research-article

Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing

Authors:

Lakmal Meegahapola,

Florian Labhart,

Thanh-Trung Phan,

Daniel Gatica-PerezAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 5, Issue 3

Article No.: 121, Pages 1 - 26

https://doi.org/10.1145/3478126

Published: 14 September 2021 Publication History

Abstract

According to prior work, the type of relationship between a person consuming alcohol and others in the surrounding (friends, family, spouse, etc.), and the number of those people (alone, with one person, with a group) are related to many aspects of alcohol consumption, such as the drinking amount, location, motives, and mood. Even though the social context is recognized as an important aspect that influences the drinking behavior of young adults in alcohol research, relatively little work has been conducted in smartphone sensing research on this topic. In this study, we analyze the weekend nightlife drinking behavior of 241 young adults in a European country, using a dataset consisting of self-reports and passive smartphone sensing data over a period of three months. Using multiple statistical analyses, we show that features from modalities such as accelerometer, location, application usage, bluetooth, and proximity could be informative about different social contexts of drinking. We define and evaluate seven social context inference tasks using smartphone sensing data, obtaining accuracies of the range 75%-86% in four two-class and three three-class inferences. Further, we discuss the possibility of identifying the sex composition of a group of friends using smartphone sensor data with accuracies over 70%. The results are encouraging towards supporting future interventions on alcohol consumption that incorporate users' social context more meaningfully and reducing the need for user self-reports when creating drink logs for self-tracking tools and public health studies.

Supplementary Material

meegahapola (meegahapola.zip)

Supplemental movie, appendix, image and software files for, Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing

Download
94.72 KB

References

[1]

2014. Youth@Night. Retrieved February 8, 2021 from http://www.youth-night.ch/

[2]

Birgitta Ander, Agneta Abrahamsson, and Disa Bergnehr. 2017. 'It is ok to be drunk, but not too drunk': party socialising, drinking ideals, and learning trajectories in Swedish adolescent discourse on alcohol use. Journal of Youth Studies 20, 7 (Aug. 2017), 841--854.

[3]

Z. Arnold, D. Larose, and E. Agu. 2015. Smartphone Inference of Alcohol Consumption Levels from Gait. In 2015 International Conference on Healthcare Informatics. 417--426.

[4]

Sophie Attwood, Hannah Parke, John Larsen, and Katie Morton. 2017. Using a mobile health application to reduce alcohol consumption: a mixed-methods evaluation of the drinkaware track & calculate units application. BMC Public Health 17 (05 2017).

[5]

Sangwon Bae, Tammy Chung, Denzil Ferreira, Anind K. Dey, and Brian Suffoletto. 2018. Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions. Addictive Behaviors 83 (2018), 42--47. Ambulatory Assessment of Addictive Disorders.

[6]

Sangwon Bae, Denzil Ferreira, Brian Suffoletto, Juan C. Puyana, Ryan Kurtz, Tammy Chung, and Anind K. Dey. 2017. Detecting Drinking Episodes in Young Adults Using Smartphone-Based Sensors. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 2, Article 5 (June 2017), 36 pages.

Digital Library

[7]

Yatan Balhara and Shachi Mathur. 2012. Alcohol: A Major Public Health Problem - South Asian Perspective. Addictive Disorders & Their Treatment 11 (06 2012), 101--120.

[8]

Kenneth Beck, Amelia Arria, Kimberly Caldeira, Kathryn Vincent, Kevin O'Grady, and Eric Wish. 2008. Social Context of Drinking and Alcohol Problems Among College Students. American journal of health behavior 32 (07 2008), 420--30.

[9]

Abdelkareem Bedri, Richard Li, Malcolm Haynes, Raj Prateek Kosaraju, Ishaan Grover, Temiloluwa Prioleau, Min Yan Beh, Mayank Goel, Thad Starner, and Gregory Abowd. 2017. EarBit: Using Wearable Sensors to Detect Eating Episodes in Unconstrained Environments. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 3, Article 37 (Sept. 2017), 20 pages.

Digital Library

[10]

Edward J. Bedrick. 2005. Biserial Correlation. American Cancer Society.

[11]

Melina Bersamin, Sharon Lipperman-Kreda, Christina Mair, Joel Grube, and Paul Gruenewald. 2016. Identifying Strategies to Limit Youth Drinking in the Home. Journal of Studies on Alcohol and Drugs 77, 6 (Nov. 2016), 943--949.

[12]

Joan-Isaac Biel, Nathalie Martin, David Labbe, and Daniel Gatica-Perez. 2018. Bites'N'Bits: Inferring Eating Behavior from Contextual Mobile Data. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 4, Article 125 (Jan. 2018), 33 pages.

Digital Library

[13]

Yekta Said Can, Bert Arnrich, and Cem Ersoy. 2019. Stress detection in daily life scenarios using smart phones and wearable sensors: A survey. Journal of Biomedical Informatics 92 (2019), 103139.

