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The Future of Quantified Self in Healthcare

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How data collected from devices & tests will be used in the future to diagnose & pre-empt diseases in the future

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The Future of Quantified Self in Healthcare

  1. 1. The potential of self-monitoring for Health Promotion
  2. 2. Health Promotion  Health promotion is the process of enabling people to increase control over, and to improve, their health.  It moves beyond a focus on individual behaviour towards a wide range of social and environmental interventions
  3. 3. The Ottawa Charter for Health Promotion (WHO)
  4. 4. Quantified Self for Health Promotion  Developing Personal Skills  Self-awareness & self-optimisation  understanding data  Strengthening Community Action  Asthmapolis  Google flu trends  Building Healthy Public Policies  Big data to guide policy makers  Creating Supportive Environments  Providing resources for tracking  Re-orientating Healthcare Services  Prevention practises over curative measures  Promotion of QS by Health care professionals
  5. 5. Research Aim  To explore the experiences and impact self-monitoring and data collection has had on the lives of self- trackers, and the potential for their data to be used to better understand behaviour change mechanisms for human health and wellbeing.
  6. 6. Objectives  Explore the driving factors and reasons behind individuals’ self-tracking and self-monitoring habits.  Gain insight into what participants have discovered through their self-tracking and self-monitoring habits.  Discover the perceived benefits and barriers to self- tracking.  Gain insight into the experiences self-trackers have had with their chosen habit.  Explore participants’ views on the potential for integration of self-tracking and self-monitoring behaviour into mainstream daily life
  7. 7. Methodology  Mixed methods approach  Online questionnaire  semi-structured interviews  Data Collection & Analysis  Survey Monkey  Skype  SPSS  Inductive thematic analysis
  8. 8. Results  Demographics  25 respondents, 11male 8 female 6 undisclosed  Age range: 18-84 (M=25-34)  High socio-economic status
  9. 9. 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% PercentageofUsers Catagories of Tools Tools Used
  10. 10. Benefits Evidence based approach to health Improve/ optimise health Greater understanding of oneself Mindfulness Self- awareness/ self- knowledge Preventative
  11. 11. Barriers Consistency/ forgetfulness Time-costs Frequency and effort Poor inter- operability Skills required Lack of privacy Cheating
  12. 12. 0% 50% 100% Yes No Confidentiality Concerns 0% 20% 40% 60% Yes No Familiarity with privacy Terms and Conditions of Devices 0% 20% 40% 60% Yes No I don't know Devices used Sharing with a Third Party 0% 50% 100% Yes No Willing to Share Data Anonymously
  13. 13.  Overall positive experience of self tracking  Satisfied with the available tools, process and usability of the devices  Self-awareness and self-knowledge to optimise health  Desire to anonymously share data
  14. 14. Interview Results  Demographics  11 Interviews, 8 males, 3 females  Experience 6 months- 40 years  High Socio-economic Status  22 variables of tracking  20 tools mentioned  Time-costs:2.5mins-1hours
  15. 15. Motivations & data Usage  Motivations  Cue to action  Curiosity  Sports  school or work requirements  Usage  surgery, treatment and illness management  self-diagnosis  Accountability  self-knowledge  objective decision making  prevention
  16. 16. Process & Impact  Process  quick and easy  Consistency  Cheating  passive tracking  Stressful  obsessive  Impact  rational decision making  Confidence  Support  self-efficacy
  17. 17. Benefits and Barriers  Barriers  Inter-operability  Correlations  passive tracking  interpretation of data & context  self-doubt  Benefits  Motivating  objective viewpoint  improve performance  Lifestyle  overall health
  18. 18. London Survey Comparisons  Respondents:  London: 105  Dublin: 25  Measuring weight:  London: 47%  Dublin: 70%  Pen and paper still being used to track  London: 28%  Dublin: 32%  Willingness to share data  London:84%  Dublin: 90%
  19. 19. Summary of Results  Motivations  Self-knowledge  self-optimization  curiosity  Engagement  Fitness  Weight  Nutrition  sleep  Perception of time consumption  Data Usage  informed choices  Motivate  Empowerment  self-awareness  self-efficacy  Barriers  interpretation of data  Correlations & Context  Consistency  psychological stresses
  20. 20. Self-awareness  “It is like when you are driving a car and you see the fuel gauge. If you couldn’t see the fuel gauge you would just drive on but because you see it, you say ‘oh I am running low on fuel’ so I suppose if you see your weight going up or down, you can take action”
  21. 21. Psychological Stress  “I staggered home with my flashlight knowing that I’d advance to sixty-five thousand, and that there will be no end to it until my feet snap off at the ankles. Then it’ll just be my jagged bones stabbing into the soft ground. Why is it some people can manage a thing like a Fitbit, while others go off the rails and allow it to rule, and perhaps even ruin, their lives?” (Sedaris, 2014)
  22. 22. Future Integration in Ireland  Reputation: “nerdy”, time consuming  Early adopters & innovators  Role of Health Care Professionals  Adaption of devices to better suit the needs of individuals
  23. 23. Recommendations for Technology  Securing continued engagement from its users  Avoiding early drop-offs in usage  Increasing devices passive tracking abilities  Providing more cross-connection and correlations of variables between devices  Must convey the meaning behind the data  Make more suggestions to the user as to how to improve their results
  24. 24. Recommendations for Research  Study the defining characteristics of self-trackers, for example, personality types and traits  Study the current and potential uses of self-tracking within alternative social classes, for which the experiences, perceived benefits and barriers may vary widely  Research on the actual impact self-tracking has on its users published work focused on initial integration and adoption, neglected to look at the effects of long term adoption and sustainable behaviour change  Evidence base required to promote more active integration of self- monitoring in to health promotion and primary care practices

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