Quantifyme: An open-source automated single-case experimental design platform

Sensors (Switzerland)

Volumn 18, Issue 4, 2018, Pages

  Taylor, Sara ^a   Sano, Akane ^a   Ferguson, Craig ^a   Mohan, Akshay ^a   Picard, Rosalind W ^a  

^a MIT MEDIA LAB   (United States)

Author keywords

Mobile health; Self experiment; Self tracking; Single case experimental design; Wearable sensors

Indexed keywords

MHEALTH; OPEN SOURCE SOFTWARE; SLEEP RESEARCH; SMARTPHONES; STATISTICS; WEARABLE SENSORS;

ADMINISTRATIVE COST; DATA COLLECTION; HUMAN HEALTH; LEISURE TIME; OPEN SOURCES; PILOT STUDIES; SELF-TRACKING; SLEEP EFFICIENCIES;

DESIGN OF EXPERIMENTS;

EID: 85045100671     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s18041097     Document Type: Article

References (56)

1. IDC. IDC Forecasts Wearables Shipments to Reach 213.6 Million Units. 2016. Available online: http://www. idc.com/getdoc.jsp?containerId=prUS41530816 (accessed on 29 September 2017).
2. IDC. Worldwide Mobile Phone Forecast, 2016-2020. Available online: https://www.idc.com/getdoc.jsp? containerId=US41084116 (accessed on 29 September 2017).
3. Higgins, J.P. Smartphone applications for patients’ health and fitness. Am. J. Med. 2016, 129, 11-19.
4. Poushter, J. Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies; Pew Research Center: Washington, DC, USA, 2016.
5. Epstein, D.A. Personal informatics in everyday life. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, Osaka, Japan, 7-11 September 2015; pp. 429-434.
6. CDC. Why Walk? Why Not! 2017. Available online: https://www.cdc.gov/physicalactivity/walking/(accessed on 29 January 2017).
7. Science, L. The Truth About 10,000 Steps a Day. 2017. Available online: http://www.livescience.com/43956-walking-10000-steps-healthy.html (accessed on 29 January 2017).
8. Bearder, M. Stephen Wolfram on Personal Analytics. MIT Technol. Rev. July/August 2013. Available online: https://www.technologyreview.com/s/514356/stephen-wolfram-on-personal-analytics/(accessed on 2 April 2018).
9. Swan, M. The quantified self: Fundamental disruption in big data science and biological discovery. Big Data 2013, 1, 85-99.
10. Choe, E.K.; Lee, N.B.; Lee, B.; Pratt, W.; Kientz, J.A. Understanding quantified-selfers practices in collecting and exploring personal data. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, Toronto, ON, Canada, 26 April-1 May 2014; pp. 1143-1152.
11. Mohan, A.; Picard, R. Health0: A new health and lifestyle management paradigm. Stud. Health Technol. Inform. 2003, 108, 43-48.
12. Pentland, A. Healthwear: Medical technology becomes wearable. Computer 2004, 37, 42-49.
13. Eslick, I.S. Crowdsourcing Health Discoveries: From Anecdotes to Aggregated Self-Experiments. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 2013.
14. FitBit. FitBit App. 2017. Available online: https://www.fitbit.com/app (accessed on 29 January 2017).
15. Jawbone. Jawbone Up App. 2017. Available online: https://jawbone.com/(accessed on 30 April 2017).
16. Withings, I. Withings Steel HR Watch. 2017. Available online: https://www.withings.com/es/en/products/steel-hr (accessed on 30 April 2017).
17. Choudhury, T.; Consolvo, S.; Harrison, B.; Hightower, J.; LaMarca, A.; LeGrand, L.; Rahimi, A.; Rea, A.; Bordello, G.; Hemingway, B.; et al. The mobile sensing platform: An embedded activity recognition system. IEEE Pervasive Comput. 2008, 7, doi:10.1109/MPRV.2008.39.
18. Lester, J.; Choudhury, T.; Borriello, G. A practical approach to recognizing physical activities. In International Conference on Pervasive Computing; Springer: Berlin/Heidelberg, Germany, 2006; pp. 1-16.
19. MyFitnessPal Inc. 2017. Available online: https://www.myfitnesspal.com/(accessed on 29 January 2017).
20. Cordeiro, F.; Epstein, D.A.; Thomaz, E.; Bales, E.; Jagannathan, A.K.; Abowd, G.D.; Fogarty, J. Barriers and negative nudges: Exploring challenges in food journaling. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea, 18-23 April 2015; pp. 1159-1162.
21. Thomaz, E.; Essa, I.; Abowd, G.D. A practical approach for recognizing eating moments with wrist-mounted inertial sensing. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 7-11 September 2015; pp. 1029-1040.
22. Bedri, A.; Verlekar, A.; Thomaz, E.; Avva, V.; Starner, T. A wearable system for detecting eating activities with proximity sensors in the outer ear. In Proceedings of the 2015 ACM International Symposium on Wearable Computers, Osaka, Japan, 7-11 September 2015; pp. 91-92.
23. Kay, M.; Choe, E.K.; Shepherd, J.; Greenstein, B.; Watson, N.; Consolvo, S.; Kientz, J.A. Lullaby: A capture & access system for understanding the sleep environment. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, Pennsylvania, 5-8 September 2012; pp. 226-234.
24. Min, J.K.; Doryab, A.; Wiese, J.; Amini, S.; Zimmerman, J.; Hong, J.I. Toss’n’turn: Smartphone as sleep and sleep quality detector. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, ON, Canada, 26 April 26-1 May 2014; pp. 477-486.
25. Hao, T.; Xing, G.; Zhou, G. iSleep: Unobtrusive sleep quality monitoring using smartphones. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, Roma, Italy, 11-15 November 2013; p. 4.
26. Team, U. Sleep as Android. 2017. Available online: https://play.google.com/store/apps/details?id=com. urbandroid.sleep&hl=en (accessed on 29 January 2017).
27. Ali, A.A.; Hossain, S.M.; Hovsepian, K.; Rahman, M.M.; Plarre, K.; Kumar, S. mPuff: Automated detection of cigarette smoking puffs from respiration measurements. In Proceedings of the ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN), Beijing, China, 16-20 April 2012; pp. 269-280.
28. Sano, A. Measuring College Students’ Sleep, Stress, Mental Health and Wellbeing with Wearable Sensors and Mobile Phones. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 2016.
29. Hernandez Rivera, J. Towards Wearable Stress Measurement. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 2015.
30. Morris, M.; Guilak, F. Mobile heart health: Project highlight. IEEE Pervasive Comput. 2009, 8, doi:10.1109/MPRV.2009.31.
31. Madan, A.; Moturu, S.T.; Lazer, D.; Pentland, A.S. Social sensing: Obesity, unhealthy eating and exercise in face-to-face networks. Wirel. Health 2010, 104-110, doi:10.1145/1921081.1921094.
32. Rooksby, J.; Rost, M.; Morrison, A.; Chalmers, M.C. Personal tracking as lived informatics. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems, Toronto, ON, Canada, 26 April-1 May 2014; pp. 1163-1172.
33. Asimakopoulos, S.; Asimakopoulos, G.; Spillers, F. Motivation and User Engagement in Fitness Tracking: Heuristics for Mobile Healthcare Wearables. Informatics 2017, 4, 5, doi:10.3390/informatics4010005.
34. Gouveia, R.; Karapanos, E.; Hassenzahl, M. How do we engage with activity trackers? A longitudinal study of Habito. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 7-11 September 2015; pp. 1305-1316.
35. Harrison, D.; Marshall, P.; Bianchi-Berthouze, N.; Bird, J. Activity tracking: Barriers, workarounds and customisation. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 7-11 September 2015; pp. 617-621.
36. Evans, B. PACO Personal Analytics Companion. 2017. Available online: https://quantifiedself.appspot.com/(accessed on 29 January 2017).
37. Daskalova, N.; Metaxa-Kakavouli, D.; Tran, A.; Nugent, N.; Boergers, J.; McGeary, J.; Huang, J. SleepCoacher: A Personalized Automated Self-Experimentation System for Sleep Recommendations. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology, Tokyo, Japan, 16-19 October 2016; pp. 347-358.
38. Karkar, R.; Schroeder, J.; Epstein, D.A.; Pina, L.R.; Scofield, J.; Fogarty, J.; Kientz, J.A.; Munson, S.A.; Vilardaga, R.; Zia, J. TummyTrials: A Feasibility Study of Using Self-Experimentation to Detect Individualized Food Triggers. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver, CO, USA, 6-11 May 2017; pp. 6850-6863.
39. Karkar, R.; Zia, J.; Vilardaga, R.; Mishra, S.R.; Fogarty, J.; Munson, S.A.; Kientz, J.A. A framework for self-experimentation in personalized health. J. Am. Med. Inform. Assoc. 2015, 23, 440-448.
40. Daskalova, N.; Desingh, K.; Papoutsaki, A.; Schulze, D.; Sha, H.; Huang, J. Lessons Learned from Two Cohorts of Personal Informatics Self-Experiments. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2017, 1, 1-22.
41. Meldrum, M.L. A brief history of the randomized controlled trial: From oranges and lemons to the gold standard. Hematol./Oncol. Clin. N. Am. 2000, 14, 745-760.
42. Barlow, D.H.; Hersen, M. Single Case Experimental Designs; Pergamon Press: Oxford, UK, 1984.
43. Neuringer, A. Self-experimentation: A call for change. Behaviorism 1981, 9, 79-94.
44. Lillie, E.O.; Patay, B.; Diamant, J.; Issell, B.; Topol, E.J.; Schork, N.J. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Pers. Med. 2011, 8, 161-173.
45. Kratochwill, T.R.; Hitchcock, J.H.; Horner, R.H.; Levin, J.R.; Odom, S.L.; Rindskopf, D.M.; Shadish, W.R. Single-case intervention research design standards. Remedial Spec. Educ. 2013, 34, 26-38.
46. Newman, F.L.; Wong, S.E. Progress and outcome assessment of individual patient data: Selecting single subject design and statistical procedures. In The Use of Psychological Testing for Treatment Planning & Outcome Assessment, 3rd ed.; Lawrence Erlbaum Associates: Mahwah, NJ, USA, 2004; Volume 1, pp. 273-289.
47. Van, d.N.; Onghena, P. Combining single-case experimental data using hierarchical linear models. Sch. Psychol. Q. 2003, 18, 325-346.
48. Anderson, K.; Burford, O.; Emmerton, L. Mobile Health Apps to Facilitate Self-Care: A Qualitative Study of User Experiences. PLoS ONE 2016, 11, e0156164, doi:10.1371/journal.pone.0156164.
49. Cohen, S.; Kamarck, T.; Mermelstein, R. A global measure of perceived stress. J. Health Soc. Behav. 1983, 24, 385-396.
50. John, O.P.; Srivastava, S. The Big Five trait taxonomy: History, measurement, and theoretical perspectives. Handb. Personal. Theory Res. 1999, 2, 102-138.
51. Brooke, J. SUS-A quick and dirty usability scale. Usabil. Evaluation Ind. 1996, 189, 4-7.
52. Srivastava, S.; John, O.P.; Gosling, S.D.; Potter, J. Development of personality in early and middle adulthood: Set like plaster or persistent change? J. Personal. Soc. Psychol. 2003, 84, 1041-1053.
53. Sauro, J. SUStisfied? Little-Known System Usability Scale Facts. 2011. Available online: http://uxpamagazine. org/sustified/(accessed on 12 February 2018).
54. Hilliard, R.C.; Brewer, B.W.; Cornelius, A.E.; Van Raalte, J.L. Big Five Personality Characteristics and Adherence to Clinic-Based Rehabilitation Activities after ACL Surgery: A Prospective Analysis. Open Rehabil. J. 2014, 7, 1-5.
55. Wu, J.R.; Song, E.K.; Moser, D.K. Type D personality, self-efficacy, and medication adherence in patients with heart failure—A mediation analysis. Heart Lung J. Acute Crit. Care 2015, 44, 276-281.
56. Halko, S.; Kientz, J.A. Personality and Persuasive Technology: An Exploratory Study on Health-Promoting Mobile Applications. In Persuasive Technology; Springer: Berlin/Heidelberg, Germany, 2010; pp. 150-161.