CoChat
Sign Up
Cited by 48Impact of Technology on AdolescentsChild Development and Digital TechnologySexuality, Behavior, and Technology

A model for risk factors harms and of smartphone addiction among nursing students: A scoping review

Zhou, Bo, Mui, Lim Gek, Li, Jinnan, Yang, Yang, Hu, Jiahui·2024·Nurse Education in Practice
DOI 10.1016/j.nepr.2024.103874
Open this paper in CoChat — free account

Abstract

Aim To delineate the prevalence of smartphone addiction among nursing students. Further, to identify associated harms and underlying risk factors based on established theoretical models. Background Smartphones have become indispensable tools for students. However, excessive use can lead to smartphone addiction, causing physiological, psychological and social harm. Nursing students represent a unique population whose smartphone use may differ from other disciplines due to clinical training demands. Methods A scoping review was conducted following the Arksey and O'Malley framework. Seven databases were systematically searched from inception to August 2023. Inclusion criteria encompassed original research on smartphone addiction, harms and risks among nursing students. Data were extracted and thematically synthesized. Results Studies (n=39) met inclusion criteria, representing 15 countries. Rates of smartphone addiction among nursing students ranged from 19% to 72%, averaging 40-50%. Incorporated into Engel's biopsychosocial models, the harm is emphasized across individual inclinations, emotional aspects, cognitive processes and executive functions. Physiological harms include sleep disruption, vision concerns,other physiological concerns. psychologically, addiction correlated with increased anxiety and depression,decline in self-esteem, learning and attention and other psychological concerns. socially, it encompasses harms such as interpersonal relationships challenges, career development and decline in social abilities. The I-PACE model identifies various risk factors for smartphone addiction among nursing students, including personal factors such as interpersonal relationship anxiety and perceived academic pressure, affective factors like high stress and learning burnout, cognitive factors such as the need for online social interaction and low perception of social support, as well as executive factors like extended usage duration, poor self-control and usage before sleep. Conclusion Smartphone addiction among nursing students presents tangible harms. A proposed theoretical model integrating established frameworks provides avenues to better comprehend addiction genesis and potential intervention strategies. Given addiction's multi-factorial nature, future research investigating harm mitigation through optimizing predisposing, precipitating and perpetuating factors is warranted.

Integrity signals

Registry

Crossref · 10.1016/j.nepr.2024.103874

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

  • Many users report using Instagram late into the night, which interferes with sleep quality and leads to daytime fatigue.

    “Many users report using Instagram late into the night, which interferes with sleep quality and leads to daytime fatigue.”

    From The Scrolling Paradox: Analyzing Instagram Addiction Patterns in The Digital Age (2025) · cites this paper as Zhou et al. 2024

  • Instagram addiction is linked to physical health concerns, particularly through excessive screen time and disrupted sleep patterns.

    “Moreover, Instagram addiction is linked to physical health concerns, particularly through excessive screen time and disrupted sleep patterns.”

    From The Scrolling Paradox: Analyzing Instagram Addiction Patterns in The Digital Age (2025) · cites this paper as Zhou et al. 2024

Cites

Flagged first, then by citations

3 of the 54 references couldn’t be matched to a known paper.

Research works printed but not linked: 12

  1. Gutiérrez-Puertas, 2019, Comparative study of nomophobia among Spanish and Portuguese nursing students, Nurse Educ. Today, 34, 79, 10.1016/j.nepr.2018.11.010 · DOI
  2. Jin-Hwa, 2017, The relationship between interpersonal anxiety, college life adjustment, self-control and smartphone addiction in nursing students, J. Korean Data, 28, 185
  3. Kim, 2018, Relationships among smartphone use level, communication competence, social avoidance and distress of college nursing students, Asia Life Sci., 17, 1
  4. Kwon, 2021, The effect of stress on smartphone addiction in nursing students: the mediating effect of interpersonal relationship, Ann. Rom. Soc. Cell Biol., 25, 1293
  5. Kwon, 2021, The effect of stress on smartphone addiction in nursing students: the mediating effect of interpersonal relationship, Ann. Rom. Soc. Cell Biol., 25, 1293
  6. Kwon, 2021, The effect of stress on smartphone addiction in nursing students: the mediating effect of interpersonal relationship, Ann. Rom. Soc. Cell Biol., 25, 1293
  7. Meng, 2022, The association of learning burnout and mobile phone addiction among nursing students in china: the mediating role of negative emotions. Tobacco Regulatory, Sci. (TRS), 382
  8. Mohammadi Benmar, 2023, The association between perceived stress and mobile phone addiction in COVID-19 epidemic: the role of rumination and Self-Control, J. Psychol. Stud., 19, 93
  9. Panma, 2022, Academic stress affects smartphone addiction in nursing student, J. Keperawatan Komprehensif, 8
  10. Sok, S. R., Seong, M. H. Ryu, M. H. 2019. Differences of Self-Control, Daily Life Stress, and Communication Skills between Smartphone Addiction Risk Group and General Group in Korean Nursing Students. Psychiatric Quarterly 90, 1-9. doi: 10.1007/s11126-018-9596-1. · DOI
  11. Sunhee, 2018, Smartphone addiction and interpersonal competence of nursing students, Iran. J. Public Health, 47, 342
  12. Ventola, 2014, Mobile devices and apps for health care professionals: uses and benefits, Pharm. Ther., 39, 356

Cited by

Sorted by citations

Related

Authors
Zhou, B., Mui, L. G., Li, J., Yang, Y., & Hu, J.
Affiliations
  • Mahsa University (MY)
  • Huangshan Vocational College of Health (CN)
Corresponding author
Mui, Lim Gek
ORCID
Venue
Nurse Education in Practice
Publisher
Elsevier BV
Published
2024-02
Volume
75
Pages
103874
ISSN
1471-5953 (ISSN-L)
DOI
10.1016/j.nepr.2024.103874
OpenAlex
W4390887244
Cited by
48
References
54
Citations by year
2026: 17 · 2025: 21 · 2024: 10
FWCI
5.02
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Nurse Education in Practice (publishedVersion)
  • PubMed (publishedVersion)
Funders
Ministry of Education of the People's Republic of China (2023kt088)
Topics
  • Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
  • Social Sciences › Education › Child Development and Digital Technology
  • Social Sciences › Psychology › Clinical Psychology › Sexuality, Behavior, and Technology
Keywords
smartphone addiction, nursing students, risk factors, biopsychosocial model, I-PACE model, sleep disruption, anxiety and depression, academic stress, burnout, social support, self-control, excessive screen time
MeSH terms
Smartphone, Internet Addiction Disorder, Anxiety, Humans, Risk Factors, Students, Nursing, Behavior, Addictive, Social Skills

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