Evidence map›Paper›PMID 41493477›Full record

ArticleJournal of behavioral addictions2026

Multidimensional analysis of smartphone overuse in insomnia: Integrating digital phenotyping with clinical assessment.

Emma Matsushita, Hyungju Kim, Minji Kim, Seojin Yoon, Sujin Kim, Ji Won Yeom, Seung Pil Pack, Heon-Jeong Lee, Taesu Cheong, Chul-Hyun Cho

Abstract read
In one paragraph

Article in Journal of behavioral addictions, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Emma Matsushita1Keio University School of Medicine, Tokyo, Japan.ORCID https://orcid.org/0009-0008-0097-1524
Hyungju Kim2School of Industrial Management Engineering, Korea University, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0009-0935-7747
Minji Kim3Korea University College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0006-2770-6056
Seojin Yoon3Korea University College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0002-2791-2918
Sujin Kim4Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0009-1323-7561
Ji Won Yeom4Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0003-0038-2101
Seung Pil Pack5Department of Biotechnology and Bioinformatics, Korea University, Sejong, Republic of Korea.ORCID https://orcid.org/0000-0002-4874-0265
Heon-Jeong Lee4Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-9560-2383
Taesu Cheong2School of Industrial Management Engineering, Korea University, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-8340-825X
Chul-Hyun Cho4Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-1663-9680

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aims: This study aimed to identify the differences in characteristics between high- and low-risk smartphone users among individuals with insomnia symptoms using digital phenotyping and clinical assessments. Methods: A total of 246 participants with insomnia symptoms (M = 31.14, SD = 10.09) were monitored for four weeks using the smartphone application and wearable devices. The participants were divided into high-(n = 141) and low-risk (n = 105) smartphone overuse groups based on a Smartphone Overuse Screening Questionnaire. Clinical scale results and wearable data were analyzed using ANCOVA and logistic regression, controlling for age, sex, and BMI. Results: After covariate adjustment, the high-risk group showed significantly greater biological rhythm disruption (K-BRIAN: LS-mean difference = 6.86, p < 0.000), more severe insomnia (ISI index: aOR: 2.63, p = 0.0005), and poorer sleep quality (PSQI-K: aOR: 2.41, p = 0.0015). Psychological distress, including depression (PHQ-9 index: aOR: 2.77, p = 0.0001) and anxiety (GAD-7 index: aOR: 1.59, p = 0.0059), was more pronounced in the high-risk group. Bedtime procrastination (BPS index: aOR: 1.96, p = 0.0173) and stress reactivity to insomnia (FIRST index: aOR: 1.67, p = 0.0574) were significantly elevated. Digital phenotyping revealed persistent differences in minimum daytime heart rate and exercise intensity patterns, while many activity-related measures lost significance after adjustment. Discussion and conclusions: Smartphone overuse is independently associated with severe circadian disruption, insomnia, and psychological distress. The integrated assessment approach revealed critical biomarkers and behavioral patterns. Targeted interventions focused on circadian stabilization and behavioral sleep patterns may improve sleep quality and mental health outcomes in this population. Longitudinal research is needed to establish causality.

Indexed as

Internet Addiction DisorderSleep Initiation and Maintenance DisordersSmartphoneStress, PsychologicalAdultAnxietyDepressionFemaleHumansMaleMiddle AgedPhenotypeWearable Electronic DevicesYoung Adultcircadian rhythmdigital phenotypinginsomniamental healthsleep qualitysmartphone overuse

Identifiers

PMID41493477
PMCPMC13132408

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.