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Toward Lightweight In-situ Self-reporting: An Exploratory Study of Alternative Smartwatch Interface Designs in Context

Authors:

Bingjian Huang,

Sun Young Park,

Mark W. NewmanAuthors Info & Claims

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

Article No.: 158, Pages 1 - 22

https://doi.org/10.1145/3432212

Published: 18 December 2020 Publication History

Abstract

In-situ self-reporting is an important measurement method used for capturing daily experience data right-in-the-moment in dynamic contexts. Research has been conducted to reduce the demand placed on users for manually reporting data in context. In this regard, smartwatches offer inherent benefits for making self-reporting more convenient and facilitate data gathering. However, self-reporting on the small touchscreen under various contextual conditions can be burdensome and challenging. In this study, to gain insights into designing smartwatch-based self-report interfaces, we conducted an exploratory user study with eight design probes and twenty-four participants under three simulated scenarios: walking, gaming, and social chatting. Findings showed that users' subjective perception of interface features (e.g., input methods and option layouts) varied with changes in context. Participants leveraged different features (e.g., hierarchical layout and discrete input) to micro-schedule self-report tasks (i.e., create one or multiple opportune moments) or to conduct eyes-free interaction with the assistance of smartwatch attributes (e.g., the physical frame of a smartwatch). We discuss implications for smartwatch-based self-report interface designs by considering context and designing interface features to support users' coping strategies.

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 (CHI '18). ACM, New York, NY, USA, Article 502, 502:1--502:13 pages. https://doi.org/10.1145/3173574.3174076

Digital Library

[2]

Phil Adams, Elizabeth L Murnane, Michael Elfenbein, Elaine Wethington, and Geri Gay. 2017. Supporting the self-management of chronic pain conditions with tailored momentary self-assessments. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 1065--1077.

Digital Library

[3]

Android. 2019. Design for Wear OS | Android Developers. Website. Retrieved January 31, 2020 from https://developer.android.com/design/wear.

[4]

Shaikh Shawon Arefin Shimon, Courtney Lutton, Zichun Xu, Sarah Morrison-Smith, Christina Boucher, and Jaime Ruiz. 2016. Exploring non-touchscreen gestures for smartwatches. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 3822--3833.

Digital Library

[5]

Daniel Lee Ashbrook. 2010. Enabling mobile microinteractions. Ph.D. Dissertation. Georgia Institute of Technology.

Digital Library

[6]

Anna L Beukenhorst, Jamie C Sergeant, Max A Little, John McBeth, and William G Dixon. 2018. Consumer Smartwatches for Collecting Self-Report and Sensor Data: App Design and Engagement. In MIE. 291--295.

[7]

Niall Bolger, Angelina Davis, and Eshkol Rafaeli. 2003. Diary methods: Capturing life as it is lived. Annual review of psychology 54, 1 (2003), 579--616.

[8]

Yung-Ju Chang, Gaurav Paruthi, and Mark W Newman. 2015. A field study comparing approaches to collecting annotated activity data in real-world settings. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 671--682.

Digital Library

[9]

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

[10]

Tamlin Conner and Eliza Bliss-Moreau. 2006. Sampling human experience in naturalistic settings. Emergent methods in social research (2006), 109--129.

[11]

Michael P Craven, Kirusnapillai Selvarajah, Robert Miles, Holger Schnädelbach, Adam Massey, Kavita Vedhara, Nicholas Raine-Fenning, and John Crowe. 2013. User requirements for the development of smartphone self-reporting applications in healthcare. In International Conference on Human-Computer Interaction. Springer, 36--45.

[12]

Nils Dahlbäck, Arne Jönsson, and Lars Ahrenberg. 1993. Wizard of Oz studies: why and how. In Proceedings of the 1st international conference on Intelligent user interfaces. 193--200.

Digital Library

[13]

Anind K Dey, Katarzyna Wac, Denzil Ferreira, Kevin Tassini, Jin-Hyuk Hong, and Julian Ramos. 2011. Getting closer: an empirical investigation of the proximity of user to their smart phones. In Proceedings of the 13th international conference on Ubiquitous computing. 163--172.

Digital Library

[14]

Victor Dibia. 2016. Foqus: A smartwatch application for individuals with adhd and mental health challenges. In Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility. ACM, 311--312.

