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764 results for “Physical exercise”
The exercise paradox: Avoiding physical inactivity stimuli requires higher response inhibition
<p><strong>Dataset related to the paper on Response inhibition to physical inactivity stimuli using go/no-go tasks. </strong></p> <p>This dataset includes:</p> <p><strong>1) A codebook (including the name of the main variables)</strong></p> <p>--> "code_book_Go_noGo_Miller.xlsx"</p> <p><strong>2) Raw data of the behavioral outcomes (i.e., reaction times) of the affective go/no-go task</strong></p> <p>--> "corrected.behavioral.data.csv"</p> <p>--> "correct_Order.csv"</p> <p><strong>3) Self-reported data </strong></p> <p>--> "Self_report_data.csv"</p> <p><strong>3) EEG data </strong></p> <p>--> "gng_data"</p> <p><strong>5) R script for the data management (i.e., from the raw data to data ready to be analyzed)</strong></p> <p>--> "Data_management_Self_report_go_no_go_Miller.R" for the self-reported data (return the file: "Data_SR_final.RData")</p> <p>--> "Data_management_behav_go_no_go_Miller.R" for the behavioral outcomes (return the file: "Data_GNG_behav.RData")</p> <p>--> Data ready to be analyzed "Data_GNG_final_all.RData"</p> <p><strong>6) Eprime script for the affective go/no-go task ("Go_no_go_task.zip")</strong></p> <p>--> Images depicting physical activity and physical inactivity stimuli were kindly Share by Kullmann et al. (2014)</p> <p><strong>7) R script for the models tested</strong></p> <p><strong>--> "</strong>Models_GoNogo_Miller_VZenodo.R" for behavioral data</p> <p>--> "Models_EEG_GoNogo.R" for EEG data</p>
A database of physical therapy exercises with variability of execution collected by wearable sensors
<p>The PHYTMO database contains data from physical therapy exercises and gait variations recorded with magneto-inertial sensors, including information from an optical reference system. PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations commonly prescribed in physical therapies were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. Four magneto-inertial sensors were placed on the lower-or upper-limbs for the recording of the motions together with passive optical reflectors. The files include the specifications of the inertial sensors and the cameras. The database includes magneto-inertial data (linear acceleration, turn rate and magnetic field), together with a highly accurate location and orientation in the 3D space provided by the optical system (errors are lower than 1mm). The database files were stored in CSV format to ensure usability with common data processing software. The main aim of this dataset is the availability of inertial data for two main purposes: the analysis of different techniques for the identification and evaluation of exercises monitored with inertial wearable sensors and the validation of inertial sensor-based algorithms for human motion monitoring that obtains segments orientation in the 3D space. Furthermore, the database stores enough data to train and evaluate Machine Learning-based algorithms. The age range of the participants can be useful for establishing age-based metrics for the exercises evaluation or the study of differences in motions between different aged groups. Finally, the MATLAB function <em>features_extraction</em>, developed by the authors, is also given. This function splits signals using a sliding window, returning its segments, and extract signal features, in the time and frequency domains, based on prior studies of the literature.</p>
REHAB24-6: A multi-modal dataset of physical rehabilitation exercises
<p>To enable the evaluation of HPE models and the development of exercise feedback systems, we produced a new rehabilitation dataset (REHAB24-6). The main focus is on a diverse range of exercises, views, body heights, lighting conditions, and exercise mistakes. With the publicly available RGB videos, skeleton sequences, repetition segmentation, and exercise correctness labels, this dataset offers the most comprehensive testbed for exercise-correctness-related tasks.</p> <h2>Contents</h2> <ul> <li>65 recordings (184,825 frames, 30 FPS): <ul> <li>RGB videos from two cameras (<code>videos.zip</code>, horizontal = Camera17, vertical = Camera18);</li> <li>3D and 2D projected positions of 41 motion capture marker (<code><2/3>d_markers.zip</code>, marker labels in <code>marker_names.txt</code>);</li> <li>3D and 2D projected positions of 26 skeleton joints (<code><2/3>d_joints.zip</code>, joint labels in <code>joint_names.txt</code>);</li> </ul> </li> <li>Annotation of 1,072 exercise repetitions (<code>Segmentation.csv</code>, indexed based <strong>only on</strong> 30 FPS data, described in <code>Segmentation.txt</code>): <ul> <li>Temporal segmentation (start/end frame, most between 2–5 seconds);</li> <li>Binary correctness label (around 90 from each category in each exercise, except Ex3 with around 50);</li> <li>Exercise direction (around 90 from each direction in each exercise);</li> <li>Lighting conditions label.</li> </ul> </li> </ul> <h2>Recording Conditions</h2> <p>Our laboratory setup included 18 synchronized sensors (2 RGB video cameras, 16 ultra-wide motion capture cameras) spread around an 8.2 × 7 m room. The RGB cameras were located in the corners of the room, one in a horizontal position (hor.), providing a larger field of view (FoV), and one in a vertical (ver.), resulting in a narrower FoV. Both types of cameras were synchronized with a sampling frequency of 30 frames per second (FPS).</p> <p>The subjects wore motion capture body suits with 41 markers attached to them, which were detected by optical cameras. The OptiTrack Motive 2.3.0 software inferred the 3D positions of the markers in virtual centimeters and converted them into a skeleton with 26 joints, forming our human pose 3D ground truth (GT).</p> <p>To acquire a 2D version of the ground truth in pixel coordinates, we applied a projection of the virtual coordinates into the camera using the simplified pinhole model. We estimated the parameters for this projection as follows. First, the virtual position of the cameras was estimated using measuring tape and knowledge of the virtual origin. Then, the orientation of the cameras was optimized by matching the virtual marker positions with their position in the videos.</p> <p>We also simulated changes in lighting conditions: a few videos were shot in the natural evening light, which resulted in worse visibility, while the rest were under artificial lighting.</p> <h2>Exercises</h2> <p>10 subjects participated in our recording and consented to release the data publicly: 6 males and 4 females of different ages (from 25 to 50) and fitness levels. A physiotherapist instructed the subjects on how to perform the exercises so that at least five repetitions were done in what he deemed the correct way and five more incorrectly. The participants had a certain degree of freedom, e.g., in which leg they used in Ex4 and Ex5. Similarly, the physiotherapist suggested different exercise mistakes for each subject.</p> <ul> <li><strong>Ex1 = Arm abduction</strong>: sideway raising of the straightened right arm;</li> <li><strong>Ex2 = Arm VW</strong>: fluent transition of arms between V (arms straight up) and W (elbows down, hands up) shape;</li> <li><strong>Ex3 = Push-ups</strong>: push-ups with hands on a table;</li> <li><strong>Ex4 = Leg abduction</strong>: sideway raising of the straightened leg;</li> <li><strong>Ex5 = Leg lunge</strong>: pushing a knee of the back leg down while keeping a right angle on the front knee;</li> <li><strong>Ex6 = Squats</strong>.</li> </ul> <p>Every exercise was also executed in two directions, resulting in different views of the subject depending on the camera. Facing the horizontal camera resulted in a front view for that camera and a profile from the other. Facing the wall between the cameras shows the subject from half-profile in both cameras. A rare direction, only used for push-ups due to the use of the table, was facing the vertical camera, with the views being reversed compared to the first orientation.</p> <h2>Citation</h2> <p>Cite the related conference paper:</p> <p>Černek, A., Sedmidubsky, J., Budikova P.: REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods. 17th International Conference on Similarity Search and Applications (SISAP). Springer, 14 pages, 2024.</p> <h2>License</h2> <p>This dataset is for academic or non-profit organization noncomercial research use only. By using you agree to appropriately reference the paper above in any publication making of its use. For comercial purposes contact us at info@visioncraft.ai</p>
Children's learning new word-picture pairs during physical exercise in the classroom
<p>This dataset includes memory performance and questionnaire data. In three experiments school aged children performed physical exercises in the classroom while learning new word-picture associations during motion or sitting conditions (repeated measures). The words were in an artificial language. <em>Free recall </em>memory performance was collected from a group of children (aged 8-9 years) in <em>running</em> and sitting conditions (experiment 1). <em>Recognition memory</em> performance was collected from a different group of children (aged 8-9 years) in <em>running</em> and sitting conditions (experiment 2). <em>Recognition memory</em> performance was collected from a different group of children (aged 8-9 years) in <em>stepping</em> and sitting conditions (experiment 3). Experiment 2 showed higher recognition memory performance during the sitting compared to running condition. Gender, English as an additional language (EAL), and whether the children received extra support for language work was recorded before the experiment. A survey after the experiment asked children which condition they preferred. All data were collected by Naomi Kefford in one school in Surrey (UK) during the MSc thesis at the School of Psychology at the University of Surrey. Naomi Kefford was supervised by Peter Klaver. </p>
Physical Exercise Measurements with Accelerometer and Gyroscope
<p><span>The data represent records from an accelerometer and gyroscope during seven movement tasks that were standardized by a protocol. These tasks were performed consecutively, and each had to be completed as quickly as possible. Data are obtained from 81 children aged 9-11 years.</span></p> <p><span>Description of movement tasks (performed in following order):</span></p> <p><strong><span>4x10 m Run</span></strong><span>: The track is defined by two cones placed at a distance of 10 meters from each other. The cones must have a height of at least 20 cm. It is advisable to secure the cones to the floor with adhesive tape. It is also recommended to mark the correct placement of the cones on the floor with tape. After the start, an individual begins to run towards the opposite cone, circles it from the left, and continues back to the starting cone. This cone is circled from the right. The run continues back and forth with a touch of the opposite cone. On the final run, the individual must cross over the mark where they started and then accelerate, but more freely, to the start of the second section.</span></p> <p><strong><span>Crawling</span></strong><span>: Immediately after starting the section, the individual lies down on the ground and crawls forward, with the entire torso needing to remain constantly on the mat. The total length of the course is 10 meters. The task ends at most 1 meter before the finish line (marked as No. 3 in the image). Crawling on all fours is not acceptable.</span></p> <p><strong><span>Progressive Cone Shuttle Run</span></strong><span>: The track is marked by a series of five cones placed in a line one after the other. It is advisable to secure the cones to the ground with tape. It is also recommended to mark the correct placement of the cones on the floor with tape. The first cone (marked as 'A' – "return cone") is 1 meter away from the start, the second cone is 2 meters away. Each subsequent cone is always 1 meter away from the previous one (marked 'first' to 'fourth'). After the start, the participant runs to the first cone, touches it, and runs back to the return cone A. In this manner, from cone A, the participant runs progressively to cones two through four, returning to cone A each time. The participant must always touch each cone with one hand. This way, the length of the run progressively increases by 1 meter. After completing the run to the fourth cone, the participant circles outside all the cones and runs to the next station (marked as station 4). All runs are performed on the outside of the cones.</span></p> <p><strong><span>Ring Collection and Placement Run:</span></strong><span> The track is marked by six equally sized sections of a Swedish ladder and one cone located 10 meters away from the start of the section. The individual sections are placed alternately on the left and right side of the line between the start and the end cone, each touching with one corner of the section. Inside each section, at its centre, rings are loosely placed. Immediately after the start, the participant runs and progressively moves through all the sections towards the cone, collecting rings. After circling the cone, the participant returns along the same path, placing the rings back into each section. It's important to emphasize that the rings must not be thrown but placed on the ground. From the last section, the participant runs directly to the next station, number 5.</span></p> <p><strong><span>10x Position Change Drill</span></strong><span>: The task is performed on a mat. The station is directly in the centre of the mat. Immediately after starting the section, the individual performs 10 cycles of position changes. One cycle consists of transitioning from standing upright to lying on the stomach, rolling over onto the back, and transitioning back to an upright standing position. When performing the cycles, it is necessary to ensure that each position is correctly maintained. After completing the last cycle, the individual immediately moves on to the next station, number 6.</span></p> <p><strong><span>10x Gymnastic Hoops Passing</span></strong><span>: Immediately after starting the section, the individual performs 10 movement cycles, where one cycle consists of crawling through a gymnastic hoop from a standing position, moving from the head to the feet. At the end of each cycle, the individual always lies on the ground, then steps out of it and stands upright. Only after this does the individual lean towards the hoop again and begin a new cycle. Immediately after the last cycle, there is a run to station number 7.</span></p> <p><strong><span>10x Bench Jumping</span></strong><span> (pushing off with one foot): Immediately after starting the section, the individual runs to the right edge (outer side) of the bench at its beginning and starts jumping over the bench back and forth. The jump is made from one foot, landing on the other. Between jumps, it is necessary to touch the ground with both feet. The jumps must be performed in a way that the individual continuously moves forward and does not jump in place. After completing the last jump, the individual immediately runs to the final finish mark, where a triple hit to the back is also performed. (identical to station number 2)</span></p> <p><strong><span>Crawling</span></strong><span>: This section is carried out according to the same instructions and at the same location where the previous crawling (section 2) took place. The end of the section is at station number 3, which is also the finish line of the entire course.</span></p> <p> </p> <p>The main data file is physical_exercise.csv. Two figures are attached describing the protocol:</p> <p><span>Figure 1: Protocol – Spatial delineation for movement task execution (Protocol_physical_activities_measurements.png)</span></p> <p><span>Figure 2: Protocol – Location and sequence of movement tasks (Protocol_physical_activities.png)</span></p> <p> </p> <p>Definition of measurement conditions and settings:</p> <table> <tbody> <tr> <td>The measurement device</td> <td>Axivity AX6 device (Axivity Ltd, United Kingdom)</td> </tr> <tr> <td>Recording frequency</td> <td>100 Hz with a range of ±16 G for accelerometer and ±2000 °/s for gyroscope</td> </tr> <tr> <td>Device orientation</td> <td>x, y, and z axes determine the mediolateral, craniocau-<br>dal, and sagittal directions, respectively</td> </tr> <tr> <td>Device positioning on the body</td> <td>on the back between upper angles of the shoulder blades</td> </tr> </tbody> </table> <p> </p>
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 2. Energy-VAS subjective measures for the participants (n=12) under two conditions (control and exercise involved cognitive task).
<p>As shown in Figure 2, it was found that there was a statistically significant interaction in the<br> percentage of mental fatigue between the condition type and time-on-task factor times (F(6, 22) =<br> 492.19, p < 0.001) as well as there was a significant main effect of time-on-task (time5 to time30)<br> (F(5, 22) = 463.794, p < 0.001). In addition, there was also a significant main effect in the condition type (F (1, 22) = 713.133, p < 0.001) which represented a large effect size. For the physical fatigue<br> subjective measure, there was a significant difference between the two experimental conditions (p <<br> 0.001), and within the subject test times (p < 0.001). However, there was no significant difference<br> between the means of the concentration visual analogue scale for these two experimental conditions<br> (p = 0.057) despite a significant difference (p < 0.001) in the time-on-task repeated measures.</p>
Delving into the relationship between regular physical exercise and cardiac interoception in two cross-sectional studies.
<p>This repository contains raw data from two studies corresponding to the article "No evidence of a relationship between regular physical exercise and cardiac interoception" by Yoris et al. In Study I, 45 resting EEG files are included for the Active (N = 24) and Inactive (N = 21) groups, both for the eyes closed and eyes open conditions. For Study II, there are 60 resting EEG files (30 Active/30 Inactive). Data are in EEGLAB format .set/fdt. The project is publicly available for free use and can be accessed at <a href="https://osf.io/xrsgn/">https://osf.io/xrsgn/</a>.</p>
Effect of Exercise Training on Physical, Cognitive, and Behavioral Function in People With TBI
ClinicalTrials.gov study NCT02504866. IPD Sharing: YES. Countries: 1. Publications: 3.
Extended data for behavioural change for Parkinson's disease: A randomised controlled feasibility study to promote physical activity and exercise adherence among people with Parkinson's, study protocol
<p><strong>Background: </strong>Parkinson's is a common progressive neurological condition characterized by motor and non-motor deficits. Physical activity and exercise can improve health, but many people with Parkinson's have trouble reaching the recommended dosage. Our recent literature review found improvements in exercise adherence with behavioural change interventions, but it remains unclear which are most effective. Further qualitative research and patient and public involvement has informed a novel behavioural change support intervention to be tested alongside an existing exercise program.<strong> </strong></p> <p><strong>Objective: </strong>To examine the feasibility of behavioural change support techniques delivered alongside an exercise programme to improve physical activity, function, and self-efficacy in PwP (and study procedures) to inform a future pilot RCT trial.<strong> </strong></p> <p><strong>Methods: </strong>A parallel-arm single blinded randomised feasibility study. Twenty participants with Parkinson's (Hoehn and Yahr stage 1-3) will be recruited from a physiotherapy primary-care waiting list. Following written consent, and baseline assessment, the participants will be randomly allocated to the intervention (n=10) or the control group (n=10). Both groups will receive usual care, which includes a weekly program of a multidisciplinary education, a supervised exercise class and a prescribed home exercise program. The intervention group will receive additional behavioural change techniques, targeting behaviour regulation, belief about capabilities and social influences. Class and home exercise adherence, behavioural component uptake and adherence, and negative events will be recorded. Outcomes will include enrolment and maintenance rates, physical function, falls, physical activity, and exercise self-efficacy measured pre- and post- the 12- week program (in-person). Surveys will be used to compare experiences and satisfaction between groups. Exit interviews will be completed with the intervention group only, exploring their experience of the behavioural change techniques. </p> <p><strong>Discussion: </strong>The results will help inform a future pilot RCT, based on the intervention acceptability, consent rate, maintenance, and protocol integrity.</p>
Features computed from physical exercises measurements
<p><span> </span><span>The data represents time series features from an accelerometer and gyroscope extracted from</span> <span><a href="../records/10984138">Physical Exercise Measurements with Accelerometer and Gyroscope (zenodo.org)</a></span><span>. The data consists of 5 feature sets.</span></p> <p><span>Description of feature sets:</span></p> <p><strong><span>1. </span></strong><strong><span>RQA features set</span></strong></p> <p><span>• "RR" - Recurrence rate</span><span><br></span><span>• "DET" - Determinism, count recurrence points in diagonal lines of length >= lmin</span><span><br></span><span>• "RATIO" - DET/RR</span><span><br></span><span>• "AVG" - average length of diagonal lines of length >= lmin</span><span><br></span><span>• "MAX" - maximal length of diagonal lines of length >= lmin</span><span><br></span><span>• "DIV" - Divergence, 1/MAX</span><span><br></span><span>• "LAM" - Laminarity, VLRP/TR</span><span><br></span><span>• "TT" - Trapping time, average length of vertical lines of length >= lmin</span><span><br></span><span>• "MAX_V" - maximal length of vertical lines of length >= lmin</span><span><br></span><span>• "TR" - Total number of recurrence points</span><span><br></span><span>• "DLRP" - Recurrence points on the diagonal lines of length of length >= lmin</span><span><br></span><span>• "DLC" - Count of diagonal lines of length of length >= lmin</span><span><br></span><span>• "VLRP" - Recurrence points on the vertical lines of length of length >= lmin</span><span><br></span><span>• "VLC" - Count of vertical lines of length of length >= lmin</span></p> <p><span>Was calculated by Chaos01 R package.</span></p> <p><span><a href="https://cran.r-project.org/package=Chaos01">https://CRAN.R-project.org/package=Chaos01</a></span></p> <p><span>The parameters were chosen so that the embedding will create a vector of one value of each axis of the accelerometer/gyroscope measurements. Therefore the used parameters were:</span></p> <table> <tbody> <tr> <td>Function argument</td> <td>Value</td> </tr> <tr> <td>embedding dimension (dim)</td> <td>3</td> </tr> <tr> <td>embedding lag (lag)</td> <td>time series length</td> </tr> <tr> <td>Minimal length of recurrence line (lmin)</td> <td>20</td> </tr> </tbody> </table> <p><strong><span>For Chaos01 we change eps argument and calculated it by following formula:</span></strong></p> <p><code><span># Calculate eps for acc and gyro Chaos 01----</span></code></p> <p><code><span>get_eps <- function(input_data, scale = 1) {</span></code></p> <p><code><span> # Calculate eps for acc and gyro</span></code></p> <p><code><span> eps_a <-</span></code></p> <p><code><span> purrr::map_dbl(input_data$data,</span></code></p> <p><code><span> ~ .x |></span></code></p> <p><code><span> select(Ax, Ay, Az) |></span></code></p> <p> <code><span> as.matrix() |></span></code></p> <p> <code><span> as.vector() |></span></code></p> <p> <code><span> sd()) |></span></code></p> <p> <code><span> mean() * scale</span></code></p> <p><code><span> eps_g <-</span></code></p> <p><code><span> purrr::map_dbl(input_data$data,</span></code></p> <p><code><span> ~ .x |></span></code></p> <p> <code><span> select(Gx, Gy, Gz) |> </span></code></p> <p> <code><span> as.matrix() </span></code></p> <p> <code><span> as.vector() |></span></code></p> <p> <code><span> sd()) |></span></code></p> <p> <code><span> mean() * scale</span></code></p> <p><code><span>return(list(a = eps_a, g = eps_g))</span></code></p> <p><code><span>}</span></code></p> <p><span>"TREND" - Trend of the number of recurrent points depending on the distance to the main diagonal.</span></p> <p><span>Was calculated by nonlinearTseries R package.</span></p> <p><span> </span><span><a href="https://cran.r-project.org/package=nonlinearTseries">https://CRAN.R-project.org/package=nonlinearTseries</a></span></p> <p><strong><span> </span></strong></p> <p><strong><span>All following feature sets was calculated by Python package</span></strong></p> <p><span><span><strong>https://tsfresh.readthedocs.io/en/latest/index.html</strong></span></span></p> <p><strong><span> </span></strong></p> <p><span>The used dictionary is included in file named tsfresh_autocorr_spectral_features.py.</span></p> <p><strong><span> </span></strong></p> <p><strong><span>2. </span></strong><strong><span>Autocorrelation features set</span></strong></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.autocorrelation</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.agg_autocorrelation</span></p> <p><span> </span></p> <p><strong><span>3. </span></strong><strong><span>Spectral features set</span></strong></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fft_aggregated</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.approximate_entropy</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fft_coefficient</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fourier_entropy</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.spkt_welch_density</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.ar_coefficient</span></p> <p><span> </span></p> <p><strong><span>4. </span></strong><strong><span>Mix RQA/Spectral/Autocorrelation features set</span></strong></p> <p><strong><span> </span></strong></p> <p><strong><span>5. </span></strong><strong><span>Tsfresh all features set</span></strong></p> <p><span>#</span><span><span>https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html</span></span></p> <p><span> </span></p> <p><strong><span>Versions of the software:</span></strong></p> <p><span>Python (version 3.8.10) </span></p> <p><span>tsfresh = 0.20.2</span></p> <p><span>R (version 4.3.2) </span></p> <p><span>Chaos01 = Version 1.2.1 </span></p> <p><span>nonlinearTseries = 0.3.0 </span></p>
REXCO Project :Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults
<p><strong>Methods and design</strong></p> <p><em>Participants</em></p> <p>We recruited 20 young males (19-32 years old, average age 23.8 years old) from the University of Granada (Spain). All participants met the inclusion criteria of normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders, were taking no medication and reporting less than 3 hours of moderate exercise per week. Participants were required to maintain regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense exercise 24 hours before each session. From the 20 participants, one was excluded from the analyses because he did not attend to the last experimental session and another one because of technical issues. Thus, only data from the remaining 18 participants are reported. All subjects gave written informed consent before the study and received 20 euros for their participation. The protocol was approved in accordance with both the ethical guidelines of the University of Granada and the Declaration of Helsinki of 1964.</p> <p><em>Apparatus and materials</em></p> <p>All participants were fitted with a Polar RS800 CX monitor (Polar Electro Öy, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Flanker task stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 50 cm between the head of participants and the center of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.</p> <p><em>Procedure</em></p> <p>Participants completed two counterbalanced experimental sessions of approximately 120 min each. Sessions were scheduled on different days allowing a time interval of 48–72 hours between them to avoid possible fatigue and/or training effects. On each experimental session (see Fig. 1), participants completed a 15’ resting state period sitting in a comfortable chair with closed eyes. Subsequently, they performed 10’ warm-up on a cycle-ergometer at a power load of 20% of their individual VO<sub>2</sub> VAT, following by 30’ exercise at 80% (moderate-intensity exercise session) or at 20% (light intensity exercise session) of their VO<sub>2</sub> VAT (see Table 1). Upon completion of the exercise, a 10’ cool down period at 20% VO<sub>2</sub> VAT of intensity followed. Each participant set his preferred cadence (between 60-90 rpm • min-1) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions in terms of dual-task demands. Later, participants waited sitting in a comfortable chair until their heart rate returned to within their 130% of heart rate at resting (average waiting time 5’ 44’’). The first flanker task was then performed for 6’, followed by a 15’ resting period with closed eyes. Finally, they again completed the 6’ flanker task.</p> <p><em>Flanker task</em></p> <p>We used a modified version of the Eriksen flanker task based on that reported in Eriksen and Eriksen (1974). The task consisted of a random presentation of a set arrows flanked by other arrows that faced the same or the opposite direction. In the congruent trials, the central arrow is flanked by arrows in the same direction (e.g., <<<<< or >>>>>), while in the incongruent trials, the central arrow is flanked by arrows in the opposite direction (e.g., <<><< or >><>>). Stimuli were displayed sequentially on the center of the screen on a black background. Each trial started with the presentation of a white fixation cross in a black background with random duration between 1000 and 1500 ms. Then, the stimulus was presented during 150 ms and a variable interstimulus interval (1000–1500 ms). Participants were instructed to respond by pressing the left tab button with their left index finger when the central arrow (regardless of condition) faced to the left and the right tab button with their right index finger when the central arrow faced to the right. Participants were encouraged to respond as quick as possible, being accurate. A total of 120 trials were randomly presented (60 congruent and 60 incongruent trials) in each task. Each task lasted for 6 minutes approximately without breaks.</p> <p><em>EEG recording and analysis</em></p> <p>EEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ). Participants were instructed to avoid body movements as much as possible, and to keep their gaze on the center of the screen during the exercise. Electrode impedances were kept below 10 kΩ. EEG preprocessing was conducted using custom Matlab scripts and the EEGLAB (Delorme & Makeig, 2004) and Fieldtrip (Oostenveld et al., 2011) Matlab toolboxes. EEG data were resampled at 500 Hz, bandpass filtered offline from 1 and 40 Hz to remove signal drifts and line noise, and re-referenced to a common average reference. Horizontal electrooculograms (EOG) were recorded by bipolar external electrodes for the offline detection of ocular artifacts. The potential influence of electromyographic (EMG) activity in the EEG signal was minimized by using the available EEGLAB routines (Delorme & Makeig, 2004). Independent component analysis was used to detect and remove EEG components reflecting eye blinks (Hoffmann and Falkenstein, 2008). Abnormal spectra epochs which spectral power deviated from the mean by +/-50 dB in the 0-2 Hz frequency window (useful for catching eye movements) and by +25 or -100 dB in the 20-40 Hz frequency window (useful for detecting muscle activity) were rejected. On average, 5.1% of epochs per participant were rejected.</p> <p><em>Spectral power analysis</em>. Processed EEG data from each experimental period (Resting 1, Warm-up, Exercise, Cool Down, Flanker Task 1, Resting 2, Flanker Task 2) were subsequently segmented to 1-s epochs. The spectral decomposition of each epoch was computed using Fast Fourier Transformation (FFT) applying a symmetric Hamming window and the obtained power values were averaged across experimental periods.</p> <p><em>Event-Related Spectral Perturbation (ERSP) analysis.</em> Task-evoked spectral EEG activity was assessed by computing ERSP in epochs extending from –500 ms to 500 ms time-locked to stimulus onset for frequencies between 4 and 40 Hz. Spectral decomposition was performed using sinusoidal wavelets with 3 cycles at the lowest frequency and increasing by a factor of 0.8 with increasing frequency. Power values were normalized with respect to a −300 ms to 0 ms pre-stimulus baseline and transformed into the decibel scale.</p>
REXCO Project :Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults
<p><strong>Methods and design</strong></p> <p><em>Participants</em></p> <p>We recruited 20 young males (19-32 years old, average age 23.8 years old) from the University of Granada (Spain). All participants met the inclusion criteria of normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders, were taking no medication and reporting less than 3 hours of moderate exercise per week. Participants were required to maintain regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense exercise 24 hours before each session. From the 20 participants, one was excluded from the analyses because he did not attend to the last experimental session and another one because of technical issues. Thus, only data from the remaining 18 participants are reported. All subjects gave written informed consent before the study and received 20 euros for their participation. The protocol was approved in accordance with both the ethical guidelines of the University of Granada and the Declaration of Helsinki of 1964.</p> <p><em>Apparatus and materials</em></p> <p>All participants were fitted with a Polar RS800 CX monitor (Polar Electro Öy, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Flanker task stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 50 cm between the head of participants and the center of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.</p> <p><em>Procedure</em></p> <p>Participants completed two counterbalanced experimental sessions of approximately 120 min each. Sessions were scheduled on different days allowing a time interval of 48–72 hours between them to avoid possible fatigue and/or training effects. On each experimental session (see Fig. 1), participants completed a 15’ resting state period sitting in a comfortable chair with closed eyes. Subsequently, they performed 10’ warm-up on a cycle-ergometer at a power load of 20% of their individual VO<sub>2</sub> VAT, following by 30’ exercise at 80% (moderate-intensity exercise session) or at 20% (light intensity exercise session) of their VO<sub>2</sub> VAT (see Table 1). Upon completion of the exercise, a 10’ cool down period at 20% VO<sub>2</sub> VAT of intensity followed. Each participant set his preferred cadence (between 60-90 rpm • min-1) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions in terms of dual-task demands. Later, participants waited sitting in a comfortable chair until their heart rate returned to within their 130% of heart rate at resting (average waiting time 5’ 44’’). The first flanker task was then performed for 6’, followed by a 15’ resting period with closed eyes. Finally, they again completed the 6’ flanker task.</p> <p><em>Flanker task</em></p> <p>We used a modified version of the Eriksen flanker task based on that reported in Eriksen and Eriksen (1974). The task consisted of a random presentation of a set arrows flanked by other arrows that faced the same or the opposite direction. In the congruent trials, the central arrow is flanked by arrows in the same direction (e.g., <<<<< or >>>>>), while in the incongruent trials, the central arrow is flanked by arrows in the opposite direction (e.g., <<><< or >><>>). Stimuli were displayed sequentially on the center of the screen on a black background. Each trial started with the presentation of a white fixation cross in a black background with random duration between 1000 and 1500 ms. Then, the stimulus was presented during 150 ms and a variable interstimulus interval (1000–1500 ms). Participants were instructed to respond by pressing the left tab button with their left index finger when the central arrow (regardless of condition) faced to the left and the right tab button with their right index finger when the central arrow faced to the right. Participants were encouraged to respond as quick as possible, being accurate. A total of 120 trials were randomly presented (60 congruent and 60 incongruent trials) in each task. Each task lasted for 6 minutes approximately without breaks.</p> <p><em>EEG recording and analysis</em></p> <p>EEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ). Participants were instructed to avoid body movements as much as possible, and to keep their gaze on the center of the screen during the exercise. Electrode impedances were kept below 10 kΩ. EEG preprocessing was conducted using custom Matlab scripts and the EEGLAB (Delorme & Makeig, 2004) and Fieldtrip (Oostenveld et al., 2011) Matlab toolboxes. EEG data were resampled at 500 Hz, bandpass filtered offline from 1 and 40 Hz to remove signal drifts and line noise, and re-referenced to a common average reference. Horizontal electrooculograms (EOG) were recorded by bipolar external electrodes for the offline detection of ocular artifacts. The potential influence of electromyographic (EMG) activity in the EEG signal was minimized by using the available EEGLAB routines (Delorme & Makeig, 2004). Independent component analysis was used to detect and remove EEG components reflecting eye blinks (Hoffmann and Falkenstein, 2008). Abnormal spectra epochs which spectral power deviated from the mean by +/-50 dB in the 0-2 Hz frequency window (useful for catching eye movements) and by +25 or -100 dB in the 20-40 Hz frequency window (useful for detecting muscle activity) were rejected. On average, 5.1% of epochs per participant were rejected.</p> <p><em>Spectral power analysis</em>. Processed EEG data from each experimental period (Resting 1, Warm-up, Exercise, Cool Down, Flanker Task 1, Resting 2, Flanker Task 2) were subsequently segmented to 1-s epochs. The spectral decomposition of each epoch was computed using Fast Fourier Transformation (FFT) applying a symmetric Hamming window and the obtained power values were averaged across experimental periods.</p> <p><em>Event-Related Spectral Perturbation (ERSP) analysis.</em> Task-evoked spectral EEG activity was assessed by computing ERSP in epochs extending from –500 ms to 500 ms time-locked to stimulus onset for frequencies between 4 and 40 Hz. Spectral decomposition was performed using sinusoidal wavelets with 3 cycles at the lowest frequency and increasing by a factor of 0.8 with increasing frequency. Power values were normalized with respect to a −300 ms to 0 ms pre-stimulus baseline and transformed into the decibel scale.</p>
Effectiveness of self-management of medication and self-monitoring of blood pressure, diet, and physical exercise on blood pressure in patients with poorly controlled hypertension (MEDICHY study): randomized and controlled trial
<p>Dataset study medichy ISRCTN144433778</p>
Physical Exercise and Its Impact on Signs of Inflammation in Fibromyalgia
ClinicalTrials.gov study NCT00643006. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Physical Exercise and Responses Measured by HIF-1 in COPD
ClinicalTrials.gov study NCT04955977. IPD Sharing: NO. Countries: 1. Publications: 9.
Effects of Coordinative Exercise on Physical Fitness, Motor Competence, and Inhibitory Control in Preschoolers
ClinicalTrials.gov study NCT06631248. IPD Sharing: YES. Countries: 1. Publications: 3.
Study of Exercise Capacity and Physical Activity in Children With Congenital Heart Disease
ClinicalTrials.gov study NCT06945003. IPD Sharing: NO. Countries: 1. Publications: 13.
Physical Exercise in Postoperative Bariatric Surgery Patients
ClinicalTrials.gov study NCT04235842. IPD Sharing: NO. Countries: 1. Publications: 29.
Supervised Manual Physical Therapy Exercise for Ankle Disability After Motor Vehicle Accidents.
ClinicalTrials.gov study NCT06010706. IPD Sharing: YES. Countries: 1. Publications: 1.
Rates of Recovery From Strenuous Exercise in Physically Active Older Adults
ClinicalTrials.gov study NCT02899650. IPD Sharing: NO. Countries: 1. Publications: 4.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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