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Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile Sensing

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Abstract

Recent years have seen a growth in the spread of digital technologies for self-tracking and personal informatics. Smartphones‚ in particular, stand out as being an ideal self-tracking technology that permits both active logging (via self-reports) and passive tracking of information (via phone logs and mobile sensors). In this chapter, we present the results of a literature review of smartphone-based personal informatics studies across three different disciplinary databases (computer science, psychology, and communication). In doing so, we propose a conceptual framework for organizing the smartphone-based personal informatics literature. Our framework situates self-tracking studies based on their substantive focus across two domains: (1) the measurement domain (whether the study uses subjective or objective data) and (2) the outcome of interest domain (whether the study aims to promote insight or change in physical and/or mental characteristics). We use this framework to identify and discuss research trends and gaps in the literature. For example, most research has been concentrated on tracking of objective measurements to change either physical or mental characteristics, while less research used subjective measures to study a physical outcome of interest. We conclude by pointing to promising future directions for research on self-tracking and personal informatics and emphasize the need for a greater appreciation of individual differences in future self-tracking research.

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References

  • Abney A, White B, Glick J, Bermudez A, Breckow P, Yow J, Heath P et al (2014) Evaluation of recording methods for user test sessions on mobile devices. In: Proceedings of the first ACM SIGCHI annual symposium on Computer-human interaction in play. ACM, pp 1–8

    Google Scholar 

  • Ajzen I (1985) From intentions to actions: a theory of planned behavior. In: Action control. Springer, Berlin, pp 11–39

    Google Scholar 

  • Athukorala K, Lagerspetz E, Von Kügelgen M, Jylhä A, Oliner AJ, Tarkoma S, Jacucci G (2014) How carat affects user behavior: implications for mobile battery awareness applications. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 1029–1038

    Google Scholar 

  • Bai Y, Xu B, Jiang S, Yang H, Cui J (2013) Can you form healthy habit?: predicting habit forming states through mobile phone. In: Proceedings of the 8th international conference on body area networks. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp 144–147

    Google Scholar 

  • Bandura A (2004) Health promotion by social cognitive means. Health Educ Behav 31(2):143–164

    Article  Google Scholar 

  • Barbarin AM, Saslow LR, Ackerman MS, Veinot TC (2018) Toward health information technology that supports overweight/obese women in addressing emotion-and stress-related eating. In: Proceedings of the 2018 CHI conference on human factors in computing systems. ACM, p 321

    Google Scholar 

  • Bentley F, Tollmar K (2013) The power of mobile notifications to increase wellbeing logging behavior. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 1095–1098

    Google Scholar 

  • Bentley F, Tollmar K, Stephenson P, Levy L, Jones B, Robertson S, Wilson J et al (2013) Health Mashups: presenting statistical patterns between wellbeing data and context in natural language to promote behavior change. ACM Trans Comput-Human Interact (TOCHI) 20(5):30

    Google Scholar 

  • Bexheti A, Fedosov A, Findahl J, Langheinrich M, Niforatos E (2015) Re-live the moment: visualizing run experiences to motivate future exercises. In: Proceedings of the 17th international conference on human-computer interaction with mobile devices and services adjunct. ACM, pp 986–993

    Google Scholar 

  • Bickmore TW, Kimani E, Trinh H, Pusateri A, Paasche-Orlow MK, Magnani JW (2018) Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th international conference on intelligent virtual agents. ACM, pp 119–124

    Google Scholar 

  • Brewer RS, Verdezoto N, Holst T, Rasmussen MK (2015) Tough shift: exploring the complexities of shifting residential electricity use through a casual mobile game. In: Proceedings of the 2015 annual symposium on computer-human interaction in play. ACM, pp 307–317

    Google Scholar 

  • Campbell AT, Lane ND (2013) Smartphone sensing: a game changer for behavioral science. Workshop held at the summer institute for social and personality psychology. The University of California, Davis

    Google Scholar 

  • Canzian L, Musolesi M (2015) Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 1293–1304

    Google Scholar 

  • Chaudhry BM, Schaefbauer C, Jelen B, Siek KA, Connelly K (2016) Evaluation of a food portion size estimation interface for a varying literacy population. In: Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, pp 5645–5657

    Google Scholar 

  • Chen Y, Randriambelonoro M, Geissbuhler A, Pu P (2016) Social Incentives in pervasive fitness apps for obese and diabetic patients. In: Proceedings of the 19th ACM conference on computer supported cooperative work and social computing companion. ACM, pp 245–248

    Google Scholar 

  • Choi HS, Lee HK, Ha JC (2012) The influence of smartphone addiction on mental health, campus life and personal relations-focusing on K university students. J Korean Data Inf Sci Soc 23(5):1005–1015

    Google Scholar 

  • Choudhury T, Borriello G, Consolvo S, Haehnel D, Harrison B, Hemingway B, LeGrand L et al (2008) The mobile sensing platform: an embedded activity recognition system. IEEE Pervasive Comput 7(2):32–41

    Google Scholar 

  • Ciman M, Donini M, Gaggi O, Aiolli F (2016) Stairstep recognition and counting in a serious game for increasing users’ physical activity. Pers Ubiquit Comput 20(6):1015–1033

    Article  Google Scholar 

  • Cuttone A, Larsen JE (2014) The long tail issue in large scale deployment of personal informatics. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing: adjunct publication. ACM, pp 691–694

    Google Scholar 

  • Di Lascio E, Gashi S, Krasic D, Santini S (2017) In-classroom self-tracking for teachers and students: preliminary findings from a pilot study. In: Proceedings of the 2017 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2017 ACM international symposium on wearable computers. ACM, pp 865–870

    Google Scholar 

  • Doryab A, Frost M, Faurholt-Jepsen M, Kessing LV, Bardram JE (2015) Impact factor analysis: combining prediction with parameter ranking to reveal the impact of behavior on health outcome. Pers Ubiquit Comput 19(2):355–365

    Article  Google Scholar 

  • Du H, Youngblood GM, Pirolli P (2014) Efficacy of a smartphone system to support groups in behavior change programs. In: Proceedings of the wireless health 2014 on national institutes of health. ACM, pp 1–8

    Google Scholar 

  • Du J, Wang Q, de Baets L, Markopoulos P (2017) Supporting shoulder pain prevention and treatment with wearable technology. In: Proceedings of the 11th EAI international conference on pervasive computing technologies for healthcare. ACM, pp 235–243

    Google Scholar 

  • Epstein DA, Ping A, Fogarty J, Munson SA (2015) A lived informatics model of personal informatics. In: Proceedings of the UbiComp 2015 international joint conference on pervasive and ubiquitous computing. ACM, New York

    Google Scholar 

  • Fang B, Xu Q, Park T, Zhang M (2016) AirSense: an intelligent home-based sensing system for indoor air quality analytics. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 109–119

    Google Scholar 

  • Fox S, Duggan M (2013) Tracking for health. Available from http://www.pewinternet.org/2013/01/28/tracking-for-health

  • Fujiki Y, Kazakos K, Puri C, Pavlidis I, Starren J, Levine J (2007) NEAT-o-games: ubiquitous activity-based gaming. In: CHI2007 extended abstracts on Human factors in computing systems. ACM, pp 2369–2374

    Google Scholar 

  • Gerson J, Plagnol AC, Corr PJ (2017) Passive and active facebook use measure (PAUM): validation and relationship to the reinforcement sensitivity theory. Personality Individ Differ 117:81–90

    Article  Google Scholar 

  • Glanz K, Rimer BK, Viswanath K (eds) (2008) Health behavior and health education: theory, research, and practice. Wiley, New York

    Google Scholar 

  • Götz FM, Stieger S, Reips UD (2017) Users of the main smartphone operating systems (iOS, Android) differ only little in personality. PLoS ONE 12(5):e0176921

    Article  Google Scholar 

  • Gouveia R, Karapanos E, Hassenzahl M (2015) 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. ACM, pp 1305–1316

    Google Scholar 

  • Greis M, Dingler T, Schmidt A, Schmandt C (2017) Leveraging user-made predictions to help understand personal behavior patterns. In: Proceedings of the 19th international conference on human-computer interaction with mobile devices and services. ACM, p 104

    Google Scholar 

  • Grimes A, Kantroo V, Grinter RE (2010) Let’s play!: mobile health games for adults. In: Proceedings of the 12th ACM international conference on Ubiquitous computing. ACM, pp 241–250

    Google Scholar 

  • Gui X, Chen Y, Caldeira C, Xiao D, Chen Y (2017) When fitness meets social networks: investigating fitness tracking and social practices on werun. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM, pp 1647–1659

    Google Scholar 

  • Gweon G, Kim B, Kim J, Lee KJ, Rhim J, Choi J (2018) MABLE: mediating young children’s smart media usage with augmented reality. In: Proceedings of the 2018 CHI conference on human factors in computing systems. ACM, p 13

    Google Scholar 

  • Harari GM, Gosling SD, Wang R, Chen F, Chen Z, Campbell AT (2017a) Patterns of behavior change in students over an academic term: A preliminary study of activity and sociability behaviors using smartphone sensing methods. Comput Hum Behav 67:129–138

    Article  Google Scholar 

  • Harari GM, Müller SR, Aung MS, Rentfrow PJ (2017b) Smartphone sensing methods for studying behavior in everyday life. Curr Opin Behav Sci 18:83–90

    Article  Google Scholar 

  • Harari GM, Müller SR, Gosling SD (2018) Naturalistic assessment of situations using mobile sensing methods. In: The Oxford handbook of psychological situations

    Google Scholar 

  • Henrich J, Heine SJ, Norenzayan A (2010) The weirdest people in the world? Behav Brain Sci 33(2–3):61–83

    Article  Google Scholar 

  • Hirano SH, Farrell RG, Danis CM, Kellogg WA (2013) WalkMinder: encouraging an active lifestyle using mobile phone interruptions. In: CHI2013 extended abstracts on human factors in computing systems. ACM, pp 1431–1436

    Google Scholar 

  • Hollis V, Konrad A, Springer A, Antoun M, Antoun C, Martin R, Whittaker S (2017) What does all this data mean for my future mood? actionable analytics and targeted reflection for emotional well-being. Human–Computer Interact 32(5–6):208–267

    Article  Google Scholar 

  • Hsu A, Yang J, Yilmaz YH, Haque MS, Can C, Blandford AE (2014) Persuasive technology for overcoming food cravings and improving snack choices. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 3403–3412

    Google Scholar 

  • Huang Y, Xiong H, Leach K, Zhang Y, Chow P, Fua K, Barnes LE et al (2016) Assessing social anxiety using GPS trajectories and point-of-interest data. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 898–903

    Google Scholar 

  • Hwang C, Pushp S (2018) A mobile system for investigating the user’s stress causes in daily life. In: Proceedings of the 2018 ACM international joint conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computers. ACM, pp 66–69

    Google Scholar 

  • Johansen B, Petersen MK, Pontoppidan NH, Sandholm P, Larsen JE (2017) Rethinking hearing aid fitting by learning from behavioral patterns. In: Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems. ACM, pp 1733–1739

    Google Scholar 

  • Jylhä A, Nurmi P, Sirén M, Hemminki S, Jacucci G (2013) Matkahupi: a persuasive mobile application for sustainable mobility. In: Proceedings of the 2013 ACM conference on pervasive and ubiquitous computing adjunct publication. ACM,pp 227–230

    Google Scholar 

  • Kadomura A, Li CY, Tsukada K, Chu HH, Siio I (2014) Persuasive technology to improve eating behavior using a sensor-embedded fork. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 319–329

    Google Scholar 

  • Kamphorst BA, Klein MC, Van Wissen A (2014) Autonomous E-coaching in the wild: empirical validation of a model-based reasoning system. In: Proceedings of the 2014 international conference on autonomous agents and multi-agent systems. International Foundation for Autonomous Agents and Multiagent Systems, pp 725–732

    Google Scholar 

  • Kersten-van Dijk ET, Westerink JH, Beute F, IJsselsteijn WA (2017) Personal informatics, self-insight, and behavior change: a critical review of current literature. Human–Computer Interact 32(5–6):268–296

    Google Scholar 

  • Ko M, Choi S, Yang S, Lee J, Lee U (2015) FamiLync: facilitating participatory parental mediation of adolescents’ smartphone use. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 867–878

    Google Scholar 

  • Kocielnik R, Avrahami D, Marlow J, Lu D, Hsieh G (2018) Designing for workplace reflection: a chat and voice-based conversational agent. In: Proceedings of the 2018 on designing interactive systems conference 2018. ACM, pp 881–894

    Google Scholar 

  • Kocielnik R, Xiao L, Avrahami D, Hsieh G (2018b) Reflection companion: a conversational system for engaging users in reflection on physical activity. Proc ACM Interact Mob Wearable Ubiquitous Technol 2(2):70

    Article  Google Scholar 

  • Kuo PYP (2018) Design for self-experimentation: participant reactions to self-generated behavioral prompts for sustainable living. In Proceedings of the 2018 ACM international joint conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computers. ACM, pp 802–808

    Google Scholar 

  • Lacroix J, Saini P, Holmes R (2008) The relationship between goal difficulty and performance in the context of a physical activity intervention program. In: Proceedings of the 10th international conference on Human computer interaction with mobile devices and services. ACM,pp 415–418

    Google Scholar 

  • Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150

    Article  Google Scholar 

  • Lee ML, Dey AK (2014) Real-time feedback for improving medication taking. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 2259–2268

    Google Scholar 

  • Lee J, Cho D, Kim J, Im E, Bak J, Lee KH, Kim J (2017) Itchtector: a wearable-based mobile system for managing itching conditions. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM, pp 893–905

    Google Scholar 

  • Li I, Dey A, Forlizzi J (2010) A stage-based model of personal informatics systems. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 557–566

    Google Scholar 

  • Li N, Zhao C, Choe EK, Ritter FE (2015) HHeal: a personalized health app for flu tracking and prevention. In: Proceedings of the 33rd annual ACM conference extended abstracts on human factors in computing systems. ACM, pp 1415–1420

    Google Scholar 

  • Li Y, Cao Z, Wang J (2017) Gazture: design and implementation of a gaze based gesture control system on tablets. Proc ACM Interact, Mob, Wearable Ubiquitous Technol 1(3):74

    Google Scholar 

  • Lien CH, Cao Y (2014) Examining WeChat users’ motivations, trust, attitudes, and positive word-of-mouth: Evidence from China. Comput Hum Behav 41:104–111

    Article  Google Scholar 

  • LiKamWa R, Liu Y, Lane ND, Zhong L (2013) Moodscope: building a mood sensor from smartphone usage patterns. In: Proceeding of the 11th annual international conference on Mobile systems, applications, and services. ACM, pp 389–402

    Google Scholar 

  • Luhanga ET (2015) Evaluating effectiveness of stimulus control, time management and self-reward for weight loss behavior change. In: Adjunct proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing and Proceedings of the 2015 ACM international symposium on wearable computers. ACM, pp 441–446

    Google Scholar 

  • Lupton D (2013) Quantifying the body: monitoring and measuring health in the age of mHealth technologies. Crit Public Health 23(4):393–403

    Article  Google Scholar 

  • Lupton D (2014) Self-tracking cultures: towards a sociology of personal informatics. In: Proceedings of the 26th Australian computer-human interaction conference on designing futures: the future of design. ACM, pp 77–86

    Google Scholar 

  • Madan A, Moturu ST, Lazer D, Pentland AS (2010) Social sensing: obesity, unhealthy eating and exercise in face-to-face networks. In: Wireless Health 2010. ACM, pp 104–110

    Google Scholar 

  • Marcu G, Misra A, Caro K, Plank M, Leader A, Barsevick A (2018) Bounce: designing a physical activity intervention for breast cancer survivors. In: Proceedings of the 12th EAI international conference on pervasive computing technologies for healthcare. ACM, pp 25–34

    Google Scholar 

  • Mashhadi A, Kawsar F, Mathur A, Dugan C, Shami NS (2016) Let’s talk about the quantified workplace. In: Proceedings of the 19th ACM conference on computer supported cooperative work and social computing companion. ACM, pp 522–528

