According to a survey conducted in March 2022, advertising content with nutritional value and health benefits was best associated with eating healthy, as stated by more than ** percent of Gen Z respondents in Vietnam. In comparison, around **** percent of respondents indicated that advertisements on food that tackles or prevent certain diseases and sustainably grown food had a weak correlation to healthy eating.

Column Name	Description
Age	Age of the participant (in years).
Gender	Biological gender (Male/Female).
Weight (kg)	Weight of the individual in kilograms.
Height (m)	Height of the individual in meters.
Max_BPM	Maximum heart rate recorded during a workout session.
Avg_BPM	Average heart rate maintained during the session.
Resting_BPM	Resting heart rate before starting the workout.
Session_Duration (hours)	Duration of the workout session in hours.
Calories_Burned	Total calories burned during the session.
Workout_Type	Type of workout performed (e.g., Strength, HIIT, Cardio).
Fat_Percentage	Body fat percentage of the individual.
Water_Intake (liters)	Average daily water consumption in liters.
Workout_Frequency (days/week)	Number of workout days per week.
Experience_Level	Fitness experience level (1=Beginner, 2=Intermediate, 3=Advanced).
BMI	Body Mass Index, a measure of body fat based on height and weight.
Daily meals frequency	Number of meals consumed daily.
Physical exercise	Indicates the type or frequency of physical activity.
Carbs	Daily carbohydrate intake (grams).
Proteins	Daily protein intake (grams).
Fats	Daily fat intake (grams).
Calories	Total daily calorie intake from food.
meal_name	Name of the meal (e.g., Breakfast, Lunch, Dinner).
meal_type	Type of meal (e.g., Snack, Main, Beverage).
diet_type	Type of diet followed (e.g., Keto, Vegan, Balanced).
sugar_g	Sugar content in grams per meal.
sodium_mg	Sodium content in milligrams per meal.
cholesterol_mg	Cholesterol content in milligrams per meal.
serving_size_g	Portion size of the meal in grams.
cooking_method	Cooking method used (e.g., Boiled, Fried, Grilled).
prep_time_min	Preparation time in minutes.
cook_time_min	Cooking time in minutes.
rating	Meal or workout rating (typically 1–5 scale).
is_healthy	Boolean indicator (True/False) of whether the meal/workout is healthy.
Name of Exercise	Name of the exercise performed.
Sets	Number of sets completed in the exercise.
Reps	Number of repetitions per set.
Benefit	Description of the exercise’s physical benefit.
Burns Calories (per 30 min)	Estimated calories burned in 30 minutes of that exercise.
Target Muscle Group	Main muscle group targeted by the exercise.
Equipment Needed	Equipment required to perform the exercise.
Difficulty Level	Exercise difficulty level (Beginner, Intermediate, Advanced).
Body Part	Primary body part involved (e.g., Arms, Legs, Chest).
Type of Muscle	Type of muscle engaged (e.g., Upper, Core, Grip Strength). ...

This report presents information on obesity, physical activity and diet drawn together from a variety of sources for England. More information can be found in the source publications which contain a wider range of data and analysis. Each section provides an overview of key findings, as well as providing links to relevant documents and sources. Some of the data have been published previously by NHS Digital. A data visualisation tool (link provided within the key facts) allows users to select obesity related hospital admissions data for any Local Authority (as contained in the data tables), along with time series data from 2013/14. Regional and national comparisons are also provided. The report includes information on: Obesity related hospital admissions, including obesity related bariatric surgery. Obesity prevalence. Physical activity levels. Walking and cycling rates. Prescriptions items for the treatment of obesity. Perception of weight and weight management. Food and drink purchases and expenditure. Fruit and vegetable consumption. Key facts cover the latest year of data available: Hospital admissions: 2018/19 Adult obesity: 2018 Childhood obesity: 2018/19 Adult physical activity: 12 months to November 2019 Children and young people's physical activity: 2018/19 academic year

According to a survey conducted in March 2022, advertising content with nutritional value and health benefits was best associated with eating healthy, as stated by more than ** percent of Millennial respondents in Vietnam. In comparison, *** percent of respondents indicated that advertisements on food that tackles or prevent certain diseases had a weak correlation to healthy eating.

According to a survey of users conducted in January 2022 in four leading global markets (Australia, Germany, the United Kingdom, and the United States), minimizing the risk of losing sensitive information was indicated as one of the possible benefits of super apps by ** percent of Convenience-seekers - namely, consumers who would like to see all their digital experiences integrated into one mobile app. An additional ** percent of Commerce-seekers and ** percent of Financial wellness-seekers felt the same. Around ** percent of Financial wellness-seekers appeared to consider minimizing trust concerns and the need for many security measures as a benefit of using super apps.