Digital Library

[14]

Luca Canzian and Mirco Musolesi. 2015. Trajectories of Depression: Unobtrusive Monitoring of Depressive States by Means of Smartphone Mobility Traces Analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Osaka, Japan) (UbiComp '15). Association for Computing Machinery, New York, NY, USA, 1293--1304.

Digital Library

[15]

Giuseppe Carra, Cristina Crocamo, Francesco Bartoli, Daniele Carretta, Alessandro Schivalocchi, Paul Bebbington, and Massimo Clerici. 2016. Impact of a Mobile E-Health Intervention on Binge Drinking in Young People: The Digital-Alcohol Risk Alertness Notifying Network for Adolescents and Young Adults Project. Journal of Adolescent Health 58 (05 2016), 520--6.

[16]

Nitesh Chawla, Kevin Bowyer, Lawrence Hall, and W. Kegelmeyer. 2002. SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. (JAIR) 16 (06 2002), 321--357.

[17]

Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. CoRR abs/1603.02754 (2016).

Digital Library

[18]

François Chollet. 2015. keras. https://github.com/fchollet/keras.

[19]

Varoth Chotpitayasunondh and Karen M. Douglas. 2016. How phubbing becomes the norm: The antecedents and consequences of snubbing via smartphone. Computers in Human Behavior 63 (2016), 9--18.

Digital Library

[20]

M. J. Cox, K. Sewell, K. L. Egan, S. Baird, C. Eby, K. Ellis, and J. Kuteh. 2019. A systematic review of high-risk environmental circumstances for adolescent drinking. Journal of Substance Use 24, 5 (Sept. 2019), 465--474.

[21]

David Crane, Claire Garnett, Susan Michie, Robert West, and Jamie Brown. 2018. A smartphone app to reduce excessive alcohol consumption: Identifying the effectiveness of intervention components in a factorial randomised control trial. Scientific Reports 8 (03 2018).

[22]

Adele Cutler, David Cutler, and John Stevens. 2011. Random Forests. Vol. 45. 157--176.

[23]

J. Dai, J. Teng, X. Bai, Z. Shen, and D. Xuan. 2010. Mobile phone based drunk driving detection. In 2010 4th International Conference on Pervasive Computing Technologies for Healthcare. 1--8.

[24]

Victor-Alexandru Darvariu, Laura Convertino, Abhinav Mehrotra, and Mirco Musolesi. 2020. Quantifying the Relationships between Everyday Objects and Emotional States through Deep Learning Based Image Analysis Using Smartphones. 4, 1, Article 7 (March 2020), 21 pages.

Digital Library

[25]

Emma L Davies, Adam J Lonsdale, Sarah E Hennelly, Adam R Winstock, and David R Foxcroft. 2017. Personalized Digital Interventions Showed no Impact on Risky Drinking in Young Adults: A Pilot Randomized Controlled Trial. Alcohol and Alcoholism 52, 6 (08 2017), 671--676.

[26]

Danielle M. Dick, Jason L. Pagan, Candice Holliday, Richard Viken, Lea Pulkkinen, Jaakko Kaprio, and Richard J. Rose. 2007. Gender Differences in Friends Influences on Adolescent Drinking: A Genetic Epidemiological Study. Alcoholism: Clinical and Experimental Research 31, 12 (2007), 2012--2019.

[27]

Patrick Dulin, Vivian Gonzalez, Diane King, Danielle Giroux, and Samantha Bacon. 2013. Smartphone-Based, Self-Administered Intervention System for Alcohol Use Disorders: Theory and Empirical Evidence Basis. Alcoholism treatment quarterly 31 (07 2013).

[28]

Bridget Freisthler, Sharon Lipperman-Kreda, Melina Bersamin, and Paul Gruenewald. 2014. Tracking the when, where, and with whom of alcohol use: Integrating ecological momentary assessment and geospatial data to examine risk for alcohol-related problems. Alcohol Research: Current Reviews 36, 1 (2014).

[29]

Bettina Friese and Joel W. Grube. 2014. Teen Parties: Who Has Parties, What Predicts Whether There Is Alcohol and Who Supplies the Alcohol? The Journal of Primary Prevention 35, 6 (Dec. 2014), 391--396.

[30]

Owen Gallupe and Martin Bouchard. 2013. Adolescent parties and substance use: A situational approach to peer influence. Journal of Criminal Justice 41, 3 (May 2013), 162--171.

[31]

Conor Gilligan, Kypros Kypri, Natalie Johnson, Marita Lynagh, and Stephanie Love. 2012. Parental supply of alcohol and adolescent risky drinking: Parental supply of alcohol and adolescent drinking. Drug and Alcohol Review 31, 6 (Sept. 2012), 754--762.

