Self-assess momentary mood in mobile devices: a case study with mature female participants
Senette C, Buzzi Mc, Paratore Mt
Design Input methods Momentary mood Mobile devices FOS: Computer and information sciences Well-being apps Computer Science - Human-Computer Interaction Human-Computer Interaction (cs.HC) Well-being Apps
Starting from the assumption that mood has a central role in domain-specific persuasion systems for well-being, the main goal of this study was to investigate the feasibility and acceptability of single-input methods to assess momentary mood as a medium for further interventions in health-related mobile apps destined for mature women. To this aim, we designed a very simple android App providing four user interfaces, each one showing one interactive widget to self-assess mood. Two widgets report a hint about the momentary mood they represent; the last two do not have the hints but were previously refined through questionnaires administered to 63 women (age 45-65) in order to reduce their expressive ambiguity. Next, fifteen women (age 45-65 years) were recruited to use the app for 15 days. Participants were polled about their mood four times a day and data were saved in a remote database. Moreover, users were asked to fill out a preliminary questionnaire, at the first access to the app, and a feedback questionnaire at the end of the testing period. Results appear to prove the feasibility and acceptability of this approach to self-assess momentary mood in the target population and provides some potential input methods to be used in this context.
[2] S. Brave and C. Nass, “Emotion in human-computer interaction,” in The human-computer interaction handbook. CRC Press, 2007, pp. 103-118.
[3] L. Chan, V. D. Swain, C. Kelley, K. de Barbaro, G. D. Abowd, and L. Wilcox, “Students' experiences with ecological momentary assessment tools to report on emotional well-being,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, no. 1, pp. 1-20, 2018.
[4] S. J. Czaja, N. Charness, A. D. Fisk, C. Hertzog, S. N. Nair, W. A. Rogers, and J. Sharit, “Factors predicting the use of technology: findings from the center for research and education on aging and technology enhancement (create).” Psychology and aging, vol. 21, no. 2, p. 333, 2006.
[5] P. M. Desmet, M. H. Vastenburg, and N. Romero, “Mood measurement with pick-a-mood: review of current methods and design of a pictorial self-report scale,” Journal of Design Research, vol. 14, no. 3, pp. 241- 279, 2016.
[6] M. Friedman, “The use of ranks to avoid the assumption of normality implicit in the analysis of variance,” Journal of the american statistical association, vol. 32, no. 200, pp. 675-701, 1937.
[7] N. H. Frijda et al., “Varieties of affect: Emotions and episodes, moods, and sentiments.” 1994.
[8] A. Holzinger, G. Searle, and A. Nischelwitzer, “On some aspects of improving mobile applications for the elderly,” in International Conference on Universal Access in Human-Computer Interaction. Springer, 2007, pp. 923-932.
[9] E. C. M. C. E. Horvitz, “Notification, disruption, and memory: Effects of messaging interruptions on memory and performance,” in HumanComputer Interaction: INTERACT, vol. 1, 2001, p. 263.
[10] M. Lee, B.-c. Koo, H.-s. Jeong, J. Park, J. Cho, and J.-d. Cho, “Understanding women's needs in menopause for development of mhealth,” in proceedings of the 2015 Workshop on Pervasive Wireless Healthcare, 2015, pp. 51-56.
[11] M. Nahum, T. M. Van Vleet, V. S. Sohal, J. J. Mirzabekov, V. R. Rao, D. L. Wallace, M. B. Lee, H. Dawes, A. Stark-Inbar, J. T. Jordan et al., “Immediate mood scaler: tracking symptoms of depression and anxiety using a novel mobile mood scale,” JMIR mHealth and uHealth, vol. 5, no. 4, p. e6544, 2017.
[12] V. Pejovic and M. Musolesi, “Interruptme: designing intelligent prompting mechanisms for pervasive applications,” in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014, pp. 897-908.
[13] R. Plutchik, Emotions and life: Perspectives from psychology, biology, and evolution. American Psychological Association, 2003.
[14] J. P. Pollak, P. Adams, and G. Gay, “Pam: a photographic affect meter for frequent, in situ measurement of affect,” in Proceedings of the SIGCHI conference on Human factors in computing systems, 2011, pp. 725-734.
[15] A. Quartiroli, P. C. Terry, and G. J. Fogarty, “Development and initial validation of the italian mood scale (itams) for use in sport and exercise contexts,” Frontiers in psychology, vol. 8, p. 1483, 2017.
[16] S. J. Richmond, A. Keding, M. Hover, R. Gabe, B. Cross, D. Torgerson, and H. MacPherson, “Feasibility, acceptability and validity of sms text messaging for measuring change in depression during a randomised controlled trial,” BMC psychiatry, vol. 15, no. 1, pp. 1-13, 2015.
[17] J. A. Russell, “A circumplex model of affect.” Journal of personality and social psychology, vol. 39, no. 6, p. 1161, 1980.
[18] J. A. Russell and L. F. Barrett, “Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant.” Journal of personality and social psychology, vol. 76, no. 5, p. 805, 1999.
[19] J. A. Russell, A. Weiss, and G. A. Mendelsohn, “Affect grid: a singleitem scale of pleasure and arousal.” Journal of personality and social psychology, vol. 57, no. 3, p. 493, 1989.
[20] Y. Sakairi, K. Nakatsuka, and T. Shimizu, “Development of the t wod imensional m ood s cale for self-monitoring and self-regulation of momentary mood states,” Japanese Psychological Research, vol. 55, no. 4, pp. 338-349, 2013.
[21] S. Schwartz, S. Schultz, A. Reider, and E. F. Saunders, “Daily mood monitoring of symptoms using smartphones in bipolar disorder: a pilot study assessing the feasibility of ecological momentary assessment,” Journal of Affective Disorders, vol. 191, pp. 88-93, 2016.
[22] C. Senette, M. C. Buzzi, M. T. Paratore, and A. Trujillo, “Persuasive design of a mobile coaching app to encourage a healthy lifestyle during menopause,” in proceedings of the 17th International Conference on Mobile and Ubiquitous Multimedia, 2018, pp. 47-58.
[23] S. Shiffman, A. A. Stone, and M. R. Hufford, “Ecological momentary assessment,” Annu. Rev. Clin. Psychol., vol. 4, pp. 1-32, 2008.
[24] D. Spruijt-Metz and W. Nilsen, “Dynamic models of behavior for just-intime adaptive interventions,” IEEE Pervasive Computing, vol. 13, no. 3, pp. 13-17, 2014.
[25] T. J. Trull and U. W. Ebner-Priemer, “Using experience sampling methods/ecological momentary assessment (esm/ema) in clinical assessment and clinical research: introduction to the special section.” 2009.
[26] N. Van Berkel, D. Ferreira, and V. Kostakos, “The experience sampling method on mobile devices,” ACM Computing Surveys (CSUR), vol. 50, no. 6, pp. 1-40, 2017.
[27] T. Wallbaum, W. Heuten, and S. Boll, “Comparison of in-situ mood input methods on mobile devices,” in Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia, 2016, pp. 123-127.
[28] D. Watson, L. A. Clark, and A. Tellegen, “Development and validation of brief measures of positive and negative affect: the panas scales.” Journal of personality and social psychology, vol. 54, no. 6, p. 1063, 1988.
[29] F. Wilcoxon, “Some rapid approximate statistical procedures,” Annals of the New York Academy of Sciences, vol. 52, no. 6, pp. 808-814, 1950.
Metrics
Back to previous page
@inproceedings{oai:it.cnr:prodotti:474670,
title = {Self-assess momentary mood in mobile devices: a case study with mature female participants},
author = {Senette C and Buzzi Mc and Paratore Mt},
doi = {10.1109/icct56969.2023.10075891 and 10.48550/arxiv.2211.01015},
year = {2023}
}
0000-0001-7818-0601Deposited version
Deposited version
DOI
10.1109/icct56969.2023.10075891
10.48550/arxiv.2211.01015