Abstract The practice of physical activity has been considered as an important factor in the area of public health, as it helps in the prevention and treatment of various diseases. Thus, understanding the facilitators for participation and benefits from healthy lifestyle can contribute to population awareness. The aim of this study was to analyze facilitators for body practice and benefits perceived by participants of body practice groups of two basic family health units of Santa Rosa/RS. This qualitative research included 25 participants. Data were obtained by the focal group technique. Motivation/incentive, mainly linked to family support, the pedagogical practice of the Physical Education professional, good health status and social life were aspects considered facilitators for adherence to body practice groups. Physical and psychological gains, prevention and control of diseases, lifestyle changes, cognitive improvement and decreased use of medications were pointed as benefits. Motivating participants to participate in body practice programs is an important factor for adherence and participation in these activities can provide biopsychosocial benefits that can contribute to health promotion and quality of life of users of basic family health units.

Abstract Introduction regular fish consumption has been associated with health benefits and prevention of risk factors of chronic diseases; however, consumption is heterogeneous worldwide. Consumption is higher among populations that have fish as a traditional food or among people who tend to adopt a more active and healthy lifestyle. Objectives to evaluate the level of physical activity and perception of life quality among groups with higher and lower frequency of fish consumption. Material and methods quantitative and cross-sectional study carried out via the Internet using the WHOQOL- bref tools to assess perception of life quality. The IPAQ short version normal week was used to evaluate the level of physical activity and proper tool to assess fish consumption. The frequency of fish consumption stratified into two groups (higher and lower frequency) was used as predictor variable. As outcome variable, it was considered the perception of life quality and level of physical activity among groups. Results the group of participants with the highest fish consumption presented better perception of life quality and are more physically active. Discussion and conclusion the results reinforces that regular fish consumption may be related to healthier lifestyle that, consequently, lead to a better perception of life quality.

The General Household Survey (GHS) was a continuous national survey of people living in private households conducted on an annual basis, by the Social Survey Division of the Office for National Statistics (ONS). The main aim of the survey was to collect data on a range of core topics, covering household, family and individual information. This information was used by government departments and other organisations for planning, policy and monitoring purposes, and to present a picture of households, family and people in Great Britain. From 2008, the General Household Survey became a module of the Integrated Household Survey (IHS). In recognition, the survey was renamed the General Lifestyle Survey (GLF). The GLF closed in 2011.

Secure Access GLF
The Secure Access version includes additional, detailed variables not included in either the standard 'End User Licence' (EUL) version (see under GN 33090). Not all variables are available for all years, but extra variables that can typically be found in the Secure Access version but not in the EUL version relate to:

• geography: postcodes (anonymised prior to 2009)
• employment details, including economic status, self-employment, number of employees
• employment and training schemes
• reason for reduction in income
• looking for work
• benefits
• borrowing money and bill arrears
• nationality
• migration, including when arrived in UK and previous country of residence
• ethnicity
• religious identity

Prospective users of the Secure Access version of the GLF will need to fulfil additional requirements, commencing with the completion of an extra application form to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access users must also complete face-to-face training and agree to Secure Access' User Agreement and Breaches Penalties Policy (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access version. Further details and links for all GLF studies available from the UK Data Archive can be found via the General Lifestyle Survey series web page.

Geographical references: postcodes
The postcodes available in the Secure Access version of the data prior to 2009 are pseudo-anonymised postcodes. The real postcodes were not available due to the potential risk of identification of the observations. However, these replacement postcodes retain the inherent nested characteristics of real postcodes, and will allow researchers to aggregate observations to other geographic units, e.g. wards, super output areas, etc. In the dataset, the variable of the replacement postcode is 'new_PC'.

History
The GHS started in 1971 and has been carried out continuously since then, except for breaks in 1997-1998 when the survey was reviewed, and in 1999-2000 when the survey was redeveloped. Following the 1997 review, the survey was relaunched from April 2000 with a different design. The relevant development work and the changes made are fully described in the Living in Britain report for the 2000-2001 survey. Following its review, the GHS was changed to comprise two elements: the continuous survey and extra modules, or 'trailers'. The continuous survey remained unchanged from 2000 to 2004, apart from essential adjustments to take account of, for example, changes in benefits and pensions. The GHS retained its modular structure and this allowed a number of different trailers to be included for each of those years, to a plan agreed by sponsoring government departments.

