Sustainable, Holistic, Adaptable, Real-Time, and Precise (SHARP) Approach Towards Developing Health and Wellness Systems

  • Farhaan MirzaEmail author
  • Asfahaan Mirza
  • Claris Yee Seung Chung
  • David Sundaram
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 670)


As populations age and chronic diseases become more prevalent, new strategies are required to help people live well. Traditional models of episodic health care will not be sufficient to meet changing health care needs and the reorientation of services towards maintaining function as opposed to treating illness. One strategy to meet these challenges is an increased focus on self-care via use of broader social networks and seamless integration of applications with lifestyle activities, particularly for people with chronic diseases including diabetes, cardiovascular disease, and respiratory conditions. There has also been a rapid increase in a range of technologies for connecting different components of the health system and delivering services through smartphones and connected devices. Our proposal is to pursue systems development in healthcare in a way that considers a range of aspects known as SHARP: Sustainable, Holistic, Adaptive, Real-time and Precise. This approach will provide solutions that will be useful and effective for managing the long-term well-being of individuals.


Sustainable health systems Precision health Disease management Adaptive health systems Self-managed healthcare applications 


  1. 1.
    Health Research Council of New Zealand: HRC Investment Signals 2016 - General Guidelines. HRC, Auckland (2016)Google Scholar
  2. 2.
    Ministry of Health: Sharing Health Information: Toward Better, Safer Care. Ministry of Health, Wellington (2013)Google Scholar
  3. 3.
    Bate, P., Robert, G.: Bringing User Experience to Healthcare Improvement: The Concepts, Methods and Practices of Experience-Based Design. Radcliffe Publishing, London (2007)Google Scholar
  4. 4.
    Oeppen, J., Vaupel, J.W.: Broken limits to life expectancy. Science 296, 1029–1031 (2002)CrossRefGoogle Scholar
  5. 5.
    Krebs, J.D., Parry-Strong, A., Gamble, E., McBain, L., Bingham, L.J., Dutton, E.S., Tapu-Ta’Ala, S., Howells, J., Metekingi, H., Smith, R.B.W., Coppell, K.J.: A structured, group-based diabetes self-management education (DSME) programme for people, families and whanau with type 2 diabetes (T2DM) in New Zealand: an observational study. Prim. Care Diabetes. 7, 151–158 (2013)CrossRefGoogle Scholar
  6. 6.
    Ma, J., Rosas, L.G., Lv, N.: Precision lifestyle medicine: a new frontier in the science of behavior change and population health. Am. J. Prev. Med. 50, 395 (2016)Google Scholar
  7. 7.
    Ross, S.E., Todd, J., Moore, L.A., Beaty, B.L., Wittevrongel, L., Lin, C.-T.: Expectations of patients and physicians regarding patient-accessible medical records. J. Med. Internet Res. 7, e13 (2005)CrossRefGoogle Scholar
  8. 8.
    Kankeu, H., Saksena, P., Xu, K., Evans, D.B.: The financial burden from non-communicable diseases in low- and middle-income countries: a literature review. Health Res. Policy Syst. 11, 31 (2013)CrossRefGoogle Scholar
  9. 9.
    Devol, R., Bedroussian, A.: an unhealthy america: the economic burden of chronic disease. Stroke (2007)Google Scholar
  10. 10.
    Stewart, W.F., Ricci, J.A., Chee, E., Morganstein, D.: Lost productive work time costs from health conditions in the United States: results from the American Productivity Audit. J. Occup. Environ. Med. 45, 1234–1246 (2003)CrossRefGoogle Scholar
  11. 11.
    Pukeliene, V., Starkauskiene, V.: Quality of life: factors determining its measurement complexity. Eng. Econ. 22, 147–156 (2011)CrossRefGoogle Scholar
  12. 12.
    Dunn, H.L.: What high-level wellness means. Can. J. Public Health 50, 447–457 (1959)Google Scholar
  13. 13.
    Swarbrick, M.A.: Integrated care: wellness-oriented peer approaches: a key ingredient for integrated care. Psychiatr. Serv. 64, 723–726 (2013)CrossRefGoogle Scholar
  14. 14.
    Morris, S., Paradiso, J.: Shoe-integrated sensor system for wireless gait analysis and real-time feedback. In: Engineering in Medicine and Biology, 2002. 24th Annual Coference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint (2002)Google Scholar
  15. 15.
    Wing, C., Yang, H.: FitYou: integrating health profiles to real-time contextual suggestion. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR 2014, pp. 1263–1264 (2014)Google Scholar
  16. 16.
    Boyes, A., Newell, S., Girgis, A., McElduff, P.: Does routine assessment and real‐time feedback improve cancer patients’ psychosocial well‐being? Eur. J. Cancer Care (Engl).15, (2006)Google Scholar
  17. 17.
    Jara, A.J., Zamora, M.A., Skarmeta, A.F.G.: An internet of things–based personal device for diabetes therapy management in ambient assisted living (AAL). Pers. Ubiquitous Comput. 15, 431–440 (2011)CrossRefGoogle Scholar
  18. 18.
    Dankwa-Mullan, I., Bull, J., Sy, F.: Precision medicine and health disparities: advancing the science of individualizing patient care. Am. J. Public Health 105, S368–S368 (2015)CrossRefGoogle Scholar
  19. 19.
    Comings & Goings, JIM 63–8. J. Investig. Med. 63, 893–897 (2015)Google Scholar
  20. 20.
  21. 21.
    Precision Medicine: delivering the right treatment to the right patient at the right time through early diagnosis and individually tailored treatments.
  22. 22.
    Empowering data-driven health.
  23. 23.
    Fox, J.L.: Obama catapults patient-empowered Precision Medicine. Nat. Biotechnol. 33, 325 (2015)CrossRefGoogle Scholar
  24. 24.
    August, G., Cicchetti, D., Gewirtz, A.: Moving toward precision healthcare in children’s mental health: new perspectives, methodologies, and technologies in therapeutics and prevention. Dev. Psychopathol. 28, 889 (2016)CrossRefGoogle Scholar
  25. 25.
    Cully, M.: Anticancer drugs: advancing precision medicine in silico. Nat. Rev. Drug Discov. 14, 311 (2015)CrossRefGoogle Scholar
  26. 26.
    Collins, F.S., Varmus, H.: A new initiative on precision medicine (2015).
  27. 27.
    Harrer, S.: Measuring life: sensors and analytics for precision medicine, 1 June 2015Google Scholar
  28. 28.
    Ford, E.S., Croft, J.B., Posner, S.F., Goodman, R.A., Giles, W.H.: Co-occurrence of leading lifestyle-related chronic conditions among adults in the United States, 2002-2009. Prev. Chronic Dis. 10, 120316 (2013)CrossRefGoogle Scholar
  29. 29.
    Cohen, R.A., Villarroel, M.A.: Strategies used by adults to reduce their prescription drug costs: United States, 2013. NCHS Data Brief. vol. 184, pp. 1–8 (2015)Google Scholar
  30. 30.
    Horne, R., Weinman, J.: Patients’ beliefs about prescribed medicines and their role in adherence to treatment in chronic physical illness. J. Psychosom. Res. 47, 555–567 (1999)CrossRefGoogle Scholar
  31. 31.
    Conn, V.S., Ruppar, T.M., Enriquez, M., Cooper, P.: Medication adherence interventions that target subjects with adherence problems: systematic review and meta-analysis. Res. Soc. Adm. Pharm. 12, 218–246 (2016)CrossRefGoogle Scholar
  32. 32.
    Beni, J.B.: Technology and the healthcare system: implications for patient adherence. Int. J. Electron. Healthc. 6, 117 (2011)CrossRefGoogle Scholar
  33. 33.
    Atreja, A., Bellam, N., Levy, S.R.: Strategies to enhance patient adherence: making it simple. MedGenMed 7, 4 (2005)Google Scholar
  34. 34.
    Lehmann, A., Aslani, P., Ahmed, R., Celio, J., Gauchet, A., Bedouch, P., Bugnon, O., Allenet, B., Schneider, M.P.: Assessing medication adherence: options to consider. Int. J. Clin. Pharm. 36, 55–69 (2014)CrossRefGoogle Scholar
  35. 35.
    Scragg, R., Baker, J., Metcalf, P., Dryson, E.: Prevalence of diabetes mellitus and impaired glucose tolerance in a New Zealand multiracial workforce. N. Z. Med. J. 104, 395–397 (1991)Google Scholar
  36. 36.
    Nait-Charif, H., McKenna, S.J.: Activity summarisation and fall detection in a supportive home environment. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 4, pp. 323–326. IEEE (2004)Google Scholar
  37. 37.
    Garcia, N.M., Rodrigues, J.J.P.C.: Ambient Assisted Living. CRC Press, Boca Raton (2015)CrossRefGoogle Scholar
  38. 38.
    Mathers, C.D., Sadana, R., Salomon, J.A., Murray, C.J., Lopez, A.D.: Healthy life expectancy in 191 countries, 1999. Lancet 357, 1685–1691 (2001)CrossRefGoogle Scholar
  39. 39.
    Chan, M., Campo, E., Estève, D., Fourniols, J.-Y.: Smart homes — current features and future perspectives. Maturitas 64, 90–97 (2009)CrossRefGoogle Scholar
  40. 40.
    Tricco, A.C., Ivers, N.M., Grimshaw, J.M., Moher, D., Turner, L., Galipeau, J., Halperin, I., Vachon, B., Ramsay, T., Manns, B., Tonelli, M., Shojania, K.: Effectiveness of quality improvement strategies on the management of diabetes: a systematic review and meta-analysis. Lancet 379, 2252–2261 (2012)CrossRefGoogle Scholar
  41. 41.
    Vodanovich, S., Sundaram, D., Myers, M.: Research commentary —digital natives and ubiquitous information systems. Inf. Syst. Res. 21, 711–723 (2010)CrossRefGoogle Scholar
  42. 42.
    Fotheringham, M.J., Owies, D., Leslie, E., Owen, N.: Interactive health communication in preventive medicine: Internet-based strategies in teaching and research. Am. J. Prev. Med. 19, 113–120 (2000)CrossRefGoogle Scholar
  43. 43.
    Loeppke, R., Edington, D., Bender, J., Reynolds, A.: The association of technology in a workplace wellness program with health risk factor reduction. J. Occup. Environ. Med. 55, 259–264 (2013)CrossRefGoogle Scholar
  44. 44.
    Bayer, R., Galea, S.: Public health in the precision-medicine era. N. Engl. J. Med. 373, 499–501 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Farhaan Mirza
    • 1
    Email author
  • Asfahaan Mirza
    • 2
  • Claris Yee Seung Chung
    • 2
  • David Sundaram
    • 2
  1. 1.Department of Information Technology and Software EngineeringAuckland University of TechnologyAucklandNew Zealand
  2. 2.Department of Information Systems and Operations ManagementUniversity of AucklandAucklandNew Zealand

Personalised recommendations