseases Cox proportional hazards regression models identified significant associations between physical activity metrics and chronic disease risk (Table 2 , Supplementary Figures. 2–9). A 2,000-step increase in daily steps was associated with a reduced risk of obesity (HR = 0.85; 95% CI: 0.80–0.90), morbid obesity (HR = 0.79; 0.73–0.86), and type 2 diabetes (HR = 0.78; 0.72–0.84). Higher step counts were also associated with a reduced risk of major depressive disorder (HR = 0.83; 0.77–0.90). The risk of OSA decreased with increased step count (HR = 0.84; 0.79–0.89). Other dyspnea (HR = 0.84; 0.80–0.89) and essential hypertension (HR = 0.88; 0.84–0.92) also demonstrated a reduced risk with higher step counts. Table 2 Cox proportional hazards models for the association between Fitbit activity metric and chronic diseases. Steps a Elevation b Sedentary c Lightly active c Very active c Chronic disease HR (95% CI) Obesity 0.846 (0.800-0.895) 0.858 (0.777–0.948) 1.077 (1.055–1.099) 0.848 (0.793–0.907) 0.712 (0.498–1.018) Morbid obesity 0.792 (0.731–0.857) 0.785 (0.668–0.923) 1.054 (1.025–1.083) 0.868 (0.793–0.950) 0.450 (0.244–0.830) Type 2 diabetes 0.776 (0.716–0.843) 0.653 (0.531–0.804) 1.039 (1.010–1.070) 0.821 (0.744–0.906) 0.357 (0.200-0.637) Major depressive disorder 0.832 (0.770–0.898) 0.820 (0.708–0.950) 1.026 (0.998–1.055) 0.838 (0.765–0.918) 0.565 (0.342–0.935) Obstructive sleep apnea 0.835 (0.788–0.885) 0.830 (0.746–0.923) 1.033 (1.011–1.056) 0.873 (0.812–0.939) 0.474 (0.322–0.698) Other dyspnea 0.841 (0.799–0.886) 0.866 (0.793–0.945) 1.041 (1.022–1.062) 0.909 (0.852–0.970) 0.320 (0.221–0.465) Essential hypertension 0.875 (0.835–0.917) 0.924 (0.862–0.991) 1.054 (1.035–1.073) 0.883 (0.831–0.934) 0.668 (0.507–0.881) Chronic pain syndrome 0.931 (0.799–1.085) 0.889 (0.683–1.158) 1.051 (0.989–1.116) 0.778 (0.640–0.946) 0.185 (0.044–0.772) a . 2,000 steps b . 100 m c . Hours Each 100-meter increase in elevation gain was associated with a lower risk of obesity (HR = 0.86; 0.78–0.95), morbid obesity (HR = 0.79; 0.67–0.92), type 2 diabetes (HR = 0.65; 0.53–0.80), and major depressive disorder (HR = 0.82; 0.71–0.95). OSA (HR = 0.83; 0.75–0.92), other dyspnea (HR = 0.87; 0.79–0.95), and essential hypertension (HR = 0.92; 0.86–0.99) were also inversely associated with elevation gain. Greater sedentary time was positively associated with an increased risk of multiple conditions. Each additional hour of sedentary time was associated with a higher risk of obesity (HR = 1.08; 1.06–1.10), morbid obesity (HR = 1.05; 1.03–1.08), type 2 diabetes (HR = 1.04; 1.01–1.07), and OSA (HR = 1.03; 1.01–1.06). Other dyspnea (HR = 1.04; 1.02–1.06) and essential hypertension (HR = 1.05; 1.04–1.07) also exhibited increased risk with prolonged sedentary time. Greater time spent in light activity was associated with a lower risk of several conditions. Each additional hour of light activity was associated with reduced risk of obesity (HR = 0.85; 0.79–0.91), morbid obesity (HR = 0.87; 0.79–0.95), type 2 diabetes (HR = 0.82; 0.74–0.91), major depressive disorder (HR = 0.84; 0.77–0.92), OSA (HR = 0.87; 0.81–0.94), other dyspnea (HR = 0.91; 0.85–0.97), essential hypertension (HR = 0.88; 0.83–0.93), and chronic pain syndrome (HR = 0.78; 0.64–0.95). Each additional hour of very active time was associated with a significantly lower risk of morbid obesity (HR = 0.45; 0.24–0.83), type 2 diabetes (HR = 0.34; 0.20–0.64), OSA (HR = 0.47; 0.32–0.70), other dyspnea (HR = 0.32; 0.22–0.47), essential hypertension (HR = 0.67; 0.51–0.88), and major depressive disorder (HR = 0.19; 0.44–0.77). Sensitivity Analysis using body mass index (BMI) for Obesity classification To validate our findings, we performed a sensitivity analysis using BMI-defined obesity as an alternative outcome measure Logistic regression models assessing associations between physical activity metrics and BMI-defined obesity confirmed the primary findings. Higher daily step counts were associated with a significantly lower risk of obesity (OR = 0.79; 95% CI: 0.71–0.88; p < 0.01), as were increased elevation gain (OR = 0.77; 0.69–0.86; p < 0.01) and time spent in very active intensity (OR = 0.80; 0.72–0.88; p < 0.01). Conversely, BMR calories were positively associated with an increased risk of obesity (OR = 2.17; 1.82–2.60; p < 0.01), suggesting a potential compensatory metabolic effect. Cox proportional hazards models further corroborated these findings, demonstrating consistent associations between physical activity metrics and the risk of developing BMI-defined obesity over time (Supplementary Figure. 10). Higher daily step counts, increased elevation gain, and greater time spent in very active intensity were significantly associated with a lower hazard of obesity onset. These results underscore the robustness of our primary analysis and emphasize the protective role of increased physical activity against obesity, regardless of diagnostic criteria. Discussion This study examined the associations between Fitbit-derived physical activity metrics and chronic disease incidence using data from the AoU Research Program. We utilized the latest AoU Curated Data Repository (CDR) version 8, released in February 2025, incorporating participant data up to October 1, 2023. With over 59,000 participants contributing Fitbit data, this dataset uniquely enables the exploration of long-term associations between objectively measured physical activity and chronic disease risk in a diverse cohort 15 . Utilizing this comprehensive dataset enhances the robustness and generalizability of our findings, ensuring their relevance to public health efforts and evidence-based physical activity interventions for disease prevention. Our findings suggest that higher physical activity levels—measured by daily step count, elevation gain, and activity intensity—are associated with a reduced risk of chronic diseases, including obesity, type 2 diabetes, major depressive disorder, and OSA. Conversely, prolonged sedentary time was linked to an increased risk of these conditions. These findings align with growing evidence emphasizing the role of regular physical activity in improving health and mitigating disease risk. The inverse association between daily step count and the risk of obesity, type 2 diabetes, and major depressive disorder is consistent with prior research demonstrating the protective effects of increased step counts on cardiometabolic health and mental well-being 5 , 16 – 18 . Our results indicate that higher step counts are significantly associated with a lower risk of these conditions. This aligns with the concept that increased physical activity enhances energy expenditure, improves metabolic function, and alleviates depressive symptoms 4 , 19 . These findings reinforce public health recommendations advocating higher daily step counts as a practical strategy for chronic disease prevention. Our study identified elevation gain, a less frequently examined metric, as significantly associated with a lower risk of several chronic diseases, including type 2 diabetes, morbid obesity, and OSA. Prior research suggests that elevation-related activities, such as stair climbing, may confer health benefits, though further evidence is needed to confirm their effects on cardiovascular and metabolic health 18 , 20 . These findings imply that integrating elevation-related activities into daily routines may offer additional health benefits. Further studies are warranted to elucidate the role of elevation-related activities in chronic disease prevention. This study further highlighted the association between activity intensity and health outcomes. Greater time in very active intensity was significantly associated with a lower risk of obesity, type 2 diabetes, and OSA, while increased fairly active and lightly active time correlated with reduced risks of several chronic conditions, including major depressive disorder. These findings align with prior research demonstrating the benefits of varying physical activity intensities on cardiometabolic and mental health outcomes 21 , 22 . However, fairly active intensity was also associated with an increased risk of bursitis, joint pain, and osteoarthrosis, suggesting that while high-intensity activity is beneficial for chronic disease prevention, it may increase musculoskeletal strain. These findings underscore the need for personalized physical activity recommendations, particularly for individuals at risk of musculoskeletal injuries 23 . Conversely, prolonged sedentary time was consistently associated with a higher risk of multiple chronic diseases, including obesity, type 2 diabetes, and essential hypertension. These findings align with previous studies highlighting the adverse effects of prolonged sedentary behavior on metabolic health and its association with an increased risk of cardiometabolic diseases 24 – 26 . The strong association between sedentary behavior and chronic disease risk underscores the importance of reducing sedentary time as a complementary strategy to increasing physical activity for improving health outcomes. To evaluate the robustness of our findings, we performed a sensitivity analysis using BMI-defined obesity as an alternative outcome measure. Logistic regression analyses corroborated our primary findings, showing that higher daily step counts, increased elevation gain, and greater time spent in very active intensity were significantly associated with a lower risk of obesity. Cox proportional hazards models further reinforced these associations, demonstrating that physical activity metrics were consistently linked to a lower hazard of obesity development over time. These findings enhance the validity of our primary results, indicating that the observed associations persist regardless of whether obesity is defined by BMI criteria or clinical diagnosis. The sensitivity analysis underscores the robustness of the association between physical activity and obesity risk, suggesting that increasing daily movement and engaging in higher-intensity activities may confer benefits across varying obesity definitions. A key strength of this study is the use of real-world wearable data, offering objective and granular insights into long-term physical activity patterns. Unlike traditional epidemiological studies relying on self-reported physical activity, wearable-derived metrics provide greater accuracy and allow for detailed assessments of activity intensity, duration, and elevation. However, several limitations should be considered. First, as an observational study, causality cannot be established. Although we mitigated reverse causation by excluding participants diagnosed within the initial 180 days, residual confounding remains possible. Furthermore, reliance on Fitbit users may introduce selection bias, as these individuals tend to be more health-conscious than the general population. In conclusion, our findings highlight the importance of increasing daily steps, incorporating elevation-related activities, and engaging in diverse physical activity intensities to lower chronic disease risk. Equally important is reducing sedentary behavior to mitigate chronic disease risk. These findings offer valuable insights for designing targeted public health interventions that promote physical activity and reduce sedentary behavior, ultimately enhancing population health outcomes. Future research should investigate the underlying mechanisms of these associations and develop personalized activity recommendations to optimize health benefits while minimizing risks. With the growing application of machine learning in precision medicine 27 – 32 , integrating these approaches into future research could enhance the development of tailored physical activity interventions, enabling more precise health outcome predictions and personalized recommendations. Methods Study Participants All participants provided informed consent to join the All of Us (AoU) Research Program, a large-scale National Institutes of Health (NIH)-funded initiative designed to advance research in health and disease 13 , 14 . Eligible participants included all U.S. adults (aged ≥ 18 years), excluding individuals who were incarcerated or unable to provide independent consent. This study utilized controlled-tier data (C2024Q3R4) from the AoU Researcher Workbench. At the time of analysis, data were available from 633,540 individuals enrolled between May 2018 and October 1, 2023 15 . Data collection included demographic information, survey responses from digital enrollment, physical measurements and vital signs recorded by partner healthcare provider organizations, voluntarily shared electronic health records (EHRs), and Fitbit data linked via participants’ Google Fitbit accounts within the AoU portal 13 , 14 , 33 . Fitbit activity data This study utilized Fitbit-derived daily activity data to capture detailed physical activity patterns of participants. The dataset included activity calories, basal metabolic rate (BMR) calories, total calories burned, elevation gain, activity minutes at different intensity levels, and total step count. Fitbit data provides an advantage over traditional self-reported activity measures by offering objective, continuous, and non-invasive tracking. The inclusion of detailed activity metrics, such as light, moderate, and vigorous intensity levels, enables a comprehensive assessment of participants’ activity profiles over time. Fitbit data were collected from participants who linked their Fitbit accounts through the AoU portal, enabling longitudinal tracking of activity behaviors. We summarized daily activity metrics by averaging activity patterns over each participant’s monitoring period to derive a representative activity profile. This approach facilitated characterization of individual variability and investigation of associations between activity behaviors and health outcomes. Additionally, these metrics enabled differentiation of activity intensities, offering insights into both total activity volume and its distribution throughout the day. Preprocessing The dataset was accessed via Google BigQuery, extracting all participants with available Fitbit data. SQL queries were used to extract activity summary records, including activity calories, BMR, total calories burned, elevation gain, activity minutes (light, moderate, and vigorous), and total steps. A cohort filtering process was applied to ensure participants met key inclusion criteria (supplement Figure. 1). First, participants aged 18 years or older were included. Age was determined by comparing the first recorded Fitbit activity date with the participant's date of birth, retaining only those classified as adults at their first activity record. Next, participants were required to have at least six months (≥ 180 days) of recorded Fitbit activity. This criterion, commonly applied in prior research 34 – 36 , ensured sufficient data quality and excluded participants with limited activity records that could yield unreliable results. Lastly, only participants with linked EHR data were included. This criterion ensured the availability of EHR data for all participants, allowing assessment of associations between Fitbit-derived activity metrics and clinical outcomes. Outcomes and covariates Study outcomes were derived from diagnostic events recorded in participants' EHRs. Diagnostic events were identified using International Classification of Diseases (ICD) codes from both ICD-9 and ICD-10 versions. ICD codes were mapped to 1,765 distinct phecodes using the PheWAS catalog's Phecode Maps 37 – 39 , ensuring consistent diagnosis categorization. Each phecode was associated with its corresponding phenotype and diagnostic category. To ensure statistical robustness, phecodes with fewer than 100 cases were excluded. Covariates included age and sex, extracted from demographic data. Statistical analyses We used multiple logistic regression models to assess the association between physical activity metrics (e.g., steps, activity calories, and minutes at different activity intensities) and health outcomes. Study outcomes were defined as the presence or absence of specific diagnoses, represented by phecodes derived from EHR data. To enhance robustness, participants diagnosed with a given phecode within the first 180 days of Fitbit monitoring were excluded. Analyses were adjusted for age, sex, and body mass index (BMI) at baseline. Bonferroni correction was applied to adjust for multiple comparisons. We further performed Cox proportional hazards regression models to explore the association between physical activity metrics and selected chronic diseases. Chronic diseases were selected based on prior research linking them to physical activity 40 – 47 . Primary predictor variables were Fitbit-derived activity metrics, including activity calories, elevation, activity minutes at different intensities (e.g., fairly active, lightly active, very active), and step count. Outcomes were defined as EHR-recorded diagnosis events, represented by phecodes. For each phecode, baseline activity metrics were calculated as the average values over the initial 180 days following each participant’s first recorded Fitbit activity. Participants with fewer than six months of recorded activity during the baseline period were excluded to maintain data reliability. To mitigate reverse causation, participants diagnosed before or within the first 180 days of Fitbit activity monitoring were excluded from analysis for that specific phecode. Cox models were fitted with individual activity metrics as predictors, adjusting for age, sex, and BMI. The event variable indicated whether a diagnosis occurred during follow-up, while the duration variable represented the number of days from baseline to diagnosis or the end of follow-up. Daily steps were analyzed in 2,000-step increments to assess the impact of step count increases on disease risk. Elevation gain was analyzed in 100-meter increments to evaluate its effect on disease risk. Durations of different activity intensities (e.g., lightly active, very active) and sedentary time were converted to hours to assess the impact of each additional hour on disease risk. Statistical analyses were performed using Python (v3.8, https://www.python.org/ ) on the AoU Researcher Workbench, a secure cloud-based platform. The analyses were conducted by the libraries including Pandas 48 , NumPy 49 , Statsmodels 50 , and Lifelines 51 . Sensitivity Analysis using BMI for Obesity classification To evaluate the robustness of our findings, we conducted a sensitivity analysis by defining obesity based on BMI rather than electronic health record (EHR)-based ICD codes. This alternative classification aimed to capture cases that might not have been explicitly diagnosed in the EHR but met the clinical criteria for obesity (BMI ≥ 30 kg/m²). Height and weight records were extracted from the AoU. Participants were classified as having BMI-defined obesity if they had at least one recorded BMI ≥ 30 kg/m² at any point. To reduce the risk of reverse causation, we excluded participants who had a BMI ≥ 30 kg/m² before the start of Fitbit monitoring or within the first 180 days of Fitbit activity tracking. We repeated the logistic regression and Cox proportional hazards regression models analyses using BMI-defined obesity as the outcome variable. Declarations Data availability This study used data from the All of Us Research Program’s Control Tier Dataset v8, available to authorized users on the Researcher Workbench, which can be accessed via https://workbench.researchallofus.org/ . Code availability Code used for this study is available to approved researchers upon request. Researchers can access the code on the All of Us Research Workbench platform by contacting our study team. Conflict of interests The authors state that they have no competing interests to declare. Author contributions Y.H. and R. Z. conceived the study. Y. H. served as the principal author, extracting data, designing and conducting the entire experiment and writing the manuscript. E. C. assisted in conceiving the study design and reviewed the manuscript. S. I., K. L., L. C., M. H. provided clinical expertise and reviewed the manuscript. All authors contributed to the production of the final manuscript. Acknowledgements This work was supported by the National Institutes of Health’s National Institute on Minority Health and Health Disparities, grant number 1R21MD019134-01. The content is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health. We gratefully acknowledge All of Us participants for their contributions, without whom this research would not have been possible. We also thank the National Institutes of Health’s All of Us Research Program for making available the participant data examined in this study. References Noncommunicable diseases. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. Flegal, K. M., Kit, B. K., Orpana, H. & Graubard, B. I. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA 309 , 71–82 (2013). Malhi, G. S. & Mann, J. J. Depression. Lancet 392 , 2299–2312 (2018). Warburton, D. E. R., Nicol, C. W. & Bredin, S. S. D. Health benefits of physical activity: the evidence. CMAJ 174 , 801–809 (2006). Lee, I.-M. et al. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 380 , 219–229 (2012). Haskell, W. L., Blair, S. N. & Hill, J. O. Physical activity: health outcomes and importance for public health policy. Prev. Med. 49 , 280–282 (2009). Zhao, A., Cui, E., Leroux, A., Lindquist, M. A. & Crainiceanu, C. M. Evaluating the prediction performance of objective physical activity measures for incident Parkinson’s disease in the UK Biobank. J. Neurol. 270 , 5913–5923 (2023). Meng, Q. et al. Quantifying the association between objectively measured physical activity and multiple sclerosis in the UK Biobank. Med. Sci. Sports Exerc. 55 , 2194–2202 (2023). Prince, S. A. et al. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int. J. Behav. Nutr. Phys. Act. 5 , 56 (2008). Schrack, J. A. et al. Assessing the “physical cliff”: detailed quantification of age-related differences in daily patterns of physical activity. J. Gerontol. A Biol. Sci. Med. Sci. 69 , 973–979 (2014). Adam Noah, J., Spierer, D. K., Gu, J. & Bronner, S. Comparison of steps and energy expenditure assessment in adults of Fitbit Tracker and Ultra to the Actical and indirect calorimetry. J. Med. Eng. Technol. 37 , 456–462 (2013). Evenson, K. R., Goto, M. M. & Furberg, R. D. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int. J. Behav. Nutr. Phys. Act. 12 , 159 (2015). All of Us Research Program Investigators et al. The “All of Us” Research Program. N. Engl. J. Med 381 , 668–676 (2019). Mayo, K. R. et al. The All of Us data and Research Center: Creating a secure, scalable, and sustainable ecosystem for biomedical research. Annu. Rev. Biomed. Data Sci. 6 , 443–464 (2023). Curated Data Repository (CDR) version 8 Release Notes. User Support https://support.researchallofus.org/hc/en-us/articles/30294451486356-Curated-Data-Repository-CDR-version-8-Release-Notes. Schuch, F. B. et al. Physical activity and incident depression: A meta-analysis of prospective cohort studies. Am. J. Psychiatry 175 , 631–648 (2018). Arem, H. et al. Leisure time physical activity and mortality: a detailed pooled analysis of the dose-response relationship. JAMA Intern. Med. 175 , 959–967 (2015). Del Pozo Cruz, B., Ahmadi, M. N., Lee, I.-M. & Stamatakis, E. Prospective associations of daily step counts and intensity with cancer and cardiovascular disease incidence and mortality and all-cause mortality. JAMA Intern. Med. 182 , 1139–1148 (2022). Reiner, M., Niermann, C., Jekauc, D. & Woll, A. Long-term health benefits of physical activity--a systematic review of longitudinal studies. BMC Public Health 13 , 813 (2013). Lee, I.-M. et al. Association of step volume and intensity with all-cause mortality in older women. JAMA Intern. Med. 179 , 1105–1112 (2019). Ekelund, U. et al. Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis. BMJ 366 , l4570 (2019). Piercy, K. L. et al. The Physical Activity Guidelines for Americans. JAMA 320 , 2020–2028 (2018). Bennell, K., Matheson, G., Meeuwisse, W. & Brukner, P. Risk factors for stress fractures. Sports Med. 28 , 91–122 (1999). Katzmarzyk, P. T., Church, T. S., Craig, C. L. & Bouchard, C. Sitting time and mortality from all causes, cardiovascular disease, and cancer. Med. Sci. Sports Exerc. 41 , 998–1005 (2009). Wilmot, E. G. et al. Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia 55 , 2895–2905 (2012). Biswas, A. et al. Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: a systematic review and meta-analysis. Ann. Intern. Med. 162 , 123–132 (2015). Su, C. et al. Identification of Parkinson’s disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data. NPJ Digit. Med. 7 , 184 (2024). Yang, H. S. et al. Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning. Clin. Chem. 66 , 1396–1404 (2020). Yang, H. S. et al. Machine learning highlights downtrending of COVID-19 patients with a distinct laboratory profile. Health Data Sci. 2021 , 7574903 (2021). Brendel, M., Su, C., Hou, Y., Henchcliffe, C. & Wang, F. Comprehensive subtyping of Parkinson’s disease patients with similarity fusion: a case study with BioFIND data. NPJ Parkinsons Dis. 7 , 83 (2021). Zang, C. et al. Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts. Commun. Med. (Lond.) 4 , 130 (2024). Zang, C. et al. Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records. Transl. Psychiatry 14 , 316 (2024). Master, H. et al. How Fitbit data are being made available to registered researchers in All of Us Research Program. Pac. Symp. Biocomput. 28 , 19–30 (2023). Zheng, N. S. et al. Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program. Nat. Med. 30 , 2648–2656 (2024). Leroux, A. et al. NHANES 2011-2014: Objective physical activity is the strongest predictor of all-cause mortality. Med. Sci. Sports Exerc. 56 , 1926–1934 (2024). Leroux, A. et al. Quantifying the predictive performance of objectively measured physical activity on mortality in the UK Biobank. J. Gerontol. A Biol. Sci. Med. Sci. 76 , 1486–1494 (2021). Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31 , 1102–1110 (2013). Wei, W.-Q. et al. Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. PLoS One 12 , e0175508 (2017). Wu, P. et al. Mapping ICD-10 and ICD-10-CM codes to phecodes: Workflow development and initial evaluation. JMIR Med. Inform. 7 , e14325 (2019). Perry, A. S. et al. Association of longitudinal activity measures and diabetes risk: An analysis from the national institutes of health All of Us Research Program. J. Clin. Endocrinol. Metab. 108 , 1101–1109 (2023). Althoff, T. et al. Large-scale physical activity data reveal worldwide activity inequality. Nature 547 , 336–339 (2017). Schuch, F. et al. Physical activity and sedentary behavior in people with major depressive disorder: A systematic review and meta-analysis. J. Affect. Disord. 210 , 139–150 (2017). Vancampfort, D. et al. Sedentary behavior and physical activity levels in people with schizophrenia, bipolar disorder and major depressive disorder: a global systematic review and meta-analysis. World Psychiatry 16 , 308–315 (2017). Mendelson, M. et al. Obstructive sleep apnea syndrome, objectively measured physical activity and exercise training interventions: A systematic review and meta-analysis. Front. Neurol. 9 , 73 (2018). Hall, K. A., Singh, M., Mukherjee, S. & Palmer, L. J. Physical activity is associated with reduced prevalence of self-reported obstructive sleep apnea in a large, general population cohort study. J. Clin. Sleep Med. 16 , 1179–1187 (2020). Paffenbarger, R. S., Jr, Jung, D. L., Leung, R. W. & Hyde, R. T. Physical activity and hypertension: an epidemiological view. Ann. Med. 23 , 319–327 (1991). Diaz, K. M. & Shimbo, D. Physical activity and the prevention of hypertension. Curr. Hypertens. Rep. 15 , 659–668 (2013). Team, T. P. D. Pandas-Dev/Pandas: Pandas . (Zenodo, 2020). doi:10.5281/zenodo.3509134. Harris, C. R. et al. Array programming with NumPy. Nature 585 , 357–362 (2020). Seabold, S. & Perktold, J. statsmodels: Econometric and statistical modeling with python. in 9th Python in Science Conference (2010). Davidson-Pilon, C. lifelines: survival analysis in Python. Journal of Open Source Software 4 , 1317 (2019). Additional Declarations There is NO Competing Interest. Supplementary Files Fitbitactivitychronicdiseasencsupp.docx Association of Physical Activity from Wearable Devices and Chronic Disease Risk: Insights from the All of Us Research Program Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6263507","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":435578476,"identity":"c7ce6d8a-2709-4c5c-ae38-ee12ed7836a4","order_by":0,"name":"Rui Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYFACxgZmEMUP4TGToEWygXgtUGUGB4jVYj4juflzQc0du803stMeMFRYJzYQ0iJzI7HBeMaxZ8nbbuRuN2A4k05Yi4REYkMyD9vhZLMbudskGNsOE6flMM+/w8nGM0Ba/hGnpbGZt+2wnYEESEsDMVp4HjYz8/YdTpA483abRMKxdGPCWtjTH3/m+XbYnr8daMuHGmtZglpgAOKeBGKVg4A9KYpHwSgYBaNghAEANgI+i397l8IAAAAASUVORK5CYII=","orcid":"","institution":"University of Minnesota","correspondingAuthor":true,"prefix":"","firstName":"Rui","middleName":"","lastName":"Zhang","suffix":""},{"id":435578477,"identity":"37dbd54e-ea13-4b93-bd29-38dc4d0bc08a","order_by":1,"name":"Yu Hou","email":"","orcid":"https://orcid.org/0009-0009-7184-6592","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Hou","suffix":""},{"id":435578478,"identity":"e6f7f9a1-ae8f-4d0b-a9ed-f7bc57092cfa","order_by":2,"name":"Erjia Cui","email":"","orcid":"","institution":"University of Minnesota, Division of Biostatistics","correspondingAuthor":false,"prefix":"","firstName":"Erjia","middleName":"","lastName":"Cui","suffix":""},{"id":435578479,"identity":"49084d40-a8a3-4122-a211-fb9ae0f6f30e","order_by":3,"name":"Kelvin Lim","email":"","orcid":"https://orcid.org/0000-0002-2390-7268","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Kelvin","middleName":"","lastName":"Lim","suffix":""},{"id":435578480,"identity":"fdd4aa23-c9ff-4db2-8b80-52911935fc00","order_by":4,"name":"Lisa Chow","email":"","orcid":"https://orcid.org/0000-0002-6210-2307","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"","lastName":"Chow","suffix":""},{"id":435578481,"identity":"03fcf82c-26ac-412b-b7de-e1a8cab006d6","order_by":5,"name":"Michael Howell","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Howell","suffix":""},{"id":435578482,"identity":"763a8f80-eeab-472b-8ccf-4929d9237f00","order_by":6,"name":"Sayeed Ikramuddin","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Sayeed","middleName":"","lastName":"Ikramuddin","suffix":""}],"badges":[],"createdAt":"2025-03-19 16:55:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6263507/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6263507/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82175906,"identity":"a9d2fb6d-a877-468c-a963-28bdec58fe93","added_by":"auto","created_at":"2025-05-07 11:10:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":649475,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between physical activity patterns and chronic disease incidence. (a). Major depressive disorder, obstructive sleep apnea, etc., are associated with daily steps. (b). Type II diabetes, obesity, etc., are associated with very active minutes. (c). Morbid obesity is associated with calories out. (d). Obesity, obstructive sleep apnea, etc., are associated with elevation. (f). Torsion dystonia, etc., are associated with lightly active minutes. (g). Morbid obesity and obesity are associated with calories BMR. (h). Pain in joint, etc., are associated with fairly active minutes.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6263507/v1/a2243a8f0ca1582d5d0af566.png"},{"id":82179259,"identity":"3e94e806-16a3-40c3-9ff4-f9148bbb5342","added_by":"auto","created_at":"2025-05-07 11:34:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1178294,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of significant phenotypes across the physical activity patterns.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6263507/v1/94704c151acebc59a39a3c3c.png"},{"id":82180234,"identity":"880772c2-9236-490e-b238-1a787b8ffe59","added_by":"auto","created_at":"2025-05-07 11:42:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2799889,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6263507/v1/276f5347-eb04-4514-8302-0bac3eeb3311.pdf"},{"id":82175907,"identity":"b7dfc1cc-196a-4ea3-92c5-763b16bf7226","added_by":"auto","created_at":"2025-05-07 11:10:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":789483,"visible":true,"origin":"","legend":"Association of Physical Activity from Wearable Devices and Chronic Disease Risk: Insights from the All of Us Research Program","description":"","filename":"Fitbitactivitychronicdiseasencsupp.docx","url":"https://assets-eu.researchsquare.com/files/rs-6263507/v1/519968dd2fad262aa3407e85.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Association of Physical Activity from Wearable Devices and Chronic Disease Risk: Insights from the All of Us Research Program","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic diseases, including cardiometabolic disorders, obesity, and major depressive disorder (MDD), are the main causes of morbidity and mortality worldwide\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Given the critical role of lifestyle factors in their onset and progression, physical activity has garnered increasing attention as a key modifiable determinant\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Wearable devices enable continuous, non-invasive monitoring of physical activity, offering novel opportunities to explore its association with chronic disease outcomes. Prior research has established links between physical activity and multiple chronic diseases, such as cardiovascular disease, type 2 diabetes, depression, Parkinson\u0026rsquo;s disease, and multiple sclerosis\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, many studies rely on self-reported activity data or short-term objective assessments via accelerometers or pedometers. Self-reported data, though informative, often diverge from objectively measured activity levels\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe emergence of commercial wearables, such as Fitbit, offers a means to address these limitations by enabling long-term, objective activity tracking. Fitbit-based studies have validated their accuracy in measuring steps, heart rate, and energy expenditure\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These devices generate longitudinal data, providing novel insights into individual activity patterns and facilitating large-scale epidemiological investigations of activity-related chronic disease risk.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eAll of Us\u003c/em\u003e Research Program (AoU), a National Institutes of Health initiative, seeks to collect health data from over one million individuals across diverse U.S. populations\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The program integrates electronic health records (EHRs), genomic data, physical measurements, and participant surveys. As of Version 8, more than 59,000 participants have contributed Fitbit data, encompassing activity and sleep metrics. This dataset uniquely enables the first large-scale exploration of longitudinal associations between objectively measured activity behaviors and chronic disease outcomes in a diverse U.S. cohort.\u003c/p\u003e \u003cp\u003eThis study leverages extensive Fitbit activity data and longitudinal EHRs from AoU to examine associations between real-world activity patterns and chronic disease incidence. We aim to elucidate how activity intensity, duration, and regularity influence chronic disease risk, with findings informing evidence-based lifestyle recommendations for disease prevention in the general U.S. population.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe AoU Research Program included 58,785 participants with available Fitbit activity data. Among them, 22,019 participants had both corresponding EHR data and at least six months of Fitbit monitoring (supplementary Figure. 1). The median participants age was 52 years (interquartile range: 38 to 63 years). The majority of participants were female (71.4%), followed by males (28.2%), with 0.4% having unknown sex information. Race distribution was as follows: White (77.5%), Black (6.3%), Asian (3.4%), Other (7.3%), and Unknown (5.5%). Hispanic or Latino participants comprised 7.8%, while 90.4% were non-Hispanic, and 1.9% had unknown ethnicity. The median Fitbit monitoring duration was 2.53 years (1.42, 4.32). Participants had a median daily step count of 6,166 steps (3,698, 8,600). Participants had a median daily step count of 6,166 steps (484.3, 1050.2), with a median basal metabolic rate (BMR) of 1,474.0 kcal calories (1,287.9, 1,686.9). The median total daily calories burned was 2,101.6 kcal (1,787.4, 2,507.4). Median daily elevation gain was 33.9 meters (4.3, 91.0). Daily median activity intensity metrics included sedentary time (738 min; 12.1\u0026ndash;931.3), lightly active time (177.0 min; 118.3\u0026ndash;227.5), fairly active time (9.6 min; 2.6\u0026ndash;19.0), and very active time (13.0 min; 6.0\u0026ndash;24.7) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFitbit activity participants\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.00 (38.00, 63.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,724 (71.41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,214 (28.22%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,060 (77.48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,382 (6.28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e743 (3.37%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,615 (7.33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,219 (5.54%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic or Latino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,716 (7.79%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic or Latino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,896 (90.36%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e407 (1.85%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitbit monitoring length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.53 (1.42, 4.32) years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily steps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,166 (3,698, 8,600)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily elevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.92 (4.30, 91.00) meters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eActivity intensity (minutes)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLightly active\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177.02 (118.28, 227.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery active\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.96 (6.03, 24.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFairly active\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.61 (2.62, 18.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedentary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e737.98 (12.05, 931.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCalories\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily activity calories \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e765.68 (484.26, 1,123.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily BMR calories \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,474.02 (1,287.86, 1,686.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily calories burned \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,101.64 (1,787.42, 2,507.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e The number of calories burned for the day during periods the user was active above the sedentary level. This includes both activities burned calories and BMR.\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Total BMR calories burned for the day.\u003c/p\u003e \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Total calories burned for the day\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between physical activity patterns and chronic disease incidence\u003c/h2\u003e \u003cp\u003eAfter Bonferroni correction, 43 significant associations were identified between physical activity metrics and health outcomes. Daily step count was significantly associated with 27 diseases, including major depressive disorder and obstructive sleep apnea, while activity calories correlated with seven diseases, including sleep apnea and chronic fatigue syndrome. Morbid obesity and obesity were significantly associated with BMR-calories burned, with additional associations observed for total daily energy expenditure. Elevation gain was significantly linked to 11 diseases, including obesity and obstructive sleep apnea. Lightly active minutes were significantly associated with torsion dystonia and inflammatory neuropathy, fairly active minutes with three diseases (including joint pain), and very active minutes with 16 diseases (including type 2 diabetes and obesity) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the associations between physical activity metrics and chronic disease incidence. An increase in daily step count was significantly associated with a lower risk of type 2 diabetes with neurological manifestations (odds ratio (OR)\u0026thinsp;=\u0026thinsp;0.25; 95% confidence interval (CI)\u0026thinsp;=\u0026thinsp;0.14\u0026ndash;0.44; p-value\u0026thinsp;=\u0026thinsp;0.01), major depressive disorder (OR\u0026thinsp;=\u0026thinsp;0.63; 0.54\u0026ndash;0.73; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), sleep apnea (OR\u0026thinsp;=\u0026thinsp;0.49; 0.37\u0026ndash;0.64; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), chronic fatigue syndrome (OR\u0026thinsp;=\u0026thinsp;0.46; 0.35\u0026ndash;0.59; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), chronic pain syndrome (OR\u0026thinsp;=\u0026thinsp;0.20; 0.12\u0026ndash;0.35; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and obstructive sleep apnea (OSA) (OR\u0026thinsp;=\u0026thinsp;0.57; 0.47\u0026ndash;0.69; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Neurological disorders (OR\u0026thinsp;=\u0026thinsp;0.35; 0.25\u0026ndash;0.51; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and shortness of breath (OR\u0026thinsp;=\u0026thinsp;0.49; 0.37\u0026ndash;0.64; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were inversely associated with step count. Increased step count was also significantly associated with a lower risk of tachycardia NOS (OR\u0026thinsp;=\u0026thinsp;0.45; 0.33\u0026ndash;0.60; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and acute renal failure (OR\u0026thinsp;=\u0026thinsp;0.27; 0.18\u0026ndash;0.42; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eHigher BMR calories were significantly associated with an increased risk of obesity (OR\u0026thinsp;=\u0026thinsp;1.62; 1.44\u0026ndash;1.82; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and morbid obesity (OR\u0026thinsp;=\u0026thinsp;2.22; 1.90\u0026ndash;2.59; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Higher activity calories were associated with a reduced risk of sleep apnea (OR\u0026thinsp;=\u0026thinsp;0.76; 0.68\u0026ndash;0.85; p\u0026thinsp;=\u0026thinsp;0.02), OSA (OR\u0026thinsp;=\u0026thinsp;0.83; 0.77\u0026ndash;0.90; p\u0026thinsp;=\u0026thinsp;0.03), and chronic fatigue syndrome (OR\u0026thinsp;=\u0026thinsp;0.70; 0.61\u0026ndash;0.81; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eGreater daily elevation gain was significantly associated with a reduced risk of multiple conditions. Higher elevation gain was linked to lower risk of abdominal pain (OR\u0026thinsp;=\u0026thinsp;0.83; 0.77\u0026ndash;0.90; p\u0026thinsp;=\u0026thinsp;0.01), urinary tract infection (OR\u0026thinsp;=\u0026thinsp;0.65; 0.55\u0026ndash;0.75; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), gastroesophageal reflux disease (GERD) (OR\u0026thinsp;=\u0026thinsp;0.81; 0.75\u0026ndash;0.88; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and OSA (OR\u0026thinsp;=\u0026thinsp;0.79; 0.72\u0026ndash;0.87; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Elevation gain was also inversely associated with obesity risk (OR\u0026thinsp;=\u0026thinsp;0.78; 0.71\u0026ndash;0.86; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Major depressive disorder was also significantly associated with increased elevation gain (OR\u0026thinsp;=\u0026thinsp;0.80; 0.73\u0026ndash;0.88; p\u0026thinsp;=\u0026thinsp;0.02), as were sleep apnea (OR\u0026thinsp;=\u0026thinsp;0.68; 0.59\u0026ndash;0.79; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Other conditions significantly associated with increased elevation gain included other dyspnea (OR\u0026thinsp;=\u0026thinsp;0.81; 0.74\u0026ndash;0.88; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and hypovolemia (OR\u0026thinsp;=\u0026thinsp;0.52; 0.40\u0026ndash;0.66; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eGreater time spent in very active minutes was significantly associated with a lower risk of OSA (OR\u0026thinsp;=\u0026thinsp;0.79; 0.73\u0026ndash;0.86; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), obesity (OR\u0026thinsp;=\u0026thinsp;0.79; 0.73\u0026ndash;0.87; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and dyspnea (OR\u0026thinsp;=\u0026thinsp;0.84; 0.77\u0026ndash;0.90; p\u0026thinsp;=\u0026thinsp;0.02). Fairly active minutes were significantly associated with an increased risk of bursitis (OR\u0026thinsp;=\u0026thinsp;1.31; 1.19\u0026ndash;1.45; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), joint pain (OR\u0026thinsp;=\u0026thinsp;1.13; 1.08\u0026ndash;1.19; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and osteoarthrosis (OR\u0026thinsp;=\u0026thinsp;1.14; 1.08\u0026ndash;1.20; p\u0026thinsp;=\u0026thinsp;0.02). Lightly active minutes were inversely associated with torsion dystonia risk (OR\u0026thinsp;=\u0026thinsp;0.53; 0.40\u0026ndash;0.69; p\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCox Model Results for Physical Activity and Chronic Diseases\u003c/h3\u003e\n\u003cp\u003eCox proportional hazards regression models identified significant associations between physical activity metrics and chronic disease risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Figures. 2\u0026ndash;9). A 2,000-step increase in daily steps was associated with a reduced risk of obesity (HR\u0026thinsp;=\u0026thinsp;0.85; 95% CI: 0.80\u0026ndash;0.90), morbid obesity (HR\u0026thinsp;=\u0026thinsp;0.79; 0.73\u0026ndash;0.86), and type 2 diabetes (HR\u0026thinsp;=\u0026thinsp;0.78; 0.72\u0026ndash;0.84). Higher step counts were also associated with a reduced risk of major depressive disorder (HR\u0026thinsp;=\u0026thinsp;0.83; 0.77\u0026ndash;0.90). The risk of OSA decreased with increased step count (HR\u0026thinsp;=\u0026thinsp;0.84; 0.79\u0026ndash;0.89). Other dyspnea (HR\u0026thinsp;=\u0026thinsp;0.84; 0.80\u0026ndash;0.89) and essential hypertension (HR\u0026thinsp;=\u0026thinsp;0.88; 0.84\u0026ndash;0.92) also demonstrated a reduced risk with higher step counts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox proportional hazards models for the association between Fitbit activity metric and chronic diseases.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSteps \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElevation \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSedentary \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLightly active \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVery active \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObesity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.846 (0.800-0.895)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.858 (0.777\u0026ndash;0.948)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.077 (1.055\u0026ndash;1.099)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.848 (0.793\u0026ndash;0.907)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.712 (0.498\u0026ndash;1.018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMorbid obesity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.792 (0.731\u0026ndash;0.857)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.785 (0.668\u0026ndash;0.923)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.054 (1.025\u0026ndash;1.083)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.868 (0.793\u0026ndash;0.950)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.450 (0.244\u0026ndash;0.830)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType 2 diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.776 (0.716\u0026ndash;0.843)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.653 (0.531\u0026ndash;0.804)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.039 (1.010\u0026ndash;1.070)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.821 (0.744\u0026ndash;0.906)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.357 (0.200-0.637)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMajor depressive disorder\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.832 (0.770\u0026ndash;0.898)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.820 (0.708\u0026ndash;0.950)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.026 (0.998\u0026ndash;1.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.838 (0.765\u0026ndash;0.918)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.565 (0.342\u0026ndash;0.935)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObstructive sleep apnea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.835 (0.788\u0026ndash;0.885)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.830 (0.746\u0026ndash;0.923)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.033 (1.011\u0026ndash;1.056)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.873 (0.812\u0026ndash;0.939)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.474 (0.322\u0026ndash;0.698)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther dyspnea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.841 (0.799\u0026ndash;0.886)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.866 (0.793\u0026ndash;0.945)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.041 (1.022\u0026ndash;1.062)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.909 (0.852\u0026ndash;0.970)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.320 (0.221\u0026ndash;0.465)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEssential hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.875 (0.835\u0026ndash;0.917)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.924 (0.862\u0026ndash;0.991)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.054 (1.035\u0026ndash;1.073)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.883 (0.831\u0026ndash;0.934)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.668 (0.507\u0026ndash;0.881)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic pain syndrome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.931 (0.799\u0026ndash;1.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.889 (0.683\u0026ndash;1.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.051 (0.989\u0026ndash;1.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.778 (0.640\u0026ndash;0.946)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.185 (0.044\u0026ndash;0.772)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e. 2,000 steps\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e. 100 m\u003c/p\u003e \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e. Hours\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEach 100-meter increase in elevation gain was associated with a lower risk of obesity (HR\u0026thinsp;=\u0026thinsp;0.86; 0.78\u0026ndash;0.95), morbid obesity (HR\u0026thinsp;=\u0026thinsp;0.79; 0.67\u0026ndash;0.92), type 2 diabetes (HR\u0026thinsp;=\u0026thinsp;0.65; 0.53\u0026ndash;0.80), and major depressive disorder (HR\u0026thinsp;=\u0026thinsp;0.82; 0.71\u0026ndash;0.95). OSA (HR\u0026thinsp;=\u0026thinsp;0.83; 0.75\u0026ndash;0.92), other dyspnea (HR\u0026thinsp;=\u0026thinsp;0.87; 0.79\u0026ndash;0.95), and essential hypertension (HR\u0026thinsp;=\u0026thinsp;0.92; 0.86\u0026ndash;0.99) were also inversely associated with elevation gain.\u003c/p\u003e \u003cp\u003eGreater sedentary time was positively associated with an increased risk of multiple conditions. Each additional hour of sedentary time was associated with a higher risk of obesity (HR\u0026thinsp;=\u0026thinsp;1.08; 1.06\u0026ndash;1.10), morbid obesity (HR\u0026thinsp;=\u0026thinsp;1.05; 1.03\u0026ndash;1.08), type 2 diabetes (HR\u0026thinsp;=\u0026thinsp;1.04; 1.01\u0026ndash;1.07), and OSA (HR\u0026thinsp;=\u0026thinsp;1.03; 1.01\u0026ndash;1.06). Other dyspnea (HR\u0026thinsp;=\u0026thinsp;1.04; 1.02\u0026ndash;1.06) and essential hypertension (HR\u0026thinsp;=\u0026thinsp;1.05; 1.04\u0026ndash;1.07) also exhibited increased risk with prolonged sedentary time.\u003c/p\u003e \u003cp\u003eGreater time spent in light activity was associated with a lower risk of several conditions. Each additional hour of light activity was associated with reduced risk of obesity (HR\u0026thinsp;=\u0026thinsp;0.85; 0.79\u0026ndash;0.91), morbid obesity (HR\u0026thinsp;=\u0026thinsp;0.87; 0.79\u0026ndash;0.95), type 2 diabetes (HR\u0026thinsp;=\u0026thinsp;0.82; 0.74\u0026ndash;0.91), major depressive disorder (HR\u0026thinsp;=\u0026thinsp;0.84; 0.77\u0026ndash;0.92), OSA (HR\u0026thinsp;=\u0026thinsp;0.87; 0.81\u0026ndash;0.94), other dyspnea (HR\u0026thinsp;=\u0026thinsp;0.91; 0.85\u0026ndash;0.97), essential hypertension (HR\u0026thinsp;=\u0026thinsp;0.88; 0.83\u0026ndash;0.93), and chronic pain syndrome (HR\u0026thinsp;=\u0026thinsp;0.78; 0.64\u0026ndash;0.95). Each additional hour of very active time was associated with a significantly lower risk of morbid obesity (HR\u0026thinsp;=\u0026thinsp;0.45; 0.24\u0026ndash;0.83), type 2 diabetes (HR\u0026thinsp;=\u0026thinsp;0.34; 0.20\u0026ndash;0.64), OSA (HR\u0026thinsp;=\u0026thinsp;0.47; 0.32\u0026ndash;0.70), other dyspnea (HR\u0026thinsp;=\u0026thinsp;0.32; 0.22\u0026ndash;0.47), essential hypertension (HR\u0026thinsp;=\u0026thinsp;0.67; 0.51\u0026ndash;0.88), and major depressive disorder (HR\u0026thinsp;=\u0026thinsp;0.19; 0.44\u0026ndash;0.77).\u003c/p\u003e\n\u003ch3\u003eSensitivity Analysis using body mass index (BMI) for Obesity classification\u003c/h3\u003e\n\u003cp\u003eTo validate our findings, we performed a sensitivity analysis using BMI-defined obesity as an alternative outcome measure Logistic regression models assessing associations between physical activity metrics and BMI-defined obesity confirmed the primary findings. Higher daily step counts were associated with a significantly lower risk of obesity (OR\u0026thinsp;=\u0026thinsp;0.79; 95% CI: 0.71\u0026ndash;0.88; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as were increased elevation gain (OR\u0026thinsp;=\u0026thinsp;0.77; 0.69\u0026ndash;0.86; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and time spent in very active intensity (OR\u0026thinsp;=\u0026thinsp;0.80; 0.72\u0026ndash;0.88; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Conversely, BMR calories were positively associated with an increased risk of obesity (OR\u0026thinsp;=\u0026thinsp;2.17; 1.82\u0026ndash;2.60; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting a potential compensatory metabolic effect. Cox proportional hazards models further corroborated these findings, demonstrating consistent associations between physical activity metrics and the risk of developing BMI-defined obesity over time (Supplementary Figure. 10). Higher daily step counts, increased elevation gain, and greater time spent in very active intensity were significantly associated with a lower hazard of obesity onset. These results underscore the robustness of our primary analysis and emphasize the protective role of increased physical activity against obesity, regardless of diagnostic criteria.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the associations between Fitbit-derived physical activity metrics and chronic disease incidence using data from the AoU Research Program. We utilized the latest AoU Curated Data Repository (CDR) version 8, released in February 2025, incorporating participant data up to October 1, 2023. With over 59,000 participants contributing Fitbit data, this dataset uniquely enables the exploration of long-term associations between objectively measured physical activity and chronic disease risk in a diverse cohort\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Utilizing this comprehensive dataset enhances the robustness and generalizability of our findings, ensuring their relevance to public health efforts and evidence-based physical activity interventions for disease prevention. Our findings suggest that higher physical activity levels\u0026mdash;measured by daily step count, elevation gain, and activity intensity\u0026mdash;are associated with a reduced risk of chronic diseases, including obesity, type 2 diabetes, major depressive disorder, and OSA. Conversely, prolonged sedentary time was linked to an increased risk of these conditions. These findings align with growing evidence emphasizing the role of regular physical activity in improving health and mitigating disease risk.\u003c/p\u003e \u003cp\u003eThe inverse association between daily step count and the risk of obesity, type 2 diabetes, and major depressive disorder is consistent with prior research demonstrating the protective effects of increased step counts on cardiometabolic health and mental well-being\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Our results indicate that higher step counts are significantly associated with a lower risk of these conditions. This aligns with the concept that increased physical activity enhances energy expenditure, improves metabolic function, and alleviates depressive symptoms\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These findings reinforce public health recommendations advocating higher daily step counts as a practical strategy for chronic disease prevention.\u003c/p\u003e \u003cp\u003eOur study identified elevation gain, a less frequently examined metric, as significantly associated with a lower risk of several chronic diseases, including type 2 diabetes, morbid obesity, and OSA. Prior research suggests that elevation-related activities, such as stair climbing, may confer health benefits, though further evidence is needed to confirm their effects on cardiovascular and metabolic health\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These findings imply that integrating elevation-related activities into daily routines may offer additional health benefits. Further studies are warranted to elucidate the role of elevation-related activities in chronic disease prevention.\u003c/p\u003e \u003cp\u003eThis study further highlighted the association between activity intensity and health outcomes. Greater time in very active intensity was significantly associated with a lower risk of obesity, type 2 diabetes, and OSA, while increased fairly active and lightly active time correlated with reduced risks of several chronic conditions, including major depressive disorder. These findings align with prior research demonstrating the benefits of varying physical activity intensities on cardiometabolic and mental health outcomes\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, fairly active intensity was also associated with an increased risk of bursitis, joint pain, and osteoarthrosis, suggesting that while high-intensity activity is beneficial for chronic disease prevention, it may increase musculoskeletal strain. These findings underscore the need for personalized physical activity recommendations, particularly for individuals at risk of musculoskeletal injuries\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConversely, prolonged sedentary time was consistently associated with a higher risk of multiple chronic diseases, including obesity, type 2 diabetes, and essential hypertension. These findings align with previous studies highlighting the adverse effects of prolonged sedentary behavior on metabolic health and its association with an increased risk of cardiometabolic diseases\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The strong association between sedentary behavior and chronic disease risk underscores the importance of reducing sedentary time as a complementary strategy to increasing physical activity for improving health outcomes.\u003c/p\u003e \u003cp\u003eTo evaluate the robustness of our findings, we performed a sensitivity analysis using BMI-defined obesity as an alternative outcome measure. Logistic regression analyses corroborated our primary findings, showing that higher daily step counts, increased elevation gain, and greater time spent in very active intensity were significantly associated with a lower risk of obesity. Cox proportional hazards models further reinforced these associations, demonstrating that physical activity metrics were consistently linked to a lower hazard of obesity development over time. These findings enhance the validity of our primary results, indicating that the observed associations persist regardless of whether obesity is defined by BMI criteria or clinical diagnosis. The sensitivity analysis underscores the robustness of the association between physical activity and obesity risk, suggesting that increasing daily movement and engaging in higher-intensity activities may confer benefits across varying obesity definitions.\u003c/p\u003e \u003cp\u003eA key strength of this study is the use of real-world wearable data, offering objective and granular insights into long-term physical activity patterns. Unlike traditional epidemiological studies relying on self-reported physical activity, wearable-derived metrics provide greater accuracy and allow for detailed assessments of activity intensity, duration, and elevation. However, several limitations should be considered. First, as an observational study, causality cannot be established. Although we mitigated reverse causation by excluding participants diagnosed within the initial 180 days, residual confounding remains possible. Furthermore, reliance on Fitbit users may introduce selection bias, as these individuals tend to be more health-conscious than the general population.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings highlight the importance of increasing daily steps, incorporating elevation-related activities, and engaging in diverse physical activity intensities to lower chronic disease risk. Equally important is reducing sedentary behavior to mitigate chronic disease risk. These findings offer valuable insights for designing targeted public health interventions that promote physical activity and reduce sedentary behavior, ultimately enhancing population health outcomes. Future research should investigate the underlying mechanisms of these associations and develop personalized activity recommendations to optimize health benefits while minimizing risks. With the growing application of machine learning in precision medicine\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, integrating these approaches into future research could enhance the development of tailored physical activity interventions, enabling more precise health outcome predictions and personalized recommendations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants\u003c/h2\u003e \u003cp\u003eAll participants provided informed consent to join the \u003cem\u003eAll of Us\u003c/em\u003e (AoU) Research Program, a large-scale National Institutes of Health (NIH)-funded initiative designed to advance research in health and disease\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Eligible participants included all U.S. adults (aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years), excluding individuals who were incarcerated or unable to provide independent consent. This study utilized controlled-tier data (C2024Q3R4) from the AoU Researcher Workbench. At the time of analysis, data were available from 633,540 individuals enrolled between May 2018 and October 1, 2023\u003csup\u003e15\u003c/sup\u003e. Data collection included demographic information, survey responses from digital enrollment, physical measurements and vital signs recorded by partner healthcare provider organizations, voluntarily shared electronic health records (EHRs), and Fitbit data linked via participants\u0026rsquo; Google Fitbit accounts within the AoU portal\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFitbit activity data\u003c/h3\u003e\n\u003cp\u003eThis study utilized Fitbit-derived daily activity data to capture detailed physical activity patterns of participants. The dataset included activity calories, basal metabolic rate (BMR) calories, total calories burned, elevation gain, activity minutes at different intensity levels, and total step count. Fitbit data provides an advantage over traditional self-reported activity measures by offering objective, continuous, and non-invasive tracking. The inclusion of detailed activity metrics, such as light, moderate, and vigorous intensity levels, enables a comprehensive assessment of participants\u0026rsquo; activity profiles over time. Fitbit data were collected from participants who linked their Fitbit accounts through the AoU portal, enabling longitudinal tracking of activity behaviors.\u003c/p\u003e \u003cp\u003eWe summarized daily activity metrics by averaging activity patterns over each participant\u0026rsquo;s monitoring period to derive a representative activity profile. This approach facilitated characterization of individual variability and investigation of associations between activity behaviors and health outcomes. Additionally, these metrics enabled differentiation of activity intensities, offering insights into both total activity volume and its distribution throughout the day.\u003c/p\u003e\n\u003ch3\u003ePreprocessing\u003c/h3\u003e\n\u003cp\u003eThe dataset was accessed via Google BigQuery, extracting all participants with available Fitbit data. SQL queries were used to extract activity summary records, including activity calories, BMR, total calories burned, elevation gain, activity minutes (light, moderate, and vigorous), and total steps.\u003c/p\u003e \u003cp\u003eA cohort filtering process was applied to ensure participants met key inclusion criteria (supplement Figure. 1). First, participants aged 18 years or older were included. Age was determined by comparing the first recorded Fitbit activity date with the participant's date of birth, retaining only those classified as adults at their first activity record. Next, participants were required to have at least six months (\u0026ge;\u0026thinsp;180 days) of recorded Fitbit activity. This criterion, commonly applied in prior research\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, ensured sufficient data quality and excluded participants with limited activity records that could yield unreliable results. Lastly, only participants with linked EHR data were included. This criterion ensured the availability of EHR data for all participants, allowing assessment of associations between Fitbit-derived activity metrics and clinical outcomes.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes and covariates\u003c/h2\u003e \u003cp\u003eStudy outcomes were derived from diagnostic events recorded in participants' EHRs. Diagnostic events were identified using International Classification of Diseases (ICD) codes from both ICD-9 and ICD-10 versions. ICD codes were mapped to 1,765 distinct phecodes using the PheWAS catalog's Phecode Maps\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, ensuring consistent diagnosis categorization. Each phecode was associated with its corresponding phenotype and diagnostic category. To ensure statistical robustness, phecodes with fewer than 100 cases were excluded. Covariates included age and sex, extracted from demographic data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eWe used multiple logistic regression models to assess the association between physical activity metrics (e.g., steps, activity calories, and minutes at different activity intensities) and health outcomes. Study outcomes were defined as the presence or absence of specific diagnoses, represented by phecodes derived from EHR data. To enhance robustness, participants diagnosed with a given phecode within the first 180 days of Fitbit monitoring were excluded. Analyses were adjusted for age, sex, and body mass index (BMI) at baseline. Bonferroni correction was applied to adjust for multiple comparisons.\u003c/p\u003e \u003cp\u003eWe further performed Cox proportional hazards regression models to explore the association between physical activity metrics and selected chronic diseases. Chronic diseases were selected based on prior research linking them to physical activity\u003csup\u003e\u003cspan additionalcitationids=\"CR41 CR42 CR43 CR44 CR45 CR46\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Primary predictor variables were Fitbit-derived activity metrics, including activity calories, elevation, activity minutes at different intensities (e.g., fairly active, lightly active, very active), and step count. Outcomes were defined as EHR-recorded diagnosis events, represented by phecodes. For each phecode, baseline activity metrics were calculated as the average values over the initial 180 days following each participant\u0026rsquo;s first recorded Fitbit activity. Participants with fewer than six months of recorded activity during the baseline period were excluded to maintain data reliability. To mitigate reverse causation, participants diagnosed before or within the first 180 days of Fitbit activity monitoring were excluded from analysis for that specific phecode. Cox models were fitted with individual activity metrics as predictors, adjusting for age, sex, and BMI. The event variable indicated whether a diagnosis occurred during follow-up, while the duration variable represented the number of days from baseline to diagnosis or the end of follow-up. Daily steps were analyzed in 2,000-step increments to assess the impact of step count increases on disease risk. Elevation gain was analyzed in 100-meter increments to evaluate its effect on disease risk. Durations of different activity intensities (e.g., lightly active, very active) and sedentary time were converted to hours to assess the impact of each additional hour on disease risk.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using Python (v3.8, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.python.org/\u003c/span\u003e\u003cspan address=\"https://www.python.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) on the AoU Researcher Workbench, a secure cloud-based platform. The analyses were conducted by the libraries including Pandas\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, NumPy\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, Statsmodels\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and Lifelines\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis using BMI for Obesity classification\u003c/h2\u003e \u003cp\u003eTo evaluate the robustness of our findings, we conducted a sensitivity analysis by defining obesity based on BMI rather than electronic health record (EHR)-based ICD codes. This alternative classification aimed to capture cases that might not have been explicitly diagnosed in the EHR but met the clinical criteria for obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;). Height and weight records were extracted from the AoU. Participants were classified as having BMI-defined obesity if they had at least one recorded BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2; at any point. To reduce the risk of reverse causation, we excluded participants who had a BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2; before the start of Fitbit monitoring or within the first 180 days of Fitbit activity tracking. We repeated the logistic regression and Cox proportional hazards regression models analyses using BMI-defined obesity as the outcome variable.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThis study used data from the All of Us Research Program\u0026rsquo;s Control Tier Dataset v8, available to authorized users on the Researcher Workbench, which can be accessed via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://workbench.researchallofus.org/\u003c/span\u003e\u003cspan address=\"https://workbench.researchallofus.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eCode used for this study is available to approved researchers upon request. Researchers can access the code on the All of Us Research Workbench platform by contacting our study team.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eConflict of interests\u003c/h2\u003e \u003cp\u003eThe authors state that they have no competing interests to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eY.H. and R. Z. conceived the study. Y. H. served as the principal author, extracting data, designing and conducting the entire experiment and writing the manuscript. E. C. assisted in conceiving the study design and reviewed the manuscript. S. I., K. L., L. C., M. H. provided clinical expertise and reviewed the manuscript. All authors contributed to the production of the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Institutes of Health\u0026rsquo;s National Institute on Minority Health and Health Disparities, grant number 1R21MD019134-01. The content is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health. We gratefully acknowledge All of Us participants for their contributions, without whom this research would not have been possible. We also thank the National Institutes of Health\u0026rsquo;s All of Us Research Program for making available the participant data examined in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNoncommunicable diseases. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases.\u003c/li\u003e\n\u003cli\u003eFlegal, K. M., Kit, B. K., Orpana, H. \u0026amp; Graubard, B. I. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. \u003cem\u003eJAMA\u003c/em\u003e \u003cstrong\u003e309\u003c/strong\u003e, 71\u0026ndash;82 (2013).\u003c/li\u003e\n\u003cli\u003eMalhi, G. S. \u0026amp; Mann, J. J. Depression. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e392\u003c/strong\u003e, 2299\u0026ndash;2312 (2018).\u003c/li\u003e\n\u003cli\u003eWarburton, D. E. R., Nicol, C. W. \u0026amp; Bredin, S. S. D. Health benefits of physical activity: the evidence. \u003cem\u003eCMAJ\u003c/em\u003e \u003cstrong\u003e174\u003c/strong\u003e, 801\u0026ndash;809 (2006).\u003c/li\u003e\n\u003cli\u003eLee, I.-M. \u003cem\u003eet al.\u003c/em\u003e Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e380\u003c/strong\u003e, 219\u0026ndash;229 (2012).\u003c/li\u003e\n\u003cli\u003eHaskell, W. L., Blair, S. N. \u0026amp; Hill, J. O. Physical activity: health outcomes and importance for public health policy. \u003cem\u003ePrev. Med.\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 280\u0026ndash;282 (2009).\u003c/li\u003e\n\u003cli\u003eZhao, A., Cui, E., Leroux, A., Lindquist, M. A. \u0026amp; Crainiceanu, C. M. Evaluating the prediction performance of objective physical activity measures for incident Parkinson\u0026rsquo;s disease in the UK Biobank. \u003cem\u003eJ. Neurol.\u003c/em\u003e \u003cstrong\u003e270\u003c/strong\u003e, 5913\u0026ndash;5923 (2023).\u003c/li\u003e\n\u003cli\u003eMeng, Q. \u003cem\u003eet al.\u003c/em\u003e Quantifying the association between objectively measured physical activity and multiple sclerosis in the UK Biobank. \u003cem\u003eMed. Sci. Sports Exerc.\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 2194\u0026ndash;2202 (2023).\u003c/li\u003e\n\u003cli\u003ePrince, S. A. \u003cem\u003eet al.\u003c/em\u003e A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. \u003cem\u003eInt. J. Behav. Nutr. Phys. Act.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 56 (2008).\u003c/li\u003e\n\u003cli\u003eSchrack, J. A. \u003cem\u003eet al.\u003c/em\u003e Assessing the \u0026ldquo;physical cliff\u0026rdquo;: detailed quantification of age-related differences in daily patterns of physical activity. \u003cem\u003eJ. Gerontol. A Biol. Sci. Med. Sci.\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e, 973\u0026ndash;979 (2014).\u003c/li\u003e\n\u003cli\u003eAdam Noah, J., Spierer, D. K., Gu, J. \u0026amp; Bronner, S. Comparison of steps and energy expenditure assessment in adults of Fitbit Tracker and Ultra to the Actical and indirect calorimetry. \u003cem\u003eJ. Med. Eng. Technol.\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 456\u0026ndash;462 (2013).\u003c/li\u003e\n\u003cli\u003eEvenson, K. R., Goto, M. M. \u0026amp; Furberg, R. D. Systematic review of the validity and reliability of consumer-wearable activity trackers. \u003cem\u003eInt. J. Behav. Nutr. Phys. Act.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 159 (2015).\u003c/li\u003e\n\u003cli\u003eAll of Us Research Program Investigators et al. The \u0026ldquo;All of Us\u0026rdquo; Research Program. \u003cem\u003eN. Engl. J. Med\u003c/em\u003e \u003cstrong\u003e381\u003c/strong\u003e, 668\u0026ndash;676 (2019).\u003c/li\u003e\n\u003cli\u003eMayo, K. R. \u003cem\u003eet al.\u003c/em\u003e The All of Us data and Research Center: Creating a secure, scalable, and sustainable ecosystem for biomedical research. \u003cem\u003eAnnu. Rev. Biomed. Data Sci.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 443\u0026ndash;464 (2023).\u003c/li\u003e\n\u003cli\u003eCurated Data Repository (CDR) version 8 Release Notes. \u003cem\u003eUser Support\u003c/em\u003e https://support.researchallofus.org/hc/en-us/articles/30294451486356-Curated-Data-Repository-CDR-version-8-Release-Notes.\u003c/li\u003e\n\u003cli\u003eSchuch, F. B. \u003cem\u003eet al.\u003c/em\u003e Physical activity and incident depression: A meta-analysis of prospective cohort studies. \u003cem\u003eAm. J. Psychiatry\u003c/em\u003e \u003cstrong\u003e175\u003c/strong\u003e, 631\u0026ndash;648 (2018).\u003c/li\u003e\n\u003cli\u003eArem, H. \u003cem\u003eet al.\u003c/em\u003e Leisure time physical activity and mortality: a detailed pooled analysis of the dose-response relationship. \u003cem\u003eJAMA Intern. Med.\u003c/em\u003e \u003cstrong\u003e175\u003c/strong\u003e, 959\u0026ndash;967 (2015).\u003c/li\u003e\n\u003cli\u003eDel Pozo Cruz, B., Ahmadi, M. N., Lee, I.-M. \u0026amp; Stamatakis, E. Prospective associations of daily step counts and intensity with cancer and cardiovascular disease incidence and mortality and all-cause mortality. \u003cem\u003eJAMA Intern. Med.\u003c/em\u003e \u003cstrong\u003e182\u003c/strong\u003e, 1139\u0026ndash;1148 (2022).\u003c/li\u003e\n\u003cli\u003eReiner, M., Niermann, C., Jekauc, D. \u0026amp; Woll, A. Long-term health benefits of physical activity--a systematic review of longitudinal studies. \u003cem\u003eBMC Public Health\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 813 (2013).\u003c/li\u003e\n\u003cli\u003eLee, I.-M. \u003cem\u003eet al.\u003c/em\u003e Association of step volume and intensity with all-cause mortality in older women. \u003cem\u003eJAMA Intern. Med.\u003c/em\u003e \u003cstrong\u003e179\u003c/strong\u003e, 1105\u0026ndash;1112 (2019).\u003c/li\u003e\n\u003cli\u003eEkelund, U. \u003cem\u003eet al.\u003c/em\u003e Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis. \u003cem\u003eBMJ\u003c/em\u003e \u003cstrong\u003e366\u003c/strong\u003e, l4570 (2019).\u003c/li\u003e\n\u003cli\u003ePiercy, K. L. \u003cem\u003eet al.\u003c/em\u003e The Physical Activity Guidelines for Americans. \u003cem\u003eJAMA\u003c/em\u003e \u003cstrong\u003e320\u003c/strong\u003e, 2020\u0026ndash;2028 (2018).\u003c/li\u003e\n\u003cli\u003eBennell, K., Matheson, G., Meeuwisse, W. \u0026amp; Brukner, P. Risk factors for stress fractures. \u003cem\u003eSports Med.\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 91\u0026ndash;122 (1999).\u003c/li\u003e\n\u003cli\u003eKatzmarzyk, P. T., Church, T. S., Craig, C. L. \u0026amp; Bouchard, C. Sitting time and mortality from all causes, cardiovascular disease, and cancer. \u003cem\u003eMed. Sci. Sports Exerc.\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 998\u0026ndash;1005 (2009).\u003c/li\u003e\n\u003cli\u003eWilmot, E. G. \u003cem\u003eet al.\u003c/em\u003e Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. \u003cem\u003eDiabetologia\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 2895\u0026ndash;2905 (2012).\u003c/li\u003e\n\u003cli\u003eBiswas, A. \u003cem\u003eet al.\u003c/em\u003e Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: a systematic review and meta-analysis. \u003cem\u003eAnn. Intern. Med.\u003c/em\u003e \u003cstrong\u003e162\u003c/strong\u003e, 123\u0026ndash;132 (2015).\u003c/li\u003e\n\u003cli\u003eSu, C. \u003cem\u003eet al.\u003c/em\u003e Identification of Parkinson\u0026rsquo;s disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data. \u003cem\u003eNPJ Digit. Med.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 184 (2024).\u003c/li\u003e\n\u003cli\u003eYang, H. S. \u003cem\u003eet al.\u003c/em\u003e Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning. \u003cem\u003eClin. Chem.\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e, 1396\u0026ndash;1404 (2020).\u003c/li\u003e\n\u003cli\u003eYang, H. S. \u003cem\u003eet al.\u003c/em\u003e Machine learning highlights downtrending of COVID-19 patients with a distinct laboratory profile. \u003cem\u003eHealth Data Sci.\u003c/em\u003e \u003cstrong\u003e2021\u003c/strong\u003e, 7574903 (2021).\u003c/li\u003e\n\u003cli\u003eBrendel, M., Su, C., Hou, Y., Henchcliffe, C. \u0026amp; Wang, F. Comprehensive subtyping of Parkinson\u0026rsquo;s disease patients with similarity fusion: a case study with BioFIND data. \u003cem\u003eNPJ Parkinsons Dis.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 83 (2021).\u003c/li\u003e\n\u003cli\u003eZang, C. \u003cem\u003eet al.\u003c/em\u003e Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts. \u003cem\u003eCommun. Med. (Lond.)\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 130 (2024).\u003c/li\u003e\n\u003cli\u003eZang, C. \u003cem\u003eet al.\u003c/em\u003e Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records. \u003cem\u003eTransl. Psychiatry\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 316 (2024).\u003c/li\u003e\n\u003cli\u003eMaster, H. \u003cem\u003eet al.\u003c/em\u003e How Fitbit data are being made available to registered researchers in All of Us Research Program. \u003cem\u003ePac. Symp. Biocomput.\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 19\u0026ndash;30 (2023).\u003c/li\u003e\n\u003cli\u003eZheng, N. S. \u003cem\u003eet al.\u003c/em\u003e Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program. \u003cem\u003eNat. Med.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 2648\u0026ndash;2656 (2024).\u003c/li\u003e\n\u003cli\u003eLeroux, A. \u003cem\u003eet al.\u003c/em\u003e NHANES 2011-2014: Objective physical activity is the strongest predictor of all-cause mortality. \u003cem\u003eMed. Sci. Sports Exerc.\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 1926\u0026ndash;1934 (2024).\u003c/li\u003e\n\u003cli\u003eLeroux, A. \u003cem\u003eet al.\u003c/em\u003e Quantifying the predictive performance of objectively measured physical activity on mortality in the UK Biobank. \u003cem\u003eJ. Gerontol. A Biol. Sci. Med. Sci.\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e, 1486\u0026ndash;1494 (2021).\u003c/li\u003e\n\u003cli\u003eDenny, J. C. \u003cem\u003eet al.\u003c/em\u003e Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. \u003cem\u003eNat. Biotechnol.\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1102\u0026ndash;1110 (2013).\u003c/li\u003e\n\u003cli\u003eWei, W.-Q. \u003cem\u003eet al.\u003c/em\u003e Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, e0175508 (2017).\u003c/li\u003e\n\u003cli\u003eWu, P. \u003cem\u003eet al.\u003c/em\u003e Mapping ICD-10 and ICD-10-CM codes to phecodes: Workflow development and initial evaluation. \u003cem\u003eJMIR Med. Inform.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, e14325 (2019).\u003c/li\u003e\n\u003cli\u003ePerry, A. S. \u003cem\u003eet al.\u003c/em\u003e Association of longitudinal activity measures and diabetes risk: An analysis from the national institutes of health All of Us Research Program. \u003cem\u003eJ. Clin. Endocrinol. Metab.\u003c/em\u003e \u003cstrong\u003e108\u003c/strong\u003e, 1101\u0026ndash;1109 (2023).\u003c/li\u003e\n\u003cli\u003eAlthoff, T. \u003cem\u003eet al.\u003c/em\u003e Large-scale physical activity data reveal worldwide activity inequality. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e547\u003c/strong\u003e, 336\u0026ndash;339 (2017).\u003c/li\u003e\n\u003cli\u003eSchuch, F. \u003cem\u003eet al.\u003c/em\u003e Physical activity and sedentary behavior in people with major depressive disorder: A systematic review and meta-analysis. \u003cem\u003eJ. Affect. Disord.\u003c/em\u003e \u003cstrong\u003e210\u003c/strong\u003e, 139\u0026ndash;150 (2017).\u003c/li\u003e\n\u003cli\u003eVancampfort, D. \u003cem\u003eet al.\u003c/em\u003e Sedentary behavior and physical activity levels in people with schizophrenia, bipolar disorder and major depressive disorder: a global systematic review and meta-analysis. \u003cem\u003eWorld Psychiatry\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 308\u0026ndash;315 (2017).\u003c/li\u003e\n\u003cli\u003eMendelson, M. \u003cem\u003eet al.\u003c/em\u003e Obstructive sleep apnea syndrome, objectively measured physical activity and exercise training interventions: A systematic review and meta-analysis. \u003cem\u003eFront. Neurol.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 73 (2018).\u003c/li\u003e\n\u003cli\u003eHall, K. A., Singh, M., Mukherjee, S. \u0026amp; Palmer, L. J. Physical activity is associated with reduced prevalence of self-reported obstructive sleep apnea in a large, general population cohort study. \u003cem\u003eJ. Clin. Sleep Med.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 1179\u0026ndash;1187 (2020).\u003c/li\u003e\n\u003cli\u003ePaffenbarger, R. S., Jr, Jung, D. L., Leung, R. W. \u0026amp; Hyde, R. T. Physical activity and hypertension: an epidemiological view. \u003cem\u003eAnn. Med.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 319\u0026ndash;327 (1991).\u003c/li\u003e\n\u003cli\u003eDiaz, K. M. \u0026amp; Shimbo, D. Physical activity and the prevention of hypertension. \u003cem\u003eCurr. Hypertens. Rep.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 659\u0026ndash;668 (2013).\u003c/li\u003e\n\u003cli\u003eTeam, T. P. D. \u003cem\u003ePandas-Dev/Pandas: Pandas\u003c/em\u003e. (Zenodo, 2020). doi:10.5281/zenodo.3509134.\u003c/li\u003e\n\u003cli\u003eHarris, C. R. \u003cem\u003eet al.\u003c/em\u003e Array programming with NumPy. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e585\u003c/strong\u003e, 357\u0026ndash;362 (2020).\u003c/li\u003e\n\u003cli\u003eSeabold, S. \u0026amp; Perktold, J. statsmodels: Econometric and statistical modeling with python. in \u003cem\u003e9th Python in Science Conference\u003c/em\u003e (2010).\u003c/li\u003e\n\u003cli\u003eDavidson-Pilon, C. lifelines: survival analysis in Python. \u003cem\u003eJournal of Open Source Software\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 1317 (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6263507/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6263507/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e​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]\u0026thinsp;=\u0026thinsp;0.85, 95% confidence interval [CI]: 0.80\u0026ndash;0.90), type 2 diabetes (HR\u0026thinsp;=\u0026thinsp;0.78, CI: 0.72\u0026ndash;0.84), and major depressive disorder (HR\u0026thinsp;=\u0026thinsp;0.83, CI: 0.77\u0026ndash;0.90). Elevation gain was inversely associated with obesity (HR\u0026thinsp;=\u0026thinsp;0.86, CI: 0.78\u0026ndash;0.95) and type 2 diabetes (HR\u0026thinsp;=\u0026thinsp;0.65, CI: 0.53\u0026ndash;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\u0026thinsp;=\u0026thinsp;1.08, CI: 1.06\u0026ndash;1.10) and essential hypertension (HR\u0026thinsp;=\u0026thinsp;1.05, CI: 1.04\u0026ndash;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.\u003c/p\u003e","manuscriptTitle":"Association of Physical Activity from Wearable Devices and Chronic Disease Risk: Insights from the All of Us Research Program","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 11:10:40","doi":"10.21203/rs.3.rs-6263507/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"81f46f01-2597-41bf-90dc-d06982845d57","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":46383947,"name":"Health sciences/Risk factors"},{"id":46383948,"name":"Health sciences/Health care/Disease prevention/Lifestyle modification"}],"tags":[],"updatedAt":"2025-05-07T11:10:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 11:10:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6263507","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6263507","identity":"rs-6263507","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}