According to a survey on health and sports habits in China conducted in December 2020, over 70 percent of respondents who owned health smart devices or apps had recorded their health-related data in most cases. As for sports smart device or app users, almost 65 percent of such respondents in China did record exercise data most of the time.

As of 2023, ** percent of respondents surveyed in the United States would want to share their wearable device's data by opening the app in the device and reviewing the health data with the doctor in person during an appointment. Another method just under ************** would be willing to carry out is answering questions about health data while completing intake paperwork before an appointment. Less preferred methods included automatic data sharing and sending screenshots of health data to the doctor. Effectiveness and adoption of wearables across age groups The popularity of wearable health devices is supported by their perceived effectiveness. In 2023, ** percent of U.S. adults found their wearable fitness or wellness devices effective in helping them reach their goals. This high satisfaction rate spans across age groups, with even ** percent of users aged 65 and over reporting the devices as effective to some extent. Impact on healthcare choices The integration of wearable data into healthcare is influencing patient preferences. In 2023, ** percent of U.S. patients reported being somewhat likely to choose a doctor who uses personal wearable data for treatment plans over one who does not. This suggests a growing expectation for data-driven healthcare among patients. Additionally, the primary motivations for using these devices include achieving fitness goals and tracking health data, indicating a proactive approach to personal health management.

The data set offers additional information for the study on "Rewarding Fitness Tracking – The Communication and Promotion of Health Insurers’ Bonus Programs and the Usage of Self-Monitored Data", to be submitted at HCII 2018.

The data set includes the full lists of German and Australian Health Insurers investigated, including a link to their apps.

This study aims at giving an overview on the current status quo of health insurances that investigate self-tracking opportunities and possible rewards for customers that share their fitness and health activities. We are interested in how insurers promote their health and well-being programs (intended program goals) and motivate customers to live healthier (incentives). We introduce research in progress while firstly focusing on the countries Germany and Australia. We discuss the current situation of health insurance clients’ data use, data security issues as well as long-term health benefits regarding those programs based on recent research on self-tracking activities. The research questions are:

1. Which health insurers offer options for client to self-track health and fitness data?
2. How do insurers communicate about the programs?
3. How do those insurers communicate about data security?

Keeping track of your health is, for many people, a continuous task. Monitoring what you eat, how often you exercise and how much water you drink can be time-consuming, fortunately there are tens of...

Digital Health Tracking Apps Market Outlook

The global digital health tracking apps market size was valued at approximately USD 10 billion in 2023 and is projected to reach around USD 30 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. This significant growth is driven by the rising adoption of smartphones, increasing awareness of personal health management, and the growing prevalence of chronic diseases which require constant monitoring and management.

One of the primary growth factors for the digital health tracking apps market is the increasing penetration of smartphones and wearable devices globally. With advances in technology, smartphones are now equipped with various sensors that can track a wide range of health metrics. This has made it easier for individuals to monitor their health and fitness on-the-go. Additionally, the surge in wearable technology like smartwatches and fitness bands has significantly contributed to the popularity and effectiveness of these apps, enabling real-time health tracking.

Another significant driver is the growing awareness and emphasis on preventive healthcare. Consumers are increasingly becoming proactive about their health, preferring to take preventive measures rather than seeking treatment after a health issue arises. Digital health tracking apps empower users by providing them with the tools needed to monitor their diet, physical activity, sleep patterns, and medication adherence, thus promoting a healthier lifestyle. Furthermore, these apps offer personalized insights and recommendations based on the data collected, enhancing their appeal and utility.

The integration of artificial intelligence (AI) and machine learning (ML) into digital health tracking apps is also a major growth factor. These advanced technologies enable more accurate and personalized health tracking, predictive analytics, and early detection of potential health issues. For instance, AI-driven algorithms can analyze a user's health data to predict future health risks and provide tailored recommendations. Additionally, the rise in telehealth services, especially highlighted during the COVID-19 pandemic, has augmented the demand for digital health tracking apps as they facilitate remote monitoring and consultations.