Digital Library

[15]

Michael Dietz, Ilhan Aslan, Dominik Schiller, Simon Flutura, Anika Steinert, Robert Klebbe, and Elisabeth André. 2019. Stress Annotations from Older Adults - Exploring the Foundations for Mobile ML-Based Health Assistance. In Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare (Trento, Italy) (PervasiveHealth'19). Association for Computing Machinery, New York, NY, USA, 149--158. https://doi.org/10.1145/3329189.3329197

Digital Library

[16]

David Dobbelstein, Gabriel Haas, and Enrico Rukzio. 2017. The Effects of Mobility, Encumbrance, and (Non-)Dominant Hand on Interaction with Smartwatches. In Proceedings of the 2017 ACM International Symposium on Wearable Computers (Maui, Hawaii) (ISWC '17). ACM, New York, NY, USA, 90--93. https://doi.org/10.1145/3123021.3123033

Digital Library

[17]

Genevieve Fridlund Dunton, Eldin Dzubur, Keito Kawabata, Brenda Yanez, Bin Bo, and Stephen Intille. 2014. Development of a smartphone application to measure physical activity using sensor-assisted self-report. Frontiers in public health 2 (2014), 12.

[18]

Julian Frommel, Katja Rogers, Julia Brich, Daniel Besserer, Leonard Bradatsch, Isabel Ortinau, Ramona Schabenberger, Valentin Riemer, Claudia Schrader, and Michael Weber. 2015. Integrated questionnaires: maintaining presence in game environments for self-reported data acquisition. In Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play. 359--368.

Digital Library

[19]

Andrew G Miner, Theresa M Glomb, and Charles Hulin. 2005. Experience sampling mood and its correlates at work. Journal of Occupational and Organizational Psychology 78, 2 (2005), 171--193.

[20]

Jun Gong, Xing-Dong Yang, and Pourang Irani. 2016. WristWhirl: One-Handed Continuous Smartwatch Input Using Wrist Gestures. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (Tokyo, Japan) (UIST '16). Association for Computing Machinery, New York, NY, USA, 861--872. https://doi.org/10.1145/2984511.2984563

Digital Library

[21]

Mitchell Gordon, Tom Ouyang, and Shumin Zhai. 2016. WatchWriter: Tap and gesture typing on a smartwatch miniature keyboard with statistical decoding. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 3817--3821.

Digital Library

[22]

Katrin Hänsel, Akram Alomainy, and Hamed Haddadi. 2016. Large scale mood and stress self-assessments on a smartwatch. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. 1180--1184.

Digital Library

[23]

Seongkook Heo, Michelle Annett, Benjamin Lafreniere, Tovi Grossman, and George Fitzmaurice. 2017. No Need to Stop What You'Re Doing: Exploring No-Handed Smartwatch Interaction. In Proceedings of the 43rd Graphics Interface Colongtnference (GI '17). Canadian Human-Computer Communications Society, School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada, 107--114. https://doi.org/10.20380/GI2017.14

Digital Library

[24]

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 (MobileHCI '16). ACM, New York, NY, USA, 195--205. https://doi.org/10.1145/2935334.2935340

Digital Library

[25]

Wilhelm Hofmann and Paresh V Patel. 2015. SurveySignal: A convenient solution for experience sampling research using participants' own smartphones. Social Science Computer Review 33, 2 (2015), 235--253.

Digital Library

[26]

Stefan E Hormuth. 1986. The sampling of experiences in situ. Journal of personality 54, 1 (1986), 262--293.

[27]

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 (UbiComp '16). ACM, New York, NY, USA, 1124--1128. https://doi.org/10.1145/2971648.2971717

Digital Library

[28]

Stephen S Intille, John Rondoni, Charles Kukla, Isabel Ancona, and Ling Bao. 2003. A context-aware experience sampling tool. In CHI'03 extended abstracts on Human factors in computing systems. 972--973.

Digital Library

[29]

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.

[30]

Zachary D. King, Judith Moskowitz, Begum Egilmez, Shibo Zhang, Lida Zhang, Michael Bass, John Rogers, Roozbeh Ghaffari, Laurie Wakschlag, and Nabil Alshurafa. 2019. micro-Stress EMA: A Passive Sensing Framework for Detecting In-the-wild Stress in Pregnant Mothers. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 3, Article 91 (Sept. 2019), 22 pages. https://doi.org/10.1145/3351249

Digital Library

[31]

Reed Larson and Mihaly Csikszentmihalyi. 2014. The experience sampling method. In Flow and the foundations of positive psychology. Springer, 21--34.

[32]

Todd Matthew Manini, Tonatiuh Mendoza, Manoj Battula, Anis Davoudi, Matin Kheirkhahan, Mary Ellen Young, Eric Weber, Roger Benton Fillingim, and Parisa Rashidi. 2019. Perception of Older Adults Toward Smartwatch Technology for Assessing Pain and Related Patient-Reported Outcomes: Pilot Study. JMIR mHealth and uHealth 7, 3 (2019), e10044.

[33]

Andreas Möller, Matthias Kranz, Barbara Schmid, Luis Roalter, and Stefan Diewald. 2013. Investigating self-reporting behavior in long-term studies. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2931--2940.

Digital Library

[34]

Vivian Genaro Motti and Kelly Caine. 2015. Micro interactions and multi dimensional graphical user interfaces in the design of wrist worn wearables. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 59. SAGE Publications Sage CA: Los Angeles, CA, 1712--1716.

[35]

Inez Myin-Germeys, Margreet Oorschot, Dina Collip, Johan Lataster, Philippe Delespaul, and Jim Van Os. 2009. Experience sampling research in psychopathology: opening the black box of daily life. Psychological medicine 39, 9 (2009), 1533--1547.

[36]

Camille Nadal, Corina Sas, and Gavin Doherty. 2020. Acceptance of smartwatches for automated self-report in mental health interventions. In 25th annual international CyberPsychology, CyberTherapy & Social Networking Conference.

[37]

Antti Oulasvirta, Sakari Tamminen, Virpi Roto, and Jaana Kuorelahti. 2005. Interaction in 4-second Bursts: The Fragmented Nature of Attentional Resources in Mobile HCI. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Portland, Oregon, USA) (CHI '05). ACM, New York, NY, USA, 919--928. https://doi.org/10.1145/1054972.1055101

Digital Library

[38]

Gaurav Paruthi, Shriti Raj, Seungjoo Baek, Chuyao Wang, Chuan-che Huang, Yung-Ju Chang, and Mark W. Newman. 2018. Heed: Exploring the Design of Situated Self-Reporting Devices. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 3, Article 132 (Sept. 2018), 132:1-132:21 pages. https://doi.org/10.1145/3264942

[39]

Aditya Ponnada, Caitlin Haynes, Dharam Maniar, Justin Manjourides, and Stephen Intille. 2017. Microinteraction Ecological Momentary Assessment Response Rates: Effect of Microinteractions or the Smartwatch? Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 3, Article 92 (Sept. 2017), 16 pages. https://doi.org/10.1145/3130957

Digital Library

[40]

William T Riley, Daniel E Rivera, Audie A Atienza, Wendy Nilsen, Susannah M Allison, and Robin Mermelstein. 2011. Health behavior models in the age of mobile interventions: are our theories up to the task? Translational behavioral medicine 1, 1 (2011), 53--71.

[41]

Daniel E Rivera and Holly B Jimison. 2013. Systems modeling of behavior change: Two illustrations from optimized interventions for improved health outcomes. IEEE pulse 4, 6 (2013), 41--47.

[42]

Richard W Robins, Holly M Hendin, and Kali H Trzesniewski. 2001. Measuring global self-esteem: Construct validation of a single-item measure and the Rosenberg Self-Esteem Scale. Personality and social psychology bulletin 27, 2 (2001), 151--161.

[43]

John Christopher Rondoni. 2003. Context-aware experience sampling for the design and study of ubiquitous technologies. Ph.D. Dissertation. Massachusetts Institute of Technology.

[44]

Samsung. 2019. Galaxy Watch - Design | Samsung Developers. Website. Retrieved January 31, 2020 from https://developer.samsung.com/galaxy-watch-design/wearables/overview.html.

[45]

Niilo Saranummi, Donna Spruijt-Metz, Stephen S Intille, Ilkka Korhone, Wendy J Nilsen, and Misha Pavel. 2013. Moving the science of behavior change into the 21st century: novel solutions to prevent disease and promote health. IEEE pulse 4, 5 (2013), 22--24.

[46]

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. 909--920.

Digital Library

[47]

Hasti Seifi and Kent Lyons. 2016. Exploring the design space of touch-based vibrotactile interactions for smartwatches. In Proceedings of the 2016 ACM International Symposium on Wearable Computers. 156--165.

Digital Library

[48]

Dong-Hee Shin and Frank Biocca. 2017. Health experience model of personal informatics: The case of a quantified self. Computers in Human Behavior 69 (2017), 62--74.

Digital Library

[49]

Gaganpreet Singh, William Delamare, and Pourang Irani. 2018. D-SWIME: A Design Space for Smartwatch Interaction Techniques Supporting Mobility and Encumbrance. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI '18). ACM, New York, NY, USA, Article 634, 13 pages. https://doi.org/10.1145/3173574.3174208

Digital Library

[50]

Donna Spruijt-Metz and Wendy Nilsen. 2014. Dynamic models of behavior for just-in-time adaptive interventions. IEEE Pervasive Computing 13, 3 (2014), 13--17.

[51]

Janko Timmermann, Wilko Heuten, and Susanne Boll. 2015. Input methods for the Borg-RPE-scale on smartwatches. In Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare. ICST (Institute for Computer Sciences, Social-Informatics and ..., 80--83.

[52]

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 (CHI '14). ACM, New York, NY, USA, 3635--3644. https://doi.org/10.1145/2556288.2557044

[53]

Niels Van Berkel, Denzil Ferreira, and Vassilis Kostakos. 2017. The experience sampling method on mobile devices. ACM Computing Surveys (CSUR) 50, 6 (2017), 1--40.

Digital Library

[54]

Madelon LM Van Hooff, Sabine AE Geurts, Michiel AJ Kompier, and Toon W Taris. 2007. "How fatigued do you currently feel?" Convergent and discriminant validity of a single-item fatigue measure. Journal of Occupational Health 49, 3 (2007), 224--234.

[55]

Julio Vega, Samuel Couth, Ellen Poliakoff, Sonja Kotz, Matthew Sullivan, Caroline Jay, Markel Vigo, and Simon Harper. 2018. Back to Analogue: Self-Reporting for Parkinson's Disease. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA, Article 74, 74:1-74:13 pages. https://doi.org/10.1145/3173574.3173648

Digital Library

[56]

Philippe Verduyn, Ellen Delvaux, Hermina Van Coillie, Francis Tuerlinckx, and Iven Van Mechelen. 2009. Predicting the duration of emotional experience: Two experience sampling studies. Emotion 9, 1 (2009), 83.

[57]

Aku Visuri, Niels van Berkel, Chu Luo, Jorge Goncalves, Denzil Ferreira, and Vassilis Kostakos. 2017. Predicting Interruptibility for Manual Data Collection: A Cluster-based User Model. In Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services (Vienna, Austria) (MobileHCI '17). ACM, New York, NY, USA, Article 12, 14 pages. https://doi.org/10.1145/3098279.3098532

Digital Library

[58]

Cheng-Yao Wang, Min-Chieh Hsiu, Po-Tsung Chiu, Chiao-Hui Chang, Liwei Chan, Bing-Yu Chen, and Mike Y. Chen. 2015. PalmGesture: Using Palms As Gesture Interfaces for Eyes-free Input. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services (Copenhagen, Denmark) (MobileHCI '15). ACM, New York, NY, USA, 217--226. https://doi.org/10.1145/2785830.2785885

Digital Library

[59]

John P Wanous, Arnon E Reichers, and Michael J Hudy. 1997. Overall job satisfaction: how good are single-item measures? Journal of applied Psychology 82, 2 (1997), 247.

[60]

Jacob O Wobbrock. 2019. Situationally-induced impairments and disabilities. In Web Accessibility. Springer, 59--92.

[61]

Xinghui Yan, Katy Madier, Sun Young Park, and Mark Newman. 2019. Towards Low-burden In-situ Self-reporting: A Design Space Exploration. In Companion Publication of the 2019 on Designing Interactive Systems Conference 2019 Companion (San Diego, CA, USA) (DIS '19 Companion). ACM, New York, NY, USA, 337--346. https://doi.org/10.1145/3301019.3323905

Digital Library

[62]

Eman MG Younis, Eiman Kanjo, and Alan Chamberlain. 2019. Designing and evaluating mobile self-reporting techniques: crowdsourcing for citizen science. Personal and Ubiquitous Computing 23, 2 (2019), 329--338.

Digital Library

[63]

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 (CHI '16). ACM, New York, NY, USA, 5658--5664. https://doi.org/10.1145/2858036.2858360

Digital Library

Cited By

Cook DWalker AMinor BLuna CTomaszewski Farias SWiese LWeaver RSchmitter-Edgecombe M(2024)Understanding the Relationship between EMA Methods, Sensed Behavior, and Responsiveness: A Cross-Study Analysis (Preprint)JMIR mHealth and uHealth10.2196/57018Online publication date: 6-Feb-2024
https://doi.org/10.2196/57018
Yan XBaroudi LBei RBoudalia LCain SBarton KShorter KNewman M(2024)Proposing a Context-informed Layer-based Framework: Incorporating Context into Designing mHealth Technology for Fatigue ManagementProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661615(571-583)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661615
Kunchay SLinden-Carmichael AAbdullah S(2023)Design and Formative Evaluation of a Smartwatch Application to Understand Young Adult Substance Use (Preprint)JMIR Human Factors10.2196/50795Online publication date: 20-Jul-2023
https://doi.org/10.2196/50795
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Index Terms

Toward Lightweight In-situ Self-reporting: An Exploratory Study of Alternative Smartwatch Interface Designs in Context
1. Human-centered computing
  1. Interaction design
    1. Empirical studies in interaction design
  2. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing

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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 4, Issue 4

December 2020

1356 pages

EISSN:2474-9567

DOI:10.1145/3444864

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Copyright © 2020 ACM.

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Published: 18 December 2020

Published in IMWUT Volume 4, Issue 4

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Cited By

Cook DWalker AMinor BLuna CTomaszewski Farias SWiese LWeaver RSchmitter-Edgecombe M(2024)Understanding the Relationship between EMA Methods, Sensed Behavior, and Responsiveness: A Cross-Study Analysis (Preprint)JMIR mHealth and uHealth10.2196/57018Online publication date: 6-Feb-2024
https://doi.org/10.2196/57018
Yan XBaroudi LBei RBoudalia LCain SBarton KShorter KNewman M(2024)Proposing a Context-informed Layer-based Framework: Incorporating Context into Designing mHealth Technology for Fatigue ManagementProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661615(571-583)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661615
Kunchay SLinden-Carmichael AAbdullah S(2023)Design and Formative Evaluation of a Smartwatch Application to Understand Young Adult Substance Use (Preprint)JMIR Human Factors10.2196/50795Online publication date: 20-Jul-2023
https://doi.org/10.2196/50795
Markopoulos PKhanshan ABormans STisza GKang LVan Gorp P(2023)Comparative Evaluation of Touch-Based Input Techniques for Experience Sampling on SmartwatchesProceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia10.1145/3626705.3627768(74-86)Online publication date: 3-Dec-2023
https://dl.acm.org/doi/10.1145/3626705.3627768
Rey BLee BChoe EIrani P(2023)Investigating In-Situ Personal Health Data Queries on SmartwatchesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694816:4(1-19)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3569481
Yan XFeldman JBentley FKhwaja MGilbert M(2023)A Case Study Exploring Users’ Perceptions and Expectations of Shapes for Dialog DesignsExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544549.3573845(1-8)Online publication date: 19-Apr-2023
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Zhang QCavuoto L(2023)Investigating the Use of Changes in Facial Features as Indicators of Physical WorkloadIISE Transactions on Occupational Ergonomics and Human Factors10.1080/24725838.2023.222832911:1-2(48-58)Online publication date: 16-Jul-2023
https://doi.org/10.1080/24725838.2023.2228329
Volsa SBatinic BStieger S(2022)Self-Reports in the Field Using Smartwatches: An Open-Source Firmware SolutionSensors10.3390/s2205198022:5(1980)Online publication date: 3-Mar-2022
https://doi.org/10.3390/s22051980
Bardot SRey BAudette LFan KHuang DLi JLi WIrani P(2022)One Ring to Rule Them AllProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35503156:3(1-20)Online publication date: 7-Sep-2022
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Kim YChou DLee BDanilovich MLazar AConroy DKacorri HChoe E(2022)MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with SpeechProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517457(1-21)Online publication date: 29-Apr-2022
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