{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:05:07Z","timestamp":1772136307824,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,5]],"date-time":"2018-04-05T00:00:00Z","timestamp":1522886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MIT MindHandHeart Innovation Fund"},{"name":"MIT Media Lab Consortium"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Smartphones and wearable sensors have enabled unprecedented data collection, with many products now providing feedback to users about recommended step counts or sleep durations. However, these recommendations do not provide personalized insights that have been shown to be best suited for a specific individual. A scientific way to find individualized recommendations and causal links is to conduct experiments using single-case experimental design; however, properly designed single-case experiments are not easy to conduct on oneself. We designed, developed, and evaluated a novel platform, QuantifyMe, for novice self-experimenters to conduct proper-methodology single-case self-experiments in an automated and scientific manner using their smartphones. We provide software for the platform that we used (available for free on GitHub), which provides the methodological elements to run many kinds of customized studies. In this work, we evaluate its use with four different kinds of personalized investigations, examining how variables such as sleep duration and regularity, activity, and leisure time affect personal happiness, stress, productivity, and sleep efficiency. We conducted a six-week pilot study (N = 13) to evaluate QuantifyMe. We describe the lessons learned developing the platform and recommendations for its improvement, as well as its potential for enabling personalized insights to be scientifically evaluated in many individuals, reducing the high administrative cost for advancing human health and wellbeing.<\/jats:p>","DOI":"10.3390\/s18041097","type":"journal-article","created":{"date-parts":[[2018,4,5]],"date-time":"2018-04-05T16:50:58Z","timestamp":1522947058000},"page":"1097","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["QuantifyMe: An Open-Source Automated Single-Case Experimental Design Platform"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4133-9230","authenticated-orcid":false,"given":"Sara","family":"Taylor","sequence":"first","affiliation":[{"name":"Affective Computing Group, MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akane","family":"Sano","sequence":"additional","affiliation":[{"name":"Affective Computing Group, MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Craig","family":"Ferguson","sequence":"additional","affiliation":[{"name":"Affective Computing Group, MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akshay","family":"Mohan","sequence":"additional","affiliation":[{"name":"Affective Computing Group, MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rosalind","family":"Picard","sequence":"additional","affiliation":[{"name":"Affective Computing Group, MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,5]]},"reference":[{"key":"ref_1","unstructured":"IDC (2017, September 29). IDC Forecasts Wearables Shipments to Reach 213.6 Million Units. Available online: http:\/\/www.idc.com\/getdoc.jsp?containerId=prUS41530816."},{"key":"ref_2","unstructured":"IDC (2017, September 29). Worldwide Mobile Phone Forecast, 2016\u20132020. Available online: https:\/\/www.idc.com\/getdoc.jsp?containerId=US41084116."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.amjmed.2015.05.038","article-title":"Smartphone applications for patients\u2019 health and fitness","volume":"129","author":"Higgins","year":"2016","journal-title":"Am. J. Med."},{"key":"ref_4","unstructured":"Poushter, J. (2016). Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies, Pew Research Center."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Epstein, D.A. (2015, January 7\u201311). Personal informatics in everyday life. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium onWearable Computers, Osaka, Japan.","DOI":"10.1145\/2800835.2801643"},{"key":"ref_6","unstructured":"CDC (2017, January 29). Why Walk? Why Not!, Available online: https:\/\/www.cdc.gov\/physicalactivity\/walking\/."},{"key":"ref_7","unstructured":"Science, L. (2017, January 29). The Truth About 10,000 Steps a Day. Available online: http:\/\/www.livescience.com\/43956-walking-10000-steps-healthy.html."},{"key":"ref_8","unstructured":"Bearder, M. (2018, April 02). Stephen Wolfram on Personal Analytics. Available online: https:\/\/www.technologyreview.com\/s\/514356\/stephen-wolfram-on-personal-analytics\/."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1089\/big.2012.0002","article-title":"The quantified self: Fundamental disruption in big data science and biological discovery","volume":"1","author":"Swan","year":"2013","journal-title":"Big Data"},{"key":"ref_10","unstructured":"Choe, E.K., Lee, N.B., Lee, B., Pratt, W., and Kientz, J.A. (May, January 26). Understanding quantified-selfers practices in collecting and exploring personal data. Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, Toronto, ON, Canada."},{"key":"ref_11","first-page":"43","article-title":"Health0: A new health and lifestyle management paradigm","volume":"108","author":"Mohan","year":"2003","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MC.2004.1297238","article-title":"Healthwear: Medical technology becomes wearable","volume":"37","author":"Pentland","year":"2004","journal-title":"Computer"},{"key":"ref_13","unstructured":"Eslick, I.S. (2013). Crowdsourcing Health Discoveries: From Anecdotes to Aggregated Self-Experiments. [Ph.D. Thesis, Massachusetts Institute of Technology]."},{"key":"ref_14","unstructured":"FitBit (2017, January 29). FitBit App. Available online: https:\/\/www.fitbit.com\/app."},{"key":"ref_15","unstructured":"Jawbone (2017, April 30). Jawbone Up App. Available online: https:\/\/jawbone.com\/."},{"key":"ref_16","unstructured":"Withings, I. (2017, April 30). Withings Steel HR Watch. Available online: https:\/\/www.withings.com\/es\/en\/products\/steel-hr."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Choudhury, T., Consolvo, S., Harrison, B., Hightower, J., LaMarca, A., LeGrand, L., Rahimi, A., Rea, A., Bordello, G., and Hemingway, B. (2008). The mobile sensing platform: An embedded activity recognition system. IEEE Pervasive Comput., 7.","DOI":"10.1109\/MPRV.2008.39"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lester, J., Choudhury, T., and Borriello, G. (2006). A practical approach to recognizing physical activities. International Conference on Pervasive Computing, Springer.","DOI":"10.1007\/11748625_1"},{"key":"ref_19","unstructured":"(2017, January 29). MyFitnessPal Inc.. Available online: https:\/\/www.myfitnesspal.com\/."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cordeiro, F., Epstein, D.A., Thomaz, E., Bales, E., Jagannathan, A.K., Abowd, G.D., and Fogarty, J. (2015, January 18\u201323). Barriers and negative nudges: Exploring challenges in food journaling. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea.","DOI":"10.1145\/2702123.2702155"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Thomaz, E., Essa, I., and Abowd, G.D. (2015, January 7\u201311). A practical approach for recognizing eating moments with wrist-mounted inertial sensing. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan.","DOI":"10.1145\/2750858.2807545"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bedri, A., Verlekar, A., Thomaz, E., Avva, V., and Starner, T. (2015, January 7\u201311). A wearable system for detecting eating activities with proximity sensors in the outer ear. Proceedings of the 2015 ACM International Symposium on Wearable Computers, Osaka, Japan.","DOI":"10.1145\/2818346.2820767"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kay, M., Choe, E.K., Shepherd, J., Greenstein, B., Watson, N., Consolvo, S., and Kientz, J.A. (2012, January 5\u20138). Lullaby: A capture & access system for understanding the sleep environment. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, Pennsylvania.","DOI":"10.1145\/2370216.2370253"},{"key":"ref_24","unstructured":"Min, J.K., Doryab, A., Wiese, J., Amini, S., Zimmerman, J., and Hong, J.I. (26\u20131, January 26). Toss\u2019n\u2019turn: Smartphone as sleep and sleep quality detector. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, ON, Canada."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hao, T., Xing, G., and Zhou, G. (2013, January 11\u201315). iSleep: Unobtrusive sleep quality monitoring using smartphones. Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, Roma, Italy.","DOI":"10.1145\/2517351.2517359"},{"key":"ref_26","unstructured":"Team, U. (2017, January 29). Sleep as Android. Available online: https:\/\/play.google.com\/store\/apps\/details?id=com.urbandroid.sleep&hl=en."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ali, A.A., Hossain, S.M., Hovsepian, K., Rahman, M.M., Plarre, K., and Kumar, S. (2012, January 16\u201320). mPuff: Automated detection of cigarette smoking puffs from respiration measurements. Proceedings of the ACM\/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN), Beijing, China.","DOI":"10.1145\/2185677.2185741"},{"key":"ref_28","unstructured":"Sano, A. (2016). Measuring College Students\u2019 Sleep, Stress, Mental Health and Wellbeing with Wearable Sensors and Mobile Phones. [Ph.D. Thesis, Massachusetts Institute of Technology]."},{"key":"ref_29","unstructured":"Hernandez Rivera, J. (2015). Towards Wearable Stress Measurement. [Ph.D. Thesis, Massachusetts Institute of Technology]."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Morris, M., and Guilak, F. (2009). Mobile heart health: Project highlight. IEEE Pervasive Comput., 8.","DOI":"10.1109\/MPRV.2009.31"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Madan, A., Moturu, S.T., Lazer, D., and Pentland, A.S. (2010). Social sensing: Obesity, unhealthy eating and exercise in face-to-face networks. Wirel. Health, 104\u2013110.","DOI":"10.1145\/1921081.1921094"},{"key":"ref_32","unstructured":"Rooksby, J., Rost, M., Morrison, A., and Chalmers, M.C. (May, January 26). Personal tracking as lived informatics. Proceedings of the 32nd annual ACM conference on Human factors in computing systems, Toronto, ON, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Asimakopoulos, S., Asimakopoulos, G., and Spillers, F. (2017). Motivation and User Engagement in Fitness Tracking: Heuristics for Mobile Healthcare Wearables. Informatics, 4.","DOI":"10.3390\/informatics4010005"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gouveia, R., Karapanos, E., and Hassenzahl, M. (2015, January 7\u201311). How do we engage with activity trackers? A longitudinal study of Habito. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan.","DOI":"10.1145\/2750858.2804290"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Harrison, D., Marshall, P., Bianchi-Berthouze, N., and Bird, J. (2015, January 7\u201311). Activity tracking: Barriers, workarounds and customisation. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan.","DOI":"10.1145\/2750858.2805832"},{"key":"ref_36","unstructured":"Evans, B. (2017, January 29). PACO Personal Analytics Companion. Available online: https:\/\/quantifiedself.appspot.com\/."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Daskalova, N., Metaxa-Kakavouli, D., Tran, A., Nugent, N., Boergers, J., McGeary, J., and Huang, J. (2016, January 16\u201319). SleepCoacher: A Personalized Automated Self-Experimentation System for Sleep Recommendations. Proceedings of the 29th Annual Symposium on User Interface Software and Technology, Tokyo, Japan.","DOI":"10.1145\/2984511.2984534"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Karkar, R., Schroeder, J., Epstein, D.A., Pina, L.R., Scofield, J., Fogarty, J., Kientz, J.A., Munson, S.A., Vilardaga, R., and Zia, J. (2017, January 6\u201311). TummyTrials: A Feasibility Study of Using Self-Experimentation to Detect Individualized Food Triggers. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver, CO, USA.","DOI":"10.1145\/3025453.3025480"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1093\/jamia\/ocv150","article-title":"A framework for self-experimentation in personalized health","volume":"23","author":"Karkar","year":"2015","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3130911","article-title":"Lessons Learned from Two Cohorts of Personal Informatics Self-Experiments","volume":"1","author":"Daskalova","year":"2017","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/S0889-8588(05)70309-9","article-title":"A brief history of the randomized controlled trial: From oranges and lemons to the gold standard","volume":"14","author":"Meldrum","year":"2000","journal-title":"Hematol.\/Oncol. Clin. N. Am."},{"key":"ref_42","unstructured":"Barlow, D.H., and Hersen, M. (1984). Single Case Experimental Designs, Pergamon Press."},{"key":"ref_43","first-page":"79","article-title":"Self-experimentation: A call for change","volume":"9","author":"Neuringer","year":"1981","journal-title":"Behaviorism"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"161","DOI":"10.2217\/pme.11.7","article-title":"The n-of-1 clinical trial: the ultimate strategy for individualizing medicine?","volume":"8","author":"Lillie","year":"2011","journal-title":"Pers. Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1177\/0741932512452794","article-title":"Single-case intervention research design standards","volume":"34","author":"Kratochwill","year":"2013","journal-title":"Remedial Spec. Educ."},{"key":"ref_46","first-page":"273","article-title":"Progress and outcome assessment of individual patient data: Selecting single subject design and statistical procedures","volume":"Volume 1","author":"Newman","year":"2004","journal-title":"The Use of Psychological Testing for Treatment Planning & Outcome Assessment"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1521\/scpq.18.3.325.22577","article-title":"Combining single-case experimental data using hierarchical linear models","volume":"18","author":"Van","year":"2003","journal-title":"Sch. Psychol. Q."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Anderson, K., Burford, O., and Emmerton, L. (2016). Mobile Health Apps to Facilitate Self-Care: A Qualitative Study of User Experiences. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0156164"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"385","DOI":"10.2307\/2136404","article-title":"A global measure of perceived stress","volume":"24","author":"Cohen","year":"1983","journal-title":"J. Health Soc. Behav."},{"key":"ref_50","first-page":"102","article-title":"The Big Five trait taxonomy: History, measurement, and theoretical perspectives","volume":"2","author":"John","year":"1999","journal-title":"Handb. Personal. Theory Res."},{"key":"ref_51","first-page":"4","article-title":"SUS-A quick and dirty usability scale","volume":"189","author":"Brooke","year":"1996","journal-title":"Usabil. Evaluation Ind."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1037\/0022-3514.84.5.1041","article-title":"Development of personality in early and middle adulthood: Set like plaster or persistent change?","volume":"84","author":"Srivastava","year":"2003","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_53","unstructured":"Sauro, J. (2018, February 12). SUStisfied? Little-Known System Usability Scale Facts. Available online: http:\/\/uxpamagazine.org\/sustified\/."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2174\/1874943701407010001","article-title":"Big Five Personality Characteristics and Adherence to Clinic-Based Rehabilitation Activities after ACL Surgery: A Prospective Analysis","volume":"7","author":"Hilliard","year":"2014","journal-title":"Open Rehabil. J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.hrtlng.2015.03.006","article-title":"Type D personality, self-efficacy, and medication adherence in patients with heart failure\u2014A mediation analysis","volume":"44","author":"Wu","year":"2015","journal-title":"Heart Lung J. Acute Crit. Care"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Halko, S., and Kientz, J.A. (2010). Personality and Persuasive Technology: An Exploratory Study on Health-Promoting Mobile Applications. Persuasive Technology, Springer.","DOI":"10.1007\/978-3-642-13226-1_16"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1097\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:59:41Z","timestamp":1760194781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1097"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,5]]},"references-count":56,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["s18041097"],"URL":"https:\/\/doi.org\/10.3390\/s18041097","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,5]]}}}