Sleep Tracker Apps have become an integral part of the digital health tracking ecosystem, offering users a comprehensive way to monitor and improve their sleep quality. As sleep is increasingly recognized as a vital component of overall health, these apps provide valuable insights into sleep patterns, duration, and disturbances. By leveraging advanced algorithms and sensor data from smartphones and wearables, Sleep Tracker Apps can offer personalized recommendations to enhance sleep hygiene. This not only aids in improving individual health outcomes but also contributes to a broader understanding of sleep-related disorders, which are becoming more prevalent in today's fast-paced world. The growing demand for effective sleep management solutions underscores the importance of Sleep Tracker Apps in promoting better health and well-being.

Regionally, North America holds the largest share of the digital health tracking apps market due to the high adoption rate of advanced healthcare technologies and the presence of major market players. Europe follows closely due to increasing healthcare expenditure and supportive government initiatives promoting digital health. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the growing smartphone user base, increasing disposable income, and rising health awareness among the population. Emerging markets in Latin America and the Middle East & Africa are also showing substantial growth potential due to increasing investments in healthcare infrastructure and technology.

Type Analysis

The digital health tracking apps market is segmented based on the type of tracking provided by the app. This includes fitness tracking, diet and nutrition, sleep tracking, medication adherence, and others. Fitness tracking apps are among the most popular, driven by the increasing trend of fitness and wellness. These apps offer features such as step counting, exercise tracking, heart rate monitoring, and more, appealing to a wide range of users from casual exercisers to professional athletes. The shift towards maintaining an active lifestyle to prevent health issues further propels the dema

The Environmental Protection Agency (EPA) provides air pollution data about ozone and particulate matter (PM2.5) to CDC for the Tracking Network. The EPA maintains a database called the Air Quality System (AQS) which contains data from approximately 4,000 monitoring stations around the country, mainly in urban areas. Data from the AQS is considered the "gold standard" for determining outdoor air pollution. However, AQS data are limited because the monitoring stations are usually in urban areas or cities and because they only take air samples for some air pollutants every three days or during times of the year when air pollution is very high. CDC and EPA have worked together to develop a statistical model (Downscaler) to make modeled predictions available for environmental public health tracking purposes in areas of the country that do not have monitors and to fill in the time gaps when monitors may not be recording data. This data does not include "Percent of population in counties exceeding NAAQS (vs. population in counties that either meet the standard or do not monitor PM2.5)". Please visit the Tracking homepage for this information.View additional information for indicator definitions and documentation by selecting Content Area "Air Quality" and the respective indicator at the following website: http://ephtracking.cdc.gov/showIndicatorsData.action

The digital health tracking app market is experiencing robust growth, driven by increasing smartphone penetration, rising health consciousness among consumers, and the expanding adoption of telehealth services. The market, estimated at $50 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $150 billion by 2033. This expansion is fueled by several key trends, including the integration of AI and machine learning for personalized health insights, the proliferation of wearable technology seamlessly syncing with these apps, and a growing demand for remote patient monitoring solutions. The market's segmentation includes diverse applications catering to weight management, fitness tracking, mental health, women's health, medication management, and chronic disease management. The competitive landscape is dynamic, with established players like MyFitnessPal and newer entrants constantly innovating to capture market share. Major restraints include data privacy concerns, the need for robust cybersecurity measures to protect sensitive user health data, and the varying levels of app efficacy and user engagement. Overcoming these challenges through transparent data handling practices, user-friendly interfaces, and personalized health guidance will be crucial for continued market growth. The geographical distribution of the market reveals significant regional variations, with North America and Europe currently dominating, however, Asia-Pacific is poised for significant growth fueled by increasing smartphone usage and rising disposable incomes. The future of the digital health tracking app market hinges on continued technological advancements, addressing user concerns regarding data security, and expanding accessibility across diverse demographics and geographical locations.

This survey charted Finnish citizens' as well as social and healthcare service professionals' attitudes and views concerning secondary use of health and social care data in research and development of services. The study contained two target groups: (1) persons who suffered or had a close relative or acquaintance who suffered from one or more chronic conditions, diseases or disorders, and (2) social and healthcare service professionals. First, the respondents' opinions on the reliability of a variety of authorities and organisations were examined (e.g. the police, Kela, register and statistics authorities, universities) as well as trust in appropriate handling of personal data. They were also asked which type of information they deemed personal or not (e.g. bank account number and balance, purchase history at a grocery store, web browsing history, patient records, genetic information, social security number, phone number). They were asked to evaluate which principles they considered important in handling personal health data (e.g. being able to access one's personal data and to have inaccurate data rectified, and being able to restrict data processing), and the study also surveyed how interested the respondents were in keeping track of the use of their health data, and how willing they would be to permit the use of anonymous health data and genetic information for a variety of purposes (e.g. medicine and treatment development, development of equipment and services, and operations of insurance companies). Next, it was examined whether the respondents kept track of their physical activity with a smartphone or a fitness tracker, for instance, and if they would be willing to permit the use of anonymous data concerning physical activity for a variety of purposes. In addition, the respondents' attitudes were charted with regard to developing medicine research by combining anonymous health data and patient records with other data on, for instance, physical activity, alcohol use, grocery store purchase history, web browsing history, and social media use. The study also examined the willingness to permit access to personal health data for social and healthcare service professionals in a service situation, as well as for social and healthcare authorities and other authorities outside of a service situation. Finally, it was charted how important the respondents deemed different factors relating to data collection (e.g. being able to decide for which purposes personal data, or even anonymous data, can be used, and increasing awareness on how health data can be utilised in scientific research). The reliability of a variety of authorities and organisations, such as social welfare/healthcare organisations, academic researchers and pharmaceutical companies, was also examined in terms of data security and purposes for using data. Background variables included, among others, mother tongue, marital status, household composition, housing tenure, socioeconomic class, political party preference, left-right political self-placement, gross income, economic activity and occupational status, and respondent group (citizen/healthcare service professional/social service professional).

There are two datasets related to the County Level Prevention Agenda Tracking Indicators posted on this site. Each dataset consists of county level data for 70 health tracking indicators and sub-indicators for the Prevention Agenda 2019-2024: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. The data sets also include indicators about major cross-cutting health outcomes and about health disparities. Each dataset includes tracking indicators for the five Priority Areas of the Prevention Agenda 2019-2024. The most recent year dataset includes the most recent county level data for all indicators. The trend dataset includes the most recent county level data and historical data, where available. Each dataset also includes the Prevention Agenda 2024 state objectives for the indicators. Sub-indicators are included in these datasets to measure health disparities among socioeconomic groups.

The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

For each hospital web page, we only recorded data requests that initiated data transfers to third-party domains. We then used the webXray database to determine the corporation associated with the third-party domain and the majority owner of the corporation (i.e., the “parent company”) at the time the study occurred (August 2021). For example, the corporation associated with the third-party domain doubleclick.net was determined to be Google, which is majority owned by Alphabet.

Our research does not indicate that the corporations or parent companies listed in our report received data from hospital website browsing; rather, the parent companies listed owned a corporation affiliated with the third-party domains initiating these data requests. We observed only data transfers from the browser to the domain; we did not observe the subsequent use of the data. We do not claim that any third-party domain listed was requesting or receiving this data in a manner that violated applicable laws or regulations governing consumer data privacy.

Below is a table that lists each parent company and the listed domains associated with a corporation owned by such parent company.

Real‑time Health Data Analytics Market Outlook

According to our latest research, the global real-time health data analytics market size reached USD 16.2 billion in 2024, and is projected to grow at a robust CAGR of 18.4% from 2025 to 2033, reaching an estimated value of USD 80.2 billion by 2033. This substantial growth is primarily driven by the increasing adoption of digital health solutions, the proliferation of connected medical devices, and the rising demand for instant, actionable healthcare insights to improve patient outcomes and operational efficiency worldwide.

One of the primary growth factors fueling the real-time health data analytics market is the rapid digitization of healthcare systems. Hospitals, clinics, and other healthcare providers are increasingly deploying electronic health records (EHRs), wearable devices, and remote monitoring solutions that generate vast volumes of real-time patient data. These technologies enable continuous tracking of vital signs, medication adherence, and other health metrics, allowing clinicians to make timely decisions and intervene early in case of anomalies. The integration of artificial intelligence (AI) and machine learning (ML) algorithms with real-time analytics platforms further enhances the ability to detect patterns, predict adverse events, and personalize treatment plans. As healthcare organizations strive to transition from reactive to proactive care models, the demand for sophisticated real-time analytics solutions is expected to surge.

Another significant driver for the real-time health data analytics market is the increasing emphasis on value-based care and population health management. Governments and payers across the globe are incentivizing healthcare providers to improve quality while reducing costs, which necessitates the use of advanced analytics for tracking patient outcomes, identifying high-risk populations, and optimizing resource allocation. Real-time analytics platforms empower healthcare professionals to aggregate and analyze data from multiple sources, including EHRs, claims, and social determinants of health, providing a holistic view of patient populations. By enabling early identification of trends and gaps in care, these solutions facilitate targeted interventions, reduce hospital readmissions, and support evidence-based decision-making, thereby aligning with the objectives of value-based healthcare delivery.

Moreover, the ongoing COVID-19 pandemic has underscored the critical importance of real-time health data analytics in managing public health crises. Governments and healthcare organizations worldwide have leveraged real-time analytics to monitor the spread of the virus, allocate resources, and optimize vaccination campaigns. The pandemic has accelerated the adoption of telemedicine, remote patient monitoring, and cloud-based analytics platforms, further expanding the scope of real-time data utilization. As the world continues to face emerging infectious diseases and chronic health challenges, the ability to rapidly analyze and act upon real-time health data will remain a strategic priority for both public and private sector stakeholders.

From a regional perspective, North America currently dominates the real-time health data analytics market, accounting for the largest revenue share in 2024, driven by advanced healthcare infrastructure, widespread adoption of digital health technologies, and strong regulatory support for interoperability and data sharing. Europe follows closely, with significant investments in health IT modernization and data-driven healthcare initiatives. The Asia Pacific region is poised for the fastest growth during the forecast period, fueled by expanding healthcare access, increasing government spending on digital health, and a burgeoning population of tech-savvy consumers. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by ongoing healthcare reforms and rising awareness regarding the benefits of real-time analytics in improving care delivery.

Component Analysi

As of June 2022, the reproductive and women's health app Femometer presented the highest number of data trackers on iOS, around **. Pregnancy App & Baby Tracker (Babycenter) presented the highest number of data trackers for Android users, collecting data across ** different categories. Mobile app Clue had approximately ** different data trackers on iOS and Android, respectively. Mobile app Flo had five trackers on iOS and only two trackers on Android.

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides state-aggregated data for hospital utilization in a timeseries format dating back to January 1, 2020. These are derived from reports with facility-level granularity across three main sources: (1) HHS TeleTracking, (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities and (3) National Healthcare Safety Network (before July 15).

The file will be updated regularly and provides the latest values reported by each facility within the last four days for all time. This allows for a more comprehensive picture of the hospital utilization within a state by ensuring a hospital is represented, even if they miss a single day of reporting.

No statistical analysis is applied to account for non-response and/or to account for missing data.

The below table displays one value for each field (i.e., column). Sometimes, reports for a given facility will be provided to more than one reporting source: HHS TeleTracking, NHSN, and HHS Protect. When this occurs, to ensure that there are not duplicate reports, prioritization is applied to the numbers for each facility.

On April 27, 2022 the following pediatric fields were added: