Journal of Healthcare Informatics Research

, Volume 3, Issue 1, pp 124–155 | Cite as

A Patient-Centered Proposal for Bayesian Analysis of Self-Experiments for Health

  • Jessica SchroederEmail author
  • Ravi Karkar
  • James Fogarty
  • Julie A. Kientz
  • Sean A. Munson
  • Matthew Kay
Research Article
Part of the following topical collections:
  1. Special Issue on Health Behavior in the Information Age


The rise of affordable sensors and apps has enabled people to monitor various health indicators via self-tracking. This trend encourages self-experimentation, a subset of self-tracking in which a person systematically explores potential causal relationships to try to answer questions about their health. Although recent research has investigated how to support the data collection necessary for self-experiments, less research has considered the best way to analyze data resulting from these self-experiments. Most tools default to using traditional frequentist methods. However, the US Agency for Healthcare Research and Quality recommends using Bayesian analysis for n-of-1 studies, arguing from a statistical perspective. To develop a complementary patient-centered perspective on the potential benefits of Bayesian analysis, this paper describes types of questions people want to answer via self-experimentation, as informed by (1) our experiences engaging with irritable bowel syndrome patients and their healthcare providers and (2) a survey investigating what questions individuals want to answer about their health and wellness. We provide examples of how those questions might be answered using (1) frequentist null hypothesis significance testing, (2) frequentist estimation, and (3) Bayesian estimation and prediction. We then provide design recommendations for analyses and visualizations that could help people answer and interpret such questions. We find the majority of the questions people want to answer with self-experimentation data are better answered with Bayesian methods than with frequentist methods. Our results therefore provide patient-centered support for the use of Bayesian analysis for n-of-1 studies.


Self-experiment N-of-1 Interface design User-centered design Self-tracking Bayesian analysis 



We thank Eric B. Heckler and Roger Vilardaga for conversations that informed this research.

Funding information

This research was funded in part by a University of Washington Innovation Research Award, the National Science Foundation under awards IIS-1553167 and SCH-1344613, and the Agency for Healthcare Research Quality under award 1R21HS023654.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflicts of interest.


  1. 1.
    Global Status Report on Noncommunicable Diseases. Geneva: World Health Organization; 2014Google Scholar
  2. 2.
    Mamykina L, Mynatt ED, Kaufman DR (2006) Investigating health management practices of individuals with diabetes. Proc SIGCHI Conf Hum Factors Comput Syst - CHI ‘06. :927Google Scholar
  3. 3.
    Riggare S, Unruh KT, Sturr J, Domingos J (2017) Patient-driven n-of-1 in Parkinson’s disease. 123–8Google Scholar
  4. 4.
    Mamykina L, Heitkemper EM, Smaldone AM, Kukafka R, Cole-Lewis HJ, Davidson PG, Mynatt ED, Cassells A, Tobin JN, Hripcsak G (2017) Personal discovery in diabetes self-management: discovering cause and effect using self-monitoring data. J Biomed Inform. 76(June):1–8CrossRefGoogle Scholar
  5. 5.
    Cepeda MS, Acevedo JC, Hernando A, Miranda N, Cortes C, Carr DB (2008) An n-of-1 trial as an aid to decision-making prior to implanting a permanent spinal cord stimulator. Pain Med (United States) 9(2):235–239CrossRefGoogle Scholar
  6. 6.
    Choe EK, Lee NB, Lee B, Pratt W, Kientz JA (2014) Understanding quantified-selfers’ practices in collecting and exploring personal data. In: Proc ACM Conf Hum Factors Comput Syst (CHI 2014). New York, New York, USA; p. 1143–52Google Scholar
  7. 7.
    Nediyana D, Metaxa-Kakavouli D, Tran A, Nugent N, Boergers J, McGeary J, Huang J (2016) SleepCoacher: a personalized automated self-experimentation system for sleep recommendations. In: Proc ACM Symp User Interface Softw Technol (UIST 2016). p. 347–58Google Scholar
  8. 8.
    Karkar R, Zia JK, Vilardaga R, Mishra SR, Fogarty J, Munson SA, Kientz JA (2016) A framework for self-experimentation in personalized health. J Am Med Informatics Assoc. 23(3):440–448CrossRefGoogle Scholar
  9. 9.
    Karkar R, Schroeder J, Epstein DA, Pina LR, Scofield J, Fogarty J, Kientz JA, Munson SA, Vilardaga R, Zia JK (2017) TummyTrials: a feasibility study of using self-experimentation to detect individualized food triggers. In: Proc ACM Conf Hum Factors Comput Syst (CHI 2017). p. 6850–63Google Scholar
  10. 10.
    Kravitz RL, Duan MSPHN (2014) Panel De.M.C.N.-1 G. Design and implementation of n-of-1 trials: a user’s guide. Agency Healthc Res Qual 13(14):1–88Google Scholar
  11. 11.
    Gelman A, Weakliem D (2008) Of beauty, sex, and power: statistical challenges in estimating small effects. Am Sci 97(4):310–316CrossRefGoogle Scholar
  12. 12.
    Gelman A, Carlin J (2014) Beyond power calculations: assessing type S (sign) and type M (magnitude) errors. Perspect Psychol Sci. 9(6):641–651CrossRefGoogle Scholar
  13. 13.
    Kay M, Nelson GL, Hekler EB (2016) Researcher-centered design of statistics: why Bayesian statistics better fit the culture and incentives of HCI. Proc 2016 CHI Conf Hum Factors Comput Syst. :4521–32Google Scholar
  14. 14.
    Schroeder J, Hoffswell J, Chung C-F, Fogarty J, Munson S, Zia JK (2017) Supporting patient-provider collaboration to identify individual triggers using food and symptom journals. Proc 2017 ACM Conf Comput Support Coop Work Soc Comput - CSCW ‘17. :1726–39Google Scholar
  15. 15.
    Li I, Dey AK, Forlizzi J (2010) A stage-based model of personal informatics systems. In: Proc ACM Conf Hum Factors Comput Syst (CHI 2010). New York, New York, USA; p. 557–66Google Scholar
  16. 16.
    Epstein DA, Ping A, Fogarty J, Munson SA (2015) A lived informatics model of personal informatics. In: Proc ACM Int Jt Conf Pervasive Ubiquitous Comput (UbiComp 2015). p. 731–42Google Scholar
  17. 17.
    Mamykina L, Smaldone AM, Bakken SR (2015) Adopting the sensemaking perspective for chronic disease self-management. J Biomed Inform. 56:406–417CrossRefGoogle Scholar
  18. 18.
    Rooksby J, Rost M, Morrison A, Chalmers MC (2014) Personal tracking as lived informatics. In: Proc ACM Conf Hum Factors Comput Syst (CHI 2014). New York, New York, USA; p. 1163–72Google Scholar
  19. 19.
    Chung C-F, Cook J, Bales E, Zia JK, Munson SA (2015) More than telemonitoring: health provider use and nonuse of life-log data in irritable bowel syndrome and weight management. J Med Internet Res 17(8):e203CrossRefGoogle Scholar
  20. 20.
    Park SY, Chen Y (2015) Individual and social recognition: challenges and opportunities in migraine management. In: Proc ACM Conf Comput Support Coop Work Soc Comput. ACM Press, New York, USA, pp 1540–1551Google Scholar
  21. 21.
    Mamykina L, Mynatt E, Davidson P, Greenblatt D (2008) MAHI: investigation of social scaffolding for reflective thinking in diabetes management. In: Proc SIGCHI Conf Hum Factors Comput Syst (CHI 2008). p. 477–86Google Scholar
  22. 22.
    Schroeder J, Chung C-F, Epstein DA, Karkar R, Parsons A, Murinova N, Fogarty J, Munson SA (2018) Examining self-tracking by people with migraine: goals, needs, and opportunities in a chronic health condition. In: Proc ACM Conf Des Interact Syst (DIS 2018) To Appear.
  23. 23.
    Consolvo S, McDonald DW, Toscos T, Chen MY, Froehlich JE, Harrison BL, Klasnja P, La Marca A, Le Grand L, Libby R, Smith IE, Landay JA (2008) Activity sensing in the wild: a field trial of Ubifit Garden. In: Proc ACM Conf Hum Factors Comput Syst (CHI 2008). p. 1797–806Google Scholar
  24. 24.
    Fitbit [Internet]Google Scholar
  25. 25.
    Jawbone UpBand [Internet]Google Scholar
  26. 26.
    Larklife [Internet]Google Scholar
  27. 27.
    Lin J.J., Mamykina L, Lindtner S, Delajoux G, Strub HB (2006) Fish’n’Steps: encouraging physical activity with an interactive computer game. Ubiquitous Comput (UbiComp 2006). 261–78Google Scholar
  28. 28.
    Nike Fuelband [Internet]Google Scholar
  29. 29.
    Kay M, Choe EK, Shepherd J, Greenstein B, Watson NF, Consolvo S, Kientz JA (2012) Lullaby: a capture & access system for understanding the sleep environment. In: Proc ACM Conf Ubiquitous Comput (UbiComp 2012). p. 226–34Google Scholar
  30. 30.
    Baumer EPS, Katz SJ, Freeman JE, Adams P, Gonzales AL, Pollak J, Retelny D, Niederdeppe J, Olson CM, Gay GK (2012) Prescriptive persuasion and open-ended social awareness: expanding the design space of mobile health. In: Proc ACM Conf Comput Support Coop Work (CSCW 2012). p. 475–84Google Scholar
  31. 31.
    Cordeiro F, Bales E, Cherry E, Fogarty J (2015) Rethinking the mobile food journal: exploring opportunities for lightweight photo-based capture. In: Proc ACM Conf Hum Factors Comput Syst (CHI 2015). p. 3207–16Google Scholar
  32. 32.
    Ali AA, Hossain SM, Hovsepian K, Plarre K, Kumar S (2012) mPuff: automated detection of cigarette smoking puffs from respiration measurements. In: Proc Conf Inf Process Sens Networks (ISPN 2012). p. 269–80Google Scholar
  33. 33.
    Morris M, Guilak F (2009) Mobile heart health: project highlight. IEEE Pervasive Comput. 8(2):57–61CrossRefGoogle Scholar
  34. 34.
    Jorgensen JT (2009) New era of personalized medicine: a 10-year anniversary. Oncologist. 14(5):557–558CrossRefGoogle Scholar
  35. 35.
    Swan M (2009) Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int J Environ Res Public Health. 6(2):492–525CrossRefGoogle Scholar
  36. 36.
    Lillie EO, Patay B, Diamant J, Issell B, Topol EJ, Schork NJ (2011) The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Per Med 8(2):161–173CrossRefGoogle Scholar
  37. 37.
    Riley WT, Glasgow RE, Etheredge L, Abernethy AP (2013) Rapid, responsive, relevant (r3) research: a call for a rapid learning health research enterprise. Clin Transl Med 2(1):10CrossRefGoogle Scholar
  38. 38.
    Barlow DH, Hayes SC (1979) Alternating treatments design: one strategy for comparing the effects of two treatments in a single subject. J Appl Behav Anal. 12(2):199–210CrossRefGoogle Scholar
  39. 39.
    Larson EB (1990) N-of-1 clinical trials: a technique for improving medical therapeutics. West J Med 152(1):52–56Google Scholar
  40. 40.
    Barlow DH, Nock MK, Hersen M (2008) Single case experimental designs: strategies for studying behavior change. Third. Pearson; 416Google Scholar
  41. 41.
    Barr C, Marois M, Sim I, Schmid CH, Wilsey B, Ward D, Duan N, Hays RD, Selsky J, Servadio J, Schwartz M, Dsouza C, Dhammi N, Holt Z, Baquero V, MacDonald S, Jerant A, Sprinkle R, Kravitz RL (2015) The PREEMPT study—evaluating smartphone-assisted n-of-1 trials in patients with chronic pain: study protocol for a randomized controlled trial. Trials 16:67CrossRefGoogle Scholar
  42. 42.
    PACO: The Personal Analytics Companion [Internet]Google Scholar
  43. 43.
    Tiralist - ohmage [Internet]Google Scholar
  44. 44.
    Daskalova N, Desingh K, Kim JY, Zhang L, Papoutsaki A, Huang J (2017) Lessons learned from two cohorts of personal informatics self-experiments. In: Proc ACM Conf Ubiquitous Comput. p. 46Google Scholar
  45. 45.
    Lee J, Walker E, Burleson W, Kay M, Buman M, Hekler EB (2017) Self-experimentation for behavior change: design and formative evaluation of two approaches. In: Proc SIGCHI Conf Hum Factors Comput Syst. p. 6837–49Google Scholar
  46. 46.
    Kruschke JK, Liddell TM (2017) The Bayesian new statistics : hypothesis testing, estimation, meta-analysis, and planning from a Bayesian perspective. Psychon Bull Rev. :1–29Google Scholar
  47. 47.
    Gelman A, Hill J, Yajima M (2012) Why we (usually) don’t have to worry about multiple comparisons. J Res Educ Eff 5(2):189–211. CrossRefGoogle Scholar
  48. 48.
    Elsenbruch S (2011) Abdominal pain in irritable bowel syndrome: a review of putative psychological, neural and neuro-immune mechanisms. Brain Behav Immun. 25(3):386–394CrossRefGoogle Scholar
  49. 49.
    Lovell RM, Ford AC ((2012)) Effect of gender on prevalence of irritable bowel syndrome in the community: systematic review and meta-analysis. Am J Gastroenterol. 107:991–1000Google Scholar
  50. 50.
    Ladabaum U, Boyd E, Zhao WK, Mannalithara A, Sharabidze A, Singh G, Chung E, Levin TR (2012) Diagnosis, comorbidities, and management of irritable bowel syndrome in patients in a large health maintenance organization. Clin Gastroenterol Hepatol. 10(1):37–45CrossRefGoogle Scholar
  51. 51.
    Mitra D, Davis KL, Baran RW (2011) All-cause healthcare charges among managed care patients with constipation and comorbid irritable bowel syndrome. Postgrad Med. 123(3):122–132CrossRefGoogle Scholar
  52. 52.
    Harris LR, Roberts L (2008) Treatments for irritable bowel syndrome: patients’ attitudes and acceptability. BMC Complement Altern Med. 8:65CrossRefGoogle Scholar
  53. 53.
    Heitkemper M, Carter E, Ameen V, Olden K, Cheng L (2002) Women with irritable bowel syndrome: differences in patients’ and physicians’ perceptions. Gastroenterol Nurs 25(5):192–200CrossRefGoogle Scholar
  54. 54.
    Monsbakken K, Vandvik P, Farup P (2006) Perceived food intolerance in subjects with irritable bowel syndrome—etiology, prevalence and consequences. Eur J Clin Nutr 60(5):667–672CrossRefGoogle Scholar
  55. 55.
    Simrén M, Månsson A, Langkilde AM, Svedlund J, Abrahamsson H, Bengtsson U, Björnsson ES (2001) Food-related gastrointestinal symptoms in the irritable bowel syndrome. Digestion 63(2):108–115CrossRefGoogle Scholar
  56. 56.
    Zia JK, Barney P, Cain KC, Jarrett ME, Heitkemper MM (2016) A comprehensive self-management irritable bowel syndrome program produces sustainable changes in behavior after 1 year. Clin Gastroenterol Hepatol 14(2):212–219CrossRefGoogle Scholar
  57. 57.
    Parker TJ, Naylor SJ, Riordan AM, Hunter JO (1995) Management of patients with food intolerance in irritable bowel syndrome: the development and use of an exclusion diet. J Hum Nutr Diet 8(3):159–166CrossRefGoogle Scholar
  58. 58.
    American Gastroenterological Association. American Gastroenterological Association Medical Position Statement: Irritable Bowel Syndrome. Vol. 123, Gastroenterology. American Gastroenterology Association; p. 2105–72002Google Scholar
  59. 59.
    Zia JK, Chung C-F, Xu K, Dong Y, Cain KC, Munson SA, Heitkemper MM Inter-rater reliability of healthcare provider interpretations of food and gastrointestinal symptom paper diaries of patients with irritable bowel syndrome. :In PreparationGoogle Scholar
  60. 60.
    Choe EK, Duarte ME, Kientz JA (2010) Understanding and designing computing technologies that convey concerning health news. In: Proc Int Conf Des Emot (D&E 2010). p. 1–12Google Scholar
  61. 61.
    Eswaran S, Tack J, Chey WD (2011) Food: the forgotten factor in the irritable bowel syndrome. Gastroenterol Clin N Am 40(1):141–162CrossRefGoogle Scholar
  62. 62.
    Loken E, Gelman A (2017) Measurement error and the replication crisis. Science (80- ). 355(6325):584–585CrossRefGoogle Scholar
  63. 63.
    Wasserstein RL, Lazar NA (2016) The ASA’s statement on p -values: context, process, and purpose. Am Stat. 70(2):129–133MathSciNetCrossRefGoogle Scholar
  64. 64.
    Walker E, Nowacki AS (2011) Understanding equivalence and noninferiority testing. J Gen Intern Med 26(2):192–196. CrossRefGoogle Scholar
  65. 65.
    Morey RD, Hoekstra R, Rouder JN, Lee MD, Wagenmakers E-J (2016) The fallacy of placing confidence in confidence intervals. Psychon Bull Rev 23(1):103–123CrossRefGoogle Scholar
  66. 66.
    Hoekstra R, Morey RD, Rouder JN, Wagenmakers E-J (2014) Robust misinterpretation of confidence intervals. Psychon Bull Rev. 21(5):1157–1164CrossRefGoogle Scholar
  67. 67.
    Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2013) Bayesian data analysis. Third Edit. Chapman and Hall/CRC; 675 pGoogle Scholar
  68. 68.
    Goldstein DG, Rothschild D (2014) Lay understanding of probability distributions. J Soc Judgm Decis Mak 9(1):1–14Google Scholar
  69. 69.
    Gigerenzer G (2004) Mindless statistics. J Socio Econ. 33(5):587–606CrossRefGoogle Scholar
  70. 70.
    Benjamin D.J., Berger J.O., Johannesson M., Nosek B.A., Wagenmakers E.-J., Berk R., Bollen K.A., Brembs B., Johnson V.E., et al. (2017) Redefine statistical significance. Nat Hum Behav.Google Scholar
  71. 71.
    Carpenter B, Gelman A, Hoffman M, Lee D, Goodrich B, Betancourt M, Brubaker MA, Li P, Riddell A (2016) Stan: a probabilistic programming language. J Stat Softw. 76(1)Google Scholar
  72. 72.
    Ancker JS, Senathrajah Y, Kukafka R, Starren JB (2006) Design features of graphs in health risk communication : a systematic review. J Am Med Informatics Assoc 13(6):608–619. CrossRefGoogle Scholar
  73. 73.
    Kay M, Kola T, Hullman JR, Munson SA (2016) When(ish) is my bus?: user-centered visualizations of uncertainty in everyday, mobile predictive systems. Proc ACM Conf Hum Factors Comput Syst (CHI 2016). 5092–103Google Scholar
  74. 74.
    Fernandes M, Walls L, Munson S, Hullman J, Kay M (2018) Uncertainty displays using quantile dotplots or CDFs improve transit decision-making. In: Proc ACM Conf Hum Factors Comput Syst (CHI 2018). p. To AppearGoogle Scholar
  75. 75.
    Scott SL, Varian HR (2014) Predicting the present with Bayesian structural time series. Int J Math Model Numer Optim. 5(1/2). doi:
  76. 76.
    Garcia-Retamero R, Cokely ET (2013) Communicating health risks with visual aids. Curr Dir Psychol Sci. 22(5):392–399CrossRefGoogle Scholar
  77. 77.
    Jung MF, Sirkin D, Gür TM, Steinert M (2015) Displayed uncertainty improves driving experience and behavior. Proc 33rd Annu ACM Conf Hum Factors Comput Syst - CHI ‘15. (April):2201–10Google Scholar
  78. 78.
    McShane BB, Gal D, Gelman A, Robert C, Tackett JL (2017) Abandon statistical significance. 1–12Google Scholar
  79. 79.
    Cummings P (2011) Arguments for and against standardized mean differences (effect sizes). Arch Pediatr Adolesc Med. 165(7):592–596CrossRefGoogle Scholar
  80. 80.
    Betancourt M (2018) Calibrating model-based inferences and decisions. 1–35Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of WashingtonSeattleUSA
  2. 2.University of MichiganAnn ArborUSA

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