Further changes to the GHS methodology from 2005
From April 1994 to 2005, the GHS was conducted on a financial year basis, with fieldwork spread evenly from April of one year to March the following year. However, in 2005 the survey period reverted to a calendar year and the whole of the annual sample was surveyed in the nine months from April to December 2005. Future surveys will run from January to December each year, hence the title date change to single year from 2005 onwards. Since the 2005 GHS (EUL version held under SN 5640) does not cover the January-March quarter, this affects annual estimates for topics which are subject to seasonal variation. To rectify this, where the questions were the same in 2005 as in 2004-2005, the final quarter of the latter survey was added (weighted in the correct proportion) to the nine months of the 2005 survey. Furthermore, in 2005, the European Union (EU) made a legal obligation (EU-SILC) for member states to collect additional statistics on income and living conditions. In addition to this the EU-SILC data cover poverty and social exclusion. These statistics are used to help plan and monitor European social policy by comparing poverty indicators and changes over time across the EU. The EU-SILC requirement has been integrated into the GHS, leading to large-scale changes in the 2005 survey questionnaire. The trailers on 'Views of your Local Area' and 'Dental Health' were removed. Other changes were made to many of the standard questionnaire sections, details of which may be found in the GHS 2005 documentation.

Further changes to the GLF methodology from 2008
As noted above, the General Household Survey (GHS) was renamed the General Lifestyle Survey (GLF) in 2008. The sample design is the same as the GHS before, and the questionnaire remains largely the same. The main change is that the GLF then included the IHS core questions, which are common to all of the separate modules that together comprise the IHS. Some of these core questions are simply questions that were previously asked in the same or a similar format on all of the IHS component surveys (including the GLF). The core questions cover employment, smoking prevalence, general health, ethnicity, citizenship and national identity. These questions are asked by proxy if an interview is not possible with the selected respondent (that is a member of the household can answer on behalf of other respondents in the household). This is a departure from the GHS which did not ask smoking prevalence and general health questions by proxy, whereas the GLF does from 2008. For details on other changes to the GLF questionnaire, please see the GLF 2008 documentation.

Changes to the drinking section
There have been a number of revisions to the methodology that is used to produce the alcohol consumption estimates. In 2006, the average number of units assigned to the different drink types and the assumption around the average size of a wine glass was updated, resulting in significantly increased consumption estimates. In addition to the revised method, a new question about wine glass size was included in the survey in 2008. Respondents were asked whether they have consumed small (125 ml), standard (175 ml) or large (250 ml) glasses of wine. The data from this question are used when calculating the number of units of alcohol consumed by the respondent. It is assumed that a small glass contains 1.5 units, a standard glass contains 2 units and a large glass contains 3 units. (In 2006 and 2007 it was assumed that all respondents drank from a standard 175 ml glass containing 2 units.) The datasets contain the original set of variables based on the original methodology, as well as those based on the revised and (for 2008 onwards) updated methodologies. Further details on these changes are provided in the GHS 2006 and GLF/GLS 2008 documentation.

Further information may be found on the ONS GLF webpages.

Correction of erroneous variables in individual 2008 data file
The 'source of income' variables (SrcInc01-14 and SrcIncT1-T5) in the individual file for 2008 have been revised in October 2011 to correct erroneous values in the previous version.

Change in household serial number variable
The household serial number variable 'Hserial' has been replaced by the variable 'HholdId' in the 2008 individual and household files.

The second edition (September 2013) includes data for 2009-2010. Data for 2011 were added in 2017, after the ONS withdrawal of the Special Licence version.

In 2023/24, approximately ten percent of responding adults in the European Union (EU-27) stated that they were following a lactose-free diet. A combined eleven percent of respondents avoided the consumption of meat completely. This includes those who follow a pescetarian, vegan, or vegetarian diet.

Definitions and descriptive statistics for predictor variables from the EHR dataset.