    Google Scholar 

  • Mehrotra A, Hendley R, Musolesi M (2016) Towards multi-modal anticipatory monitoring of depressive states through the analysis of human-smartphone interaction. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing: adjunct. ACM, pp 1132–1138

    Google Scholar 

  • Meyer J, Heuten W, Boll S (2016) No effects but useful? long term use of smart health devices. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing: adjunct. ACM, pp 516–521

    Google Scholar 

  • Mollee JS, Middelweerd A, Velde SJT, Klein MC (2017) Evaluation of a personalized coaching system for physical activity: user appreciation and adherence. In: Proceedings of the 11th EAI international conference on pervasive computing technologies for healthcare. ACM, pp 315–324

    Google Scholar 

  • Möller A, Kranz M, Schmid B, Roalter L, Diewald S (2013) Investigating self-reporting behavior in long-term studies. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 2931–2940

    Google Scholar 

  • Montag C, Błaszkiewicz K, Lachmann B, Sariyska R, Andone I, Trendafilov B, Markowetz A (2015) Recorded behavior as a valuable resource for diagnostics in mobile phone addiction: evidence from psychoinformatics. Behav Sci 5(4):434–442

    Article  Google Scholar 

  • Montag C, Becker B, Gan C (2018) The multi-purpose application WeChat: a review on recent research. Front Psychol 9:2247

    Article  Google Scholar 

  • Muaremi A, Seiter J, Tröster G, Bexheti A (2013) Monitor and understand pilgrims: data collection using smartphones and wearable devices. In: Proceedings of the 2013 ACM conference on pervasive and ubiquitous computing adjunct publication. ACM, pp 679–688

    Google Scholar 

  • Paay J, Kjeldskov J, Skov MB, Srikandarajah N, Brinthaparan U (2015) QuittyLink: using smartphones for personal counseling to help people quit smoking. In: Proceedings of the 17th international conference on human-computer interaction with mobile devices and services. ACM, pp 98–104

    Google Scholar 

  • Pardes A (2019) Curb your time wasted on the web with this browser extension. Retrieved from https://www.wired.com/story/habitlab-browser-extension/

  • Paré G, Leaver C, Bourget C (2018) Diffusion of the digital health self-tracking movement in canada: results of a national survey. J Med Internet Res 20(5)

    Google Scholar 

  • Parecki A (2018) My GPS Logs. Retrieved from https://aaronparecki.com/gps/

  • Paredes P, Gilad-Bachrach R, Czerwinski M, Roseway A, Rowan K, Hernandez J (2014) PopTherapy: coping with stress through pop-culture. In: Proceedings of the 8th international conference on pervasive computing technologies for healthcare. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp 109–117

    Google Scholar 

  • Pipke RM, Wegerich SW, Saidi A, Stehlik J (2013) Feasibility of personalized nonparametric analytics for predictive monitoring of heart failure patients using continuous mobile telemetry. In: Proceedings of the 4th conference on wireless health. ACM, p 7

    Google Scholar 

  • Prochaska JO, Velicer WF (1997) The transtheoretical model of health behavior change. Am J Health Promot 12(1):38–48

    Article  CAS  Google Scholar 

  • Quantified Self Labs (2015) Quantified self—self knowledge through numbers. Retrieved from http://www.quantifiedself.com

  • Rabbi M, Aung MH, Zhang M, Choudhury T (2015) MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 707–718

    Google Scholar 

  • Rentfrow PJ, Gosling SD (2012) Using smart-phones as mobile sensing devices: a practical guide for psychologists to current and potential capabilities. In: Preconference for the annual meeting of the Society for personality and social psychology. San Diego, CA

    Google Scholar 

  • Rooksby J, Rost M, Morrison A, Chalmers MC (2014) Personal tracking as lived informatics. In: Proceedings of the 32nd annual ACM conference on human factors in computing systems. ACM, pp 1163–1172

    Google Scholar 

  • Sanders R (2017) Self-tracking in the digital era: biopower, patriarchy, and the new biometric body projects. Body Soc 23(1):36–63

    Article  Google Scholar 

  • Sasaki W, Nakazawa J, Okoshi T (2018) Comparing ESM timings for emotional estimation model with fine temporal granularity. In: Proceedings of the 2018 ACM international joint conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computers. ACM, pp 722–725

    Google Scholar 

  • Simon J, Jahn M, Al-Akkad A (2012) Saving energy at work: the design of a pervasive game for office spaces. In: Proceedings of the 11th international conference on mobile and ubiquitous multimedia. ACM, p 9

    Google Scholar 

  • Singh P (2012) Smartphone: the emerging gadget of choice for the urban Indian. The Nielsen Company Retrived from http://www.nielsen.com/content/dam/corporate/india/reports/2012/Featured

  • Springer A, Hollis V, Whittaker S (2018) Mood modeling: accuracy depends on active logging and reflection. Pers Ubiquitous Comput 1–15

    Google Scholar 

  • Sullivan J (2014) China’s Weibo: Is faster different? New Media Soc 16(1):24–37

    Article  Google Scholar 

  • Swan M (2012) Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0. J Sens Actuator Netw 1(3):217–253

    Google Scholar 

  • Tang LY, Hsiu PC, Huang JL, Chen MS (2013) iLauncher: an intelligent launcher for mobile apps based on individual usage patterns. In: Proceedings of the 28th annual ACM symposium on applied computing. ACM, pp 505–512

    Google Scholar 

  • Tulusan J, Staake T, Fleisch E (2012) Providing eco-driving feedback to corporate car drivers: what impact does a smartphone application have on their fuel efficiency? In: Proceedings of the 2012 ACM conference on ubiquitous computing. ACM, pp 212–215

    Google Scholar 

  • Van Bruggen D, Liu S, Kajzer M, Striegel A, Crowell CR, D’Arcy J (2013) Modifying smartphone user locking behavior. In: Proceedings of the ninth symposium on usable privacy and security. ACM, p 10

    Google Scholar 

  • Wang R, Harari G, Hao P, Zhou X, Campbell AT (2015) SmartGPA: how smartphones can assess and predict academic performance of college students. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 295–306

    Google Scholar 

  • Wang R, Aung MS, Abdullah S, Brian R, Campbell AT, Choudhury T, Tseng VW et al (2016) CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 886–897

    Google Scholar 

  • Wang W, Harari GM, Wang R, Müller SR, Mirjafari S, Masaba K, Campbell AT (2018a) Sensing behavioral change over time: using within-person variability features from mobile sensing to predict personality traits. Proc ACM Interact Mob Wearable Ubiquitous Technol 2(3):141

    Google Scholar 

  • Wang R, Wang W, daSilva A, Huckins JF, Kelley WM, Heatherton TF, Campbell AT (2018b) Tracking depression dynamics in college students using mobile phone and wearable sensing. Proc ACM Interact Mob Wearable Ubiquitous Technol 2(1):43

    Google Scholar 

  • Weiss M, Staake T, Mattern F, Fleisch E (2012) PowerPedia: changing energy usage with the help of a community-based smartphone application. Pers Ubiquit Comput 16(6):655–664

    Article  Google Scholar 

  • Wolf G (2010) The data-driven life. N Y Times 28:2010

    Google Scholar 

  • Yangjingjing X (2012) The science of the self. Global Times. Retrieved from http://www.globaltimes.cn/content/750476.shtml

  • Zheng Y, Li Q, Chen Y, Xie X, Ma WY (2008) Understanding mobility based on GPS data. In: Proceedings of the 10th international conference on Ubiquitous computing. ACM, pp 312–321

    Google Scholar 

  • Zuckerman O, Gal-Oz A (2014) Deconstructing gamification: evaluating the effectiveness of continuous measurement, virtual rewards, and social comparison for promoting physical activity. Pers Ubiquitous Comput 18(7):1705–1719

    Article  Google Scholar 

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We thank Leela Srinivasan for assistance with the literature review and helpful feedback on earlier versions of the work presented in this manuscript.

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Vaid, S.S., Harari, G.M. (2019). Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile Sensing. In: Baumeister, H., Montag, C. (eds) Digital Phenotyping and Mobile Sensing. Studies in Neuroscience, Psychology and Behavioral Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-31620-4_5

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