Key Health App StatisticsTop Health AppsHealth & Fitness App Market LandscapeHealth App RevenueHealth Revenue by AppHealth App UsageHealth App Market ShareHealth App DownloadsKeeping track of...

The dataset appears to focus on a collection of fitness applications, providing detailed information about various features, user ratings, and feedback. The columns include:

App Name: The name of the fitness application. User Rating (out of 5): The average rating given by users. OS Supported: The operating systems the app supports (e.g., iOS, Android). Key Features: A list of notable features for each app (e.g., calorie counting, workout plans). No. of Downloads (in millions): The total number of times the app has been downloaded. In-App Purchases: Indicates whether the app offers in-app purchases. Subscription Model: Information about the app's subscription options (monthly, yearly, or free). User Satisfaction (%): The percentage of users who are satisfied with the app. Common User Feedback: General user comments and feedback about the app (e.g., easy to use, accurate tracking). Positive Feedback: Specific positive aspects highlighted by users. Negative Feedback: Common criticisms or drawbacks users have encountered. This dataset provides a comprehensive comparison of popular fitness apps, allowing for an analysis of their popularity, features, pricing models, and user experiences. It includes both qualitative data (e.g., user feedback) and quantitative data (e.g., downloads, ratings, user satisfaction).

In 2022, Planet Fitness was the most popular health and fitness app in the United States, generating over ** million downloads. Sport and activity tracking Sweatcoin ranked second, amassing approximately **** million downloads. The mental wellness and meditation app Calm saw *** million downloads in the country in the examined period, ranking third as the most downloaded health app. Despite the controversies surrounding period tracking mobile apps data sharing and privacy settings in the aftermath of the Supreme Court overturning of Roe v. Wade, Flo was downloaded *** million times by U.S. users in the examined year.

Mobile Health Apps Market Outlook

As per our latest research, the global Mobile Health Apps market size reached USD 56.3 billion in 2024, underscoring its robust presence in the digital health ecosystem. The market is set to grow at a CAGR of 17.2% over the forecast period, with the total market size projected to reach USD 184.7 billion by 2033. This impressive growth trajectory is fueled by rising smartphone penetration, increasing adoption of digital health solutions, and growing consumer awareness regarding personal health and wellness management worldwide.

The primary growth driver for the Mobile Health Apps market is the global proliferation of smartphones and mobile internet connectivity. With billions of users accessing mobile devices daily, health apps have become an integral part of personal healthcare management. The convenience of tracking fitness, monitoring chronic conditions, and consulting with healthcare professionals remotely has led to widespread adoption across all age groups. Additionally, the integration of advanced technologies such as artificial intelligence, machine learning, and data analytics within these apps has significantly improved their functionality, making them indispensable tools for preventive healthcare and disease management.

Another significant factor propelling market growth is the increasing focus on chronic disease management and preventive healthcare. The rising prevalence of lifestyle-related diseases such as diabetes, hypertension, and obesity has necessitated the adoption of continuous monitoring and management tools. Mobile health apps offer personalized interventions, medication reminders, and real-time tracking, empowering users to take proactive control over their health. Healthcare providers are also leveraging these apps to remotely monitor patient vitals, streamline communication, and enhance treatment adherence, thereby improving overall health outcomes and reducing hospital readmissions.

Furthermore, the COVID-19 pandemic has acted as a catalyst, accelerating digital transformation in the healthcare sector. The need for remote consultations, telemedicine, and contactless health monitoring surged during the pandemic, leading to a dramatic uptick in mobile health app downloads and usage. Governments and regulatory bodies worldwide have also recognized the potential of digital health, introducing policies and incentives to promote the development and adoption of mobile health solutions. This supportive regulatory environment, coupled with increased funding from venture capitalists and healthcare organizations, is expected to sustain the marketÂ’s upward momentum throughout the forecast period.

As mobile health apps continue to evolve, the issue of Health App Data Privacy Insurance has become increasingly critical. With the vast amount of personal health information being collected, stored, and shared through these apps, ensuring the privacy and security of this data is paramount. Health App Data Privacy Insurance provides a safety net for app developers and users alike, protecting against potential breaches and unauthorized access. This type of insurance not only covers financial losses resulting from data breaches but also helps in managing the reputational damage that can occur. As regulatory bodies tighten data protection laws, having robust privacy insurance becomes a strategic advantage, ensuring compliance and fostering trust among users. The integration of privacy insurance into the mobile health ecosystem is expected to drive further adoption and innovation, as users feel more secure in sharing their health data.

Regionally, North America dominates the Mobile Health Apps market due to its advanced healthcare infrastructure, high smartphone penetration, and favorable reimbursement policies. However, the Asia Pacific region is anticipated to witness the fastest growth, driven by a burgeoning population, increasing digital literacy, and rising healthcare expenditures. Europe also holds a significant market share, supported by robust government initiatives and widespread adoption of e-health solutions. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, with growing investments in digital health infrastructure and increasing awareness about the benefits of mobile health applications.

Source:

Oresti Banos, Department of Computer Architecture and Computer Technology, University of Granada Rafael Garcia, Department of Computer Architecture and Computer Technology, University of Granada Alejandro Saez, Department of Computer Architecture and Computer Technology, University of Granada

Email to whom correspondence should be addressed: oresti '@' ugr.es (oresti.bl '@' gmail.com)

Data Set Information:

The MHEALTH (Mobile HEALTH) dataset comprises body motion and vital signs recordings for ten volunteers of the diverse profile while performing several physical activities. Sensors placed on the subject's chest, right wrist, and left ankle are used to measure the motion experienced by diverse body parts, namely, acceleration, rate of turn, and magnetic field orientation. The sensor positioned on the chest also provides 2-lead ECG measurements, which can be potentially used for basic heart monitoring, checking for various arrhythmias, or looking at the effects of exercise on the ECG.

DATASET SUMMARY:

• Activities: 12
• Sensor devices: 3
• Subjects: 10

EXPERIMENTAL SETUP

The collected dataset comprises body motion and vital signs recordings for ten volunteers of the diverse profile while performing 12 physical activities (Table 1). Shimmer2 [BUR10] wearable sensors were used for the recordings. The sensors were respectively placed on the subject's chest, right wrist, and left ankle and attached by using elastic straps (as shown in the figure in the attachment). The use of multiple sensors permits us to measure the motion experienced by diverse body parts, namely, the acceleration, the rate of turn, and the magnetic field orientation, thus better capturing the body dynamics. The sensor positioned on the chest also provides 2-lead ECG measurements which are not used for the development of the recognition model but rather collected for future work purposes. This information can be used, for example, for basic heart monitoring, checking for various arrhythmias, or looking at the effects of exercise on the ECG. All sensing modalities are recorded at a sampling rate of 50 Hz, which is considered sufficient for capturing human activity. Each session was recorded using a video camera. This dataset is found to generalize to common activities of daily living, given the diversity of body parts involved in each one (e.g., the frontal elevation of arms vs. knees bending), the intensity of the actions (e.g., cycling vs. sitting and relaxing) and their execution speed or dynamicity (e.g., running vs. standing still). The activities were collected in an out-of-lab environment with no constraints on the way these must be executed, with the exception that the subject should try their best when executing them.

ACTIVITY SET

The activity set is listed in the following: L1: Standing still (1 min) L2: Sitting and relaxing (1 min) L3: Lying down (1 min) L4: Walking (1 min) L5: Climbing stairs (1 min) L6: Waist bends forward (20x) L7: Frontal elevation of arms (20x) L8: Knees bending (crouching) (20x) L9: Cycling (1 min) L10: Jogging (1 min) L11: Running (1 min) L12: Jump front & back (20x) NOTE: In brackets are the number of repetitions (Nx) or the duration of the exercises (min).

A complete and illustrated description (including table of activities, sensor setup, etc.) of the dataset is provided in the papers presented in the section “Citation Requests†.

Attribute Information:

The data collected for each subject is stored in a different log file: 'mHealth_subject.log'. Each file contains the samples (by rows) recorded for all sensors (by columns). The labels used to identify the activities are similar to the abovementioned (e.g., the label for walking is '4').

The meaning of each column is detailed next: Column 1: acceleration from the chest sensor (X-axis) Column 2: acceleration from the chest sensor (Y axis) Column 3: acceleration from the chest sensor (Z axis) Column 4: electrocardiogram signal (lead 1) Column 5: electrocardiogram signal (lead 2) Column 6: acceleration from the left-ankle sensor (X-axis) Column 7: acceleration from the left-ankle sensor (Y axis) Column 8: acceleration from the left-ankle sensor (Z axis) Column 9: gyro from the left-ankle sensor (X-axis) Column 10: gyro from the left-ankle sensor (Y axis) Column 11: gyro from the left-ankle sensor (Z axis) Column 13: magnetometer from the left-ankle sensor (X-axis) Column 13: magnetometer from the left-ankle sensor (Y axis) Column 14: magnetometer from the left-ankle sensor (Z axis) Column 15: acceleration from the right-lower-arm sensor (X-axis) Column 16: acceleration from the right-lower-arm sensor (Y axis) Column 17: acceleration from the right-lower-arm sensor (Z axis) Column 18: gyro from the right-lower-arm sensor (X-axis) Column 19: gyro from the right-lower-arm sensor (Y axis) Column 20: gyro fro...

Key Fitness App StatisticsTop Fitness AppsHealth & Fitness App Market LandscapeFitness App RevenueFitness Revenue by AppFitness App UsersFitness App Market ShareFitness App DownloadsTracking...

Key Wellness StatisticsTop Wellness AppsHealth & Fitness App Market LandscapeWellness App RevenueWellness Revenue by RegionWellness Revenue by AppWellness App UsageWellness App Market...

ObjectiveTo analyse the relationship between health app quality with user ratings and the number of downloads of corresponding health apps.Materials and methodsUtilising a dataset of 881 Android-based health apps, assessed via the 300-point objective Organisation for the Review of Care and Health Applications (ORCHA) assessment tool, we explored whether subjective user-level indicators of quality (user ratings and downloads) correlate with objective quality scores in the domains of user experience, data privacy and professional/clinical assurance. For this purpose, we applied spearman correlation and multiple linear regression models.ResultsFor user experience, professional/clinical assurance and data privacy scores, all models had very low adjusted R squared values (< .02). Suggesting that there is no meaningful link between subjective user ratings or the number of health app downloads and objective quality measures. Spearman correlations suggested that prior downloads only had a very weak positive correlation with user experience scores (Spearman = .084, p = .012) and data privacy scores (Spearman = .088, p = .009). There was a very weak negative correlation between downloads and professional/clinical assurance score (Spearman = -.081, p = .016). Additionally, user ratings demonstrated a very weak correlation with no statistically significant correlations observed between user ratings and the scores (all p > 0.05). For ORCHA scores multiple linear regression had adjusted R-squared = -.002.ConclusionThis study highlights that widely available proxies which users may perceive to signify the quality of health apps, namely user ratings and downloads, are inaccurate predictors for estimating quality. This indicates the need for wider use of quality assurance methodologies which can accurately determine the quality, safety, and compliance of health apps. Findings suggest more should be done to enable users to recognise high-quality health apps, including digital health literacy training and the provision of nationally endorsed “libraries”.

In June 2025, London-based Flo was the most downloaded mobile period tracking app worldwide, with almost 5.2 million downloads during the examined month. The Period Tracker Period Calendar, published by Simple Design Ltd, ranked second with approximately 1.65 million downloads during the measured period. The Chinese developed app Meiyou ranked third, with 942,000 downloads from global users in the last examined month.

Mobile App

## Overview

Mobile App is a dataset for object detection tasks - it contains Fruit annotations for 300 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Get data on COVID Alert's impact so far.

Data includes:

• Approximate total app downloads
• Total Ontarians who alerted others

Learn more about the COVID Alert app.

Frequencies of apps and app classification(s).

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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.

There has been an increased emphasis on plant-based foods and diets. Although mobile technology has the potential to be a convenient and innovative tool to help consumers adhere to dietary guidelines, little is known about the content and quality of free, popular mobile health (mHealth) plant-based diet apps. The objective of the study was to assess the content and quality of free, popular mHealth apps supporting plant-based diets for Canadians. Free mHealth apps with high user ratings, a high number of user ratings, available on both Apple App and GooglePlay stores, and primarily marketed to help users follow plant-based diet were included. Using pre-defined search terms, Apple App and GooglePlay App stores were searched on December 22, 2020; the top 100 returns for each search term were screened for eligibility. Included apps were downloaded and assessed for quality by three dietitians/nutrition research assistants using the Mobile App Rating Scale (MARS) and the App Quality Evaluation (AQEL) scale. Of the 998 apps screened, 16 apps (mean user ratings±SEM: 4.6±0.1) met the eligibility criteria, comprising 10 recipe managers and meal planners, 2 food scanners, 2 community builders, 1 restaurant identifier, and 1 sustainability assessor. All included apps targeted the general population and focused on changing behaviors using education (15 apps), skills training (9 apps), and/or goal setting (4 apps). Although MARS (scale: 1–5) revealed overall adequate app quality scores (3.8±0.1), domain-specific assessments revealed high functionality (4.0±0.1) and aesthetic (4.0±0.2), but low credibility scores (2.4±0.1). The AQEL (scale: 0–10) revealed overall low score in support of knowledge acquisition (4.5±0.4) and adequate scores in other nutrition-focused domains (6.1–7.6). Despite a variety of free plant-based apps available with different focuses to help Canadians follow plant-based diets, our findings suggest a need for increased credibility and additional resources to complement the low support of knowledge acquisition among currently available plant-based apps. This research received no specific grant from any funding agency.

BackgroundThe use of mobile technology such as phone applications (apps) has been proposed as an efficient means of providing health and clinical information in a variety of healthcare settings. We developed the Health-e Babies app as an Android smart phone application for pregnant women attending a tertiary hospital in a low socio-economic community, with the objective of providing health information about early pregnancy that would increase maternal confidence and reduce anxiety. Based on our earlier research, this form of health communication was viewed as a preferred source of information for women of reproductive age. However, the pilot study had a poor participation rate with 76% (n = 94) not completing the study requirements. These initial findings raised some very important issues in relation to the difficulties of engaging women with a pregnancy app. This paper analyses the characteristics of the participants who did not complete the study requirements in an attempt to identify potential barriers associated with the implementation of a pregnancy app.MethodsThis retrospective review of quantitative and qualitative data collected at the commencement of the Health-e Babies App trial, related to the participant’s communication technology use, confidence in knowing where to seek help and mental health status, maternal-fetal attachment and parenting confidence. Engagement and use of the Health-e Babies App was measured by the completion of a questionnaire about the app and downloaded data from participant’s phones. Mental health status, confidence and self-efficacy were measured by questionnaires.ResultsAll women were similar in terms of age, race, marital status and level of education. Of the 94 women (76%) who did not complete the trial, they were significantly more anxious as indicated by State Trait Anxiety Inventory (p = 0.001 Student T-test) and more likely to be unemployed (50% vs 31%, p = 0.012 Student T-Test).ConclusionThis study provides important information about the challenges associated with the implementation of a pregnancy app in a socially disadvantaged community. The data suggests that factors including social and mental health issues, financial constraints and technological ability can affect women’s engagement with a mobile phone app.

The health and fitness software market is experiencing robust growth, driven by increasing health consciousness, the expanding adoption of digital technologies in fitness centers and studios, and the rising demand for personalized workout experiences. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by the end of the forecast period. Key drivers include the need for efficient scheduling and management systems, the desire for enhanced client engagement through personalized workout plans and progress tracking, and the growing adoption of mobile fitness apps. Trends include the integration of wearable technology data into software platforms, the increasing use of artificial intelligence (AI) for personalized recommendations and training programs, and the expansion of cloud-based solutions for accessibility and scalability. Restraints to market growth include the initial investment costs associated with implementing new software, the need for ongoing technical support and maintenance, and the potential security risks associated with storing sensitive client data. Segmentation is likely prevalent across software types (studio management, fitness tracking, online coaching), user types (gyms, individual trainers, studios, consumers), and geographic regions, with North America and Europe currently dominating the market. The competitive landscape is marked by both established players like MindBody and Acuity Scheduling, alongside emerging companies focusing on niche solutions. The success of companies in this market will depend on their ability to adapt to evolving technological advancements, provide seamless user experiences, and address the specific needs of their target segments. Future growth will likely be propelled by further integration of health data analytics, the development of innovative mobile-first solutions, and the expanding adoption of subscription-based models. The increasing prevalence of remote fitness options post-pandemic will continue to fuel demand for software solutions that facilitate seamless online coaching, virtual classes, and remote client management. Key players must prioritize cybersecurity and data privacy to maintain consumer trust and build sustainable market positions. Strategic partnerships and acquisitions will also play a vital role in shaping the competitive dynamics within this dynamic market.

The Personal Health Records Software Market is Segmented by Component Type (Software and Mobile Apps, and Services), Deployment Mode (Web-Based and Cloud-Based), Architecture (Payer Tethered, Provider Tethered, Standalone, and Interoperable / Third-Party), and Geography (North America, Europe, Asia-Pacific, Middle East and Africa and South America). The Market Forecasts are Provided in Terms of Value (USD).

Workout Tracking App Market Outlook

According to our latest research, the global workout tracking app market size reached USD 5.4 billion in 2024, reflecting robust demand for digital health and fitness solutions worldwide. The market is experiencing a strong growth trajectory, with a recorded CAGR of 17.2% from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a value of USD 18.2 billion. This dynamic expansion is primarily driven by the increasing adoption of smartphones, rising health consciousness, and the integration of advanced technologies such as AI and wearable devices into fitness applications.

The surge in health awareness among global populations is a significant growth factor for the workout tracking app market. Consumers are increasingly prioritizing physical activity and wellness, leading to a higher demand for personalized fitness solutions. Workout tracking apps offer tailored workout plans, real-time progress tracking, and motivational features, which resonate with users seeking convenience and efficacy in their fitness journeys. Furthermore, the COVID-19 pandemic has accelerated the shift towards digital fitness, as gym closures and social distancing measures prompted individuals to seek at-home exercise alternatives. This behavioral change has persisted, fueling a sustained increase in app downloads and active users worldwide.

Technological advancements are another major driver propelling the workout tracking app market forward. The integration of artificial intelligence, machine learning, and data analytics allows these apps to deliver highly customized user experiences, including adaptive workout recommendations and predictive analytics for performance improvement. Additionally, the proliferation of wearable devices such as smartwatches and fitness bands has enhanced the functionality of workout tracking apps by enabling seamless data synchronization and real-time health monitoring. The growing ecosystem of connected devices and apps is fostering innovation and expanding the addressable market for fitness technology providers.

The evolution of business models within the workout tracking app market is also contributing to its rapid growth. Companies are offering a variety of subscription models, including free, paid, and freemium options, to cater to diverse user preferences and maximize market penetration. The freemium model, in particular, has proven successful in attracting a broad user base, with premium features and content available for a fee. Strategic partnerships with fitness centers, sports clubs, and corporate wellness programs are further expanding the reach of workout tracking apps, as organizations increasingly recognize the value of digital health solutions for employee engagement and performance.

From a regional perspective, North America remains the largest market for workout tracking apps, accounting for a substantial share of global revenue in 2024. The region's dominance is attributed to high smartphone penetration, a strong culture of fitness and wellness, and the presence of leading industry players. However, the Asia Pacific region is witnessing the fastest growth, driven by a burgeoning middle class, increasing disposable incomes, and rising awareness of health and fitness. Europe and Latin America are also experiencing steady growth, supported by favorable government initiatives and growing investments in digital health infrastructure.

Platform Analysis

The platform segment of the workout tracking app market is primarily categorized into iOS, Android, Windows, and Others. The iOS platform continues to lead in terms of revenue, thanks to the high purchasing power and strong brand loyalty of Apple users. iOS apps are often associated with premium features and superior user experience, which appeals to fitness enthusiasts willing to invest in their health. The seamless integration with Apple’s ecosystem, including the Apple Watch and HealthKit, further enhances the value proposition for users, enabling comprehensive fitness tracking and data synchronization across devices.

Android, on the other hand, dominates in terms of market share due to its widespread adoption across diverse geographic regions, particularly in emerging markets. The open-source nature of Android allows developers to create highly customizable and affordable workout tracking apps, making fitness technology accessible to a broader audience. Android’s compatibility with a wide ran

ANN sensitivity (excluding description text data).

This study describes the development and pilot evaluation of a smartphone- delivered Ecological Momentary Intervention (EMI) for people with social anxiety symptoms. Using a software engineering framework (agile modeling, model-driven development, bottom-up development), mental health experts and software developers collaborated to develop a 4-module EMI app designed to reduce social anxiety in real-time. Fifty-five participants with social anxiety were randomly allocated to the EMI or a wait-list control arm. App downloads, usage and user satisfaction data were collected and mental health outcomes assessed at baseline and post-intervention. Software development practices allowed mental health experts to distil core elements of a psychological intervention into discrete software components but there were challenges in engaging mental health experts in the process. Relative to control there was no significant reduction in social anxiety among the EMI participants in the pilot trial. However, post-test data were available for only 4 intervention and 10 control participants and only 2 (4.0%) of the EMI participants downloaded the app. The two participants who both accessed the app and completed the post-test reported being satisfied with the intervention. Future research should address managing resources and providing additional training to support ongoing engagement with key stakeholders.