Study design
The COmPLETE study is a cross-sectional study conducted between January 2018 and December 2019 at the Department of Sport, Exercise and Health at the University of Basel, Switzerland and is registered at clinicaltrials.gov (NCT03986892). The study aimed to perform a comprehensive assessment of components of physical fitness and cardiovascular function in HEART and healthy individuals (HEALTH) as well as to identify the most important factors contributing to healthy aging [22]. More details on the study design were reported elsewhere [22]. The protocol was approved by the Ethics Committee of North-western and Central Switzerland (EKNZ 2017–01,451) and all procedures followed the Declaration of Helsinki. Signed informed consent was obtained by all participants before study onset.
Study participants
This study includes both, HEART (n = 56) and HEALTH (n = 299). A full description of the recruitment procedures can be found elsewhere [22]. To be eligible for HEART, participants had to be between 20–100 years of age and diagnosed with stable chronic heart failure according to the European Society of Cardiology guidelines for the diagnosis and treatment of acute and chronic heart failure [23]. To be eligible for HEALTH, participants had to be healthy, non-smoking or with no history of smoking within the last 10 years, body mass index < 30 kg.m−2, and aged between 20 to 100 years. Exclusion criteria were: manifested exercise limiting chronic disease (e.g., myocardial infarction; stroke; heart failure; lower-extremity artery disease; cancer with general symptoms; diabetes; clinically apparent renal failure; severe liver disease; chronic bronchitis GOLD stages II to IV; osteoporosis), pregnancy or breastfeeding, drug or alcohol abuse, hypertonic blood pressure > 160/100 mmHg, compromising orthopedic problems, Alzheimer’s disease or any other form of dementia, and inability to follow the procedures of the study [22].
Participant Screening and General Health Assessment
Before the first visit, health- and smoking status, as well as physical activity readiness, were assessed via telephone interview [22]. On-site, height was measured and body composition was evaluated using a four-segment bioelectrical impedance analysis (InBody 720, InBody Co Ltd, Seoul, South Korea). In the further course of the testing procedures, blood samples were drawn via venipuncture by trained medical staff [24]. Among other blood parameters, n-terminal pro b-type natriuretic peptide concentrations were measured using a chemiluminescent microparticle immunoassay (Architect, Abbott, IL, United States) as a blood marker indicating heart failure [24].
PA measurement
PA was objectively assessed using the GENEActiv triaxial accelerometer (Activinsights Ltd., Kimbolton, UK) [11]. The participants were asked to wear the device on their non-dominant wrist [25], 24 h per day for 14 consecutive days in their free-living conditions. PA surveillance started at midnight, the day after the participants received the device. The sampling frequency was set to 50 Hz. The collected raw data were exported using the GENEActiv software version 2.9 (Activinsights Ltd., Kimbolton, UK) and stored in binary format. All further data processing and data analyzes were done using the R-package GGIR version 2.1–3 in R (R Foundation for Statistical Computing, Vienna, Austria) [26]. As part of this, auto-calibration using local gravity as a reference and the sleep detection function were applied [27, 28]. Non-wear time was estimated based on the standard deviation and value range of the raw accelerometer data from each axis using 60 min windows [29]. The magnitude of dynamic acceleration was calculated as the vector magnitude of x-, y-, and z-axes averaged over 5-s epochs [14] and corrected for gravity with negative values rounded to zero, yielding Euclidean Norm Minus One (ENMO) in gravitational units (g) (1) [30].
$$ENMO \left(x, y, z\right)=\sqrt{{x}^{2}+{y}^{2}+{z}^{2}}-1$$
(1)
For a reliable assessment of LPA, MPA, and VPA, only subjects with wear time ≥ 10 h per day [8, 20] and valid data of at least four weekdays and one weekend day for each of the two weeks were included in the statistical analyses [14, 15]. The rationale for these rather strict criteria was to accurately assess PA patterns in both weeks, as well as during the week and on weekend days. To categorize the measured acceleration into PA intensity zones 0.03, 0.1, and 0.4 g were used as cut-offs for LPA (≥ 2 METs), MPA (≥ 3 METs), and VPA (≥ 6 METs), respectively [30, 31]. Total physical activity (TPA) was calculated by summarizing LPA, MPA, and VPA. Every activity below 0.03 g was categorized as sedentary time [30, 31].
Cardiopulmonary exercise testing
Cardiopulmonary exercise testing was performed on a magnetically braked bicycle ergometer (Ergoselect 200; ergoline GmbH, Bitz, Germany) following one of five ramp protocols described in detail elsewhere [23]. The applied exhaustion criteria can be found in Wagner et al. [32]. Parameters of ventilation and gas exchange were collected breath-by-breath and analyzed in 10 s intervals using the MetaMax 3B portable metabolic system (Cortex Biophysik GmbH, Leipzig, Germany) [33]. Peak oxygen uptake (\(\overset{.}{\mathrm{V}}\mathrm{O}_{2\text{peak}}\)) was reported as the three highest consecutive V̇O2-values at any point during the test (30 s mean).
Statistical analysis
All statistical analyses were performed in R. Figures were made with GraphPad Prism version 9.0.2. Data in text and tables are presented as mean ± SD unless stated otherwise. Figures are shown as mean ± SE. For Fig. 2, absolute intensities of 3 METs and 6 METs were chosen corresponding to the cut-offs for MPA and VPA, respectively according to Garber et al. [2]. Since the R-package GGIR merely includes data that at least have a midnight timestamp, only 13 days were available for analyses. To explore the impact of the weekdays vs. weekend days and week 1 vs. week 2 on PA patterns, linear mixed models were used. Weekend days were defined as Saturday and Sunday. To explore the impact of individual days of the week (i.e., Monday to Sunday) and the number of the measurement day (day 1 to 13) on PA patterns, again, linear mixed models were used. Exploratively, moderate-to-vigorous PA (MVPA) accumulated as ≥ 10 consecutive minutes was analyzed in the same way. Similarly, seasonal differences in PA patterns were investigated via linear mixed models. Seasons of the year were defined as Spring (March, April, and May), Summer (June, July, and August), Autumn (September, October, and November), and Winter (December, January, and February). Weighted linear mixed models were used to correct for heteroscedasticity, where applicable. All these models were done for LPA, MPA, MVPA, VPA, and TPA, respectively. The same methods were applied to wear time analyses. All models were adjusted for age, sex, and \(\overset{.}{\mathrm{V}}\mathrm{O}_{2\text{peak}}\). Models analyzing PA, additionally included daily wear time. Differences in sleeping time between weekdays and weekend days were assessed using linear regression analyses. Differences in mean wear time between HEART and HEALTH were analyzed by Wilcoxon rank-sum test. The level of statistical significance was set to P = 0.05 for two-sided tests.