Impact of Smart Health Wearables on Lifestyle

Authors

  • Sheetal Mahendher Head of Analytics, ISBR Business School, Bengaluru, 560001, India
  • Leonard L Assistant Professor and Manager, Centre for AI, ISBR Business School, Bengaluru, 560001, India
  • Krithika G PGDM Student, ISBR Business School, Bengaluru, 560001, India

DOI:

https://doi.org/10.32734/sumej.v9i3.21531

Keywords:

health monitoring, lifestyle, physical activity, sleep quality, smart wearables, survey analysis

Abstract

Background: Smart health monitoring wearables, including fitness trackers and smartwatches, are increasingly used to monitor physical activity, sleep, heart rate, stress, and health metrics. These devices provide continuous feedback that may influence users’ lifestyle choices. Objective: This study aimed to analyze the impact of smart health monitoring wearables on lifestyle choices and behaviors and assess users’ satisfaction with these devices. Methods: A survey-based study was conducted among a diverse group of individuals who use smart health monitoring wearables. Data were collected through a structured questionnaire assessing users’ experiences, lifestyle changes, perceived benefits, satisfaction, and limitations of wearable device use. Results: The findings showed that smart health monitoring wearables positively influenced users’ lifestyle choices. Participants reported greater motivation to exercise and maintain healthy sleep patterns. Real-time feedback encouraged healthier daily decisions, including choosing stairs instead of elevators and maintaining healthier eating habits. However, participants also identified limitations, including inaccurate measurements, limited medical relevance, data security concerns, over-reliance on devices, and limited battery life. Most users considered the benefits to outweigh the potential drawbacks. Conclusion: Smart health monitoring wearables can support healthier lifestyle choices through continuous monitoring and real-time feedback. Nevertheless, users should recognize their limitations and avoid relying on these devices as substitutes for medical assessment.

Downloads

Download data is not yet available.

References

[1] Zheng, Y., Tang, N., Omar, R., Hu, Z., Duong, T., Wang, J., Wu, W., & Haick, H. (2021). Smart Materials Enabled with Artificial Intelligence for Healthcare Wearables. Advanced Functional Materials, 31(51). https://doi.org/10.1002/adfm.202105482.

[2] Wang, Y., Cang, S., & Yu, H. (2019). A survey on wearable sensor modality centred human activity recognition in health care. Expert Systems With Applications, 137, 167–190. https://doi.org/10.1016/j.eswa.2019.04.057.

[3] Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys and Tutorials/IEEE Communications Surveys and Tutorials, 17(4), 2347–2376. https://doi.org/10.1109/comst.2015.2444095.

[4] Li, J., Ma, Q., Chan, A. H. S., & Man, S. S. (2019). Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Applied Ergonomics/Applied Ergonomics, 75, 162–169. https://doi.org/10.1016/j.apergo.2018.10.006

[5] Liang, Z., & Martell, M. a. C. (2021). A Multi-Level classification approach for sleep stage prediction with processed data derived from consumer wearable activity trackers. Frontiers in Digital Health, 3. https://doi.org/10.3389/fdgth.2021.665946.

[6] Hamza, M. A., Hashim, A. H. A., Alsolai, H., Gaddah, A., Othman, M., Yaseen, I., Rizwanullah, M., & Zamani, A. S. (2023). Wearables-Assisted Smart Health monitoring for sleep quality prediction using optimal deep learning. Sustainability, 15(2), 1084. https://doi.org/10.3390/su15021084.

[7] Chakrabarti, S., Biswas, N., Jones, L., Kesari, S., & Ashili, S. (2022). Smart consumer wearables as Digital Diagnostic tools: a review. Diagnostics, 12(9), 2110. https://doi.org/10.3390/diagnostics12092110.

[8] Khan, M. F., Ghazal, T. M., Said, R. A., Fatima, A., Abbas, S., Khan, M. A., Issa, G. F., Ahmad, M., & Khan, M. A. (2021). An IOMT-Enabled smart healthcare model to monitor elderly people using machine learning technique. Computational Intelligence and Neuroscience, 2021, 1–10. https://doi.org/10.1155/2021/2487759.

[9] Ghose, A., Guo, X., Li, B., & Dang, Y. (2022). Empowering patients using smart mobile health platforms: evidence of a randomized field experiment. Management Information Systems Quarterly, 46(1), 151–192. https://doi.org/10.25300/misq/2022/16201.

[10] Jeng, M., Pai, F., & Yeh, T. (2022). Antecedents for older adults’ intention to use smart Health Wearable Devices-Technology Anxiety as a moderator. Behavioral Sciences, 12(4), 114. https://doi.org/10.3390/bs12040114.

[11] Larnyo, E., Dai, B., Larnyo, A., Nutakor, J. A., Ampon-Wireko, S., Nkrumah, E. N. K., & Appiah, R. (2022). Impact of Actual Use Behavior of Healthcare Wearable Devices on Quality of Life: A Cross-Sectional Survey of People with Dementia and Their Caregivers in Ghana. Healthcare, 10(2), 275. https://doi.org/10.3390/healthcare10020275.

[12] Jeng, M., Yeh, T., & Pai, F. (2022). A performance evaluation matrix for measuring the life satisfaction of older adults using eHealth wearables. Healthcare, 10(4), 605. https://doi.org/10.3390/healthcare10040605.

[13] Sharma, V., Gupta, M., Jangir, K., Chopra, P., & Pathak, N. (2023). The impact of Post-Use consumer satisfaction on smart wearables repurchase intention in the context of AI-Based healthcare information. In Advances in marketing, customer relationship management, and e-services book series (pp. 77–101). https://doi.org/10.4018/978-1-6684-8177-6.ch007.

[14] Larnyo, E., Dai, B., Larnyo, A., Nutakor, J. A., Ampon-Wireko, S., Nkrumah, E. N. K., & Appiah, R. (2022). Impact of Actual Use Behavior of Healthcare Wearable Devices on Quality of Life: A Cross-Sectional Survey of People with Dementia and Their Caregivers in Ghana. Healthcare, 10(2), 275. https://doi.org/10.3390/healthcare10020275.

[15] Serpush, F., Menhaj, M. B., Masoumi, B., & Karasfi, B. (2022). Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System. Computational Intelligence and Neuroscience, 2022, 1–31. https://doi.org/10.1155/2022/1391906.

[16] Khoshmanesh, F., Thurgood, P., Pirogova, E., Nahavandi, S., & Baratchi, S. (2021). Wearable sensors: At the frontier of personalised health monitoring, smart prosthetics and assistive technologies. Biosensors & Bioelectronics/Biosensors & Bioelectronics (Online), 176, 112946. https://doi.org/10.1016/j.bios.2020.112946.

Downloads

Published

2026-09-01

How to Cite

1.
Mahendher S, L L, G K. Impact of Smart Health Wearables on Lifestyle. Sumat. Med. J. [Internet]. 2026 Sep. 1 [cited 2026 Sep. 1];9(3):179-88. Available from: https://talenta.usu.ac.id/smj/article/view/21531

Similar Articles

<< < 9 10 11 12 > >> 

You may also start an advanced similarity search for this article.