Analyzing Wellness Issues Through The Lens Of Natural Apps

Analyzing Wellness Issues Through the Lenses of Natural, Applied

Review your work from Modules Five and Six along with the IDS Four General Education Lenses in the provided resources. Analyze your chosen wellness issue or event through the lens of the natural and applied sciences, addressing how this issue offers a social commentary through these sciences and how scientific methods can help resolve or improve upon it. Then, analyze the same issue through the social sciences lens, exploring its interaction with social issues and how this perspective deepens understanding of the social implications involved. You are required to submit two separate analyses—one from each lens—in a single Word document, supporting your points with research evidence, including credible scholarly sources and current news where appropriate. Cite all sources in APA format.

Sample Paper For Above instruction

The integration of technology into health and wellness interventions has transformed traditional healthcare practices, representing a significant shift that can be examined through multiple academic lenses. This paper critically analyzes a contemporary wellness issue—namely, the proliferation of health tracking devices and digital health apps—through the lenses of the natural and applied sciences and social sciences. The analysis reveals not only the technological advancements’ impact on health monitoring but also their broader implications for society, ethics, and individual behaviors.

Natural and Applied Sciences Lens

The use of wearable health devices and mobile applications exemplifies significant progress within the natural and applied sciences, especially in the fields of medicine, physiology, and environmental science. These devices leverage advanced sensors and algorithms that monitor vital signs such as blood pressure, heart rate, oxygen saturation, and sleep patterns in real time (Shapiro et al., 2019). This technological evolution has revolutionized how health data is collected, interpreted, and acted upon, enabling proactive health management and disease prevention.

From a scientific perspective, these devices utilize principles from biomedical engineering, data science, and physiology. For instance, the accuracy of wearable sensors relies on advancements in bioelectronics and signal processing (Karmaker et al., 2021). Scientific research underpins these innovations, ensuring reliability and validity. For example, studies demonstrate that continuous health monitoring improves outcomes by early detection of abnormalities and personalized treatment adjustments (Banaee et al., 2013). In natural sciences, understanding human physiology informs device development, with findings on cardiovascular and respiratory systems guiding sensor placement and calibration.

These technological advances provide opportunities for scientific inquiry to further refine health monitoring tools. For example, integrating artificial intelligence with wearable data could amplify predictive analytics, helping to identify health risks before symptoms appear (Esteva et al., 2019). Additionally, environmental science plays a role, as climate and pollution levels influence health outcomes, prompting the development of wearables that incorporate environmental measures. Hence, science not only helps in enhancing current devices but also offers pathways for future innovations aimed at preventative health care.

Social Sciences Lens

The proliferation of health tracking technology intersects profoundly with social issues, affecting personal privacy, social equity, and health behaviors. From the social sciences perspective, these devices influence individual identity and social interactions through increased connectivity and shared health data. They empower individuals to take greater responsibility for their wellness but also raise concerns about data privacy and surveillance (Coughlin et al., 2017).

Moreover, these technologies can both bridge and reinforce social disparities. While some populations benefit from increased health awareness and access to personalized interventions, marginalized groups may lack access due to economic or infrastructural barriers, exacerbating health inequalities (Sabharwal et al., 2020). Social science research highlights how cultural attitudes toward health and technology influence adoption rates, with some communities skeptical of data sharing or digital health interventions (Hwang et al., 2018).

Furthermore, the social sciences examine how these technological tools shape health behaviors and societal norms. For example, health apps often encourage behavioral change through goal setting and social comparison, impacting motivation and peer influence. They can foster community engagement through challenges and shared tracking, promoting collective accountability (Mittelstadt et al., 2016). Conversely, reliance on digital health data might lead to increased anxiety or health-related stress in some individuals (Andrews et al., 2020).

In terms of ethical considerations, social sciences raise questions about informed consent, data ownership, and the potential misuse of sensitive health information. Recognizing these issues fosters a more comprehensive understanding of the societal impacts of health technology and underscores the importance of developing policies that protect individual rights while maximizing health benefits.

Conclusion

Analyzing the issue of digital health tracking through the natural and applied sciences highlights technological innovations' role in advancing personalized medicine and preventative healthcare. Simultaneously, framing the same issue through the social sciences reveals complex implications for privacy, equity, and social behavior. Together, these perspectives provide a nuanced understanding necessary for guiding responsible technological development and ethical health practices in modern society.

References

• Banaee, H., Ahmed, M. U., & Loutfi, A. (2013). Data mining for wearable sensors in health monitoring systems: a review. Journal of Biomedical Informatics, 46(4), 72-83.
• Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., Dean, J., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29.
• Hwang, R., Kim, S., & Lee, J. (2018). Cultural influences on health technology acceptance: evidence from smartphone use in South Korea. International Journal of Human-Computer Interaction, 34(1), 1-12.
• Karmaker, S., Jalil, M. A., & Kim, D. (2021). Advances in wearable biosensors for real-time health monitoring. Biosensors & Bioelectronics, 175, 112830.
• Mittelstadt, B., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.
• Sabharwal, S., Bailey, R., & Eberhardt, M. (2020). Equity implications of digital health: current evidence and future directions. Health Affairs, 39(2), 205-211.
• Shapiro, M., Kourtesis, P., & Wipfli, B. (2019). Wearable health technology: current innovations and future potential. Frontiers in Digital Health, 1, 10.