research-article

ReVibe: A Context-assisted Evening Recall Approach to Improve Self-report Adherence

Authors:

Mashfiqui Rabbi,

Predrag Klasnja,

Susan MurphyAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 4

Article No.: 149, Pages 1 - 27

https://doi.org/10.1145/3369806

Published: 14 September 2020 Publication History

Abstract

Besides passive sensing, ecological momentary assessments (EMAs) are one of the primary methods to collect in-the-moment data in ubiquitous computing and mobile health. While EMAs have the advantage of low recall bias, a disadvantage is that they frequently interrupt the user and thus long-term adherence is generally poor. In this paper, we propose a less-disruptive self-reporting method, "assisted recall," in which in the evening individuals are asked to answer questions concerning a moment from earlier in the day assisted by contextual information such as location, physical activity, and ambient sounds collected around the moment to be recalled. Such contextual information is automatically collected from phone sensor data, so that self-reporting does not require devices other than a smartphone. We hypothesized that providing assistance based on such automatically collected contextual information would increase recall accuracy (i.e., if recall responses for a moment match the EMA responses at the same moment) as compared to no assistance, and we hypothesized that the overall completion rate of evening recalls (assisted or not) would be higher than for in-the-moment EMAs. We conducted a two-week study (N=54) where participants completed recalls and EMAs each day. We found that providing assistance via contextual information increased recall accuracy by 5.6% (p = 0.032) and the overall recall completion rate was on average 27.8% (p < 0.001) higher than that of EMAs.

Supplementary Material

rabbi (rabbi.zip)

Supplemental movie, appendix, image and software files for, ReVibe: A Context-assisted Evening Recall Approach to Improve Self-report Adherence

Download
191.12 KB

References

[1]

Alexander T Adams, Elizabeth L Murnane, Phil Adams, Michael Elfenbein, Pamara F Chang, Shruti Sannon, Geri Gay, and Tanzeem Choudhury. 2018. Keppi: A Tangible User Interface for Self-Reporting Pain. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 502.

Digital Library

[2]

Phil Adams, Mashfiqui Rabbi, Tauhidur Rahman, Mark Matthews, Amy Voida, Geri Gay, Tanzeem Choudhury, and Stephen Voida. 2014. Towards personal stress informatics: Comparing minimally invasive techniques for measuring daily stress in the wild. In Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 72--79.

[3]

Flurry Analytics. 2012. App Engagement: The Matrix Reloaded,. http://flurrymobile.tumblr.com/post/113379517625/app-engagement-the-matrix-reloaded

[4]

Daniel Ashbrook, Kent Lyons, and Thad Starner. 2008. An investigation into round touchscreen wristwatch interaction. In Mobile HCI. 311--314.

[5]

Brian P Bailey and Shamsi T Iqbal. 2008. Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management. ACM Transactions on Computer-Human Interaction (TOCHI) 14, 4 (2008), 21.

Digital Library

[6]

Brian P Bailey and Joseph A Konstan. 2006. On the need for attention-aware systems: Measuring effects of interruption on task performance, error rate, and affective state. Computers in human behavior 22, 4 (2006), 685--708.

[7]

Brian P Bailey, Joseph A Konstan, and John V Carlis. 2001. The Effects of Interruptions on Task Performance, Annoyance, and Anxiety in the User Interface. In Interact, Vol. 1. 593--601.

[8]

Sumit Basu. 2002. Conversational scene analysis. Ph.D. Dissertation. MaSSachuSettS InStitute of Technology.

[9]

Patrick Baudisch and Gerry Chu. 2009. Back-of-device interaction allows creating very small touch devices. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1923--1932.

Digital Library

[10]

Robert F Belli. 1998. The structure of autobiographical memory and the event history calendar: Potential improvements in the quality of retrospective reports in surveys. Memory 6, 4 (1998), 383--406.

[11]

Audrey Boruvka, Daniel Almirall, Katie Witkiewitz, and Susan A Murphy. 2018. Assessing time-varying causal effect moderation in mobile health. J. Amer. Statist. Assoc. 113, 523 (2018), 1112--1121.

