Time distortion associated with smartphone addiction: Identifying smartphone addiction via a mobile application (App)
Abstract
Background Global smartphone penetration has brought about unprecedented addictive behaviors.Aims We report a proposed diagnostic criteria and the designing of a mobile application (App) to identify smartphone addiction.Method We used a novel empirical mode decomposition (EMD) to delineate the trend in smartphone use over one month.Results The daily use count and the trend of this frequency are associated with smartphone addiction. We quantify excessive use by daily use duration and frequency, as well as the relationship between the tolerance symptoms and the trend for the median duration of a use epoch. The psychiatrists' assisted self-reporting use time is significant lower than and the recorded total smartphone use time via the App and the degree of underestimation was positively correlated with actual smartphone use.Conclusions Our study suggests the identification of smartphone addiction by diagnostic interview and via the App-generated parameters with EMD analysis.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Smartphone addiction is associated with deficits in attention and executive function such as inhibition and working memory.
“Smartphone addiction is associated with decreased cognitive function (Lee et al., 2017) and deficits in attention and executive function such as inhibition and working memory (Lin et al., 2015).”
From The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review (2023) · cites this paper as Lin et al., 2015
The constant barrage of information and distractions that come with digital technology can overload the brain, making it difficult to process information and retain it.
“Furthermore, the constant barrage of information and distractions that come with digital technology can overload the brain, making it difficult to process information and retain it (Lin et al., 2015).”
From The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review (2023) · cites this paper as Lin et al., 2015
Smartphone addiction has been associated with reduced activity in the prefrontal cortex.
“Smartphone addiction has also been associated with reduced activity in the prefrontal cortex, which is responsible for decision-making and impulse control (Lin et al., 2015).”
From The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review (2023) · cites this paper as Lin et al., 2015
Prolonged internet use was associated with reduced gray matter density in the anterior cingulate cortex, which is involved in cognitive and emotional processing.
“Lin et al. (2015) found that prolonged internet use was associated with reduced gray matter density in the anterior cingulate cortex, which is involved in cognitive and emotional processing.”
From The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review (2023) · cites this paper as Lin et al. (2015)
The cell phone has emerged as a source of potentially addictive behavior, particularly since the arrival of smartphone devices.
“If the Internet was initially the technological addiction par excellence, the cell phone soon emerged as a source of potentially addictive behavior, particularly since the arrival of smartphone devices (22, 23), along with the evolution from a global approach to a progressive differentiation of addictions by contents and concrete applications.”
From Cell-phone addiction: a review (2016) · cites this paper as [23]
Studies on the addictive consequences of the old mobile phone, the current smartphone, and the Internet are cross-sectional, and therefore the temporal evolution of their addictive impact on the population is still unknown.
“However, studies on the addictive consequences of both the ‘old’ mobile phone [9,33,34,35,36], the current smartphone [22,24,37,38,39,40], and the Internet are cross-sectional, and therefore the temporal evolution of their addictive impact on the population is still unknown.”
From Problematic Use of the Internet and Smartphones in University Students: 2006–2017 (2018) · cites this paper as [39]
Persons have problems in assessing their smartphone consumption.
“it has been demonstrated that persons have problems in assessing their smartphone consumption”
From Addictive Features of Social Media/Messenger Platforms and Freemium Games against the Background of Psychological and Economic Theories (2019) · cites this paper as [101]
The problems persons have in assessing their smartphone consumption are probably due to time distortions.
“and this is probably due to time distortions [49,101].”
From Addictive Features of Social Media/Messenger Platforms and Freemium Games against the Background of Psychological and Economic Theories (2019) · cites this paper as [101]
Cites
Distracted Driving and Risk of Road Crashes among Novice and Experienced Drivers
Klauer, S. G., Guo, F., Simons-Morton, B. G., Ouimet, M. C., Lee, S. E., & Dingus, T. A. (2014) · New England Journal of Medicine · doi:10.1056/nejmsa1204142 · Cited by 801 · see citing works
Development and validation of the Smartphone Addiction Inventory (SPAI)
Lin, Y.-H., Chang, L.-R., Lee, Y.-H., Tseng, H.-W., Kuo, T. B. J., & Chen, S.-H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098312 · Cited by 611 · see citing works
Self-citationTime Distortion for Expert and Novice Online Game Players
Rau, P.-L. P., Peng, S.-Y., & Yang, C.-C. (2006) · CyberPsychology & Behavior · doi:10.1089/cpb.2006.9.396 · Cited by 171 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Smartphone use and smartphone addiction among young people in Switzerland
Haug, S., Castro, R. P., Kwon, M., Filler, A., Kowatsch, T., & Schaub, M. P. (2015) · Journal of Behavioral Addictions · doi:10.1556/2006.4.2015.037 · Cited by 903 · see citing works
Parry, D. A., Davidson, B. I., Sewall, C. J. R., Fisher, J. T., Mieczkowski, H., & Quintana, D. S. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01117-5 · Cited by 793 · see citing works
Cell-phone addiction: a review
De-Sola Gutiérrez, J., Rodríguez de Fonseca, F., & Rubio, G. (2016) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2016.00175 · Cited by 722 · see citing works
Does time spent using social media impact mental health?: An eight year longitudinal study
Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L., & Booth, M. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2019.106160 · Cited by 701 · see citing works
Busch, P. A., & McCarthy, S. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106414 · Cited by 657 · see citing works
Cha, S.-S., & Seo, B.-K. (2018) · Health Psychology Open · doi:10.1177/2055102918755046 · Cited by 592 · see citing works
Wolniewicz, C. A., Tiamiyu, M. F., Weeks, J. W., & Elhai, J. D. (2018) · Psychiatry Research · doi:10.1016/j.psychres.2017.09.058 · Cited by 539 · see citing works
Smartphone usage in the 21st century: who is active on WhatsApp?