[32]

Gerhard Gmel, Jacques Gaume, Mohamed Faouzi, Jean-Pierre Kulling, and Jean-Bernard Daeppen. 2008. Who Drinks Most of the Total Alcohol in Young Men---Risky Single Occasion Drinking as Normative Behaviour. Alcohol and Alcoholism 43, 6 (2008), 692--697.

[33]

Google. 2021. Choose a category and tags for your app or game. Retrieved January 29, 2021 from https://bit.ly/39r8zPp

[34]

Greenland Sander, Senn Stephen J., Rothman Kenneth J., Carlin John B., Poole Charles, Goodman Steven N., and Altman Douglas G. 2016. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European Journal of Epidemiology 31, 4 (2016), 337--350.

[35]

Ulrike Grittner, Sharon Wilsnack, Sandra Kuntsche, Thomas Greenfield, Richard Wilsnack, Arlinda Kristjanson, and Kim Bloomfield. 2019. A Multilevel Analysis of Regional and Gender Differences in the Drinking Behavior of 23 Countries. Substance Use & Misuse 55 (12 2019), 1--15.

[36]

David Gustafson, Fiona Mctavish, Ming-Yuan Chih, Amy Atwood, Roberta Johnson, Michael Boyle, Michael Levy, Hilary Driscoll, Steven Chisholm, Lisa Dillenburg, Andrew Isham, and Dhavan Shah. 2014. A Smartphone Application to Support Recovery From Alcoholism A Randomized Clinical Trial. JAMA psychiatry 71 (03 2014).

[37]

Leanne Hides, Catherine Quinn, Wendell Cockshaw, Stoyan Stoyanov, Oksana Zelenko, Daniel Johnson, Dian Tjondronegoro, Lake-Hui Quek, and David J. Kavanagh. 2018. Efficacy and outcomes of a mobile app targeting alcohol use in young people. Addictive Behaviors 77 (2018), 89--95.

[38]

Kristina M. Jackson, Jennifer E. Merrill, Nancy P. Barnett, Suzanne M. Colby, Caitlin C. Abar, Michelle L. Rogers, and Kerri L. Hayes. 2016. Contextual influences on early drinking: Characteristics of drinking and nondrinking days. Psychology of Addictive Behaviors 30, 5 (2016), 566--577.

[39]

Jisu Jung, Lyndal Wellard-Cole, Colin Cai, Irena Koprinska, Kalina Yacef, Margaret Allman-Farinelli, and Judy Kay. 2020. Foundations for Systematic Evaluation and Benchmarking of a Mobile Food Logger in a Large-Scale Nutrition Study. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4, 2, Article 47 (June 2020), 25 pages.

Digital Library

[40]

Hsin-Liu (Cindy) Kao, Bo-Jhang Ho, Allan C. Lin, and Hao-Hua Chu. 2012. Phone-Based Gait Analysis to Detect Alcohol Usage. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (Pittsburgh, Pennsylvania) (UbiComp '12). Association for Computing Machinery, New York, NY, USA, 661--662.

Digital Library

[41]

Dean Karantonis, Michael Narayanan, Merryn Mathie, Nigel Lovell, and B.G. Celler. 2006. Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring. Information Technology in Biomedicine, IEEE Transactions on 10 (02 2006), 156--167.

[42]

Mohammed Khwaja, Sumer S. Vaid, Sara Zannone, Gabriella M. Harari, A. Aldo Faisal, and Aleksandar Matic. 2019. Modeling Personality vs. Modeling Personalidad: In-the-Wild Mobile Data Analysis in Five Countries Suggests Cultural Impact on Personality Models. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 3, Article 88 (Sept. 2019), 24 pages.

Digital Library

[43]

Tae Kim. 2015. T test as a parametric statistic. Korean Journal of Anesthesiology 68 (11 2015), 540.

[44]

Takumi Kondo, Haruka Kamachi, Shun Ishii, Anna Yokokubo, and Guillaume Lopez. 2019. Robust Classification of Eating Sound Collected in Natural Meal Environment. In 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 (London, United Kingdom) (UbiComp/ISWC '19 Adjunct). Association for Computing Machinery, New York, NY, USA, 105--108.

[45]

Santosh Kumar, Wendy J. Nilsen, Amy Abernethy, Audie Atienza, Kevin Patrick, Misha Pavel, William T. Riley, Albert Shar, Bonnie Spring, Donna Spruijt-Metz, Donald Hedeker, Vasant Honavar, Richard Kravitz, R. Craig Lefebvre, David C. Mohr, Susan A. Murphy, Charlene Quinn, Vladimir Shusterman, and Dallas Swendeman. 2013. Mobile Health Technology Evaluation: The mHealth Evidence Workshop. American Journal of Preventive Medicine 45, 2 (2013), 228--236.