[12]

Michael Bostock, Vadim Ogievetsky, and Jeffrey Heer. 2011. D3 data-driven documents. IEEE transactions on visualization and computer graphics 17, 12 (2011), 2301--2309.

[13]

Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77--101.

[14]

WF Brewer. 1988. Memory for randomly sampled autobiographical events. (1988), 21--90.

[15]

Scott Carter and Jennifer Mankoff. 2005. When participants do the capturing: the role of media in diary studies. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 899--908.

Digital Library

[16]

Eun Kyoung Choe, Saeed Abdullah, Mashfiqui Rabbi, Edison Thomaz, Daniel A Epstein, Felicia Cordeiro, Matthew Kay, Gregory D Abowd, Tanzeem Choudhury, James Fogarty, et al. 2017. Semi-automated tracking: a balanced approach for self-monitoring applications. IEEE Pervasive Computing 16, 1 (2017), 74--84.

Digital Library

[17]

Eun Kyoung Choe, Bongshin Lee, Matthew Kay, Wanda Pratt, and Julie A Kientz. 2015. SleepTight: low-burden, self-monitoring technology for capturing and reflecting on sleep behaviors. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 121--132.

Digital Library

[18]

Mihaly Csikszentmihalyi. 1997. Finding flow: The psychology of engagement with everyday life. Basic Books.

[19]

Gunther Eysenbach. 2005. The law of attrition. Journal of medical Internet research 7, 1 (2005), e11.

[20]

Denzil Ferreira, Vassilis Kostakos, and Anind K Dey. 2015. AWARE: mobile context instrumentation framework. Frontiers in ICT 2 (2015), 6.

[21]

BJ Fogg. 2009. A behavior model for persuasive design. In Proceedings of the 4th international Conference on Persuasive Technology. ACM, 40.

Digital Library

[22]

Pamela Galluch. 2009. Interrupting the workplace: Examining stressors in an information technology context. (2009).

[23]

Daniel T Gilbert, Brett W Pelham, and Douglas S Krull. 1988. On cognitive busyness: When person perceivers meet persons perceived. Journal of personality and social psychology 54, 5 (1988), 733.

[24]

Tony Gillie and Donald Broadbent. 1989. What makes interruptions disruptive? A study of length, similarity, and complexity. Psychological research 50, 4 (1989), 243--250.

[25]

Duncan R Godden and Alan D Baddeley. 1975. Context-dependent memory in two natural environments: On land and underwater. British Journal of psychology 66, 3 (1975), 325--331.

[26]

Google Play Service. 2014. http://developer.android.com/google/play-services/index.html. [Online; accessed 1 April 2014].

[27]

Philip M Groves and Richard F Thompson. 1970. Habituation: a dual-process theory. Psychological review 77, 5 (1970), 419.

[28]

Javier Hernandez, Daniel McDuff, Christian Infante, Pattie Maes, Karen Quigley, and Rosalind Picard. 2016. Wearable ESM: differences in the experience sampling method across wearable devices. In Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 195--205.

Digital Library

[29]

Edward Cutrell Mary Czerwinski Eric Horvitz. 2001. Notification, disruption, and memory: Effects of messaging interruptions on memory and performance. In Human-computer Interaction: INTERACT'01: IFIP TC. 13 International Conference on Human-Comupter Interaction, 9th-13th July 2001, Tokyo, Japan. IOS Press, 263.

[30]

Google Inc. 2017. Activity Recognition. https://developers.google.com/android/reference/com/google/android/gms/location/ActivityRecognitionClient

[31]

Google Inc. 2017. Fused Location. https://developers.google.com/android/reference/com/google/android/gms/location/FusedLocationProviderClient

[32]

Stephen Intille, Caitlin Haynes, Dharam Maniar, Aditya Ponnada, and Justin Manjourides. 2016. μEMA: Microinteraction-based ecological momentary assessment (EMA) using a smartwatch. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 1124--1128.

Digital Library

[33]

Shamsi T Iqbal and Eric Horvitz. 2007. Disruption and recovery of computing tasks: field study, analysis, and directions. In CHI, Vol. 7. 677--686.

