Author(s):

  • Farhat-ul-Ain
  • Kristjan Port
  • Vladimir Tomberg

Abstract:

Purpose: Self-monitoring is one of the most effective behavior change techniques to enhance awareness and task motivation. Wearable devices provide a unique opportunity for individuals to self-monitoring compared to traditional record-keeping methods. Furthermore, digital self-monitoring helps to engage with the technologies/intelligent systems to track, collect, monitor, and display information about daily activities. This study aimed to implement a quantified self-approach for university students to explore changes in students’ attitudes and perceptions after self-monitoring of physical activity. Method: 70 university students were recruited in the study. The study was divided into four stages i. Preparation stage: participants filled out pre-survey and were instructed to use a step counter or any other fitness tracker over three weeks. ii. Collection stage: participants monitored themselves regularly for three weeks. iii. Integration stage: The collected data was analyzed and transformed for users to reflect on. iv. Reflection stage: participants reflected on the findings in a post-survey. Results: 47% of study participants reported that self-monitoring raised awareness related to physical activity in study participants. 54% of study participants felt the urge to increase physical activity after self-monitoring. Before and after self-monitoring, there was no change in the perception of being more physically active. Conclusion: The study suggested that self-quantification can raise awareness related to physical activity. Longitudinal studies can be designed to explore how self-quantification approaches would be utilized for long-term self-reflection.

Documentation: https://doi.org/10.1007/978-3-031-06388-6_34

References:

Ayobi, A., Sonne, T., Marshall, P., Cox, A.L.: Flexible and mindful self-tracking: design implications from paper bullet journals. In: Conference on Human Factors in Computing Systems – Proceedings (2018). https://doi.org/10.1145/3173574.3173602

Choe, E.K., Lee, N.B., Lee, B., Pratt, W., Kientz, J.A.: Understanding quantified-selfers’ practices in collecting and exploring personal data. In: Conference on Human Factors in Computing Systems – Proceedings, pp. 1143–1152 (2014). https://doi.org/10.1145/2556288.2557372

Jarrahi, M.H., Gafinowitz, N., Shin, G.: Activity trackers, prior motivation, and perceived informational and motivational affordances. Pers. Ubiquit. Comput. 22(2), 433–448 (2017). https://doi.org/10.1007/s00779-017-1099-9

Swan, M.: The quantified self: fundamental disruption in big data science and biological discovery. Big Data 1, 85–99 (2013). https://doi.org/10.1089/big.2012.0002

Wang, Y., Weber, I., Mitra, P.: Quantified self meets social media: sharing of weight updates on Twitter. In: DH 2016 – Proceedings of the 2016 Digital Health Conference, pp. 93–97 (2016). https://doi.org/10.1145/2896338.2896363

Shin, D.H., Biocca, F.: Health experience model of personal informatics: the case of a quantified self. Comput. Hum. Behav. 69, 62–74 (2017). https://doi.org/10.1016/j.chb.2016.12.019

Kersten-van Dijk, E.T., Westerink, J.H.D.M., Beute, F., IJsselsteijn, W.A.: Personal informatics, self-insight, and behavior change: a critical review of current literature. Hum.-Comput. Interact. 32, 268–296 (2017). https://doi.org/10.1080/07370024.2016.1276456

Khorakhun, C., Bhatti, S.N.: mHealth through quantified-self: a user study. In: 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015, pp. 329–335 (2015). https://doi.org/10.1109/HealthCom.2015.7454520

Li, I., Medynskiy, Y., Froehlich, J., Larsen, J.: Personal informatics in practice: improving quality of life through data. In: Conference on Human Factors in Computing Systems – Proceedings, pp. 2799–2802 (2012). https://doi.org/10.1145/2212776.2212724

Randriambelonoro, M., Chen, Y., Pu, P.: Can fitness trackers help diabetic and obese users make and sustain lifestyle changes? Computer 50(3), 20–29 (2017). https://doi.org/10.1109/MC.2017.92

Michie, S., Abraham, C., Whittington, C., McAteer, J., Gupta, S.: Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol. (2009). https://doi.org/10.1037/a0016136

Prochaska, J.O., Velicer, W.F.: The transtheoretical model of health behavior change. Am. J. Health Promot. 12(1), 38–48 (1997). https://doi.org/10.4278/0890-1171-12.1.38

Klein, M., Mogles, N., van Wissen, A.: Why won’t you do what’s good for you? Using intelligent support for behavior change. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 104–115. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25446-8_12

De Vries, H., et al.: The European smoking prevention framework approach (EFSA): an example of integral prevention. Health Educ. Res. 18(5), 611–626 (2003). https://doi.org/10.1093/her/cyg031

Li,I., Dey, A., Forlizzi, J.: A stage-based model of personal informatics systems. In: Conference on Human Factors in Computing Systems – Proceedings, pp. 557–566 (2010). https://doi.org/10.1145/1753326.1753409

Epstein, D.A., Ping, A., Fogarty, J., Munson, S.A.: A lived informatics model of personal informatics. In: UbiComp 2015 – Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 731–742 (2015). https://doi.org/10.1145/2750858.2804250

Butryn, M.L., Arigo, D., Raggio, G.A., Colasanti, M., Forman, E.M.: Enhancing physical activity promotion in midlife women with technology-based self-monitoring and social connectivity: a pilot study. J. Health Psychol. 21(8), 1548–1555 (2016). https://doi.org/10.1177/1359105314558895

Jakicic, J.M., et al.: Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: the IDEA randomized clinical trial. JAMA J. Am. Med. Assoc. 316(11), 1161–1171 (2016). https://doi.org/10.1001/jama.2016.12858

Kolt, G.S., Schofield, G.M., Kerse, N., Garrett, N., Ashton, T., Patel, A.: Healthy steps trial: Pedometer-based advice and physical activity for low-active older adults. Ann. Fam. Med. 10(3), 206–212 (2012). https://doi.org/10.1370/afm.1345

Fritz, T., Huang, E.M., Murphy, G.C., Zimmermann, T.: Persuasive technology in the real world: a study of long-term use of activity sensing devices for fitness. In: Conference on Human Factors in Computing Systems – Proceedings, pp. 487–496 (2014). https://doi.org/10.1145/2556288.2557383

Preusse, K.C., Mitzner, T.L., Fausset, C.B., Rogers, W.A.: Older adults’ acceptance of activity trackers. J. Appl. Gerontol. 36(2), 127–155 (2017). https://doi.org/10.1177/0733464815624151

Maitland, J., et al.: Increasing the awareness of daily activity levels with pervasive computing. In: 2006 Pervasive Health Conference and Workshops, PervasiveHealth (2006). https://doi.org/10.1109/PCTHEALTH.2006.361667

Rooksby, J., Rost, M., Morrison, A., Chalmers, M.: Personal tracking as lived informatics. In: Conference on Human Factors in Computing Systems – Proceedings, pp. 1163–1172 (2014). https://doi.org/10.1145/2556288.2557039

Attig, C., Franke, T.: I track, therefore i walk – exploring the motivational costs of wearing activity trackers in actual users. Int. J. Hum. Comput. Stud. 127, 211–224 (2019). https://doi.org/10.1016/j.ijhcs.2018.04.007

Compernolle, S., et al.: Effectiveness of interventions using self-monitoring to reduce sedentary behavior in adults: a systematic review and meta-analysis. Int. J. Behav. Nutr. Phys. Act. 16, 63 (2019). https://doi.org/10.1186/s12966-019-0824-3