No file available
To read the file of this research,
you can request a copy directly from the authors.
Fitbit-measured physical activity is inversely associated with incident atrial fibrillation among All of Us participants
Preprints and early-stage research may not have been peer reviewed yet.
Abstract
Background and Aims: Meeting weekly moderate to vigorous physical activity (MVPA) guidelines reduces risk of atrial fibrillation (AF). However existing studies have relied on subjective questionnaires or short-duration (<1 week) objective assessments using accelerometry. The objective of this research was to investigate an association between moderate to vigorous physical activity (MVPA) levels and the incidence of atrial fibrillation (AF), utilizing long-term, free-living accelerometry data.
Methods: 1-year Fitbit data, in addition to survey and electronic health record (EHR) data, were extracted from the NIH All of Us (AoU) research database. Cox proportional hazards regression was used to model the association of weekly MVPA (both continuous and categorized as <30, 30-150,151-300, and >300 minutes/week) and incident AF over a five-year follow-up period.
Results: 6086 AoU participants had usable data (51±16 years, 70% female, 83% White, BMI 28.5±4.9 kg/m2, 41±12 complete weeks of Fitbit wear). 55 individuals (0.9%) experienced incident AF in the five-year follow-up period. More time spent in MVPA was associated with lower AF risk (HR = 0.89 [0.81,0.98], p=0.02), with a step-wise reduction for 151-300 mins (p=0.07) and >300 mins (p=0.04) of weekly MVPA, respectively. In a subset of 3847 participants with genomic data, this association persisted after adjustment for AF genetic risk score.
Conclusions: Higher amounts of objectively measured MVPA, measured using free-living, long-term accelerometry data, were inversely associated with risk of incident AF, independent of clinical and genetic risk factors.
ResearchGate has not been able to resolve any citations for this publication.
The association between physical activity and human disease has not been examined using commercial devices linked to electronic health records. Using the electronic health records data from the All of Us Research Program, we show that step count volumes as captured by participants’ own Fitbit devices were associated with risk of chronic disease across the entire human phenome. Of the 6,042 participants included in the study, 73% were female, 84% were white and 71% had a college degree, and participants had a median age of 56.7 (interquartile range 41.5–67.6) years and body mass index of 28.1 (24.3–32.9) kg m–2. Participants walked a median of 7,731.3 (5,866.8–9,826.8) steps per day over the median activity monitoring period of 4.0 (2.2–5.6) years with a total of 5.9 million person-days of monitoring. The relationship between steps per day and incident disease was inverse and linear for obesity (n = 368), sleep apnea (n = 348), gastroesophageal reflux disease (n = 432) and major depressive disorder (n = 467), with values above 8,200 daily steps associated with protection from incident disease. The relationships with incident diabetes (n = 156) and hypertension (n = 482) were nonlinear with no further risk reduction above 8,000–9,000 steps. Although validation in a more diverse sample is needed, these findings provide a real-world evidence-base for clinical guidance regarding activity levels that are necessary to reduce disease risk.
Abstract Physical activity is regarded as favorable to health but effects across the spectrum of human disease are poorly quantified. In contrast to self-reported measures, wearable accelerometers can provide more precise and reproducible activity quantification. Using wrist-worn accelerometry data from the UK Biobank prospective cohort study, we test associations between moderate-to-vigorous physical activity (MVPA) – both total MVPA minutes and whether MVPA is above a guideline-based threshold of ≥150 min/week—and incidence of 697 diseases using Cox proportional hazards models adjusted for age, sex, body mass index, smoking, Townsend Deprivation Index, educational attainment, diet quality, alcohol use, blood pressure, anti-hypertensive use. We correct for multiplicity at a false discovery rate of 1%. We perform analogous testing using self-reported MVPA. Among 96,244 adults wearing accelerometers for one week (age 62 ± 8 years), MVPA is associated with 373 (54%) tested diseases over a median 6.3 years of follow-up. Greater MVPA is overwhelmingly associated with lower disease risk (98% of associations) with hazard ratios (HRs) ranging 0.70–0.98 per 150 min increase in weekly MVPA, and associations spanning all 16 disease categories tested. Overall, associations with lower disease risk are enriched for cardiac (16%), digestive (14%), endocrine/metabolic (10%), and respiratory conditions (8%) (chi-square p
Introduction:
Measurement of physical activity (PA) using commercial activity trackers such as Fitbit devices has become increasingly popular, also for people with haemophilia (PWH). The accuracy of the Fitbit model Charge 3 has not yet been examined.
Aims:
To compare the Fitbit Charge 3 against the research-grade accelerometer ActiGraph GT3X-BT in measuring average daily steps and minutes spent in different PA intensities.
Methods:
Twenty-four young PWH wore a wrist-worn Fitbit Charge 3 and hip-worn ActiGraph GT3X-BT simultaneously for seven consecutive days in free-living conditions. Correlation of and differences between the devices for daily averages of PA parameters were assessed using Pearson's correlation coefficient and paired t-test, respectively. Agreement between devices was assessed using Bland-Altman plots.
Results:
Twenty participants (mean age 21.8) were included in the analyses. We found moderate to high correlations between Fitbit and ActiGraph measured daily averages for all PA variables, but statistically significant differences between devices for all variables except daily minutes of moderate PA. Fitbit overestimated average daily steps, minutes of light, vigorous and moderate-to-vigorous PA. Bland-Altman plots showed a measurement bias between devices for all parameters with increasing overestimation by the Fitbit for higher volumes of PA.
Conclusion:
The Fitbit Charge 3 overestimated steps and minutes of light, moderate and moderate-to-vigorous PA as compared to the ActiGraph GT3X-BT, and this bias increased with PA volume. The Fitbit should therefore be used with caution in research, and we advise users of the device to be cognizant of this overestimation.
Aims:
To estimate the prevalence, incidence, mortality, and risk factors for atrial fibrillation (AF) in 195 countries and territories from 1990 to 2017.
Methods and results:
Following the methodologies used in the Global Burden of Disease study 2017, the prevalence, incidence, and mortality of AF were analyzed by age, sex, year, socio-demographic index (SDI), and location. The percentage contributions of major risk factors to age-standardised AF deaths were measured by population attributable fractions. In 2017, there were 37.57 million [95% uncertainty interval (UI) 32.55 to 42.59] prevalent cases and 3.05 million (95% UI 2.61 to 3.51) incident cases of AF globally, contributing to 287,241 (95% UI 276,355 to 304,759) deaths. The age-standardised rates of prevalent cases, incident cases, and deaths of AF in 2017 and their temporal trends from 1990 to 2017 varied significantly by SDI quintile and location. High systolic blood pressure was the leading risk factor for AF age-standardised deaths [34.3% (95% UI 27.4 to 41.5)] in 2017, followed by high body-mass index [20.7% (95% UI 11.5 to 32.2)] and alcohol use [9.4% (95% UI 7.0 to 12.2)].
Conclusion:
Our study has systematically and globally assessed the temporal trends of AF, which remains a major public heath challenge. Although AF mainly occurred in developed countries, the unfavorable trend in countries with lower SDI also deserves particular attention. More effective prevention and treatment strategies aimed at counteracting the increase in AF burden should be established in some countries.
Objective:
We examined the accuracy of data from an affordable personal monitor (Fitbit Flex) compared with that of data from a research-grade accelerometer worn simultaneously for 7 days; high accuracy would support substitution with this less-expensive personal activity monitor in future community-based arthritis research.
Methods:
Subjects (N = 35) with chronic knee symptoms were recruited for a pilot intervention study using Fitbits to increase physical activity in employees with chronic knee symptoms at an urban corporation. Subjects simultaneously wore for 7 days a Fitbit Flex (wrist-worn) and ActiGraph GT3X+ (waist-worn). Fitbit Flex data were regularly stored on a research storage service (Fitabase) by participants. Bland-Altman plots were constructed to examine the agreement between the mean daily times spent in light activity and in bouted moderate-to-vigorous physical activity (MVPA). Comparisons were calculated by matching Fitabase data from calendar days the Fitbit was worn with data from valid monitoring days (greater than or equal to 10 hours wear time) of the ActiGraph.
Results:
Participants at baseline were mostly female (69%) and white (57%) and had a mean age of 52 years and body mass index of 32 kg/m2 . Bland-Altman analyses indicated systematic bias overall (the Fitbit overestimated both light-intensity activity and MVPA compared with the ActiGraph). The average error varied in magnitude and direction with changing activity amounts.
Conclusion:
The Fitbit Flex does not appear to be an adequate substitute for research-grade accelerometry (which represents the gold standard for objective research monitoring of all physical activity intensity levels) in this population of persons with chronic knee symptoms.
Scikit-learn is a Python module integrating a wide range of state-of-the-art
machine learning algorithms for medium-scale supervised and unsupervised
problems. This package focuses on bringing machine learning to non-specialists
using a general-purpose high-level language. Emphasis is put on ease of use,
performance, documentation, and API consistency. It has minimal dependencies
and is distributed under the simplified BSD license, encouraging its use in
both academic and commercial settings. Source code, binaries, and documentation
can be downloaded from http://scikit-learn.sourceforge.net.
Multivariate Imputation by Chained Equations (MICE) is the name of software for imputing incomplete multivariate data by Fully Conditional Speci cation (FCS). MICE V1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. MICE V1.0 introduced predictor selection, passive imputation and automatic pooling. This article presents MICE V2.0, which extends the functionality of MICE V1.0 in several ways. In MICE V2.0, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. MICE V2.0 adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling and model selection. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. MICE V2.0 is freely available from CRAN as an R package mice. This article provides a hands-on, stepwise approach to using mice for solving incomplete data problems in real data.
Aims
Physical activity may be an important modifiable risk factor for atrial fibrillation (AF), but associations have been variable and generally based on self-reported activity.
Methods and results
We analysed 93 669 participants of the UK Biobank prospective cohort study without prevalent AF who wore a wrist-based accelerometer for 1 week. We categorized whether measured activity met the standard recommendations of the European Society of Cardiology, American Heart Association, and World Health Organization [moderate-to-vigorous physical activity (MVPA) ≥150 min/week]. We tested associations between guideline-adherent activity and incident AF (primary) and stroke (secondary) using Cox proportional hazards models adjusted for age, sex, and each component of the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) risk score. We also assessed correlation between accelerometer-derived and self-reported activity. The mean age was 62 ± 8 years and 57% were women. Over a median of 5.2 years, 2338 incident AF events occurred. In multivariable adjusted models, guideline-adherent activity was associated with lower risks of AF [hazard ratio (HR) 0.82, 95% confidence interval (CI) 0.75–0.89; incidence 3.5/1000 person-years, 95% CI 3.3–3.8 vs. 6.5/1000 person-years, 95% CI 6.1–6.8] and stroke (HR 0.76, 95% CI 0.64–0.90; incidence 1.0/1000 person-years, 95% CI 0.9–1.1 vs. 1.8/1000 person-years, 95% CI 1.6–2.0). Correlation between accelerometer-derived and self-reported MVPA was weak (Spearman r = 0.16, 95% CI 0.16–0.17). Self-reported activity was not associated with incident AF or stroke.
Conclusions
Greater accelerometer-derived physical activity is associated with lower risks of AF and stroke. Future preventive efforts to reduce AF risk may be most effective when targeting adherence to objective activity thresholds.
The association between objectively measured physical activity and atrial fibrillation (AF) has not been examined. Therefore, we examined the association between moderate and vigorous physical activity (MVPA) with incident AF in 5,147 participants who completed accelerometer assessment for 4 to 7 consecutive days in the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. MVPA was defined as >1065 counts/minute, and daily mean time spent in MVPA was computed. Incident AF was identified during follow-up by a study-scheduled electrocardiogram and also from self-reported medical history of a physician diagnosis. Logistic regression was used to assess the relationship between daily time in MVPA and incident AF. A total of 429 (8.3%) incident AF cases were detected after a median follow-up of 3.5 years following accelerometer assessment. Participants with higher daily time spent in MVPA were less likely to develop AF than those with lower MVPA achievement (Quartile 1=12.4%; Quartile 2=8.3%; Quartile 3=7.1%; Quartile 4=5.4%; p-trend<0.001). In a multivariable model adjusted for AF risk factors, the risk of AF decreased with higher levels of daily MVPA (Quartile 1: Ref; Quartile 2: HR=0.77, 95%CI=0.58-1.01; Quartile 3: HR=0.72, 95%CI=0.53-0.98; Quartile 4: HR=0.62, 95%CI=0.44-0.87; p-trend=0.0056). In conclusion, higher levels of objectively measured daily MVPA are protective against the development of AF suggesting that promotion of MVPA should be encouraged to reduce the current and future burden of AF.
Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, is associated with substantial morbidity, mortality, and healthcare use. Great strides have been made in stroke prevention and rhythm control strategies, yet reducing the incidence of AF has been slowed by the increasing incidence and prevalence of AF risk factors, including obesity, physical inactivity, sleep apnea, diabetes mellitus, hypertension, and other modifiable lifestyle-related factors. Fortunately, many of these AF drivers are potentially reversible, and emerging evidence supports that addressing these modifiable risks may be effective for primary and secondary AF prevention. A structured, protocol-driven multidisciplinary approach to integrate lifestyle and risk factor management as an integral part of AF management may help in the prevention and treatment of AF. However, this aspect of AF management is currently underrecognized, underused, and understudied. The purpose of this American Heart Association scientific statement is to review the association of modifiable risk factors with AF and the effects of risk factor intervention. Implementation strategies, care pathways, and educational links for achieving impactful weight reduction, increased physical activity, and risk factor modification are included. Implications for clinical practice, gaps in knowledge, and future directions for the research community are highlighted.
Aims:
Physical activity reduces cardiovascular disease burden and mortality, although its relationship with cardiac arrhythmias is less certain. The aim of this study was to assess the association between self-reported physical activity and atrial fibrillation (AF), ventricular arrhythmias and bradyarrhythmias, across the UK Biobank cohort.
Methods and results:
We included 402 406 individuals (52.5% female), aged 40-69 years, with over 2.8 million person-years of follow-up who underwent self-reported physical activity assessment computed in metabolic equivalent-minutes per week (MET-min/wk) at baseline, detailed physical assessment and medical history evaluation. Arrhythmia episodes were diagnosed through hospital admissions and death reports. Incident AF risk was lower amongst physically active participants, with a more pronounced reduction amongst female participants [hazard ratio (HR) for 1500 vs. 0 MET-min/wk: 0.85, 95% confidence interval (CI) 0.74-0.98] than males (HR for 1500 vs. 0 MET-min/wk: 0.90, 95% CI 0.82-1.0). Similarly, we observed a significantly lower risk of ventricular arrhythmias amongst physically active participants (HR for 1500 MET-min/wk 0.78, 95% CI 0.64-0.96) that remained relatively stable over a broad range of physical activity levels between 0 and 2500 MET-min/wk. A lower AF risk amongst female participants who engaged in moderate levels of vigorous physical activity was observed (up to 2500 MET-min/wk). Vigorous physical activity was also associated with reduced ventricular arrhythmia risk. Total or vigorous physical activity was not associated with bradyarrhythmias.
Conclusion:
The risk of AF and ventricular arrhythmias is lower amongst physically active individuals. These findings provide observational support that physical activity is associated with reduced risk of atrial and ventricular arrhythmias.
Knowledge gained from observational cohort studies has dramatically advanced the prevention and treatment of diseases. Many of these cohorts, however, are small, lack diversity, or do not provide comprehensive phenotype data. The All of Us Research Program plans to enroll a diverse group of at least 1 million persons in the United States in order to accelerate biomedical research and improve health. The program aims to make the research results accessible to participants, and it is developing new approaches to generate, access, and make data broadly available to approved researchers. All of Us opened for enrollment in May 2018 and currently enrolls participants 18 years of age or older from a network of more than 340 recruitment sites. Elements of the program protocol include health questionnaires, electronic health records (EHRs), physical measurements, the use of digital health technology, and the collection and analysis of biospecimens. As of July 2019, more than 175,000 participants had contributed biospecimens. More than 80% of these participants are from groups that have been historically underrepresented in biomedical research. EHR data on more than 112,000 participants from 34 sites have been collected. The All of Us data repository should permit researchers to take into account individual differences in lifestyle, socioeconomic factors, environment, and biologic characteristics in order to advance precision diagnosis, prevention, and treatment.
Background
-Observational studies have shown inverse associations among fitness, physical activity, and cardiovascular disease. However, little is known about these associations in individuals with elevated genetic susceptibility for these diseases.
Methods
-We estimated associations of grip strength, objective and subjective physical activity, and cardiorespiratory fitness with cardiovascular events and all-cause death in a large cohort of 502635 individuals from the UK Biobank (median follow-up, 6.1 years; interquartile range, 5.4-6.8 years). Then we further examined these associations in individuals with different genetic burden by stratifying individuals based on their genetic risk scores for coronary heart disease and atrial fibrillation. We compared disease risk among individuals in different tertiles of fitness, physical activity, and genetic risk using lowest tertiles as reference.
Results
-Grip strength, physical activity, and cardiorespiratory fitness showed inverse associations with incident cardiovascular events (coronary heart disease: hazard ratio [HR], 0.79; 95% confidence interval [CI], 0.77- 0.81; HR, 0.95; 95% CI, 0.93-0.97; and HR, 0.68; 95% CI, 0.63-0.74, per SD change, respectively; atrial fibrillation: HR, 0.75; 95% CI, 0.73- 0.76; HR, 0.93; 95% CI, 0.91-0.95; and HR, 0.60; 95% CI, 0.56-0.65, per SD change, respectively). Higher grip strength and cardiorespiratory fitness were associated with lower risk of incident coronary heart disease and atrial fibrillation in each genetic risk score group (Ptrend <0.001 in each genetic risk category). In particular, high levels of cardiorespiratory fitness were associated with 49% lower risk for coronary heart disease (HR, 0.51; 95% CI, 0.38-0.69) and 60% lower risk for atrial fibrillation (HR, 0.40; 95%, CI 0.30-0.55) among individuals at high genetic risk for these diseases.
Conclusions
- Fitness and physical activity demonstrated inverse associations with incident cardiovascular disease in the general population, as well as in individuals with elevated genetic risk for these diseases.
A coding scheme is presented for classifying physical activity by rate of energy expenditure, i.e., by intensity. Energy cost was established by a review of published and unpublished data. This coding scheme employs five digits that classify activity by purpose (i.e., sports, occupation, self-care), the specific type of activity, and its intensity as the ratio of work metabolic rate to resting metabolic rate (METs). Energy expenditure in kilocalories or kilocalories per kilogram body weight can be estimated for all activities, specific activities, or activity types. General use of this coding system would enhance the comparability of results across studies using self reports of physical activity.
- M O Leavitt
Leavitt MO. 2008 Physical Activity Guidelines for Americans
Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes: An Individual-Participant Data Meta-Analysis
Writing Group for the CKD Prognosis Consortium. Estimated Glomerular Filtration Rate,
Albuminuria, and Adverse Outcomes: An Individual-Participant Data Meta-Analysis. JAMA
2023;330:1266-1277.