Comprehensive Mental Health Insights: A Diverse Dataset of 1000 Individuals Across Professions, Countries, and Lifestyles

This dataset provides a rich collection of anonymized mental health data for 1000 individuals, representing a wide range of ages, genders, occupations, and countries. It aims to shed light on the various factors affecting mental health, offering valuable insights into stress levels, sleep patterns, work-life balance, and physical activity.

Key Features: Demographics: The dataset includes individuals from various countries such as the USA, India, the UK, Canada, and Australia. Each entry captures key demographic information such as age, gender, and occupation (e.g., IT, Healthcare, Education, Engineering).

Mental Health Conditions: The dataset contains data on whether the individuals have reported any mental health issues (Yes/No), along with the severity of these conditions categorized into Low, Medium, or High.

Consultation History: For individuals with mental health conditions, the dataset notes whether they have consulted a mental health professional.

Stress Levels: Each individual’s stress level is classified as Low, Medium, or High, providing insights into how different factors such as work hours or sleep may correlate with mental well-being.

Lifestyle Factors: The dataset includes information on sleep duration, work hours per week, and weekly physical activity hours, offering a detailed picture of how lifestyle factors contribute to mental health.

This dataset can be used for research, analysis, or machine learning models to predict mental health trends, uncover correlations between work-life balance and mental well-being, and explore the impact of stress and physical activity on mental health.

This dataset contains global health indicators such as life expectancy, mortality rates, vaccination coverage, and disease prevalence across different countries. It covers data from 2000 to 2023, allowing for trend analysis in global health. Columns: Country, Year, Life Expectancy, Infant Mortality Rate, Vaccination Coverage (%), Disease Prevalence (%), GDP per Capita, Region.

This dataset was originally collected for a data science and machine learning project that aimed at investigating the potential correlation between the amount of time an individual spends on social media and the impact it has on their mental health.

The project involves conducting a survey to collect data, organizing the data, and using machine learning techniques to create a predictive model that can determine whether a person should seek professional help based on their answers to the survey questions.

This project was completed as part of a Statistics course at a university, and the team is currently in the process of writing a report and completing a paper that summarizes and discusses the findings in relation to other research on the topic.

The following is the Google Colab link to the project, done on Jupyter Notebook -

https://colab.research.google.com/drive/1p7P6lL1QUw1TtyUD1odNR4M6TVJK7IYN

The following is the GitHub Repository of the project -

https://github.com/daerkns/social-media-and-mental-health

Libraries used for the Project -

Pandas
Numpy
Matplotlib
Seaborn
Sci-kit Learn

CDC National Center for Health Statistics data briefs and WONDER system outputs related to U.S. mental health trends, including prevalence, demographics, and service utilization insights.

A national dataset of de-identified all-payer claims detailing outpatient and inpatient visit volumes, stratified by provider type, location, and service line. Used to benchmark market share and care utilization trends.

The Gen Z Mental Health Market Report is Segmented by Product Type (Meditation & Mindfulness Apps, Digital Therapy Platforms, and More), Delivery Mode (Mobile Application, Web-Based, and More), Mental-Health Condition (Anxiety & Stress, Depression, and More), End-User (Individual Consumers, Enterprises & Employers, and More), and Geography (North America, Europe, and More). The Market Forecasts are Provided in Terms of Value (USD).

Population Health (BRFSS: HRQOL)

Examining Trends, Disparities and Determinants of Health in the US Population

By Health [source]

About this dataset

The Behavioral Risk Factor Surveillance System (BRFSS) offers an expansive collection of data on the health-related quality of life (HRQOL) from 1993 to 2010. Over this time period, the Health-Related Quality of Life dataset consists of a comprehensive survey reflecting the health and well-being of non-institutionalized US adults aged 18 years or older. The data collected can help track and identify unmet population health needs, recognize trends, identify disparities in healthcare, determine determinants of public health, inform decision making and policy development, as well as evaluate programs within public healthcare services.

The HRQOL surveillance system has developed a compact set of HRQOL measures such as a summary measure indicating unhealthy days which have been validated for population health surveillance purposes and have been widely implemented in practice since 1993. Within this study's dataset you will be able to access information such as year recorded, location abbreviations & descriptions, category & topic overviews, questions asked in surveys and much more detailed information including types & units regarding data values retrieved from respondents along with their sample sizes & geographical locations involved!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset tracks the Health-Related Quality of Life (HRQOL) from 1993 to 2010 using data from the Behavioral Risk Factor Surveillance System (BRFSS). This dataset includes information on the year, location abbreviation, location description, type and unit of data value, sample size, category and topic of survey questions.

Using this dataset on BRFSS: HRQOL data between 1993-2010 will allow for a variety of analyses related to population health needs. The compact set of HRQOL measures can be used to identify trends in population health needs as well as determine disparities among various locations. Additionally, responses to survey questions can be used to inform decision making and program and policy development in public health initiatives.

Research Ideas

• Analyzing trends in HRQOL over the years by location to identify disparities in health outcomes between different populations and develop targeted policy interventions.
• Developing new models for predicting HRQOL indicators at a regional level, and using this information to inform medical practice and public health implementation efforts.
• Using the data to understand differences between states in terms of their HRQOL scores and establish best practices for healthcare provision based on that understanding, including areas such as access to care, preventative care services availability, etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:-------------------------------|:----------------------------------------------------------| | Year | Year of survey. (Integer) | | LocationAbbr | Abbreviation of location. (String) | | LocationDesc | Description of location. (String) | | Category | Category of survey. (String) | | Topic | Topic of survey. (String) | | Question | Question asked in survey. (String) | | DataSource | Source of data. (String) | | Data_Value_Unit | Unit of data value. (String) | | Data_Value_Type | Type of data value. (String) | | Data_Value_Footnote_Symbol | Footnote symbol for data value. (String) | | Data_Value_Std_Err | Standard error of the data value. (Float) | | Sample_Size | Sample size used in sample. (Integer) | | Break_Out | Break out categories used. (String) | | Break_Out_Category | Type break out assessed. (String) | | **GeoLocation*...

Population estimates for U.S. metropolitan and micropolitan statistical areas from the U.S. Census Bureau, used to analyze demographic shifts and market size changes over time.

The HCUP Summary Trend Tables include monthly information on hospital utilization derived from the HCUP State Inpatient Databases (SID) and HCUP State Emergency Department Databases (SEDD). Information on emergency department (ED) utilization is dependent on availability of HCUP data; not all HCUP Partners participate in the SEDD. The HCUP Summary Trend Tables include downloadable Microsoft® Excel tables with information on the following topics: Overview of monthly trends in inpatient and emergency department utilization All inpatient encounter types Inpatient stays by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Inpatient encounter type -Normal newborns -Deliveries -Non-elective inpatient stays, admitted through the ED -Non-elective inpatient stays, not admitted through the ED -Elective inpatient stays Inpatient service line -Maternal and neonatal conditions -Mental health and substance use disorders -Injuries -Surgeries -Other medical conditions Emergency department treat-and-release visits Emergency department treat-and-release visits by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Description of the data source, methodology, and clinical criteria

For 2023, the health costs (combined medical and pharmacy benefit expenses) of U.S. employers for employees after plan and contribution changes are forecasted to increase by 6 percent. This survey represents US company's health care cost trends from 1999 to 2023.

Mental Health Apps Market Outlook

As per our latest research, the global mental health apps market size reached USD 6.2 billion in 2024, reflecting a robust upward trajectory in digital mental health solutions. The market is experiencing a strong compound annual growth rate (CAGR) of 17.8% from 2025 to 2033, and is forecasted to reach USD 25.1 billion by 2033. The primary growth factor driving this surge is the increasing prevalence of mental health disorders globally, paired with rising smartphone penetration and a greater societal emphasis on mental wellness. As per our latest research, the adoption of digital health platforms is reshaping how individuals, enterprises, and healthcare providers approach mental health management, signaling a transformative shift in the healthcare landscape.

The rapid growth of the mental health apps market is underpinned by a confluence of technological advancements and shifting societal attitudes toward mental health. The proliferation of smartphones and wearable devices has made mental health support more accessible than ever before, enabling real-time interventions and continuous monitoring. This accessibility is crucial in addressing the growing incidence of mental health conditions such as depression, anxiety, and stress-related disorders, which have been exacerbated by modern lifestyle challenges and, more recently, the global pandemic. Furthermore, the integration of artificial intelligence and machine learning into mental health apps has enhanced the personalization of interventions, making these tools more effective and user-friendly. The ability of these apps to offer tailored content, track mood changes, and provide cognitive behavioral therapy (CBT) exercises on demand has contributed significantly to their widespread acceptance and sustained market growth.

Another pivotal growth factor is the increasing recognition of mental health as a critical component of overall well-being by governments, employers, and healthcare organizations. Public health campaigns and corporate wellness programs are increasingly incorporating digital mental health solutions to support employee productivity and reduce absenteeism. Enterprises are investing in mental health apps as part of their employee benefits packages, recognizing the return on investment in terms of enhanced workforce morale and reduced healthcare costs. Simultaneously, healthcare providers are leveraging these platforms to extend their reach, offering remote consultations and ongoing patient engagement, which is particularly valuable in regions with limited access to mental health professionals. This institutional support is fostering a favorable regulatory environment and encouraging further innovation in the sector.

The regional outlook for the mental health apps market is shaped by varying levels of digital infrastructure, healthcare expenditure, and cultural attitudes toward mental health. North America continues to dominate the market, accounting for over 35% of global revenue in 2024, driven by high smartphone adoption rates and a proactive approach to mental wellness. Europe follows closely, benefiting from supportive regulatory frameworks and increasing investments in digital health. Meanwhile, the Asia Pacific region is emerging as the fastest-growing market, with a projected CAGR of over 20% through 2033, fueled by rising awareness, expanding middle-class populations, and government-led digital health initiatives. Latin America and the Middle East & Africa are also witnessing steady growth, although challenges such as limited digital literacy and infrastructure remain barriers to widespread adoption. Overall, the regional dynamics highlight a global shift toward embracing digital mental health solutions, with significant opportunities for market expansion and innovation across all continents.

In recent years, the rise of Campus Mental Health Apps has become a significant trend within the digital mental health landscape. These apps are specifically designed to cater to the unique needs of students, providing them with accessible mental health resources and support. With the increasing pressures of academic life, social interactions, and the transition to independence, students often face mental health challenges that require timely and effective interventions. Campus Mental Health Apps offer a range of features, including stress management techniq

The behavioral health market is projected to grow from USD 130.8 billion in 2025 to USD 175.8 billion by 2035, at a CAGR of 3.0%. Outpatient Counselling will dominate with a 48.5% market share, while depression will lead the disorder type segment with a 26.1% share.

health-trends.net is ranked #3642 in JP with 821.08K Traffic. Categories: . Learn more about website traffic, market share, and more!

Health, United States is an annual report on trends in health statistics. The report consists of two main sections: A chartbook containing text and figures that illustrates major trends in the health of Americans and a trend tables section that contains 156 detailed data tables. The two main components are supplemented by an executive summary, a highlights section, an extensive appendix and reference section, and an index.Note to Users: This CD is part of a collection located in the Da ta Archive of the Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

Discover the booming mental health counseling services market! This comprehensive analysis reveals key trends, growth drivers, and challenges from 2019-2033, including the rise of telehealth, regional market shares, and leading companies like BetterHelp and Talkspace. Learn about market size, CAGR, and future projections for this rapidly expanding sector.

Health, United States is an annual report on trends in health statistics, find more information at http://www.cdc.gov/nchs/hus.htm.

This product presents comparable time-series data for a range of health indicators from a number of sources including the Canadian Community Health Survey, Vital Statistics, and Canadian Cancer Registry.

The Smart Healthcare Products Market Report is Segmented by Product Type (Telemedicine, Electronic Health Records, Mhealth Solutions, Smart Pills, Smart Syringes, Smart RFID Cabinets, and More), Application (Storage and Inventory Management, Remote Monitoring, and More), End User (Hospitals, Home Care Settings, and More), and Geography (North America, Europe, and More). The Market Forecasts are Provided in Terms of Value (USD).

Dataset

This dataset was created by Rachna Gupta

Contents