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Assessing the factors that influence the adoption of healthcare wearables by the older population using an extended PMT model

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  • Singh, Nidhi
  • Misra, Richa
  • Singh, Sonali
  • Rana, Nripendra P.
  • Khorana, Sangeeta

Abstract

The present study aims to assess the intention of the older population to use healthcare wearable devices (HWDs) for wellness during life-threatening situations like COVID-19. The target population for the study was senior citizens (individuals aged above 60) living in Delhi and the national capital region. The respondents were aware that smartwatches could be used to monitor their health. Data from 534 respondents was collected using a structured questionnaire and nonprobability-based sampling method. The partial least squares structure equation model (PLS-SEM) was used to test the hypothesized model derived from the protection motivation theory (PMT) and constructs from previous studies on HWDs. Healthcare wearables offer new perspectives for gauging both health and technology-related dimensions. The present study is important as unlike existing studies, it discusses not only the utilitarian characteristics of HWDs but also their health-protective dimensions, which are crucial in times of life-threatening situations such as the COVID-19 pandemic. The findings indicate that there is a significant impact of both the protective and utilitarian dimensions of HWDs. The study assesses the perceived vulnerability and severity of the older population in COVID 19 and the intention to use HWDs to handle such health crises. The study confirms that perceived usefulness and information accuracy of HWDs, as well as self-efficacy, perceived severity, and perceived vulnerability of senior citizens are high during the COVID-19 pandemic, which significantly influences their intention to use HWDs.

Suggested Citation

  • Singh, Nidhi & Misra, Richa & Singh, Sonali & Rana, Nripendra P. & Khorana, Sangeeta, 2022. "Assessing the factors that influence the adoption of healthcare wearables by the older population using an extended PMT model," Technology in Society, Elsevier, vol. 71(C).
    • Handle: RePEc:eee:teinso:v:71:y:2022:i:c:s0160791x22002676
    DOI: 10.1016/j.techsoc.2022.102126
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    References listed on IDEAS

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    Cited by:

    1. Xuelian Hai & Jia Zhang & Yumeng Zhang & Jay Pan, 2024. "Chinese physicians’ perceptions and willingness to use telemedicine during the COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    2. Roppelt, Julia Stefanie & Kanbach, Dominik K. & Kraus, Sascha, 2024. "Artificial intelligence in healthcare institutions: A systematic literature review on influencing factors," Technology in Society, Elsevier, vol. 76(C).
    3. Jiaxin Chen & Ting Li & Hua You & Jingyu Wang & Xueqing Peng & Baoyi Chen, 2023. "Behavioral Interpretation of Willingness to Use Wearable Health Devices in Community Residents: A Cross-Sectional Study," IJERPH, MDPI, vol. 20(4), pages 1-11, February.
    4. Brantnell, Anders & Wagrell, Sofia, 2024. "Implementation of medical technology in management and engineering studies: A systematic literature review and future research agenda," Technology in Society, Elsevier, vol. 77(C).
    5. Park, Hyeyoung K. & Chung, Joohyun & Ha, Jiyeon, 2023. "Acceptance of technology related to healthcare among older Korean adults in rural areas: A mixed-method study," Technology in Society, Elsevier, vol. 72(C).
    6. Maras, Marie-Helen & Miranda, Michelle D., 2023. "Augmented body surveillance: Human microchip implantations and the omnipresent threat of function creep," Technology in Society, Elsevier, vol. 74(C).

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