Data and Code Repository This repository contains the anonymized dataset and analysis code associated with the paper titled "Promoting Sustainable Travel Modes Through Health and Active Lifestyle Messaging." Contents anonymized_data.csv: Contains the raw anonymized dataset (1 MB) used in the analysis. analysis_v1.ipynb: Python scripts for analysis, and visualization. (Last Modified July 7, 2025, 5:00PM) README.md: Description of the repository contents and usage. datadictionary.md: A detailed explanation of each variable in the final dataset. 68 unique variables, 4,840 observations. Requirements Packages required to run this analysis are pandas==2.0.3, numpy==1.24.1, statsmodel.api==0.14.1. This code was tested on Python 3.8.13 and 3.9.2 and on macOS Sequoia 15.5 and Google Colab CPUs. Structure of the code The first code block loads the dataset and required packages. The second code block has helper function that generates dataframe for statistical analysis in the later blocks. The third code block has helper variables and functions to load model specifications and formatting model coefficients for analysis in the later blocks Code blocks four and above generate statistical results used in the paper. Output This code package generates the necessary derived data consisting of odds ratios and uncertainty for Figure 1 and Figure 2 in the main document: Main Document Fig. 1 Treatment effects of air quality impacts targeting bus transit and active lifestyle messaging targeting walking or biking. Main Document Fig. 2 Comparative treatment effects of health and active lifestyle messaging for all respondents and those with underlying health conditions. This code package generates the following tables: Main Document Table 1 Heterogeneous treatment effects across key subgroups. Table S4. Treatment effects of air pollution exposure messaging in Experiment 1 (personal and community benefits targeting bus transit) Table S5. Treatment effects of air quality improvement messaging in Experiment 2 (personal and community benefits targeting bus transit) Table S6. Treatment effects of air quality improvement messaging in Experiment 2 (personal gain targeting walking) Table S7. Treatment effects of air quality improvement messaging in Experiment 2 (personal and community benefits targeting walking) Table S8. Treatment effects of air quality improvement messaging in Experiment 2 (personal gain targeting biking) Table S9. Treatment effects of air quality improvement messaging in Experiment 2 (personal and community benefits targeting biking) Table S10. Treatment effects of all active lifestyle messaging in Experiment 3 (personal and community benefits targeting walking and biking) Table S11. Treatment effects of all active lifestyle messaging in Experiment 3 (personal gain targeting biking) Table S12. Treatment effects of active lifestyle messaging in Experiment 3 (personal and community benefits targeting biking) Table S13. Treatment effects of step count messaging in Experiment 3 (personal and community benefits targeting walking) Table S14. Treatment effects of calories burned messaging in Experiment 3 (personal and community benefits targeting walking) Table S15. Treatment effects of heart health messaging in Experiment 3 (personal and community benefits targeting walking and biking) Table S16. Heterogeneous treatment effects of air pollution exposure messaging in Experiment 1 (personal gain targeting bus transit) Table S17. Heterogeneous treatment effects of air pollution exposure messaging in Experiment 1 (personal and community benefits targeting bus transit) Table S18. Heterogeneous treatment effects of air quality improvement messaging in Experiment 2 (personal gain targeting bus transit) Table S19. Heterogeneous treatment effects of air quality improvement messaging in Experiment 2 (personal and community benefits targeting bus transit) Table S20. Heterogeneous treatment effects of active lifestyle messaging in Experiment 3 (personal gain targeting walking) Table S21. Heterogeneous treatment effects of active lifestyle messaging in Experiment 3 (personal and community benefits targeting walking) Table S22. Heterogeneous treatment effects of different active lifestyle messaging in Experiment 3 targeting walking Table S23. Heterogeneous treatment effects of calories burned messaging for commuters with varying daily travel times in Experiment 3 (targeting walking) Replication Supporting replication code is also available here: https://github.com/asensio-lab/health-active-lifestyle. This code package was last replicated on July 7, 2025 by @YifanLiu0304 Declaration of generative AI and AI-assisted technologies in the coding process During the preparation of this work the authors used ChatGPT in order to debug code errors such as KeyError, syntax errors in python scripts that were used to generate statistical tables. After using this tool/service, the researchers reviewed, edited, and replicated all code.

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Health and Wellness Market Size 2025-2029

The health and wellness market size is forecast to increase by USD 2069.2 billion, at a CAGR of 7.1% between 2024 and 2029. Increasing emphasis on promotion of health and wellness activities and programs will drive the health and wellness market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 36% growth during the forecast period.
By Product Type - Beauty and personal care products segment was valued at USD 1077.50 billion in 2023
By Distribution Channel - Online segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 94.43 billion
Market Future Opportunities: USD 2069.20 billion 
CAGR : 7.1%
APAC: Largest market in 2023

Market Summary

The market is a continually evolving landscape, driven by the increasing prioritization of self-care and preventative health measures. Core technologies and applications, such as telehealth and wearable devices, are revolutionizing the way consumers manage their well-being. The service types or product categories, including fitness centers and dietary supplements, are experiencing significant growth, with thermal and mineral springs and spas gaining increasing popularity. However, challenges persist, such as frequent product recalls and stringent regulations, particularly in regions like Europe and North America.
Key companies, like Fitbit and Peloton Interactive, are seizing opportunities to innovate and expand their offerings. As we look forward, the market's evolution is set to continue, with advancements in artificial intelligence and virtual reality technologies poised to reshape the industry landscape.

What will be the Size of the Health And Wellness Market during the forecast period?

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How is the Health and Wellness Market Segmented and what are the key trends of market segmentation?

The health and wellness industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product Type

  Beauty and personal care products
  Health and wellness food
  Wellness tourism
  Fitness equipment
  Preventive and personalized health


Distribution Channel

  Online
  Offline


End-User

  Adults
  Children
  Seniors


Category Type

  Organic
  Natural
  Functional Foods
  Plant-Based


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    The Netherlands
    UK


  Middle East and Africa

    UAE


  APAC

    Australia
    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Type Insights

The beauty and personal care products segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth and innovation, with various sectors contributing to its continuous expansion. Health tracking devices, such as wearable sensors and fitness monitors, have seen a 30% increase in adoption, enabling individuals to monitor their biometric data and maintain healthy habits. Preventive medicine, including yoga and meditation practices, personalized nutrition, and wellness programs, has gained popularity, with 25% of companies offering workplace wellness initiatives. Corporate wellness, healthy eating habits, and lifestyle interventions are increasingly prioritized, with telehealth platforms and digital therapeutics facilitating remote patient monitoring and mental well-being support. Functional foods, nutritional supplements, and probiotics efficacy are essential components of personalized nutrition, growing by 22% in the past year.

Stress management techniques, such as mindfulness practices and emotional well-being initiatives, are in high demand, with 18% of businesses integrating these offerings. Physical therapy, holistic healthcare, and rehabilitation programs are essential for overall well-being, with a 20% increase in demand for these services. The integration of ergonomic design, remote patient monitoring, and mindfulness practices in various industries underscores the importance of wellbeing initiatives. The future of the market holds promising growth, with a 15% increase in demand for health coaching and nutrition counseling services expected. The market is a dynamic and evolving sector, with ongoing developments in technology, personalization, and prevention shaping its future.

Companies like L'Oreal, Procter and Gamble, and Beiersdorf are leading the way, integrating organic and natural offerings into their product lines. The market's continuous expansion underscores the growing importance of prioritizing health and well-being in our daily live

Short-term studies have shown numerous benefits of weight loss in overweight or obese patients, including improvements in glycemic control, risk factors for cardiovascular disease, quality of life, and other obesity-related coexisting illnesses. The Look AHEAD study was designed to test whether weight loss similarly improved cardiovascular morbidity and mortality in patients with type 2 diabetes. The study is a multicenter, randomized clinical trial that examines the long-term effects of an intensive lifestyle intervention program designed to achieve and maintain weight loss by decreased caloric intake and increased physical activity.

Eligible patients with type 2 diabetes and a body-mass index (BMI) of 25.0 or more were enrolled and randomly assigned either to participate in an intensive lifestyle intervention (intervention group) or to receive diabetes support and education (control group). The intensive lifestyle intervention, which included both group and individual counseling sessions, was aimed at achieving and maintaining weight loss of at least 7% by focusing on reduced caloric intake and increased physical activity. The diabetes support and education program featured sessions focusing on diet, exercise, and social support. Both the intervention and control programs occurred with decreasing frequency as the trial progressed. The primary outcome measure is the first occurrence of a composite cardiovascular outcome, which consists of death from cardiovascular causes, nonfatal myocardial infarction, nonfatal stroke, and hospitalization for angina. Participants will be followed for a planned period of 13.5 years.

An ancillary study, Look AHEAD Brain, was conducted to assess whether participation in the 10-year lifestyle intervention, as part of Look AHEAD, had an impact on white matter hyperintensity and loss of brain tissue among individuals with type 2 diabetes. A subset of Look AHEAD study participants underwent standardized brain magnetic resonance imaging in conjunction with tests assessing cognitive function 10-12 years post-randomization.

The Look AHEAD intensive lifestyle intervention ended in September, 2012. Participants continued to be followed to determine the long-term effects of the intervention on health outcomes.

The Look AHEAD Continuation Study (Look AHEAD-C) builds on the Look AHEAD study to determine the long-term impact of an intensive lifestyle intervention on 1) physical function and mobility disability, and 2) cognitive function and cognitive impairment. Collection of cognitive function measures began in Year 8 of the study and continued through Year 13.

The current data package contains data through the end of the post intervention program.

AimTo study 1-year effectiveness of an intensive, culturally targeted lifestyle intervention in general practice for weight status and metabolic profile of South-Asians at risk of type 2 diabetes.Methods536 South-Asians at risk of type 2 diabetes were randomized to an intervention (n = 283) or control (n = 253) group. The intervention, which was targeted culturally to the South-Asian population, consisted of individual lifestyle counselling, a family session, cooking classes, and supervised physical activity programme. All components of the intervention were carried out by professionals as part of their daily clinical practice. The control group received generic lifestyle advice. Change in weight status and metabolic profile were assessed after 1 year.ResultsAfter 1 year, 201 participants were lost to follow-up. Remaining participants in intervention (n = 177) and control (n = 158) group had similar baseline characteristics. Weight loss in the intervention group was 0.2±3.3 kg, weight gain in the control group was 0.4±3.1 kg (p = 0.08). Changes in other weight-related measurements did not differ significantly between groups. Furthermore, there were no differences between groups in changes of metabolic profile. All results remained similar after repeating analyses in a multiple imputed dataset.DiscussionAn intensive, culturally targeted, lifestyle intervention of 1 year did not improve weight status and metabolic profile of South-Asians at risk of type 2 diabetes. The laborious recruitment, high drop-out, and lack of effectiveness emphasise the difficulty of realising health benefits in practice and suggest that this strategy might not be the optimal approach for this population.Trial RegistrationNederlands Trial Register NTR1499

In 2025, among EU-27 consumers aged 18 to 19 years, some 12 percent of survey respondents stated they have one or more food intolerances For respondents 60 to 64 years old, the share stood at seven percent. The survey was carried out in 18 countries of the Eu-27. The highest share of food intolerances could be found among those 30 to 39 years.

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Online Fitness Course Market Size 2024-2028

The online fitness course market size is forecast to increase by USD 26.46 billion at a CAGR of 16.92% between 2023 and 2028. The market is experiencing significant growth due to several key drivers. The increasing awareness of the benefits of maintaining a healthy lifestyle is leading more individuals to seek flexible workout options that fit their schedules. Live video content provides real-time engagement and interaction with fitness instructors, enhancing the virtual fitness experience. Diverse workout options catering to various fitness levels and preferences are also attracting a wider audience. Corporate wellness programs integrating virtual fitness stations offer employers cost-effective solutions for employee health and productivity. However, privacy concerns and the need for individual fitness plans require platforms to ensure secure data handling and customized workout recommendations. Group sessions and personalized workouts offer social connection and individualized attention, respectively. The integration of Virtual Reality (VR) and Augmented Reality (AR) technology in online fitness courses adds an engaging element to the user experience. Despite the high cost of some online fitness courses, the market is expected to continue growing as consumers prioritize their health and wellness.

What will be the Size of the Market During the Forecast Period?

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In today's fast-paced world, maintaining a healthy lifestyle has become a top priority for individuals. Traditional gym workouts and in-person fitness classes may not always fit into busy schedules, leading to a growing demand for virtual fitness solutions. Online fitness courses offer convenience, flexibility, and accessibility, making it easier for people to engage in advanced fitness sessions from the comfort of their homes. The health and wellness industry has seen a significant shift towards digital platforms, with fitness apps, training videos, and wearable technology becoming increasingly popular.

Also, these solutions cater to the health consciousness of millennials and offer a more flexible approach to fitness. Health insurance providers are also recognizing the importance of online fitness solutions and are offering incentives to policyholders who incorporate these services into their routines. Augmented reality technology is revolutionizing the online fitness industry by providing engaging workout experiences. Virtual fitness competitions and live video classes offer a sense of community and engagement, keeping users motivated and committed to their fitness goals. Online instructors provide personalized training and feedback, ensuring that each workout is effective and safe. Remote workouts offer a convenient alternative to in-person workouts, allowing individuals to maintain their fitness routines even when traveling or working from home.

Further, balanced diets and mental health are essential components of a healthy lifestyle, and online fitness solutions provide access to resources and tools to help users make informed decisions about their nutrition and mental well-being. Fitness executives predict that online fitness solutions will continue to gain popularity, with live video content becoming a staple in the industry. The accessibility of these services allows individuals to prioritize their health and wellness, regardless of location or schedule. As technology continues to advance, we can expect to see even more innovative online fitness solutions that cater to the unique needs and preferences of users.

In conclusion, the online fitness industry is poised for growth, offering a convenient and accessible alternative to traditional fitness solutions. With the increasing popularity of fitness apps, training videos, and wearable technology, it is clear that virtual fitness is here to stay. By prioritizing health consciousness and offering flexible and engaging workouts, online fitness solutions are helping individuals maintain a healthy lifestyle, no matter where they are or what their schedule looks like.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  On-demand courses
  Live classes
  Hybrid courses


Revenue Stream

  Subscription-based
  Freemium
  One-time purchase
  Pay-per-class


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Type Insights

The on-demand courses segment is estimated to witness significant growth during the forecast period. The on-demand segment of The market has revolutionized how individuals approach fitness education and training. This sector

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The smart fitness market share is expected to increase by USD 34.06 billion from 2021 to 2026, at a CAGR of 13.33%.

This smart fitness market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers smart fitness market segmentation by product (gear, smart bike, ellipticals, treadmill, and others) and geography (North America, Europe, APAC, MEA, and South America). The smart fitness market report also offers information on several market vendors, including Alphabet Inc., Apple Inc., Dyaco International Inc., Fossil Group Inc., Garmin Ltd., Johnson Health Tech, Nautilus Inc., Peloton Interactive Inc., Tunturi New Fitness BV, and Zwift Inc. among others.

What will the Smart Fitness Market Size be During the Forecast Period?

Download the Free Report Sample to Unlock the Smart Fitness Market Size for the Forecast Period and Other Important Statistics

Smart Fitness Market: Key Drivers and Trends

The increasing focus on fitness and a healthy lifestyle orientation is notably driving the smart fitness market growth, although factors such as lack of data privacy and security may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the smart fitness industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Smart Fitness Market Driver

One of the key factors driving growth in the smart fitness market is the increasing focus on fitness and healthy lifestyle orientation. The rising adoption of a sedentary lifestyle is exposing people to the high risk of developing various health conditions, such as anxiety, obesity, type 2 diabetes, and osteoporosis. The hectic work schedules and increasing health issues have forced people to undertake some form of exercise daily to remain healthy and prevent various health-related issues. Thus, increasing awareness about the importance of a healthy lifestyle has led to a rise in the demand for various fitness activities, including interactive fitness. Interactive fitness activities offer several benefits, such as body coordination and the strengthening of the abdominal muscles. Promotional activities conducted by major vendors operating in the global smart fitness market to generate smart fitness products awareness have played a key role in driving the demand for interactive fitness. The wellness services industry, which also includes fitness services and a healthy lifestyle, has witnessed significant growth in the last five years. It is expected to achieve strong growth during the forecast period, owing to the increasing focus of employees on health and fitness.

Key Smart Fitness Market Challenge

The lack of data privacy and security will be a major challenge for the smart fitness market during the forecast period. Smart wearable devices can cause work interruption for users as they store a huge amount of sensitive information. Smart wearable devices also use GPS navigation systems for receiving location-based information, and at times, individuals have to share their location to get certain information. This information can also be retrieved and used by several advertisers. Security breaches can also occur because of the use of innovative technologies in these devices. The leakage of data stored in the sports wearable devices of renowned sportspersons and athletes can lead to serious security threats. The information about a subscriber's location is owned and controlled by the respective network operators of mobile carriers and mobile content providers. With network operators privy to such information, end-users are concerned about their privacy and security, in spite of the legal framework to protect it.

This smart fitness market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2022-2026.

Who are the Major Smart Fitness Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Alphabet Inc.
Apple Inc.
Dyaco International Inc.
Fossil Group Inc.
Garmin Ltd.
Johnson Health Tech
Nautilus Inc.
Peloton Interactive Inc.
Tunturi New Fitness BV
Zwift Inc.

This statistical study of the smart fitness market encompasses successful business strategies deployed by the key vendors. The smart fitness market is fragmented and the vendors are deploying growth strategies such as increasing their R&D investments to compete in the market.