[46]

Emmanuel Kuntsche and Gerhard Gmel. 2013. Alcohol consumption in late adolescence and early adulthood - where is the problem? Swiss medical weekly 143 (07 2013), 0.

[47]

Emmanuel Kuntsche, Ronald Knibbe, Gerhard Gmel, and Rutger Engels. 2005. Why do young people drink? A review of drinking motives. Clinical Psychology Review 25, 7 (2005), 841--861.

[48]

Emmanuel Kuntsche and Sandra Kuntsche. 2009. Development and Validation of the Drinking Motive Questionnaire Revised Short Form (DMQâ€"R SF). Journal of clinical child and adolescent psychology: the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53 38 (11 2009), 899--908.

[49]

Emmanuel Kuntsche, Sandra Kuntsche, Johannes Thrul, and Gerhard Gmel. 2017. Binge drinking: Health impact, prevalence, correlates and interventions. Psychology & health 32 (05 2017), 1--42.

[50]

Emmanuel Kuntsche and Florian Labhart. 2014. The future is now-using personal cellphones to gather data on substance use and related factors: Editorial. Addiction 109, 7 (July 2014), 1052--1053.

[51]

Emmanuel Kuntsche and Florian Labhart. 2014. The future is nowâ€" using personal cellphones to gather data on substance use and related factors. Addiction 109, 7 (2014), 1052--1053.

[52]

Florian Labhart, Michael Livingston, Rutger Engels, and Emmanuel Kuntsche. 2018. After how many drinks does someone experience acute consequencesâ€" determining thresholds for binge drinking based on two event-level studies. Addiction 113, 12 (2018), 2235--2244.

[53]

Florian Labhart, Skanda Muralidhar, Benoit MassÃ©, Lakmal Meegahapola, Emmanuel Kuntsche, and Daniel Gatica-Perez. 2021. Ten seconds of my nights: Exploring methods to measure brightness, loudness and attendance and their associations with alcohol use from video clips. PLOS ONE 16, 4 (04 2021), 1--21.

[54]

Florian Labhart, Flavio Tarsetti, Olivier Bornet, Darshan Santani, Jasmine Truong, Sara Landolt, Daniel Gatica-Perez, and Emmanuel Kuntsche. 2020. Capturing drinking and nightlife behaviours and their social and physical context with a smartphone application -investigation of users' experience and reactivity. Addiction Research & Theory 28, 1 (2020), 62--75.

[55]

Florian Labhart, Samantha Wells, Kathryn Graham, and Emmanuel Kuntsche. 2014. Do Individual and Situational Factors Explain the Link Between Predrinking and Heavier Alcohol Consumption? An Event-Level Study of Types of Beverage Consumed and Social Context. Alcohol and Alcoholism 49, 3 (May 2014), 327--335.

[56]

Daniël Lakens. 2013. Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol 4: 863. Frontiers in psychology 4 (11 2013), 863.

[57]

Nicholas D. Lane, Mu Lin, Mashfiqui Mohammod, Xiaochao Yang, Hong Lu, Giuseppe Cardone, Shahid Ali, Afsaneh Doryab, Ethan Berke, Andrew T. Campbell, and Tanzeem Choudhury. 2014. BeWell: Sensing Sleep, Physical Activities and Social Interactions to Promote Wellbeing. Mob. Netw. Appl. 19, 3 (June 2014), 345--359.

Digital Library

[58]

Anne-Marie Laslett, Robin Room, Jason Ferris, Claire Wilkinson, Michael Livingston, and Janette Mugavin. 2011. Surveying the range and magnitude of alcohol's harm to others in Australia. Addiction 106, 9 (2011), 1603--1611.

[59]

Katrin Leadley, Catherine L Clark, and Raul Caetano. 2000. Couples' Drinking Patterns, Intimate Partner Violence, and Alcohol-Related Partnership Problems. Journal of Substance Abuse 11, 3 (2000), 253--263.

[60]

Dong Kyu Lee. 2016. Alternatives to P value: confidence interval and effect size. In Korean journal of anesthesiology.

[61]

Kenneth Leonard and Pamela Mudar. 2003. Peer and partner drinking and the transition to marriage: A longitudinal examination of selection and influence processes. Psychology of addictive behaviors: journal of the Society of Psychologists in Addictive Behaviors 17 (07 2003), 115--25.

[62]

Robert LiKamWa, Yunxin Liu, Nicholas D. Lane, and Lin Zhong. 2013. MoodScope: Building a Mood Sensor from Smartphone Usage Patterns. In Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services (Taipei, Taiwan) (MobiSys '13). Association for Computing Machinery, New York, NY, USA, 389--402.

Digital Library

[63]

Mu Lin, Nicholas D. Lane, Mashfiqui Mohammod, Xiaochao Yang, Hong Lu, Giuseppe Cardone, Shahid Ali, Afsaneh Doryab, Ethan Berke, Andrew T. Campbell, and Tanzeem Choudhury. 2012. BeWell+: Multi-dimensional Wellbeing Monitoring with Community-guided User Feedback and Energy Optimization. In Proceedings of the Conference on Wireless Health (San Diego, California) (WH '12). ACM, New York, NY, USA, Article 10, 8 pages.

Digital Library

[64]

Tomi Lintonen, Salme Ahlstorm, and Leena Metso. 2004. The Reliability of Self-Reported Drinking in Adolescence. Alcohol and Alcoholism 39, 4 (07 2004), 362--368.

[65]

Sharon Lipperman-Kreda, Laura J. Finan, and Joel W. Grube. 2018. Social and situational characteristics associated with adolescents' drinking at party and non-party events. Addictive Behaviors 83 (Aug. 2018), 148--153.

[66]

Claire G. Lisco, Dominic J. Parrott, and Andra Teten Tharp. 2012. The role of heavy episodic drinking and hostile sexism in men's sexual aggression toward female intimate partners. Addictive Behaviors 37, 11 (2012), 1264 - 1270.

[67]

Hong Lu, Denise Frauendorfer, Mashfiqui Rabbi, Marianne Schmid Mast, Gokul T. Chittaranjan, Andrew T. Campbell, Daniel Gatica-Perez, and Tanzeem Choudhury. 2012. StressSense: Detecting Stress in Unconstrained Acoustic Environments Using Smartphones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (Pittsburgh, Pennsylvania) (UbiComp '12). ACM, New York, NY, USA, 351--360.

Digital Library

[68]

E. Jane Marshall. 2014. Adolescent Alcohol Use: Risks and Consequences. Alcohol and Alcoholism 49, 2 (01 2014), 160--164.

[69]

M.J. Mathie, B.G. Celler, Nigel Lovell, and Adelle Coster. 2004. Classification of basic daily movements using a triaxial accelerometer. Medical & biological engineering & computing 42 (10 2004), 679--87.

[70]

Dennis McCarty. 1985. Environmental factors in substance abuse. Springer, 247--281.

[71]

Lakmal Meegahapola and Daniel Gatica-Perez. 2021. Smartphone Sensing for the Well-Being of Young Adults: A Review. IEEE Access 9 (2021), 3374--3399.

[72]

Lakmal Meegahapola, Salvador Ruiz-Correa, and Daniel Gatica-Perez. 2020. Alone or With Others? Understanding Eating Episodes of College Students with Mobile Sensing. In 19th International Conference on Mobile and Ubiquitous Multimedia (Essen, Germany) (MUM 2020). Association for Computing Machinery, New York, NY, USA, 162--166.

[73]

Lakmal Meegahapola, Salvador Ruiz-Correa, and Daniel Gatica-Perez. 2020. Protecting Mobile Food Diaries from Getting Too Personal. In 19th International Conference on Mobile and Ubiquitous Multimedia (Essen, Germany) (MUM 2020). Association for Computing Machinery, New York, NY, USA, 212--222.

Digital Library

[74]

Lakmal Meegahapola, Salvador Ruiz-Correa, Viridiana del Carmen Robledo-Valero, Emilio Ernesto Hernandez-Huerfano, Leonardo Alvarez-Rivera, Ronald Chenu-Abente, and Daniel Gatica-Perez. 2021. One More Bite? Inferring Food Consumption Level of College Students using Smartphone Sensing and Self-Reports. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5, 1, Article 26 (2021), 28 pages.

[75]

Jochen Meyer, Judy Kay, Daniel A. Epstein, Parisa Eslambolchilar, and Lie Ming Tang. 2020. A Life of Data: Characteristics and Challenges of Very Long Term Self-Tracking for Health and Wellness. ACM Trans. Comput. Healthcare 1, 2, Article 11 (March 2020), 4 pages.

Digital Library

[76]

C D Mohr, S Averna, D A Kenny, and F K Del Boca. 2001. "Getting by (or getting high) with a little help from my friends": an examination of adult alcoholics' friendships. Journal of Studies on Alcohol 62, 5 (2001), 637--645.

[77]

Mehrab Bin Morshed, Samruddhi Shreeram Kulkarni, Richard Li, Koustuv Saha, Leah Galante Roper, Lama Nachman, Hong Lu, Lucia Mirabella, Sanjeev Srivastava, Munmun De Choudhury, Kaya de Barbaro, Thomas Ploetz, and Gregory D Abowd. 2020. A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study. JMIR Mhealth Uhealth 4, 3 (18 dec 2020), e20625.

[78]

Inbal Nahum-Shani, Shawna Smith, Bonnie Spring, Linda Collins, Katie Witkiewitz, Ambuj Tewari, and Susan Murphy. 2016. Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Annals of Behavioral Medicine 52 (09 2016).

[79]

Alexey Natekin and Alois Knoll. 2013. Gradient boosting machines, a tutorial. Frontiers in Neurorobotics 7 (2013), 21.

[80]

Jeremy Northcote and Michael Livingston. 2011. Accuracy of Self-Reported Drinking: Observational Verification of 'Last Occasion' Drink Estimates of Young Adults. Alcohol and Alcoholism 46, 6 (09 2011), 709--713.

[81]

Renee O'Donnell, Ben Richardson, Matthew Fuller-Tyszkiewicz, and Petra Staiger. 2019. Delivering Personalized Protective Behavioral Drinking Strategies via a Smartphone Intervention: a Pilot Study. International Journal of Behavioral Medicine 26 (06 2019).

[82]

D. Wayne Osgood, Daniel T. Ragan, Lacey Wallace, Scott D. Gest, Mark E. Feinberg, and James Moody. 2013. Peers and the Emergence of Alcohol Use: Influence and Selection Processes in Adolescent Friendship Networks. Journal of Research on Adolescence 23, 3 (2013), 500--512.

[83]

Hilde Pape and Elin K. Bye. 2017. Drinking with parents: Different measures, different associations with underage heavy drinking? Nordisk alkohol- & narkotikatidskrift: NAT 34, 6 (Dec. 2017), 445--455.

[84]

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830.

Digital Library

[85]

Peggy L. Peterson, J. David Hawkins, Robert D. Abbott, and Richard F. Catalano. 1994. Disentangling the Effects of Parental Drinking, Family Management, and Parental Alcohol Norms on Current Drinking by Black and White Adolescents. Journal of Research on Adolescence 4, 2 (1994), 203--227.

[86]

Thanh-Trung Phan, Florian Labhart, Skanda Muralidhar, and Daniel Gatica-Perez. 2020. Understanding Heavy Drinking at Night through Smartphone Sensing and Active Human Engagement. In Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare (Atlanta, GA, USA) (PervasiveHealth '20). Association for Computing Machinery, New York, NY, USA, 211--222.

Digital Library

[87]

Thanh-Trung Phan, Skanda Muralidhar, and Daniel Gatica-Perez. 2019. Drinks & Crowds: Characterizing Alcohol Consumption through Crowdsensing and Social Media. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 2, Article 59 (June 2019), 30 pages.

Digital Library

[88]

Evelien A. P. Poelen, Ron H. J. Scholte, Gonneke Willemsen, Dorret I. Boomsma, and Rutger C. M. E. Engels. 2007. Drinking by parents, siblings, and friends as predictors of regular alcohol use in adolescents and young adults: a longitudinal twin-family study. Alcohol and Alcoholism 42, 4 (05 2007), 362--369.

[89]

Marnie E. Rice and Grant T. Harris. 2005. Comparing Effect Sizes in Follow-Up Studies: ROC Area, Cohen's d, and r. Law and Human Behavior 29, 5 (01 Oct 2005), 615--620.

[90]

Irina Rish. 2001. An Empirical Study of the NaÃve Bayes Classifier. IJCAI 2001 Work Empir Methods Artif Intell 3 (01 2001).

[91]

James A. Roberts and Meredith E. David. 2016. My life has become a major distraction from my cell phone: Partner phubbing and relationship satisfaction among romantic partners. Computers in Human Behavior 54 (2016), 134--141.

Digital Library

[92]

D. Santani, T. Do, F. Labhart, S. Landolt, E. Kuntsche, and D. Gatica-Perez. 2018. DrinkSense: Characterizing Youth Drinking Behavior Using Smartphones. IEEE Transactions on Mobile Computing 17, 10 (2018), 2279--2292.

[93]

Darshan Santani and Daniel Gatica-Perez. 2015. Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular Indoor Urban Places. In Proceedings of the 23rd ACM International Conference on Multimedia (Brisbane, Australia) (MM '15). ACM, 211--220.

Digital Library

[94]

Robert E Schapire. 2013. Explaining adaboost. In Empirical inference. Springer, 37--52.

[95]

Laura Schelenz, Ivano Bison, Matteo Busso, Amalia de Götzen, Daniel Gatica-Perez, Fausto Giunchiglia, Lakmal Meegahapola, and Salvador Ruiz-Correa. 2021. The Theory, Practice, and Ethical Challenges of Designing a Diversity-Aware Platform for Social Relations. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. ACM, 11.

Digital Library

[96]

Sandra Servia-Rodríguez, Kiran K. Rachuri, Cecilia Mascolo, Peter J. Rentfrow, Neal Lathia, and Gillian M. Sandstrom. 2017. Mobile Sensing at the Service of Mental Well-Being: A Large-Scale Longitudinal Study. In Proceedings of the 26th International Conference on World Wide Web (Perth, Australia) (WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 103--112.

[97]

Saul Shiffman, Arthur A. Stone, and Michael R. Hufford. 2008. Ecological Momentary Assessment. Annual Review of Clinical Psychology 4, 1 (2008), 1--32.

[98]

Carillon J. Skrzynski and Kasey G. Creswell. 2020. A systematic review and meta-analysis on the association between solitary drinking and alcohol problems in adults. Addiction (Dec. 2020), add.15355.

[99]

Koen Smit, Martine Groefsema, Maartje Luijten, Rutger Engels, and Emmanuel Kuntsche. 2015. Drinking Motives Moderate the Effect of the Social Environment on Alcohol Use: An Event-Level Study Among Young Adults. Journal of Studies on Alcohol and Drugs 76, 6 (Nov. 2015), 971--980.

[100]

Joshua Smyth and Arthur Stone. 2003. Ecological Momentary Assessment Research in Behavioral medicine. Journal of Happiness Studies 4 (02 2003), 35--52.

[101]

Bundit Sornpaisarn, Kevin Shield, Jakob Manthey, Yuriko Limmade, Wah Yun Low, Vo Van Thang, and Jurgen Rehm. 2020. Alcohol consumption and attributable harm in middle-income South-East Asian countries: Epidemiology and policy options. International Journal of Drug Policy 83 (2020), 102856.

[102]

Dimitris Spathis, Sandra Servia-Rodriguez, Katayoun Farrahi, Cecilia Mascolo, and Jason Rentfrow. 2019. Passive Mobile Sensing and Psychological Traits for Large Scale Mood Prediction. 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, 272--281.

Digital Library

[103]

D. Spruijt-Metz and W. Nilsen. 2014. Dynamic Models of Behavior for Just-in-Time Adaptive Interventions. IEEE Pervasive Computing 13, 3 (2014), 13--17.

[104]

Abigail K. Stevely, John Holmes, Simon McNamara, and Petra S. Meier. 2020. Drinking contexts and their association with acute alcohol-related harm: A systematic review of event-level studies on adults' drinking occasions. Drug and Alcohol Review 39, 4 (May 2020), 309--320.

[105]

Richard Taylor. 1990. Interpretation of the Correlation Coefficient: A Basic Review. Journal of Diagnostic Medical Sonography 6, 1 (1990), 35--39.

[106]

Johannes Thrul and Emmanuel Kuntsche. 2015. The impact of friends on young adults' drinking over the course of the evening-an event-level analysis: Impact of friends on young adults' drinking. Addiction 110, 4 (April 2015), 619--626.

[107]

Johannes Thrul, Florian Labhart, and Emmanuel Kuntsche. 2017. Drinking with mixed-gender groups is associated with heavy weekend drinking among young adults. Alcohol Research: Current Reviews 112, 3 (2017), 432--439.

[108]

Johannes Thrul, Sharon Lipperman-Kreda, and Joel W. Grube. 2018. Do Associations Between Drinking Event Characteristics and Underage Drinking Differ by Drinking Location? Journal of Studies on Alcohol and Drugs 79, 3 (May 2018), 417--422.

[109]

Haske Van Der Vorst, Rutger C.M.E. Engels, Wim Meeus, and Maja Dekovic. 2006. The impact of alcohol-specific rules, parental norms about early drinking and parental alcohol use on adolescentsdrinking behavior. Journal of Child Psychology and Psychiatry 47, 12 (2006), 1299--1306.

[110]

Haske Van Der Vorst, Rutger C. M. E. Engels, Wim Meeus, Maja Dekovic, and Jan Van Leeuwe. 2005. The role of alcohol-specific socialization in adolescents' drinking behaviour. Addiction 100, 10 (2005), 1464--1476.

[111]

Victoria Vickerstaff, Rumana Omar, and Gareth Ambler. 2019. Methods to adjust for multiple comparisons in the analysis and sample size calculation of randomised controlled trials with multiple primary outcomes. BMC Medical Research Methodology 19 (06 2019).

[112]

Rui Wang, Weichen Wang, Alex daSilva, Jeremy F. Huckins, William M. Kelley, Todd F. Heatherton, and Andrew T. Campbell. 2018. Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1, Article 43 (March 2018), 26 pages.

Digital Library

[113]

Aaron White and Ralph Hingson. 2013. The Burden of Alcohol Use Excessive Alcohol Consumption and Related Consequences Among College Students. Alcohol research: current reviews 35 (01 2013), 201--18.

[114]

W. Yao, Y. Liu, D. Zhou, Z. Pan, M. J. Till, J. Zhao, L. Zhu, L. Zhan, Q. Tang, and Y. Liu. 2018. Impact of GPS Signal Loss and Its Mitigation in Power System Synchronized Measurement Devices. IEEE Transactions on Smart Grid 9, 2 (2018), 1141--1149.

[115]

Koji Yatani. 2016. Effect Sizes and Power Analysis in HCI. Springer International Publishing, Cham, 87--110.

[116]

Chuang-wen You, Kuo-Cheng Wang, Ming-Chyi Huang, Yen-Chang Chen, Cheng-Lin Lin, Po-Shiun Ho, Hao-Chuan Wang, Polly Huang, and Hao-Hua Chu. 2015. SoberDiary: A Phone-Based Support System for Assisting Recovery from Alcohol Dependence. Association for Computing Machinery, New York, NY, USA, 3839--3848.

Cited By

Barnett NSokolovsky AMeisel MForkus SJackson K(2024)A Bluetooth-Based Smartphone App for Detecting Peer Proximity: Protocol for Evaluating Functionality and ValidityJMIR Research Protocols10.2196/5024113(e50241)Online publication date: 5-Apr-2024
https://doi.org/10.2196/50241
Meegahapola LHassoune HGatica-Perez D(2024)M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial TrainingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595918:2(1-30)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659591
Kianpisheh MMariakakis ATruong K(2024)exHARProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435008:1(1-30)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643500
Show More Cited By

Index Terms

Recommendations

Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors

Alcohol use in young adults is common, with high rates of morbidity and mortality largely due to periodic, heavy drinking episodes (HDEs). Behavioral interventions delivered through electronic communication modalities (e.g., text messaging) can reduce ...
Social media jealousy and intimate partner violence in young adults’ romantic relationships: A longitudinal study
Highlights
- Social media are a prime location for the emergence of jealousy in romantic relationships.
Abstract
Social media have profoundly transformed young adults’ social interactions, especially within their romantic relationships. For instance, jealousy induced by the partner's activity on social media can cause conflicts that can escalate ...
Cyberbullying among young adults in Malaysia

The study explored cyberbullying prevalence among young adults in Malaysia.Social media emerged as the primary tool used.No significant differences for gender and age on cyberbullying activities.Internet frequency significantly predicts cyberbullying.A ...

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 5, Issue 3

Sept 2021

1443 pages

EISSN:2474-9567

DOI:10.1145/3486621

Issue’s Table of Contents

Copyright © 2021 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 2021

Published in IMWUT Volume 5, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
470
Total Downloads

Downloads (Last 12 months)112
Downloads (Last 6 weeks)13

Reflects downloads up to 03 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Barnett NSokolovsky AMeisel MForkus SJackson K(2024)A Bluetooth-Based Smartphone App for Detecting Peer Proximity: Protocol for Evaluating Functionality and ValidityJMIR Research Protocols10.2196/5024113(e50241)Online publication date: 5-Apr-2024
https://doi.org/10.2196/50241
Meegahapola LHassoune HGatica-Perez D(2024)M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial TrainingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595918:2(1-30)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659591
Kianpisheh MMariakakis ATruong K(2024)exHARProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435008:1(1-30)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643500
Ghosh SMandi SSen SMitra BDe P(2024)Towards Estimating Missing Emotion Self-reports Leveraging User Similarity: A Multi-task Learning ApproachProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642833(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642833
Mäder AMeegahapola LGatica-Perez D(2024)Learning About Social Context From Smartphone Data: Generalization Across Countries and Daily Life MomentsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642444(1-18)Online publication date: 11-May-2024
https://doi.org/10.1145/3613904.3642444
Jung YJung GJeong SKim CWoo WHong HLee U(2023)"Enjoy, but Moderately!": Designing a Social Companion Robot for Social Engagement and Behavior Moderation in Solitary Drinking ContextProceedings of the ACM on Human-Computer Interaction10.1145/36100287:CSCW2(1-24)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610028
Nanchen AMeegahapola LDroz WGatica-Perez D(2023)Keep Sensors in Check: Disentangling Country-Level Generalization Issues in Mobile Sensor-Based Models with Diversity ScoresProceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3600211.3604688(217-228)Online publication date: 8-Aug-2023
https://dl.acm.org/doi/10.1145/3600211.3604688
Parra MCastro LFavela J(2023)Enhancing Well-being Through Food: A Conversational Agent for Mindful Eating and CookingAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610732(423-427)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610732
Kammoun NMeegahapola LGatica-Perez D(2023)Understanding the Social Context of Eating with Multimodal Smartphone Sensing: The Role of Country DiversityProceedings of the 25th International Conference on Multimodal Interaction10.1145/3577190.3614129(604-612)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3577190.3614129
Meegahapola LDroz WKun Pde Götzen ANutakki CDiwakar SCorrea SSong DXu HBidoglia MGaskell GChagnaa AGanbold AZundui TCaprini CMiorandi DHume AZarza JCernuzzi LBison IBritez MBusso MChenu-Abente RGünel CGiunchiglia FSchelenz LGatica-Perez D(2023)Generalization and Personalization of Mobile Sensing-Based Mood Inference ModelsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694836:4(1-32)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3569483
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