This study develops a casual smart Na/K meter to measure the sodium and potassium in urine for hypertensive patients. To prevent hypertension from leading to cardiopathies, it is useful to reduce salt intake. The Omron Healthcare Co., Ltd. lunched the prototype, a casual Na/K meter to measure the salt intake from a diet. Nevertheless, it lacks the function to make the patients grasp the historical data. This study improves that meter by adding the NFC and developing the software application linked to Android smartphones and smart watches. Smartphones can store the data and display the historical data. Smart watches make up a part of their daily lives by alerts and messages. The concept of this study provides a continuous value for hypertensive patients. That value is similar to the learning value but it exists beyond the learning effect. For the learning value, after the subjects learn something and obtain the skills, ability, and knowledge, the value is fixed and completed. On the other hand, for the continuous value, the learning value is also included and the subjects receive the learning value; however, they need to continue that behavior until death. If they stop reducing salt intake, they return to hypertension. If they get satisfied with obtaining the learning value and stop their actions, they never receive the continuous value that exists beyond the learning value. The continuous value is brewed in the transtheoretical model of health behavior change. Throughout these stages, to encourage their behavioral change and obtain the continuous value, this study employs Fogg’s theory applied to developing the communication devices. The application stocks the historical data and displays it on the smartphones. The smart watches classify alerts into five colored displays from green (good) to red (bad). It can be helpful for the patients to make the reduction of salt intake as their dietary habit. In the future, the application needs to be improved for making patients adapt with their diets and motivations.

1. [1] Ministry of Health, Labour and Welfare, “The National Health and Nutrition Survey.” https://www.mhlw.go.jp/bunya/kenkou/eiyou/h24-houkoku.html [Accessed March 17, 2020]
2. [2] J. H. Society, “Guideline for treatment of hypertension.” http://www.jpnsh.jp/guideline_g.html [Accessed March 17, 2020]
3. [3] WHO, “Raised blood pressure in Global Health Observatory (GHO) data.” https://www.who.int/gho/ncd/risk_factors/blood_pressure_prevalence_text/en/ [Accessed March 17, 2020]
4. [4] T. Katsuya, K. Ishikawa, K. Sugimoto, H. Rakugi, and T. Ogihara, “Salt sensitivity of Japanese from the viewpoint of gene polymorphism,” Hypertension Research, Vol.26, No.7, pp. 521-525, doi: 10.1291/hypres.26.521, 2003.
5. [5] Ministry of Health, Labour and Welfare, “Overview of the national health care costs in 2014.” https://www.mhlw.go.jp/toukei/saikin/hw/k-iryohi/14/ [Accessed August 1, 2018]
6. [6] Intersalt Cooperative Research Group, “Intersalt: an international study of electrolyte excretion and blood pressure. Results for 24 hour urinary sodium and potassium excretion,” BMJ: British Medical J., Vol.297, No.6644, pp. 319-328, 1988.
7. [7] T. Iwahori et al., “Six random specimens of daytime casual urine on different days are sufficient to estimate daily sodium/potassium ratio in comparison to 7-day 24-h urine collections,” Hypertension Research, Vol.37, No.8, pp. 765-771, 2014.
8. [8] J. C. Ni, C. S. Yang, J. K. Huang, and L. C. Shiu, “Combining Non-Invasive Wearable Device and Intelligent Terminal in HealthCare IoT,” Procedia Computer Science, Vol.154, pp. 161-166, 2019.
9. [9] F. P. Akbulut and A. Akan, “A smart wearable system for short-term cardiovascular risk assessment with emotional dynamics,” Measurement, Vol.128, pp. 237-246, 2018.
10. [10] L.-P. Hung and C.-C. Lin, “A multiple warning and smart monitoring system using wearable devices for home care,” Int. J. of Human-Computer Studies, Vol.136, 102381, 2020.
11. [11] P. N. Ramkumar et al., “Remote patient monitoring using mobile health for total knee arthroplasty: validation of a wearable and machine learning–based surveillance platform,” The J. of Arthroplasty, Vol.34, No.10, pp. 2253-2259, 2019.
12. [12] M. Shojaee, S. Nasresfahani, M. Dordane, and M. Sheikhi, “Fully integrated wearable humidity sensor based on hydrothermally synthesized partially reduced graphene oxide,” Sensors and Actuators A: Physical, Vol.279, pp. 448-456, 2018.
13. [13] A. Colley, B. Pfleging, F. Alt, and J. Häkkilä, “Exploring public wearable display of wellness tracker data,” Int. J. of Human-Computer Studies, Vol.138, 102408, 2020.
14. [14] Z. Lou, L. Wang, K. Jiang, Z. Wei, and G. Shen, “Reviews of wearable healthcare systems: Materials, devices and system integration,” Materials Science and Engineering: R: Reports, Vol.140, 100523, 2020.
15. [15] G. Lopez, T. Tokuda, M. Oshima, K. Nkurikiyeyezu, N. Isoyama, and K. Itao, “Development and Evaluation of a Low-Energy Consumption Wearable Wrist Warming Device,” Int. J. Automation Technol., Vol.12, No.6, pp. 911-920, doi: 10.20965/ijat.2018.p0911, 2018.
16. [16] J. O. Prochaska and W. F. Velicer, “The transtheoretical model of health behavior change,” American J. of Health Promotion, Vol.12, No.1, pp. 38-48, 1997.
17. [17] C. Bundy, “Changing behaviour: using motivational interviewing techniques,” J. of the Royal Society of Medicine, Vol.97, pp. 43-47, 2004.
18. [18] T. Ito et al., “Effect of salt intake on blood pressure in patients receiving antihypertensive therapy: Shimane CoHRE Study,” European J. of Internal Medicine, Vol.28, pp. 70-73, doi: 10.1016/j.ejim.2015.10.013, 2016.
19. [19] N. Sato, K. Akai, M. Hirose, S. Okamoto, and K. Karino, “Visualization of Acquisition Experience in Sternal Compression Maneuver Using Kinect Sensoring: For Co-Creation of Medical Technique Experiential Values,” Int. J. Automation Technol., Vol.12, No.4, pp. 542-552, doi: 10.20965/ijat.2018.p0542, 2018.
20. [20] Q. An et al., “Skill Abstraction of Physical Therapists in Hemiplegia Patient Rehabilitation Using a Walking Assist Robot,” Int. J. Automation Technol., Vol.13, No.2, pp. 271-278, doi: 10.20965/ijat.2019.p0271, 2019.
21. [21] B. J. Fogg, “A behavior model for persuasive design,” Proc. of the 4th Int. Conf. on Persuasive Technology, pp. 40:1-40:7, doi: 10.1145/1541948.1541999, 2009.
22. [22] B. J. Fogg, “Persuasive technology: using computers to change what we think and do,” Morgan Kaufmann Publishers, doi: 10.1016/B978-1-55860-643-2.X5000-8, 2003.
23. [23] H. Oinas-Kukkonen and M. Harjumaa, “A systematic framework for designing and evaluating persuasive systems,” Proc. of the 3rd Int. Conf. on Persuasive Technology, Lecture Notes in Computer Science, Vol.5033, pp. 164-176, doi: 10.1007/978-3-540-68504-3_15, 2008.
24. [24] S. M. Kelders, R. N. Kok, H. C. Ossebaard, and J. E. Van Gemert-Pijnen, “Persuasive system design does matter: a systematic review of adherence to web-based interventions,” J. of Medical Internet Research, Vol.14, No.6, e152, doi: 10.2196/jmir.2104, 2012.
25. [25] G. Wildeboer, S. M. Kelders, and J. E. van Gemert-Pijnen, “The relationship between persuasive technology principles, adherence and effect of web-Based interventions for mental health: A meta-analysis,” Int. J. of Medical Informatics, Vol.96, pp. 71-85, doi: 10.1016/j.ijmedinf.2016.04.005, 2016.
26. [26] J. Matthews, K. T. Win, H. Oinas-Kukkonen, and M. Freeman, “Persuasive technology in mobile applications promoting physical activity: a systematic review,” J. of Medical Systems, Vol.40, No.3, pp. 72:1-72:13, doi: 10.1007/s10916-015-0425-x, 2016.
27. [27] WHO, “Guideline: potassium intake for adults and children,” 2012.
28. [28] M. S. Yatabe et al., “Urinary sodium-to-potassium ratio tracks the changes in salt intake during an experimental feeding study using standardized low-salt and high-salt meals among healthy Japanese volunteers,” Nutrients, Vol.9, No.9, pp. 951:1-951:113, doi: 10.3390/nu9090951, 2017.
29. [29] K. Aoki, K. Akai, K. Ujiie, T. Shinmura, and N. Nishino, “An Actual Purchasing Experiment for Investigating the Effects of Eco-Information on Consumers’ Environmental Consciousness and Attitudes Towards Agricultural Products,” Int. J. Automation Technol., Vol.8, No.5, pp. 688-697, doi: 10.20965/ijat.2014.p0688, 2014.
30. [30] H. Hutchinson et al., “Technology probes: inspiring design for and with families,” Proc. of the SIGCHI Conf. on Human Factors in Computing Systems – CHI ’03, Vol.5, No.1, pp. 17-24, doi: 10.1145/642611.642616, 2003.
31. [31] T. Takenaka, Y. Yamamoto, K. Fukuda, A. Kimura, and K. Ueda, “Enhancing products and services using smart appliance networks,” CIRP Annals, Vol.65, No.1, pp. 397-400, doi: 10.1016/j.cirp.2016.04.062, 2016.
32. [32] T. Takenaka, H. Koshiba, Y. Motomura, and K. Ueda, “Product/service variety strategy considering mixed distribution of human lifestyles,” CIRP Annals, Vol.62, No.1, pp. 463-466, doi: 10.1016/j.cirp.2013.03.087, 2013.
33. [33] T. Kaihara et al., “Value creation in production: Reconsideration from interdisciplinary approaches,” CIRP Annals, Vol.67, No.2, pp. 791-813, doi: 10.1016/j.cirp.2018.05.002, 2018.