research-article

Understanding, Discovering, and Mitigating Habitual Smartphone Use in Young Adults

Authors:

Alberto Monge Roffarello,

Luigi De RussisAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 11, Issue 2

Article No.: 13, Pages 1 - 34

https://doi.org/10.1145/3447991

Published: 21 July 2021 Publication History

Abstract

People, especially young adults, often use their smartphones out of habit: They compulsively browse social networks, check emails, and play video-games with little or no awareness at all. While previous studies analyzed this phenomena qualitatively, e.g., by showing that users perceive it as meaningless and addictive, yet our understanding of how to discover smartphone habits and mitigate their disruptive effects is limited. Being able to automatically assess habitual smartphone use, in particular, might have different applications, e.g., to design better “digital wellbeing” solutions for mitigating meaningless habitual use.

To close this gap, we first define a data analytic methodology based on clustering and association rules mining to automatically discover complex smartphone habits from mobile usage data. We assess the methodology over more than 130,000 phone usage sessions collected from users aged between 16 and 33, and we show evidence that smartphone habits of young adults can be characterized by various types of links between contextual situations and usage sessions, which are highly diversified and differently perceived across users. We then apply the proposed methodology in Socialize, a digital wellbeing app that (i) monitors habitual smartphone behaviors in real time and (ii) uses proactive notifications and just-in-time reminders to encourage users to avoid any identified smartphone habits they consider as meaningless. An in-the-wild study with 20 users (ages 19–31) demonstrates that Socialize can assist young adults in better controlling their smartphone usage with a significant reduction of their unwanted smartphone habits.

References

[1]

Google. 2018. Our commitment to Digital Wellbeing. Retrieved from https://wellbeing.google/.

[2]

Henk Aarts, Theo Paulussen, and Herman Schaalma. 1997. Physical exercise habit: On the conceptualization and formation of habitual health behaviours. Health Educ. Res. 12 (10 1997), 363–74.

[3]

Rakesh Agrawal, Tomasz Imieliński, and Arun Swami. 1993. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’93). ACM, New York, NY, 207–216.

Digital Library

[4]

Rakesh Agrawal and Ramakrishnan Srikant. 1994. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’94). Morgan Kaufmann Publishers Inc., San Francisco, CA, 487–499.

Digital Library

[5]

Morgan G. Ames. 2013. Managing mobile multitasking: The culture of iPhones on stanford campus. In Proceedings of the Conference on Computer Supported Cooperative Work (CSCW’13). ACM, New York, NY, 1487–1498.

Digital Library

[6]

Ricardo Baeza-Yates, Di Jiang, Fabrizio Silvestri, and Beverly Harrison. 2015. Predicting the next app that you are going to use. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM’15). Association for Computing Machinery, New York, NY, 285–294.

Digital Library

[7]

Nikola Banovic, Christina Brant, Jennifer Mankoff, and Anind Dey. 2014. ProactiveTasks: The short of mobile device use sessions. In Proceedings of the 16th International Conference on Human-computer Interaction with Mobile Devices & Services (MobileHCI’14). ACM, New York, NY, 243–252.

Digital Library

[8]

Joseph B. Bayer and Scott W. Campbell. 2012. Texting while driving on automatic: Considering the frequency-independent side of habit. Comput. Hum. Behav. 28, 6 (2012), 2083–2090.

Digital Library

[9]

Adriana Bianchi and James G. Phillips. 2005. Psychological predictors of problem mobile phone use. Cyberpsychol. Behav.: Impact Internet, Multimedia Virt. Real. Behav. Soc. 8 1 (2005), 39–51.

[10]

Matthias Böhmer, Brent Hecht, Johannes Schöning, Antonio Krüger, and Gernot Bauer. 2011. Falling asleep with Angry Birds, Facebook and Kindle: A large scale study on mobile application usage. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI’11). ACM, New York, NY, 47–56.

Digital Library

[11]

Juan Pablo Carrascal and Karen Church. 2015. An in-situ study of mobile app & mobile search interactions. In Proceedings of the 33rd ACM Conference on Human Factors in Computing Systems (CHI’15). Association for Computing Machinery, New York, NY, 2739–2748.

Digital Library

[12]

Rory Cellan-Jones. 2018. Confessions of a smartphone addict. Retrieved from https://www.bbc.com/news/technology-44972913.

[13]

Chen Chongyang, Kem Zhang, and Sesia J. Zhao. 2015. Examining the effects of perceived enjoyment and habit on smartphone addiction: The role of user type. Lect. Notes Bus. Inf. Proc. 209, 224–235.

[14]

Laura Dabbish, Gloria Mark, and Víctor M. González. 2011. Why do I keep interrupting myself? Environment, habit and self-interruption. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’11). ACM, New York, NY, 3127–3130.

Digital Library

[15]

Tilman Dingler, Dominik Weber, Martin Pielot, Jennifer Cooper, Chung-Cheng Chang, and Niels Henze. 2017. Language learning on-the-go: Opportune moments and design of mobile microlearning sessions. In Proceedings of the 19th International Conference on Human-computer Interaction with Mobile Devices and Services (MobileHCI’17). Association for Computing Machinery, New York, NY.

Digital Library

[16]

Brian D. Earp, Brendan Dill, Jennifer L. Harris, Joshua M. Ackerman, and John A. Bargh. 2013. No sign of quitting: Incidental exposure to “no smoking” signs ironically boosts cigarette-approach tendencies in smokers. J. Appl. Soc. Psychol. 43, 10 (2013), 2158–2162.

[17]

Gilles Einstein and Mark McDaniel. 2005. Prospective memory: Multiple retrieval processes. Curr. Direct. Psychol. Sci. 14 (12 2005).

[18]

Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD’96). AAAI Press, 226–231.

Digital Library

[19]

Denzil Ferreira, Jorge Goncalves, Vassilis Kostakos, Louise Barkhuus, and Anind K. Dey. 2014. Contextual experience sampling of mobile application micro-usage. In Proceedings of the 16th International Conference on Human-computer Interaction with Mobile Devices & Services (MobileHCI’14). ACM, New York, NY, 91–100.

Digital Library

[20]

Benjamin Gardner. 2015. A review and analysis of the use of “habit” in understanding, predicting and influencing health-related behaviour. Health Psychol. Rev. 9, 3 (2015), 277–295.

[21]

Victor M. González and Gloria Mark. 2004. “Constant, constant, multi-tasking craziness”: Managing multiple working spheres. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’04). ACM, New York, NY, 113–120.

Digital Library

[22]

Alina Hang, Alexander De Luca, Jonas Hartmann, and Heinrich Hussmann. 2013. Oh app, where art thou? On app launching habits of smartphone users. In Proceedings of the 15th International Conference on Human-computer Interaction with Mobile Devices and Services (MobileHCI’13). ACM, New York, NY, 392–395.

Digital Library

[23]

Ellie Harmon and Melissa Mazmanian. 2013. Stories of the smartphone in everyday discourse: Conflict, tension & instability. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’13). ACM, New York, NY, 1051–1060.

Digital Library

[24]

Joshua Harwood, Julian J. Dooley, Adrian J. Scott, and Richard Joiner. 2014. Constantly connected—The effects of smart-devices on mental health. Comput. Hum. Behav. 34 (2014), 267–272.

[25]

Alexis Hiniker, Sungsoo (Ray) Hong, Tadayoshi Kohno, and Julie A. Kientz. 2016. MyTime: Designing and evaluating an intervention for smartphone non-use. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’16). ACM, New York, NY, 4746–4757.

Digital Library

[26]

Alexis Hiniker, Shwetak N. Patel, Tadayoshi Kohno, and Julie A. Kientz. 2016. Why would you do that? Predicting the uses and gratifications behind smartphone-usage behaviors. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’16). Association for Computing Machinery, New York, NY, 634–645.

Digital Library

[27]

Daniel Hintze, Philipp Hintze, Rainhard D. Findling, and René Mayrhofer. 2017. A large-scale, long-term analysis of mobile device usage characteristics. Proc. ACM Interact. Mob. Wear. Ubiq. Technol. 1, 2 (June 2017).

Digital Library

[28]

Ke Huang, Chunhui Zhang, Xiaoxiao Ma, and Guanling Chen. 2012. Predicting mobile application usage using contextual information. In Proceedings of the ACM Conference on Ubiquitous Computing (UbiComp’12). ACM, New York, NY, 1059–1065.

Digital Library

[29]

NComputing Global Inc.2019. QualityTime: Discover Your Smartphone Habits. Manage Your Digital Diet. Retrieved from http://www.qualitytimeapp.com/.

[30]

Alexander J. Rothman, Paschal Sheeran, and Wendy Wood. 2009. Reflective and automatic processes in the initiation and maintenance of dietary change. Ann. Behav. Med. 38 Suppl 1 (09 2009), S4–17.

[31]

Simon L. Jones, Denzil Ferreira, Simo Hosio, Jorge Goncalves, and Vassilis Kostakos. 2015. Revisitation Analysis of Smartphone App Use. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’15). ACM, New York, NY, 1197–1208.

Digital Library

[32]

L. Kaufman and P. J. Rousseeuw. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley.

[33]

Jaejeung Kim, Joonyoung Park, Hyunsoo Lee, Minsam Ko, and Uichin Lee. 2019. LocknType: Lockout task intervention for discouraging smartphone app use. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’19). ACM, New York, NY.

Digital Library

[34]

Predrag Klasnja, Sunny Consolvo, and Wanda Pratt. 2011. How to evaluate technologies for health behavior change in HCI research. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’11). Association for Computing Machinery, New York, NY, 3063–3072.

Digital Library

[35]

Christian A. Klöckner. 2013. A comprehensive model of the psychology of environmental behaviour—A meta-analysis. Glob. Environ. Change 23, 5 (2013), 1028–1038.

[36]

Minsam Ko, Seungwoo Choi, Koji Yatani, and Uichin Lee. 2016. Lock N’ LoL: Group-based limiting assistance app to mitigate smartphone distractions in group activities. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’16). ACM, New York, NY, 998–1010.

Digital Library

[37]

Minsam Ko, Subin Yang, Joonwon Lee, Christian Heizmann, Jinyoung Jeong, Uichin Lee, Daehee Shin, Koji Yatani, Junehwa Song, and Kyong-Mee Chung. 2015. NUGU: A group-based intervention app for improving self-regulation of limiting smartphone use. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW’15). ACM, New York, NY, 1235–1245.

Digital Library

[38]

Phillippa Lally and Benjamin Gardner. 2013. Promoting habit formation. Health Psychol. Rev. 7, sup 1 (2013), S137–S158.

[39]

Phillippa Lally, Cornelia H. M. van Jaarsveld, Henry W. W. Potts, and Jane Wardle. 2010. How are habits formed: Modelling habit formation in the real world. Eur. J. Soc. Psychol. 40, 6 (2010), 998–1009.

[40]

Simone Lanette, Phoebe K. Chua, Gillian Hayes, and Melissa Mazmanian. 2018. How much is “too much”? The role of a smartphone addiction narrative in individuals’ experience of use. Proc. ACM Hum.-comput. Interact. 2, CSCW (Nov. 2018).

Digital Library

[41]

Simone Lanette and Melissa Mazmanian. 2018. The smartphone “addiction” narrative is compelling, but largely unfounded. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (CHI EA’18). Association for Computing Machinery, New York, NY.

Digital Library

[42]

Robert Larose, Jung-Hyun Kim, and Wei Peng. 2011. Social Networking: Addictive, Compulsive, Problematic or Just Another Media Habit? Routledge, 59–81.

[43]

J. K. Laurila, Daniel Gatica-Perez, I. Aad, J. Blom, Olivier Bornet, Trinh-Minh-Tri Do, O. Dousse, J. Eberle, and M. Miettinen. 2012. The mobile data challenge: Big data for mobile computing research. Retrieved from http://infoscience.epfl.ch/record/192489.

[44]

Uichin Lee, Joonwon Lee, Minsam Ko, Changhun Lee, Yuhwan Kim, Subin Yang, Koji Yatani, Gahgene Gweon, Kyong-Mee Chung, and Junehwa Song. 2014. Hooked on smartphones: An exploratory study on smartphone overuse among college students. In Proceedings of the 32nd ACM Conference on Human Factors in Computing Systems (CHI’14). ACM, New York, NY, 2327–2336.

Digital Library

[45]

Yu-Kang Lee, Chun-Tuan Chang, You Lin, and Zhao-Hong Cheng. 2014. The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress. Comput. Hum. Behav. 31 (2014), 373–383.

Digital Library

[46]

Robert LiKamWa, Yunxin Liu, Nicholas D. Lane, and Lin Zhong. 2013. MoodScope: Building a mood sensor from smartphone usage patterns. In Proceedings of the 11th International Conference on Mobile Systems, Applications, and Services (MobiSys’13). Association for Computing Machinery, New York, NY, 389–402.

Digital Library

[47]

Moez Limayem, Sabine Gabriele Hirt, and Christy M. K. Cheung. 2007. How habit limits the predictive power of intention: The case of information systems continuance. MIS Quart. 31, 4 (2007), 705–737.

Digital Library

[48]

Xi Lu, Junko Watanabe, Qingbo Liu, Masayo Uji, Masahiro Shono, and Toshinori Kitamura. 2011. Internet and mobile phone text-messaging dependency: Factor structure and correlation with dysphoric mood among Japanese adults. Comput. Hum. Behav. 27, 5 (2011), 1702–1709.

Digital Library

[49]

Kai Lukoff, Cissy Yu, Julie Kientz, and Alexis Hiniker. 2018. What makes smartphone use meaningful or meaningless?Proc. ACM Interact., Mob., Wear. Ubiq. Technol. 2, 1 (Mar. 2018).

Digital Library

[50]

Ulrik Lyngs, Kai Lukoff, Petr Slovak, Reuben Binns, Adam Slack, Michael Inzlicht, Max Van Kleek, and Nigel Shadbolt. 2019. Self-control in cyberspace: Applying dual systems theory to a review of digital self-control tools. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’19). ACM, New York, NY.

Digital Library

[51]

Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press, New York, NY.

Digital Library

[52]

John G. McHaffie, Terrence R. Stanford, Barry E. Stein, Veronique Coizet, and Peter Redgrave. 2005. Subcortical loops through the basal ganglia. Trends Neurosci. 28, 8 (2005), 401–407.

[53]

Abhinav Mehrotra, Sandrine R. Müller, Gabriella M. Harari, Samuel D. Gosling, Cecilia Mascolo, Mirco Musolesi, and Peter J. Rentfrow. 2017. Understanding the role of places and activities on mobile phone interaction and usage patterns. Proc. ACM Interact., Mob., Wear. Ubiq. Technol. 1, 3 (Sept. 2017).

Digital Library

[54]

Alberto Monge Roffarello and Luigi De Russis. 2019. The race towards digital wellbeing: Issues and opportunities. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’19). ACM, New York, NY.

Digital Library

[55]

Sheina Orbell and Bas Verplanken. 2010. The automatic component of habit in health behavior: Habit as cue-contingent automaticity. Health Psychol. 29 (07 2010), 374–83.

[56]

Antti Oulasvirta, Tye Rattenbury, Lingyi Ma, and Eeva Raita. 2012. Habits make smartphone use more pervasive. Person. Ubiq. Comput. 16, 1 (01 Jan. 2012), 105–114.

Digital Library

[57]

Abhinav Parate, Matthias Böhmer, David Chu, Deepak Ganesan, and Benjamin M. Marlin. 2013. Practical prediction and prefetch for faster access to applications on mobile phones. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’13). Association for Computing Machinery, New York, NY, 275–284.

Digital Library

[58]

Martin Pielot, Bruno Cardoso, Kleomenis Katevas, Joan Serrà, Aleksandar Matic, and Nuria Oliver. 2017. Beyond interruptibility: Predicting opportune moments to engage mobile phone users. Proc. ACM Interact., Mob., Wear. Ubiq. Technol. 1, 3 (Sept. 2017).

Digital Library

[59]

Martin Pielot, Tilman Dingler, Jose San Pedro, and Nuria Oliver. 2015. When attention is not scarce—Detecting boredom from mobile phone usage. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’15). Association for Computing Machinery, New York, NY, 825–836.

Digital Library

[60]

Charlie Pinder, Jo Vermeulen, Benjamin R. Cowan, and Russell Beale. 2018. Digital behaviour change interventions to break and form habits. ACM Trans. Comput.-hum. Interact. 25, 3 (June 2018).

Digital Library

[61]

Adam Popescu. 2018. Keep Your Head Up: How Smartphone Addiction Kills Manners and Moods. Retrieved from https://www.nytimes.com/2018/01/25/smarter-living/bad-text-posture-neckpain-mood.html.

[62]

Amanda Rebar, James Dimmock, Ben Jackson, Ryan Rhodes, Andrew Kates, Jade Starling, and Corneel Vandelanotte. 2016. A systematic review of the effects of non-conscious regulatory processes in physical activity. Health Psychol. Rev. 10 (04 2016), 1–86.

[63]

Eric Robinson, Paul Aveyard, Amanda Daley, Kate Jolly, A. Lewis, Deborah Lycett, and Suzanne Higgs. 2013. Eating attentively: A systematic review and meta-analysis of the effect of food intake memory and awareness on eating. Amer. J. Clin. Nutrit. 97 (02 2013).

[64]

Larry D. Rosen, L. Mark Carrier, and Nancy A. Cheever. 2013. Facebook and texting made me do it: Media-induced task-switching while studying. Comput. Hum. Behav. 29, 3 (May 2013), 948–958.

Digital Library

[65]

Mohammad Salehan and Arash Negahban. 2013. Social networking on smartphones: When mobile phones become addictive. Comput. Hum. Behav. 29, 6 (Nov. 2013), 2632–2639.

Digital Library

[66]

G. Salton, A. Wong, and C. S. Yang. 1975. A vector space model for automatic indexing. Commun. ACM 18, 11 (Nov. 1975), 613–620.

Digital Library

[67]

Seekrtech. 2019. Forest—Stay focused, be present. Retrieved from https://www.forestapp.cc/.

[68]

Choonsung Shin and Anind K. Dey. 2013. Automatically detecting problematic use of smartphones. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’13). ACM, New York, NY, 335–344.

Digital Library

[69]

Choonsung Shin, Jin-Hyuk Hong, and Anind K. Dey. 2012. Understanding and prediction of mobile application usage for smart phones. In Proceedings of the ACM Conference on Ubiquitous Computing (UbiComp’12). ACM, New York, NY, 173–182.

Digital Library

[70]

Manya Sleeper, Alessandro Acquisti, Lorrie Faith Cranor, Patrick Gage Kelley, Sean A. Munson, and Norman Sadeh. 2015. I would like to..., I shouldn’t..., I wish I...: Exploring behavior-change goals for social networking sites. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW’15). ACM, New York, NY, 1058–1069.

Digital Library

[71]

A. David Smith and J. Paul Bolam. 1990. The neural network of the basal ganglia as revealed by the study of synaptic connections of identified neurones. Trends. Neurosci. 13, 7 (1990), 259–265.

[72]

Michael Steinbach, George Karypis, and Vipin Kumar. 2000. A comparison of document clustering techniques. In Proceedings of the International KDD Workshop on Text Mining.

[73]

Chen Sun, Jun Zheng, Huiping Yao, Yang Wang, and D. Frank Hsu. 2013. AppRush: Using dynamic shortcuts to facilitate application launching on mobile devices. Procedia Comput. Sci. 19 (2013), 445–452.

[74]

Pang-Ning Tan, Michael Steinbach, and Vipin Kumar. 2005. Introduction to Data Mining (First Edition). Addison-Wesley Longman Publishing Co., Inc., Boston, MA.

Digital Library

[75]

Virginia Thomas, Margarita Azmitia, and Steve Whittaker. 2016. Unplugged: Exploring the costs and benefits of constant connection. Comput. Hum. Behav. 63 (2016), 540–548.

Digital Library

[76]

Chad Tossell, Philip Kortum, Ahmad Rahmati, Clayton Shepard, and Lin Zhong. 2012. Characterizing web use on smartphones. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’12). ACM, New York, NY, 2769–2778.

Digital Library

[77]

Jonathan A. Tran, Katie S. Yang, Katie Davis, and Alexis Hiniker. 2019. Modeling the engagement-disengagement cycle of compulsive phone use. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’19). ACM, New York, NY.

Digital Library

[78]

Niels van Berkel, Chu Luo, Theodoros Anagnostopoulos, Denzil Ferreira, Jorge Goncalves, Simo Hosio, and Vassilis Kostakos. 2016. A systematic assessment of smartphone usage gaps. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’16). Association for Computing Machinery, New York, NY, 4711–4721.

Digital Library

[79]

Alexander J. A. M. van Deursen, Colin L. Bolle, Sabrina M. Hegner, and Piet A. M. Kommers. 2015. Modeling habitual and addictive smartphone behavior: The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Comput. Hum. Behav. 45 (2015), 411–420.

Digital Library

[80]

Philippe Verduyn, David Lee, Jiyoung Park, Holly Shablack, Ariana Orvell, Joseph Bayer, Oscar Ybarra, John Jonides, and Ethan Kross. 2015. Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence. J. Exper. Psychol. Gen. (02 2015).

[81]

Tingxin Yan, David Chu, Deepak Ganesan, Aman Kansal, and Jie Liu. 2012. Fast app launching for mobile devices using predictive user context. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys’12). ACM, New York, NY, 113–126.

Digital Library

[82]

Sha Zhao, Julian Ramos, Jianrong Tao, Ziwen Jiang, Shijian Li, Zhaohui Wu, Gang Pan, and Anind K. Dey. 2016. Discovering different kinds of smartphone users through their application usage behaviors. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’16). ACM, New York, NY, 498–509.

Digital Library

[83]

Maayan Zhitomirsky-Geffet and Maya Blau. 2016. Cross-generational analysis of predictive factors of addictive behavior in smartphone usage. Comput. Hum. Behav. 64 (2016), 682–693.

Digital Library

Cited By

Terzimehić NHuber JAragon-Hahner SMayer S(2024)Real-World Winds: Micro Challenges to Promote Balance Post Smartphone OverloadProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642583(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642583
Haliburton LGrüning DRiedel FSchmidt ATerzimehić N(2024)A Longitudinal In-the-Wild Investigation of Design Frictions to Prevent Smartphone OveruseProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642370(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642370
Monge Roffarello ADe Russis L(2024)Hey StepByStep! Can you teach me how to use my phone better?International Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103195183:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.ijhcs.2023.103195
Show More Cited By

Index Terms

Understanding, Discovering, and Mitigating Habitual Smartphone Use in Young Adults
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
    2. HCI design and evaluation methods
      1. User models
  2. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing
    2. Ubiquitous and mobile devices
      1. Smartphones

Recommendations

Reflect, not Regret: Understanding Regretful Smartphone Use with App Feature-Level Analysis
CSCW2

Digital intervention tools against problematic smartphone usage help users control their consumption on smartphones, for example, by setting a time limit on an app. However, today's social media apps offer a mix of quasiessential and addictive features ...
Towards detecting and mitigating smartphone habits
UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers

Smartphones have the potential to produce new habits, i.e., habitual phone usage sessions consistently associated with explicit contextual cues. Despite there is evidence that habitual smartphone use is perceived as meaningless and addictive, little is ...
Modeling habitual and addictive smartphone behavior

We investigate indicators of habitual and addictive smartphone behavior.Process and social related smartphone usage are strong determinants.Social stress has a positive influence, self-regulation a negative.Emotional intelligence did not have an ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 11, Issue 2

June 2021

267 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/3465444

Editor:
Michelle X. Zhou
Juji, Inc., USA

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

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 ACM 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: 21 July 2021

Accepted: 01 January 2021

Revised: 01 January 2021

Received: 01 June 2020

Published in TIIS Volume 11, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
816
Total Downloads

Downloads (Last 12 months)350
Downloads (Last 6 weeks)43

Reflects downloads up to 11 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Terzimehić NHuber JAragon-Hahner SMayer S(2024)Real-World Winds: Micro Challenges to Promote Balance Post Smartphone OverloadProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642583(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642583
Haliburton LGrüning DRiedel FSchmidt ATerzimehić N(2024)A Longitudinal In-the-Wild Investigation of Design Frictions to Prevent Smartphone OveruseProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642370(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642370
Monge Roffarello ADe Russis L(2024)Hey StepByStep! Can you teach me how to use my phone better?International Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103195183:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.ijhcs.2023.103195
Chen XZhang KHuang Y(2023)Effect of Social Loneliness on Tourist Happiness: A Mediation Analysis Based on Smartphone UsageSustainability10.3390/su1511876015:11(8760)Online publication date: 29-May-2023
https://doi.org/10.3390/su15118760
Al-Mansoori RAl-Thani DAli R(2023)Designing for Digital Wellbeing: From Theory to Practice a Scoping ReviewHuman Behavior and Emerging Technologies10.1155/2023/99240292023(1-24)Online publication date: 2-Aug-2023
https://doi.org/10.1155/2023/9924029
Terzimehic NBemmann FHalsner MMayer S(2023)A Mixed-Method Exploration into the Mobile Phone Rabbit HoleProceedings of the ACM on Human-Computer Interaction10.1145/36042417:MHCI(1-29)Online publication date: 13-Sep-2023
https://dl.acm.org/doi/10.1145/3604241
Terzimehić NAragon-Hahner SHussmann H(2023)The Tale of a Complicated RelationshipProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35807927:1(1-34)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580792
Monge Roffarello ADe Russis L(2023)Nudging Users Towards Conscious Social Media UseProceedings of the 25th International Conference on Mobile Human-Computer Interaction10.1145/3565066.3608703(1-7)Online publication date: 26-Sep-2023
https://dl.acm.org/doi/10.1145/3565066.3608703
Monge Roffarello ALukoff KDe Russis L(2023)Defining and Identifying Attention Capture Deceptive Designs in Digital InterfacesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580729(1-19)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580729
Mallawaarachchi STieppo AHooley MHorwood S(2023)Persuasive design-related motivators, ability factors and prompts in early childhood appsComputers in Human Behavior10.1016/j.chb.2022.107492139:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.chb.2022.107492
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.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents