https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F7974466%2F69e65ffa17d835dc093fc520999e492c%2FLeonardo_Kino_XL_Create_a_vibrant_and_dynamic_digital_illustra_2.jpg?generation=1728979114108033&alt=media" alt="">

This dataset provides detailed information on 50 diverse exercises designed to promote overall health and fitness. It includes a wide range of activities suitable for beginners to advanced fitness enthusiasts, targeting various muscle groups and fitness goals. The data can be used for personal fitness planning, workout app development, or data analysis projects in health and sports science.

Column Descriptions

1. Name of Exercise: The common name of the exercise. Type: String Description: Unique identifier for each exercise in the dataset.

2. Sets: The recommended number of sets for the exercise. Type: Integer Description: Indicates how many times the group of repetitions should be performed.

3. Reps: The recommended number of repetitions per set. Type: Integer Description: Specifies how many times the exercise should be performed in each set.

4. Benefit: The primary health or fitness benefit of the exercise. Type: String Description: Briefly explains the main advantage or target of the exercise.

5. Burns Calories (per 30 min): Estimated calorie burn for a 30-minute session. Type: Integer Description: Approximates the number of calories burned by an average person (155 lbs/70 kg) performing the exercise for 30 minutes.

6. Target Muscle Group: The main muscles or muscle groups engaged during the exercise. Type: String Description: Lists the primary muscles worked, helping users target specific areas.

7. Equipment Needed: Any equipment required to perform the exercise. Type: String Description: Specifies necessary equipment, or "None" if the exercise can be performed without equipment.

8. Difficulty Level: The relative challenge level of the exercise. Type: String Description: Categorizes exercises as "Beginner," "Intermediate," or "Advanced" to guide appropriate selection based on fitness level.

Dataset Overview:

This dataset contains simulated (hypothetical) but almost realistic (based on AI) data related to sleep, heart rate, and exercise habits of 500 individuals. It includes both pre-exercise and post-exercise resting heart rates, allowing for analyses such as a dependent t-test (Paired Sample t-test) to observe changes in heart rate after an exercise program. The dataset also includes additional health-related variables, such as age, hours of sleep per night, and exercise frequency.

The data is designed for tasks involving hypothesis testing, health analytics, or even machine learning applications that predict changes in heart rate based on personal attributes and exercise behavior. It can be used to understand the relationships between exercise frequency, sleep, and changes in heart rate.

File: Filename: heart_rate_data.csv File Format: CSV

- Features (Columns):

Age: Description: The age of the individual. Type: Integer Range: 18-60 years Relevance: Age is an important factor in determining heart rate and the effects of exercise.

Sleep Hours: Description: The average number of hours the individual sleeps per night. Type: Float Range: 3.0 - 10.0 hours Relevance: Sleep is a crucial health metric that can impact heart rate and exercise recovery.

Exercise Frequency (Days/Week): Description: The number of days per week the individual engages in physical exercise. Type: Integer Range: 1-7 days/week Relevance: More frequent exercise may lead to greater heart rate improvements and better cardiovascular health.

Resting Heart Rate Before: Description: The individual’s resting heart rate measured before beginning a 6-week exercise program. Type: Integer Range: 50 - 100 bpm (beats per minute) Relevance: This is a key health indicator, providing a baseline measurement for the individual’s heart rate.

Resting Heart Rate After: Description: The individual’s resting heart rate measured after completing the 6-week exercise program. Type: Integer Range: 45 - 95 bpm (lower than the "Resting Heart Rate Before" due to the effects of exercise). Relevance: This variable is essential for understanding how exercise affects heart rate over time, and it can be used to perform a dependent t-test analysis.

Max Heart Rate During Exercise: Description: The maximum heart rate the individual reached during exercise sessions. Type: Integer Range: 120 - 190 bpm Relevance: This metric helps in understanding cardiovascular strain during exercise and can be linked to exercise frequency or fitness levels.

Potential Uses: Dependent T-Test Analysis: The dataset is particularly suited for a dependent (paired) t-test where you compare the resting heart rate before and after the exercise program for each individual.

Exploratory Data Analysis (EDA):Investigate relationships between sleep, exercise frequency, and changes in heart rate. Potential analyses include correlations between sleep hours and resting heart rate improvement, or regression analyses to predict heart rate after exercise.

Machine Learning: Use the dataset for predictive modeling, and build a beginner regression model to predict post-exercise heart rate using age, sleep, and exercise frequency as features.

Health and Fitness Insights: This dataset can be useful for studying how different factors like sleep and age influence heart rate changes and overall cardiovascular health.

License: Choose an appropriate open license, such as:

CC BY 4.0 (Attribution 4.0 International).

Inspiration for Kaggle Users: How does exercise frequency influence the reduction in resting heart rate? Is there a relationship between sleep and heart rate improvements post-exercise? Can we predict the post-exercise heart rate using other health variables? How do age and exercise frequency interact to affect heart rate?

Acknowledgments: This is a simulated dataset for educational purposes, generated to demonstrate statistical and machine learning applications in the field of health analytics.

Dataset

This dataset was created by Nargis Karimova

Released under CC0: Public Domain

Contents

This Gym Exercise Dataset offers a comprehensive examination of various exercises and their detailed components. It focuses specifically on exercises performed using machines commonly available in gym settings.

The dataset encompasses: - Detailed breakdowns of machine-based exercises - Specific components and parameters for each exercise - Information on proper form and technique - Data on muscle groups targeted by each exercise

This collection serves as a valuable resource for: - Fitness professionals developing evidence-based training programs - Researchers studying exercise biomechanics and efficiency - Gym equipment manufacturers interested in user interaction data - Data scientists exploring patterns in exercise routines and preferences

Physical fitness is a key element of a healthy life, and being overweight or lacking physical exercise will lead to health problems. Therefore, assessing an individual’s physical health status from a non-medical, cost-effective perspective is essential. This paper aimed to evaluate the national physical health status through national physical examination data, selecting 12 indicators to divide the physical health status into four levels: excellent, good, pass, and fail. The existing challenge lies in the fact that most literature on physical fitness assessment mainly focuses on the two major groups of sports athletes and school students. Unfortunately, there is no reasonable index system has been constructed. The evaluation method has limitations and cannot be applied to other groups. This paper builds a reasonable health indicator system based on national physical examination data, breaks group restrictions, studies national groups, and hopes to use machine learning models to provide helpful health suggestions for citizens to measure their physical status. We analyzed the significance of the selected indicators through nonparametric tests and exploratory statistical analysis. We used seven machine learning models to obtain the best multi-classification model for the physical fitness test level. Comprehensive research showed that MLP has the best classification effect, with macro-precision reaching 74.4% and micro-precision reaching 72.8%. Furthermore, the recall rates are also above 70%, and the Hamming loss is the smallest, i.e., 0.272. The practical implications of these findings are significant. Individuals can use the classification model to understand their physical fitness level and status, exercise appropriately according to the measurement indicators, and adjust their lifestyle, which is an important aspect of health management.

Chemistry is increasingly data centric and the undergraduate curriculum needs to adjust to keep up. To address this, we created the Air Quality Activity, a new first-year undergraduate activity where students use Microsoft Excel to analyze a unique subset of atmospheric ozone (O3) and nitrogen dioxide (NO2) measurements from the Canadian National Air Pollution Surveillance (NAPS) program. Through this activity students develop their numeracy, graphicacy, and proficiency with Excel. Moreover, students are equipped with a foundational approach to data analysis they can leverage throughout their studies. To make this activity possible, we developed an open-source webbook detailing pertinent Excel operations for first-year students, and an interactive web-app for the generation, distribution, and exploration of NAPS data. Students were excited by the analysis of real-world chemical phenomena in comparison to traditional first-year lab exercises and appreciated their acquired Excel skills. The Air Quality Activity is readily adaptable for both virtual and in-person implementation, entirely open-source, and readily deployable at any institution wishing to teach data analysis in a chemistry context.

Each R script replicates all of the example code from one chapter from the book. All required data for each script are also uploaded, as are all data used in the practice problems at the end of each chapter. The data are drawn from a wide array of sources, so please cite the original work if you ever use any of these data sets for research purposes.

Section 1: Introduction

Brief overview of dataset contents:

Current database contains anonymised data collected during exercise testing services performed on male and female participants (cycling, rowing, kayaking and running) provided by the Human Performance Laboratory, School of Medicine, Trinity College Dublin, Dublin 2, Ireland.

835 graded incremental exercise test files (285 cycling, 266 rowing / kayaking, 284 running)

Description file with each row representing a test file - COLUMNS: file name (AXXX), sport (cycling, running, rowing or kayaking)

Anthropometric data of participants by sport (age, gender, height, body mass, BMI, skinfold thickness,% body fat, lean body mass and haematological data; namely, haemoglobin concentration (Hb), haematocrit (Hct), red blood cell (RBC) count and white blood cell (WBC) count )

Test data (HR, VO2 and lactate data) at rest and across a range of exercise intensities

Derived physiological indices quantifying each individual’s endurance profile

Following a request from athletes seeking assessment by phone or e-mail the test protocol, risks, benefits and test and medical requirements, were explained verbally or by return e-mail. Subsequently, an appointment for an exercise assessment was arranged following the regulatory reflection period (7 days). Following this regulatory period each participant’s verbal consent was obtained pre-test, for participants under 18 years of age parent / guardian consent was obtained in writing. Ethics approval was obtained from the Faculty of Health Sciences ethics committee and all testing procedures were performed in compliance with Declaration of Helsinki guidelines.

All consenting participants were required to attend the laboratory on one occasion in a rested, carbohydrate loaded and well-hydrated state, and for male participants’ clean shaven in the facial region. All participants underwent a pre-test medical examination, including assessment of resting blood pressure, pulmonary function testing and haematological (Coulter Counter Act Diff, Beckmann Coulter, CA,US) review performed by a qualified medical doctor prior to exercise testing. Any person presenting with any cardiac abnormalities, respiratory difficulties, symptoms of cold or influenza, musculoskeletal injury that could impair performance, diabetes, hypertension, metabolic disorders, or any other contra-indicatory symptoms were excluded. In addition, participants completed a medical questionnaire detailing training history, previous personal and family health abnormalities, recent illness or injury, menstrual status for female participants, as well as details of recent travel and current vaccination status, and current medications, supplements and allergies. Barefoot height in metre (Holtain, Crymych, UK), body mass (counter balanced scales) in kilogram (Seca, Hamburg, Germany) and skinfold thickness in millimetre using a Harpenden skinfold caliper (Bath International, West Sussex, UK) were recorded pre-exercise.

Section 2: Testing protocols

2.1: Cycling

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on an electromagnetically braked cycle ergometer (Lode Excalibur Sport, Groningen, The Netherlands). Participants initially identified a cycling position in which they were most comfortable by adjusting saddle height, saddle fore-aft position relative to the crank axis, saddle to handlebar distance and handlebar height. Participant’s feet were secured to the ergometer using their own cycling shoes with cleats and accompanying pedals. The protocol commenced with a 15-min warm-up at a workload of 120 Watt (W), followed by a 10-min rest. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a workload of 100 or 120 W for female and male participants, respectively, and subsequently increasing by a 20, 30 or 40 W incremental increase every 3-min depending on gender and current competition category. During assessment participants maintained a constant self-selected cadence chosen during their warm-up (permitted window was 5 rev.min−1 within a permitted absolute range of 75 to 95 rev.min−1) and the test was terminated when a participant was no longer able to maintain a constant cadence.

Heart rate (HR) data were recorded continuously by radio-telemetry using a Cosmed HR monitor (Cosmed, Rome, Italy). During the test, blood samples were collected from the middle finger of the right hand at the end of the second minute of each 3-min interval. The fingertip was cleaned to remove any sweat or blood and lanced using a long point sterile lancet (Braun, Melsungen, Germany). The blood sample was collected into a heparinised capillary tube (Brand, Wertheim, Germany) by holding the tube horizontal to the droplet and allowing transfer by capillary action. Subsequently, a 25μL aliquot of whole blood was drawn from the capillary tube using a YSI syringepet (YSI, OH, USA) and added into the chamber of a YSI 1500 Sport lactate analyser (YSI, OH, USA) for determination of non-lysed [Lac] in mmol.L−1. The lactate analyser was calibrated to the manufacturer’s requirements (± 0.05 mmol.L−1) before each test using a standard solution (YSI, OH, USA) of known concentration (5 mmol.L−1) and analyser linearity was confirmed using either a 15 or 30 mmol.L-1 standard solution (YSI, OH, USA).

Gas exchange variables including respiration rate (Rf in breaths.min-1), minute ventilation (VE in L.min-1), oxygen consumption (VO2 in L.min-1 and in mL.kg-1.min-1) and carbon dioxide production (VCO2 in L.min-1), were measured on a breath-by-breath basis throughout the test, using a cardiopulmonary exercise testing unit (CPET) and an associated software package (Cosmed, Rome, Italy). Participants wore a face mask (Hans Rudolf, KA, USA) which was connected to the CPET unit. The metabolic unit was calibrated prior to each test using ambient air and an alpha certified gas mixture containing 16% O2, 5% CO2 and 79% N2 (Cosmed, Rome, Italy). Volume calibration was performed using a 3L gas calibration syringe (Cosmed, Rome, Italy). Barometric pressure recorded by the CPET was confirmed by recording barometric pressure using a laboratory grade barometer.

Following testing mean HR and mean VO2 data at rest and during each exercise increment were computed and tabulated over the final minute of each 3-min interval. A graphical plot of [Lac], mean VO2 and mean HR versus cycling workload was constructed and analysed to quantify physiological endurance indices, see Data Analysis section. Data for VO2 peak in L.min-1 (absolute) and in mL.kg-1.min-1 (relative) and VE peak in L.min-1 were reported as the peak data recorded over any 10 consecutive breaths recorded during the last minute of the final exercise increment.

2.2: Running protocol

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a motorised treadmill (Powerjog, Birmingham, UK). The running protocol, performed at a gradient of 0%, commenced with a 15-min warm-up at a velocity (km.h-1) which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity. Subsequently, the warm-up was followed by a 10 minute rest / dynamic stretching phase. From a safety perspective during all running GxT participants wore a suspended lightweight safety harness to minimise any potential falls risk. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal running velocity which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity, and subsequently increased by ≥ 1 km.h-1 every 3-min depending on gender and current competition category. The test was terminated when a participant was no longer able to maintain the imposed treadmill.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

2.3: Rowing / kayaking protocol

A discontinuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a Concept 2C rowing ergometer (Concept, VA, US) in rowers or a Dansprint kayak ergometer (Dansprint, Hvidovre, Denmark) in flat-water kayakers. The protocol commenced with a 15-min low-intensity warm-up at a workload (W) dependent on gender, sport and competition category, followed by a 10-min rest. For rowing the flywheel damping (120, 125 or 130W) was set dependent on gender and competition category. For kayaking the bungee cord tension was adjusted by individual participants to suit their requirements. A discontinuous protocol of 3-min exercise at a targeted load followed by a 1-min rest phase to facilitate stationary earlobe capillary blood sample collection and resetting of ergometer display (Dansprint ergometer) was used. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal load 80 to 120 W for rowing, 50 to 90 W for kayaking and subsequently increased by 20,30 or 40 W every 3-min depending on gender, sport and current competition category. The test was terminated when a participant was no longer able to maintain the targeted workload.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

3.1: Data analysis

Constructed graphical plots (HR, VO2 and [Lac] versus load / velocity) were analysed to quantify the following; load / velocity at TLac, HR at TLac, [Lac] at TLac, % of VO2 peak at TLac, % of HRmax at TLac, load / velocity and HR at a nominal [Lac] of 2 mmol.L-1, load / velocity, VO2 and [Lac} at a nominal HR of

Matlab codes for the solution of exercises which can be found in the book "Tensor Calculus and Differential Geometry for Engineers"

Related publications:

Shahab Sahraee, Peter Wriggers: Tensor Calculus and Differential Geometry for Engineers, Springer, Berlin, to be published.

BackgroundAttention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children, often accompanied by working memory deficits. Recently, exercise interventions have gained attention as a potential strategy to improve cognitive function in children with ADHD. However, the effects of different types of exercise on working memory remain unclear. This study aimed to assess the effects of various exercise interventions on working memory in children with ADHD using a network meta-analysis.MethodsA comprehensive search was conducted in PubMed, Cochrane, Embase, and Web of Science databases for relevant studies. After screening according to the inclusion and exclusion criteria, a total of 17 eligible studies were identified for analysis. A network meta-analysis was performed to integrate data and evaluate the effects of cognitive-aerobic exercise, ball games, mind-body exercises, interactive games, and general aerobic exercise on working memory in children with ADHD.ResultsThe results indicated significant differences in the effectiveness of various types of exercise interventions on working memory in children with ADHD. Cognitive-aerobic exercise showed the most significant effect (SMD = 0.72, 95% CI: 0.44–1.00), followed by ball games (SMD = 0.61, 95% CI: −0.12–1.35). Mind-body exercises and interactive games had moderate effects (SMD = 0.50 and 0.37, respectively), while general aerobic exercise showed relatively small effects (SMD = 0.40, 95% CI: 0.19–0.60). SUCRA analysis further confirmed the highest preference for cognitive-aerobic exercise in improving working memory. Meta-regression analysis showed that intervention frequency and total intervention duration significantly affected the effectiveness of cognitive-aerobic exercise, while other variables did not significantly moderate the effects.ConclusionCognitive-aerobic exercise had the most significant effect on improving working memory in children with ADHD. Higher intervention frequency and longer intervention duration may enhance its effects. Future research should explore the impact of these factors and consider increasing sample sizes to validate the role of these moderators.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=627915.

1) Data Introduction • The Gym Members Exercise Dataset is a dataset built to systematically collect gym members' exercise routines, body information, exercise habits, and fitness indicators to analyze individual exercise patterns and health conditions.

2) Data Utilization (1) Gym Members Exercise Dataset has characteristics that: • This dataset contains various body and exercise related numerical and categorical variables such as age, gender, weight, height, body fat percentage, BMI, exercise type (e.g., aerobic, muscular, yoga, HIIT), exercise frequency, session time, heart rate (maximum, average, rest), calorie consumption, water intake, and experience level. (2) Gym Members Exercise Dataset can be used to: • Exercise effect analysis and customized fitness strategy: Various variables such as exercise type, frequency, session time and heart rate, calorie burn, body fat percentage, etc. can be analyzed and used to establish customized exercise plans for each member and optimize exercise effectiveness. • Healthcare and Member Characteristics Based Marketing: Based on demographics and exercise habit data such as age, gender, and experience level, it can be used to develop healthcare programs, segment members, and establish targeted marketing strategies.

The open science movement produces vast quantities of openly published data connected to journal articles, creating an enormous resource for educators to engage students in current topics and analyses. However, educators face challenges using these materials to meet course objectives. I present a case study using open science (published articles and their corresponding datasets) and open educational practices in a capstone course. While engaging in current topics of conservation, students trace connections in the research process, learn statistical analyses, and recreate analyses using the programming language R. I assessed the presence of best practices in open articles and datasets, examined student selection in the open grading policy, surveyed students on their perceived learning gains, and conducted a thematic analysis on student reflections. First, articles and datasets met just over half of the assessed fairness practices, but this increased with the publication date. There was a..., Article and dataset fairness To assess the utility of open articles and their datasets as an educational tool in an undergraduate academic setting, I measured the congruence of each pair to a set of best practices and guiding principles. I assessed ten guiding principles and best practices (Table 1), where each category was scored ‘1’ or ‘0’ based on whether it met that criteria, with a total possible score of ten. Open grading policies Students were allowed to specify the percentage weight for each assessment category in the course, including 1) six coding exercises (Exercises), 2) one lead exercise (Lead Exercise), 3) fourteen annotation assignments of readings (Annotations), 4) one final project (Final Project), 5) five discussion board posts and a statement of learning reflection (Discussion), and 6) attendance and participation (Participation). I examined if assessment categories (independent variable) were weighted (dependent variable) differently by students using an analysis of ..., , # Data for: Integrating open education practices with data analysis of open science in an undergraduate course

Author: Marja H Bakermans Affiliation: Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA 01609 USA ORCID: https://orcid.org/0000-0002-4879-7771 Institutional IRB approval: IRB-24–0314

Data and file overview

The full dataset file called OEPandOSdata (.xlsx extension) contains 8 files. Below are descriptions of the name and contents of each file. NA = not applicable or no data available

1. BestPracticesData.csv
   • Description: Data to assess the adherence of articles and datasets to open science best practices.
   • Column headers and descriptions:
     • Article: articles used in the study, numbered randomly
     • F1: Findable, Data are assigned a unique and persistent doi
     • F2: Findable, Metadata includes an identifier of data
     • F3: Findable, Data are registered in a searchable database
     • A1: ...

The data if part of the tutorial supplement to the paper "A Primer on Acoustic Analysis for Landscape Ecologists" by Villanueva-Rivera et al. featured in the Landscape Ecology special issue entitled "Soundscape Ecology" (vol. 26, pages 1233-1246, doi: 10.1007/s10980-011-9636-9). Accordingly, the exercises in the tutorial are meant to be undertaken while reading the article.

Primer_Tutorial_1.3.1.pdf - pdf of the tutorial, version 1.3.1 (24june2014) Exercise1.zip - Files for exercise 1 Exercise2.zip - Files for exercise 2 The following zip files contain 1-minute versions of the files for exercise 3 (the original files were 15 minutes long). Each site was divided in 4 files: Ag1_1min_[number].zip - Files from the Ag1 site Ag2_1min_[number].zip - Files from the Ag2 site FNRFarm_1min_[number].zip - Files from the FNR Farm site Martell_1min_[number].zip - Files from the Martell site McCormick_1min_[number].zip - Files from the McCormick site PurdueWildlife_1min_[number].zip - Files from the Purdue Wildlife site Ross_1min_[number].zip - Files from the Ross site

This dataset was revised on 26Jun2014 to correct the date of the Tutorial v 1.3.1.

Exercise data set for the SAS book by Uehlinger. Sample of individual variables and cases from the data set of ZA Study 0757 (political ideology).

Topics: most important political problems of the country; political interest; party inclination; beha

To develop a systematic review with meta-analysis to summarize the effectiveness of exercise, regular physical activity, and mind-body exercise on harmful exercise habits, psychological factors, and quality of life in people clinically diagnosed with feeding and eating disorders. Randomized clinical trials and pilot randomized clinical trials were considered. Twelve studies were included. No studies evaluated athletes. No studies examined regular physical activity as the targeted intervention. Quality of life could not be meta-analyzed. Overall, meta-analyses showed that exercise or mind-body exercise was not more effective than controls in reducing depression symptoms, harmful exercise habits, eating behaviors, or emotional regulation skills. However, important methodological and clinical issues were detected in the included studies. This affected the certainty of evidence of the meta-analyzed outcomes which ranged from low to very low. No studies reported in sufficient detail their interventions to be replicated. Overall, exercise and mind-body exercise may be ineffective in improving meta-analyzed outcomes. However, the certainty of evidence ranged from low to very low and the body of knowledge in this field needs to be increased to reach robust conclusions. Exercise and mind-boy exercises were not more effective than controls for eating behaviors in eating disorders. Exercise and mind-boy exercises were not more effective than controls for depression symptoms in eating disorders. Clinicians should be aware no specific exercises can be recommended for treating psychological factors in eating disorders.

This study is a network meta-analysis of the effects of different traditional Chinese exercises on lung cancer patients. The results were obtained through the search and statistical analysis of multiple databases, aiming to understand the differences in the therapeutic effects of different traditional Chinese exercises.

The review aimed to address this knowledge gap by determining the effectiveness of and the parameters underpinning group exercise interventions among community-dwelling older persons in Southeast Asia. The outcomes of interest were physical function as measured using Time Up and Go test (TUG), activities of daily living (ADL), the physical performance included balance, muscle strength and gait speed, and fall-related outcomes. Data from selected studies were pooled to calculate the effect size through the mean and standard deviations obtained from outcome measures post-intervention for experimental and control groups. The chi-squared test and the I2 statistic were employed to evaluate heterogeneity between studies. The random modal effect model was utilized if I2 exceeded 50% and p-value > 0.1. Mean differences (MD) and 95% confidence intervals (CI) were used to analyse the studies. The value of p < 0.05 was considered statistically significant.

Each participant has to perform a set of Yoga inspired exercises.

ObjectiveTo review the effects of core stability exercise or general exercise for patients with chronic low back pain (LBP). Summary of Background DataExercise therapy appears to be effective at decreasing pain and improving function for patients with chronic LBP in practice guidelines. Core stability exercise is becoming increasingly popular for LBP. However, it is currently unknown whether core stability exercise produces more beneficial effects than general exercise in patients with chronic LBP. MethodsPublished articles from 1970 to October 2011 were identified using electronic searches. For this meta-analysis, two reviewers independently selected relevant randomized controlled trials (RCTs) investigating core stability exercise versus general exercise for the treatment of patients with chronic LBP. Data were extracted independently by the same two individuals who selected the studies. ResultsFrom the 28 potentially relevant trials, a total of 5 trials involving 414 participants were included in the current analysis. The pooling revealed that core stability exercise was better than general exercise for reducing pain [mean difference (−1.29); 95% confidence interval (−2.47, −0.11); P = 0.003] and disability [mean difference (−7.14); 95% confidence interval (−11.64, −2.65); P = 0.002] at the time of the short-term follow-up. However, no significant differences were observed between core stability exercise and general exercise in reducing pain at 6 months [mean difference (−0.50); 95% confidence interval (−1.36, 0.36); P = 0.26] and 12 months [mean difference (−0.32); 95% confidence interval (−0.87, 0.23); P = 0.25]. ConclusionsCompared to general exercise, core stability exercise is more effective in decreasing pain and may improve physical function in patients with chronic LBP in the short term. However, no significant long-term differences in pain severity were observed between patients who engaged in core stability exercise versus those who engaged in general exercise. Systematic Review Registrationhttp://www.crd.york.ac.uk/PROSPERO PROSPERO registration number: CRD42011001717.

This dataset contains the processed outcome variables and anonymized participant information underlying the statistical analysis presented in the following publication: In addition, this dataset contains supplementary information on the investigated training intervention.