To make the most of the opportunities and recover from post COVID-19 impact, marke

Abstract Background Weight gain is common after breast cancer. The aim of this study was to identify and describe the barriers to and enablers of successful weight management for women with breast cancer. Methods This was a combined inductive and deductive framework analysis of free text responses to an anonymous cross-sectional survey on weight after breast cancer. Women were recruited mainly through the Breast Cancer Network Australia Review and Survey Group. We applied deductive thematic analysis to free text responses to questions on barriers, enablers, research priorities, and one open-ended question at the end of the survey using the Capability, Opportunity, Motivation and Behaviour (COM-B) model as a framework. Subthemes that arose from the inductive analysis were mapped onto the COM-B model framework. Findings were used to identify behaviour change intervention functions. Results One hundred thirty-three women provided free text responses. Most women were of Caucasian origin and had been diagnosed with non-metastatic breast cancer, with a mean age of 59.1 years. Women's physical capability to adopt and sustain healthy lifestyle habits was significantly affected by treatment effects and physical illness, and some lacked psychological capability to self-regulate the face of stress and other triggers. Limited time and finances, and the social impact of undergoing cancer treatment affected the ability to control their diet. Frustration and futility around weight management were prominent. However, some women were confident in their abilities to self-regulate and self-monitor lifestyle behaviours, described support from friends and health professionals as enablers, and welcomed the physical and psychological benefits of being active in the context of embracing transformation and self-care after cancer. Conclusion Women need specific advice and support from peers, friends and families and health professionals. There is a substantial gap in provision of supportive care to enable women to adopt and sustain healthy lifestyles. Environmental restructuring (including financial support), incentivization (creating an expectation of looking and feeling better), persuasion and coercion (aiming to prevent recurrence), and equipping women with specific knowledge and skills, would also facilitate optimal lifestyle behaviours and weight management.

BackgroundScalable weight loss maintenance (WLM) interventions for adults with obesity are lacking but vital for the health and economic benefits of weight loss to be fully realised. We examined the effectiveness and cost-effectiveness of a low-intensity technology-mediated behavioural intervention to support WLM in adults with obesity after clinically significant weight loss (≥5%) compared to standard lifestyle advice.Methods and findingsThe NULevel trial was an open-label randomised controlled superiority trial in 288 adults recruited April 2014 to May 2015 with weight loss of ≥5% within the previous 12 months, from a pre-weight loss BMI of ≥30 kg/m2. Participants were self-selected, and the majority self-certified previous weight loss. We used a web-based randomisation system to assign participants to either standard lifestyle advice via newsletter (control arm) or a technology-mediated low-intensity behavioural WLM programme (intervention arm). The intervention comprised a single face-to-face goal-setting meeting, self-monitoring, and remote feedback on weight, diet, and physical activity via links embedded in short message service (SMS). All participants were provided with wirelessly connected weighing scales, but only participants in the intervention arm were instructed to weigh themselves daily and told that they would receive feedback on their weight. After 12 months, we measured the primary outcome, weight (kilograms), as well as frequency of self-weighing, objective physical activity (via accelerometry), psychological variables, and cost-effectiveness. The study was powered to detect a between-group weight difference of ±2.5 kg at follow-up. Overall, 264 participants (92%) completed the trial. Mean weight gain from baseline to 12 months was 1.8 kg (95% CI 0.5–3.1) in the intervention group (n = 131) and 1.8 kg (95% CI 0.6–3.0) in the control group (n = 133). There was no evidence of an effect on weight at 12 months (difference in adjusted mean weight change from baseline: −0.07 [95% CI 1.7 to −1.9], p = 0.9). Intervention participants weighed themselves more frequently than control participants and were more physically active. Intervention participants reported greater satisfaction with weight outcomes, more planning for dietary and physical activity goals and for managing lapses, and greater confidence for healthy eating, weight loss, and WLM. Potential limitations, such as the use of connected weighing study in both trial arms, the absence of a measurement of energy intake, and the recruitment from one region of the United Kingdom, are discussed.ConclusionsThere was no difference in the WLM of participants who received the NULevel intervention compared to participants who received standard lifestyle advice via newsletter. The intervention affected some, but not all, process-related secondary outcomes of the trial.Trial registrationThis trial is registered with the ISRCTN registry (ISRCTN 14657176; registration date 20 March 2014).

Observed and age-standardised proportion of adults who met the recommended guidelines of consuming five or more portions of fruit and vegetables a day. To help reduce the risk of deaths from chronic diseases such as heart disease, stroke, and cancer.The Five-a-day programme was introduced to increase fruit and vegetable consumption within the general population. Its central message is that people should eat at least five portions of fruit and vegetables a day; that a variety of fruit and vegetables should be consumed and that fresh, frozen, canned and dried fruit, vegetables and pulses all count in making up these portions. The programme includes educational initiatives to increase awareness of the Five-a-day message and the benefits of fruit and vegetable consumption, along with more direct schemes to increase access to fruit and vegetables, such as the school fruit scheme and community initiatives. Monitoring of fruit and vegetable consumption is key to evaluating the success of the policy, both at the level of individual schemes and at a more general level. Legacy unique identifier: P00860