Analyzing the changes of health condition and social capital of elderly people using wearable devices
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Rapid developments in information technology have enabled wearable devices to be applied in the health field. In elderly adults, wearable devices aid in data collection and exerts a positive effect on their social capital. This study evaluated the changes in these two parameters among elderly adults using wearable devices, and analyzed the effect of these devices on their daily lives.
We selected 18 elderly people using wearable devices, between February and May 2017. The data collected by the wearable devices included the number of steps taken, sleep duration, blood pressure, heart rate, respiratory rate, fatigue, and mood of the wearers. Using a questionnaire and the trajectory equifinality model, we interviewed and surveyed elderly adults in order to understand their health status and social capital.
The health of the participants was generally good, and most were able to achieve > 8000 steps per day (p < 0.05). Mild and moderate fatigue symptoms were noted in elderly adults for 90% of the study period (p < 0.05). The number of steps, blood pressure, and heart rate changed significantly within a month. From the commencement of using the wearable devices, a steady increase was noted in the monthly number of steps. Interviews suggested that the elderly adults perceived wearable devices as having the potential to improve health and social capital.
By using wearable devices, the participants had a better understanding of their own health, and were willing to take health-boosting measures. The participants were also more willing to increase their social capital and expand their social network.
KeywordsWearable devices Elderly Social capital ICT Health status
SZ was responsible for conducting the questionnaires and interviews, as well as data analysis, based on social capital and a statistical approach. AO lead the project and participated in the experiment design. SN contributed to the data analysis framework design. QJ participated in the data acquisition system design. All authors read and approved the final manuscript.
This work was partially supported by a 2016–2018 Masaru Ibuka Foundation Research Project on Oriental Medicine. We are grateful to Zhanwei Gu for the assistance with the experiments, and to Atsushi Saito for the valuable discussion.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
- 2.Kaidi Z, Lujun S, Xinyu M, Haitao W, Mingzhen LI. Study on the status of elderly disabled in urban and rural areas of China. People Disabil. 2011;2:11–6.Google Scholar
- 7.Liang Z, Qi L, Xue YY. A systematic exploration of the micro-blog feature space for teens stress detection. Health Inf Sci Syst. 2016;4(3):1–12.Google Scholar
- 9.Bhattacharjee B. Factors affecting computer use among older adult users: a study in the backdrop of Florida State University in College of Information. Tallahassee: The Florida State University; 2007. p. 217.Google Scholar
- 10.Renaud, K., van Biljon, J.: Predicting technology acceptance and adoption by the elderly: a qualitative study. In: Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists a Information Technologists on IT research in developing countries: riding the wave of technology, ACM, Wilderness, South Africa; 2008. pp. 210–219.Google Scholar
- 13.Bourdieu P. The forms of capital. In: Richardson JE, editor. Handbook of theory of research for the sociology of education. New York: Greenword Press; 1986. p. 241–58.Google Scholar
- 14.Lan P, Xiao-yan F. The current situation and future trend of ICT and social capital research: the achievements and predicament of both scholars and clients in international politics. J Int Med. 2011;33:75–80 (in Chinese).Google Scholar
- 16.Ammari, T., Schoenebeck, S.Y.: Understanding and supporting fathers and fatherhood on social media sites. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, ACM, New York, NY; 2015. pp. 1905–1914.Google Scholar
- 20.Charney DS, Reynolds CF, Lewis L, Lebowitz BD, Sunderland T, Alexopoulos GS, Blazer DG, Katz IR, Meyers BS, Arean PA, Borson S, Brown C, Bruce ML, Callahan CM, Charlson ME, Conwell Y, Cuthbert BN, Devanand DP, Gibson MJ, Gottlieb GL, Krishnan KR, Laden SK, Lyketsos CG, Mulsant BH, Niederehe G, Olin JT, Oslin DW, Pearson J, Persky T, Pollock BG, Raetzman S, Reynolds M, Salzman C, Schulz R, Schwenk TL, Scolnick E, Unutzer J, Weissman MM, Young RC. Depression and bipolar support alliance. Depression and bipolar support alliance consensus statement on the unmet needs in diagnosis and treatment of mood disorders in late life. Arch Gen Psychiatry. 2003;60:664–72.CrossRefPubMedGoogle Scholar
- 25.Brodie MA, Coppens MJ, Lord SR, Lovell NH, Gschwind YJ, Redmond SJ, Del Rosario MB, Wang K, Sturnieks DL, Persiani M, Delbaere K. Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different. Med Biol Eng Comput. 2016;54:663–74.CrossRefPubMedGoogle Scholar
- 26.Kousokabe T. The relationship between time perspective and the coference using TEM in the early childhood education and care conferences. Miyajiro Educ Univ Min. 2015;49:153–60.Google Scholar
- 27.Kiuchi M, Shimada H. The effect of clinical experience in teacher training program on the desire to be a teacher by means of semi-structured interview and time-series analysis. Educ Pract Res. 2012;13:31–40.Google Scholar