Montag, C., Błaszkiewicz, K., Sariyska, R., Lachmann, B., Andone, I., Trendafilov, B., Eibes, M., & Markowetz, A. (2015) · BMC Research Notes · doi:10.1186/s13104-015-1280-z · Cited by 408 · see citing works
Gökçearslan, Ş., Mumcu, F. K., Haşlaman, T., & Çevik, Y. D. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.05.091 · Cited by 424 · see citing works
Montag, C., Lachmann, B., Herrlich, M., & Zweig, K. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16142612 · Cited by 387 · see citing works
“Although literature on each of the elements in the study of SUD is scarce, it has been demonstrated that persons have problems in assessing their smartphone consumption, and this is probably due to time distortions [49,101].”
BackgroundDirect Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep
Christensen, M. A., Bettencourt, L., Kaye, L., Moturu, S. T., Nguyen, K. T., Olgin, J. E., Pletcher, M. J., & Marcus, G. M. (2016) · PLOS ONE · doi:10.1371/journal.pone.0165331 · Cited by 327 · see citing works
The Multipurpose Application WeChat: A Review on Recent Research
Montag, C., Becker, B., & Gan, C. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.02247 · Cited by 274 · see citing works
Shanmugasundaram, M., & Tamilarasu, A. (2023) · Frontiers in Cognition · doi:10.3389/fcogn.2023.1203077 · Cited by 291 · see citing works
Smartphone addiction and associated consequences: Role of loneliness and self-regulation
Mahapatra, S. (2019) · Behaviour & Information Technology · doi:10.1080/0144929x.2018.1560499 · Cited by 261 · see citing works
Problematic Use of the Internet and Smartphones in University Students: 2006–2017
Carbonell, X., Chamarro, A., Oberst, U., Rodrigo, B., & Prades, M. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15030475 · Cited by 240 · see citing works
Chen, I.-H., Strong, C., Lin, Y.-C., Tsai, M.-C., Leung, H., Lin, C.-Y., Pakpour, A. H., & Griffiths, M. D. (2020) · Addictive Behaviors · doi:10.1016/j.addbeh.2019.04.018 · Cited by 229 · see citing works
Self-citationBarnes, S. J., Pressey, A. D., & Scornavacca, E. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.09.013 · Cited by 200 · see citing works
Ellis, D. A. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.03.006 · Cited by 186 · see citing works
Excessive dependence on mobile social apps: A rational addiction perspective
Kwon, H. E., So, H., Han, S. P., & Oh, W. (2016) · Information Systems Research · doi:10.1287/isre.2016.0658 · Cited by 156 · see citing works
Determining typical smartphone usage: What data do we need?
Wilcockson, T. D. W., Ellis, D. A., & Shaw, H. (2018) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2017.0652 · Cited by 138 · see citing works
Wegmann, E., Müller, S. M., Turel, O., & Brand, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-60819-4 · Cited by 145 · see citing works
Facebook usage on smartphones and gray matter volume of the nucleus accumbens
Montag, C., Markowetz, A., Blaszkiewicz, K., Andone, I., Lachmann, B., Sariyska, R., Trendafilov, B., Eibes, M., Kolb, J., Reuter, M., et al. (2017) · Behavioural Brain Research · doi:10.1016/j.bbr.2017.04.035 · Cited by 135 · see citing works
Buctot, D. B., Kim, N., & Kim, J. J. (2020) · Children and Youth Services Review · doi:10.1016/j.childyouth.2020.104758 · Cited by 125 · see citing works
Montag, C., Błaszkiewicz, K., Lachmann, B., Sariyska, R., Andone, I., Trendafilov, B., & Markowetz, A. (2015) · Behavioral Sciences · doi:10.3390/bs5040434 · Cited by 122 · see citing works
Users of the main smartphone operating systems (iOS, Android) differ only little in personality
Götz, F. M., Stieger, S., & Reips, U.-D. (2017) · PLOS ONE · doi:10.1371/journal.pone.0176921 · Cited by 127 · see citing works
Mobile gaming and problematic smartphone use: A comparative study between Belgium and Finland
Lopez-Fernandez, O., Männikkö, N., Kääriäinen, M., Griffiths, M. D., & Kuss, D. J. (2018) · Journal of Behavioral Addictions · doi:10.1556/2006.6.2017.080 · Cited by 109 · see citing works
Schivinski, B., Brzozowska-Woś, M., Stansbury, E., Satel, J., Montag, C., & Pontes, H. M. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.617140 · Cited by 103 · see citing works
Smartphones are more reinforcing than food for students
O'Donnell, S., & Epstein, L. H. (2019) · Addictive Behaviors · doi:10.1016/j.addbeh.2018.10.018 · Cited by 101 · see citing works
Park, C. S. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.022 · Cited by 85 · see citing works
Smartphone Addiction Inventory (SPAI): psychometric properties and confirmatory factor analysis
Pavia, L., Cavani, P., Di Blasi, M., & Giordano, C. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.05.039 · Cited by 84 · see citing works
Montag, C., Sindermann, C., & Baumeister, H. (2020) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2020.03.013 · Cited by 86 · see citing works
The relationship between sleep and problematic smartphone use among adolescents: A systematic review
Mac Cárthaigh, S., Griffin, C., & Perry, J. (2020) · Developmental Review · doi:10.1016/j.dr.2020.100897 · Cited by 75 · see citing works
Annoni, A. M., Petrocchi, S., Camerini, A.-L., & Marciano, L. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18052452 · Cited by 75 · see citing works
Compulsive smartphone usage and users' ill-being among young Indians: Does personality matter?
Panda, A., & Jain, N. K. (2018) · Telematics and Informatics · doi:10.1016/j.tele.2018.03.006 · Cited by 67 · see citing works
Zhu, C., Jiang, Y., Lei, H., Wang, H., & Zhang, C. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e30346 · Cited by 60 · see citing works
Buctot, D. B., Kim, N., & Kim, S.-H. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18105219 · Cited by 53 · see citing works
Montag, C., & Rumpf, H.-J. (2021) · Current Addiction Reports · doi:10.1007/s40429-021-00376-6 · Cited by 44 · see citing works
Prasad, S., Harshe, D., Kaur, N., Jangannavar, S., Srivastava, A., Achanta, U., Khan, S., & Harshe, G. (2018) · Indian Journal of Psychological Medicine · doi:10.4103/ijpsym.ijpsym_133_18 · Cited by 31 · see citing works
Kim, H. R., & Han, M. A. (2020) · Psychiatry Investigation · doi:10.30773/pi.2020.0007 · Cited by 33 · see citing works
Meinhardt, L.-M., Elhaidary, M., Colley, M., Rietzler, M., Rixen, J. O., Purohit, A. K., & Rukzio, E. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713187 · Cited by 16 · see citing works
- Authors
- Lin, Y.-H., Lin, Y.-C., Lee, Y.-H., Lin, P.-H., Lin, S.-H., Chang, L.-R., Tseng, H.-W., Yen, L.-Y., Yang, C. C. H., & Kuo, T. B. J.
- Affiliations
- National Yang Ming University (TW)
- Tamkang University (TW)
- Koo Foundation Sun Yat-Sen Cancer Center (TW)
- Harvard University (US)
- National Taiwan University (TW)
- National Central University (TW)
- Corresponding author
- Kuo, Terry B.J.
- Venue
- Journal of Psychiatric Research
- Publisher
- Elsevier BV
- Published
- 2015-06
- Volume
- 65
- Pages
- 139-145
- ISSN
- 0022-3956 (ISSN-L)
- OpenAlex
- W2098427813
- Cited by
- 399
- References
- 3
- Citations by year
- 2026: 22 · 2025: 25 · 2024: 28 · 2023: 36 · 2022: 43 · 2021: 44 · 2020: 73 · 2019: 52 · 2018: 40 · 2017: 18 · 2016: 14 · 2015: 4
- FWCI
- 84.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Psychiatric Research (publishedVersion)
- PubMed (publishedVersion)
- Tamkang University Institutional Repository (TKUIR) (submittedVersion)
- Funders
- National Science Council
- Ministry of Education (NSC 100-2627-B-010-002)
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Psychology › Clinical Psychology › Sexuality, Behavior, and Technology
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- smartphone addiction, mobile application, empirical mode decomposition, smartphone use patterns, diagnostic criteria, smartphone overuse, time distortion
- MeSH terms
- Smartphone, Adult, Female, Humans, Interview, Psychological, Male, Models, Theoretical, Behavior, Addictive, Internet, Young Adult, Self Report, Mobile Applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review