[34]

Natasha Jaques, Sara Taylor, Akane Sano, Rosalind Picard, et al. 2017. Predicting tomorrowâĂ&Zacute;s mood, health, and stress level using personalized multitask learning and domain adaptation. In IJCAI 2017 Workshop on Artificial Intelligence in Affective Computing. 17--33.

[35]

Daniel Johnson, Sebastian Deterding, Kerri-Ann Kuhn, Aleksandra Staneva, Stoyan Stoyanov, and Leanne Hides. 2016. Gamification for health and wellbeing: A systematic review of the literature. Internet Interventions 6 (2016), 89--106.

[36]

Michael Jacob Kahana. 2012. Foundations of human memory. OUP USA.

[37]

Daniel Kahneman, Alan B Krueger, David A Schkade, Norbert Schwarz, and Arthur A Stone. 2004. The day reconstruction method (DRM). Instrument documentation.

[38]

Daniel Kahneman, Alan B Krueger, David A Schkade, Norbert Schwarz, and Arthur A Stone. 2004. A survey method for characterizing daily life experience: The day reconstruction method. Science 306, 5702 (2004), 1776--1780.

[39]

Predrag Klasnja, Sunny Consolvo, Tanzeem Choudhury, Richard Beckwith, and Jeffrey Hightower. 2009. Exploring privacy concerns about personal sensing. In International Conference on Pervasive Computing. Springer, 176--183.

Digital Library

[40]

Mobile Data 2 Knowledge. 2017. mCerebrum. https://github.com/MD2Korg/mCerebrum

[41]

Paul J Lavrakas. 2008. Encyclopedia of survey research methods. Sage Publications.

[42]

Roderick JA Little and Donald B Rubin. 2019. Statistical analysis with missing data. Vol. 793. Wiley.

[43]

Localytics. [n. d.]. 24% of Users Abandon an App After One Use. http://info.localytics.com/blog/24-of-users-abandon-an-app-after-one-use

[44]

Hong Lu, Jun Yang, Zhigang Liu, Nicholas D Lane, Tanzeem Choudhury, and Andrew T Campbell. 2010. The Jigsaw continuous sensing engine for mobile phone applications. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems. ACM, 71--84.

Digital Library

[45]

Peter Lynn. 2001. The impact of incentives on response rates to personal interview surveys: Role and perceptions of interviewers. International Journal of Public Opinion Research (2001).

[46]

Daniel McFarlane. 2002. Comparison of four primary methods for coordinating the interruption of people in human-computer interaction. Human-Computer Interaction 17, 1 (2002), 63--139.

Digital Library

[47]

Marianne Menictas, Mashfiqui Rabbi, Predrag Klasnja, and Susan Murphy. 2019. Artificial intelligence decision-making in mobile health. The Biochemist 41, 5 (2019), 20--24.

[48]

Inbal Nahum-Shani, Shawna N Smith, Ambuj Tewari, Katie Witkiewitz, Linda M Collins, Bonnie Spring, and S Murphy. 2014. Just in time adaptive interventions (jitais): An organizing framework for ongoing health behavior support. Methodology Center technical report 14-126 (2014).

[49]

Evangelos Niforatos, Caterina Cinel, Cathleen Cortis Mack, Marc Langheinrich, and Geoff Ward. 2017. Can Less be More?: Contrasting Limited, Unlimited, and Automatic Picture Capture for Augmenting Memory Recall. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 2 (2017), 21.

Digital Library

[50]

Stephen Oney, Chris Harrison, Amy Ogan, and Jason Wiese. 2013. ZoomBoard: a diminutive qwerty soft keyboard using iterative zooming for ultra-small devices. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2799--2802.

Digital Library

[51]

Gaurav Paruthi, Shriti Raj, Ankita Gupta, Chuan-Che Huang, Yung-Ju Chang, and Mark W Newman. 2017. HEED: situated and distributed interactive devices for self-reporting. 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, 181--184.

Digital Library

[52]

Richard E Petty and J Cacioppo. 1986. Elaboration likelihood model. Handbook of theories of social psychology. London, England: Sage (1986).

[53]

Aditya Ponnada, Caitlin Haynes, Dharam Maniar, Justin Manjourides, and Stephen Intille. 2017. Microinteraction Ecological Momentary Assessment Response Rates: Effect of Microinteractions or the Smartwatch? Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 1, 3 (2017), 92.

Digital Library

[54]

Mashfiqui Rabbi, Shahid Ali, Tanzeem Choudhury, and Ethan Berke. 2011. Passive and In-Situ assessment of mental and physical well-being using mobile sensors. In Proc. 13th ACM Intï£¡l Conf. Ubiquitous Computing. 385--394.

Digital Library

[55]

Mashfiqui Rabbi, Min SH Aung, Geri Gay, M Cary Reid, and Tanzeem Choudhury. 2018. Feasibility and Acceptability of Mobile Phone-Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults. Journal of medical Internet research 20, 10 (2018), e10147.

[56]

Mashfiqui Rabbi, Meredith Philyaw Kotov, Rebecca Cunningham, Erin E Bonar, Inbal Nahum-Shani, Predrag Klasnja, Maureen Walton, and Susan Murphy. 2018. Toward increasing engagement in substance use data collection: development of the Substance Abuse Research Assistant app and protocol for a microrandomized trial using adolescents and emerging adults. JMIR research protocols 7, 7 (2018), e166.

[57]

Mashfiqui Rabbi, Angela Pfammatter, Mi Zhang, Bonnie Spring, and Tanzeem Choudhury. 2015. Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults. JMIR mHealth uHealth 3, 2 (14 May 2015), e42. https://doi.org/10.2196/mhealth.4160

[58]

Tauhidur Rahman, Mi Zhang, Stephen Voida, and Tanzeem Choudhury. 2014. Towards accurate non-intrusive recollection of stress levels using mobile sensing and contextual recall. In Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 166--169.

[59]

Michael D Robinson and Gerald L Clore. 2002. Belief and feeling: evidence for an accessibility model of emotional self-report. Psychological bulletin 128, 6 (2002), 934.

[60]

Hillol Sarker, Moushumi Sharmin, Amin Ahsan Ali, Md Mahbubur Rahman, Rummana Bari, Syed Monowar Hossain, and Santosh Kumar. 2014. Assessing the availability of users to engage in just-in-time intervention in the natural environment. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 909--920.

Digital Library

[61]

Saul Shiffman, Arthur A Stone, and Michael R Hufford. 2008. Ecological momentary assessment. Annu. Rev. Clin. Psychol. 4 (2008), 1--32.

[62]

Katie A Siek, Yvonne Rogers, and Kay H Connelly. 2005. Fat finger worries: how older and younger users physically interact with PDAs. In IFIP Conference on Human-Computer Interaction. Springer, 267--280.

Digital Library

[63]

Steven M Smith, Arthur Glenberg, and Robert A Bjork. 1978. Environmental context and human memory. Memory & Cognition 6, 4 (1978), 342--353.

[64]

Steven M Smith and Edward Vela. 2001. Environmental context-dependent memory: A review and meta-analysis. Psychonomic bulletin & review 8, 2 (2001), 203--220.

[65]

Hyewon Suh, Nina Shahriaree, Eric B Hekler, and Julie A Kientz. 2016. Developing and Validating the User Burden Scale: A Tool for Assessing User Burden in Computing Systems. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 3988--3999.

Digital Library

[66]

Margaret Sullivan Pepe and Garnet L Anderson. 1994. A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data. Communications in statistics-simulation and computation 23, 4 (1994), 939--951.

[67]

Hongsuda Tangmunarunkit, Cheng-Kang Hsieh, Brent Longstaff, S Nolen, John Jenkins, Cameron Ketcham, Joshua Selsky, Faisal Alquaddoomi, Dony George, Jinha Kang, et al. 2015. Ohmage: A general and extensible end-to-end participatory sensing platform. ACM Transactions on Intelligent Systems and Technology (TIST) 6, 3 (2015), 38.

[68]

Edison Thomaz, Aman Parnami, Irfan Essa, and Gregory D Abowd. 2013. Feasibility of identifying eating moments from first-person images leveraging human computation. In Proceedings of the 4th International SenseCam & Pervasive Imaging Conference. ACM, 26--33.

Digital Library

[69]

John Torous, Jennifer Nicholas, Mark E Larsen, Joseph Firth, and Helen Christensen. 2018. Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements. Evidence-based mental health 21, 3 (2018), 116--119.

[70]

Khai N Truong, Thariq Shihipar, and Daniel J Wigdor. 2014. Slide to X: unlocking the potential of smartphone unlocking. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 3635--3644.

Digital Library

[71]

Anastasios Tsiatis. 2007. Semiparametric theory and missing data. Springer Science & Business Media.

[72]

Anastasios A Tsiatis, Marie Davidian, Min Zhang, and Xiaomin Lu. 2008. Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: a principled yet flexible approach. Statistics in medicine 27, 23 (2008), 4658--4677.

[73]

Endel Tulving et al. 1972. Episodic and semantic memory. Organization of memory 1 (1972), 381--403.

[74]

Endel Tulving and Donald M Thomson. 1973. Encoding specificity and retrieval processes in episodic memory. Psychological review 80, 5 (1973), 352.

[75]

Liam D Turner, Stuart M Allen, and Roger M Whitaker. 2015. Interruptibility prediction for ubiquitous systems: conventions and new directions from a growing field. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. ACM, 801--812.

Digital Library

[76]

Niels Van Berkel, Denzil Ferreira, and Vassilis Kostakos. 2018. The experience sampling method on mobile devices. ACM Computing Surveys (CSUR) 50, 6 (2018), 93.

[77]

Niels Van Berkel, Jorge Goncalves, Simo Hosio, and Vassilis Kostakos. 2017. Gamification of mobile experience sampling improves data quality and quantity. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 107.

[78]

Kristof Van Laerhoven, David Kilian, and Bernt Schiele. 2008. Using rhythm awareness in long-term activity recognition. In 2008 12th IEEE International Symposium on Wearable Computers. IEEE, 63--66.

Digital Library

[79]

Willem A Wagenaar. 1986. My memory: A study of autobiographical memory over six years. Cognitive psychology 18, 2 (1986), 225--252.

[80]

Rui Wang, Min SH Aung, Saeed Abdullah, Rachel Brian, Andrew T Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Michael Merrill, Emily A Scherer, 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, 886--897.

Digital Library

[81]

Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T Campbell. 2014. StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 3--14.

Digital Library

[82]

Kevin Werbach and Dan Hunter. 2012. For the win: How game thinking can revolutionize your business. Wharton Digital Press.

[83]

Danny Wyatt, Tanzeem Choudhury, and Jeff A Bilmes. 2008. Learning Hidden Curved Exponential Family Models to Infer Face-to-Face Interaction Networks from Situated Speech Data. In AAAI. 732--738.

[84]

Robert Xiao, Gierad Laput, and Chris Harrison. 2014. Expanding the input expressivity of smartwatches with mechanical pan, twist, tilt and click. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 193--196.

Digital Library

[85]

Xiaoyi Zhang, Laura R Pina, and James Fogarty. 2016. Examining unlock journaling with diaries and reminders for in situ self-report in health and wellness. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 5658--5664.

Digital Library

Cited By

Le HLakshminarayanan RLi JMishra VIntille S(2024)Collecting Self-reported Physical Activity and Posture Data Using Audio-based Ecological Momentary AssessmentProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785848:3(1-35)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678584
van Berkel NVisuri AShalawadi SEvans MTag BHosio S(2024)From Reflection to Action: Enhancing Workplace Well-Being Through Digital SolutionsInteracting with Computers10.1093/iwc/iwae049Online publication date: 26-Oct-2024
https://doi.org/10.1093/iwc/iwae049
Sobolev MAnand ADziak JPotter LLam CWetter DNahum-Shani I(2023)Time-varying model of engagement with digital self reporting: Evidence from smoking cessation longitudinal studiesFrontiers in Digital Health10.3389/fdgth.2023.11440815Online publication date: 13-Apr-2023
https://doi.org/10.3389/fdgth.2023.1144081
Show More Cited By

Index Terms

ReVibe: A Context-assisted Evening Recall Approach to Improve Self-report Adherence
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing
    2. Ubiquitous and mobile computing systems and tools

Recommendations

Heed: Exploring the Design of Situated Self-Reporting Devices

In-situ self-reporting is a widely used data collection technique for understanding people's behavior in context. Characteristics of smartphones such as their high proliferation, close proximity to their users, and heavy use have made them a popular ...
Knock, knock! who's there? Putting the user in control of managing interruptions

The abundance of communication technology, such as the omnipresence of cell phones, has not only increased our ability to reach people anytime anywhere, but also the likelihood of being interrupted. As a result, there is value in understanding how to ...
Exploring the State-of-Receptivity for mHealth Interventions

Recent advancements in sensing techniques for mHealth applications have led to successful development and deployments of several mHealth intervention designs, including Just-In-Time Adaptive Interventions (JITAI). JITAIs show great potential because they ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 3, Issue 4

December 2019

873 pages

EISSN:2474-9567

DOI:10.1145/3375704

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 September 2020

Published in IMWUT Volume 3, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

NHLBI/NIA
Michigan Institute for Data Science
NIDA
NIAAA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
592
Total Downloads

Downloads (Last 12 months)89
Downloads (Last 6 weeks)11

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Le HLakshminarayanan RLi JMishra VIntille S(2024)Collecting Self-reported Physical Activity and Posture Data Using Audio-based Ecological Momentary AssessmentProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785848:3(1-35)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678584
van Berkel NVisuri AShalawadi SEvans MTag BHosio S(2024)From Reflection to Action: Enhancing Workplace Well-Being Through Digital SolutionsInteracting with Computers10.1093/iwc/iwae049Online publication date: 26-Oct-2024
https://doi.org/10.1093/iwc/iwae049
Sobolev MAnand ADziak JPotter LLam CWetter DNahum-Shani I(2023)Time-varying model of engagement with digital self reporting: Evidence from smoking cessation longitudinal studiesFrontiers in Digital Health10.3389/fdgth.2023.11440815Online publication date: 13-Apr-2023
https://doi.org/10.3389/fdgth.2023.1144081
van Berkel NShalawadi SEvans MVisuri AHosio S(2023)A Longitudinal Analysis of Real-World Self-report DataHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42286-7_34(611-632)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-42286-7_34
Mandi SGhosh SDe PMitra B(2023)SELFI: Evaluation of Techniques to Reduce Self-report Fatigue by Using Facial Expression of EmotionHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42280-5_39(620-640)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-42280-5_39
Oh KKo JShin JKo M(2022)Using Wake-Up Tasks for Morning Behavior Change: Development and Usability StudyJMIR Formative Research10.2196/394976:9(e39497)Online publication date: 21-Sep-2022
https://doi.org/10.2196/39497
Thorpe DFouyaxis JLipschitz JNielson ALi WMurphy SBidargaddi N(2022)Cost and Effort Considerations for the Development of Intervention Studies Using Mobile Health Platforms: Pragmatic Case StudyJMIR Formative Research10.2196/299886:3(e29988)Online publication date: 31-Mar-2022
https://doi.org/10.2196/29988
Lu XThomaz EEpstein D(2022)Understanding People's Perceptions of Approaches to Semi-Automated Dietary MonitoringProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35502886:3(1-27)Online publication date: 7-Sep-2022
https://dl.acm.org/doi/10.1145/3550288
Kabir KKenfield SVan Blarigan EChan JWiese J(2022)Ask the UsersProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35346126:2(1-21)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3534612
van Berkel NMerritt TBruun ASkov M(2022)Tangible Self-Report Devices: Accuracy and Resolution of Participant InputProceedings of the Sixteenth International Conference on Tangible, Embedded, and Embodied Interaction10.1145/3490149.3501309(1-14)Online publication date: 13-Feb-2022
https://dl.acm.org/doi/10.1145/3490149.3501309
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents