Abstract
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.
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Notes
So long as one makes use of informed priors (as we advocate here) and/or applies a hierarchical modeling approach.
The widespread use of this null ritual in scientific fields is not without criticism [78]. Most pointedly, Gigerenzer went so far as to declare it a symptom of “mindless statistics” [69]. We will describe why we believe it is not applicable to small self-experiments but leave aside the question of its broader applicability to science.
Readers familiar with standardized effect sizes (like Cohen’s d) might ask why we do not use them here. Like Cummings [79], we believe that unstandardized effect sizes (e.g., mean differences) are easier to interpret, particularly for individual decision-making (a person should know what one point on a pain scale that they have used means to them; they are less likely to know what a difference of 1 standard deviation means).
We do not discuss the use of Bayes factors—one approach to Bayesian hypothesis testing—in this paper, as the sensitivity of Bayes factors to irrelevant details of the prior make them difficult even for experienced analysts to use in practice [80]. Instead, if hypothesis testing is desired, we prefer estimation-based approaches, such as regions of practical equivalence, which we believe are also easier to interpret. Regions of practical equivalence answer questions like “how likely is the effect to be 0 (or close enough to 0 that I will not care)?” [46, 80].
We used a variant of our Bayesian regression model with flat priors (i.e., priors in which all possible outcomes are equally likely, which is the implicit assumption a frequentist analysis makes) on the parameters to simulate the frequentist regression.
References
Global Status Report on Noncommunicable Diseases. Geneva: World Health Organization; 2014
Mamykina L, Mynatt ED, Kaufman DR (2006) Investigating health management practices of individuals with diabetes. Proc SIGCHI Conf Hum Factors Comput Syst - CHI ‘06. :927
Riggare S, Unruh KT, Sturr J, Domingos J (2017) Patient-driven n-of-1 in Parkinson’s disease. 123–8
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):1–8
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):235–239
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. 1143–52
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. 347–58
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):440–448
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. 6850–63
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):1–88
Gelman A, Weakliem D (2008) Of beauty, sex, and power: statistical challenges in estimating small effects. Am Sci 97(4):310–316
Gelman A, Carlin J (2014) Beyond power calculations: assessing type S (sign) and type M (magnitude) errors. Perspect Psychol Sci. 9(6):641–651
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. :4521–32
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. :1726–39
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. 557–66
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. 731–42
Mamykina L, Smaldone AM, Bakken SR (2015) Adopting the sensemaking perspective for chronic disease self-management. J Biomed Inform. 56:406–417
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. 1163–72
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):e203
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 1540–1551
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. 477–86
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. https://doi.org/10.1145/3196709.3196738
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. 1797–806
Fitbit [Internet]
Jawbone UpBand [Internet]
Larklife [Internet]
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). 261–78
Nike Fuelband [Internet]
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. 226–34
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. 475–84
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. 3207–16
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. 269–80
Morris M, Guilak F (2009) Mobile heart health: project highlight. IEEE Pervasive Comput. 8(2):57–61
Jorgensen JT (2009) New era of personalized medicine: a 10-year anniversary. Oncologist. 14(5):557–558
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):492–525
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):161–173
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):10
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):199–210
Larson EB (1990) N-of-1 clinical trials: a technique for improving medical therapeutics. West J Med 152(1):52–56
Barlow DH, Nock MK, Hersen M (2008) Single case experimental designs: strategies for studying behavior change. Third. Pearson; 416
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 16:67
PACO: The Personal Analytics Companion [Internet]
Tiralist - ohmage [Internet]
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. 46
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. 6837–49
Kruschke JK, Liddell TM (2017) The Bayesian new statistics : hypothesis testing, estimation, meta-analysis, and planning from a Bayesian perspective. Psychon Bull Rev. :1–29
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. https://doi.org/10.1080/19345747.2011.618213
Elsenbruch S (2011) Abdominal pain in irritable bowel syndrome: a review of putative psychological, neural and neuro-immune mechanisms. Brain Behav Immun. 25(3):386–394
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. 107:991–1000
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):37–45
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):122–132
Harris LR, Roberts L (2008) Treatments for irritable bowel syndrome: patients’ attitudes and acceptability. BMC Complement Altern Med. 8:65
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):192–200
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):667–672
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):108–115
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):212–219
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):159–166
American Gastroenterological Association. American Gastroenterological Association Medical Position Statement: Irritable Bowel Syndrome. Vol. 123, Gastroenterology. American Gastroenterology Association; p. 2105–72002
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 Preparation
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. 1–12
Eswaran S, Tack J, Chey WD (2011) Food: the forgotten factor in the irritable bowel syndrome. Gastroenterol Clin N Am 40(1):141–162
Loken E, Gelman A (2017) Measurement error and the replication crisis. Science (80- ). 355(6325):584–585
Wasserstein RL, Lazar NA (2016) The ASA’s statement on p -values: context, process, and purpose. Am Stat. 70(2):129–133
Walker E, Nowacki AS (2011) Understanding equivalence and noninferiority testing. J Gen Intern Med 26(2):192–196. https://doi.org/10.1007/s11606-010-1513-8
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):103–123
Hoekstra R, Morey RD, Rouder JN, Wagenmakers E-J (2014) Robust misinterpretation of confidence intervals. Psychon Bull Rev. 21(5):1157–1164
Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2013) Bayesian data analysis. Third Edit. Chapman and Hall/CRC; 675 p
Goldstein DG, Rothschild D (2014) Lay understanding of probability distributions. J Soc Judgm Decis Mak 9(1):1–14
Gigerenzer G (2004) Mindless statistics. J Socio Econ. 33(5):587–606
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.
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)
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. https://doi.org/10.1197/jamia.M2115.Introduction
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). 5092–103
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 Appear
Scott SL, Varian HR (2014) Predicting the present with Bayesian structural time series. Int J Math Model Numer Optim. 5(1/2). doi:https://doi.org/10.1504/IJMMNO.2014.059942
Garcia-Retamero R, Cokely ET (2013) Communicating health risks with visual aids. Curr Dir Psychol Sci. 22(5):392–399
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):2201–10
McShane BB, Gal D, Gelman A, Robert C, Tackett JL (2017) Abandon statistical significance. 1–12
Cummings P (2011) Arguments for and against standardized mean differences (effect sizes). Arch Pediatr Adolesc Med. 165(7):592–596
Betancourt M (2018) Calibrating model-based inferences and decisions. 1–35
Acknowledgements
We thank Eric B. Heckler and Roger Vilardaga for conversations that informed this research.
Funding
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.
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Schroeder, J., Karkar, R., Fogarty, J. et al. A Patient-Centered Proposal for Bayesian Analysis of Self-Experiments for Health. J Healthc Inform Res 3, 124–155 (2019). https://doi.org/10.1007/s41666-018-0033-x
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DOI: https://doi.org/10.1007/s41666-018-0033-x