Evidence map›Paper›PMID 36264627›Full record

ArticleJournal of medical Internet research2022

Assessment of the Popularity and Perceived Effectiveness of Smartphone Tools That Track and Limit Smartphone Use: Survey Study and Machine Learning Analysis.

Elias Aboujaoude, Germano Vera Cruz, Lucien Rochat, Robert Courtois, Farah Ben Brahim, Riaz Khan, Yasser Khazaal

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
6.7field-weighted citation impact, top 3% of its field
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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

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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

7 authors at 5 institutions in 3 countries.

Elias AboujaoudeStanford University School of Medicine, Department of Psychiatry and Behavioral Sciences, Stanford, CA, United States.ORCID 0000-0003-1216-8313
Germano Vera CruzDepartment of Psychology, University of Picardie Jules Verne, Amiens, France.ORCID 0000-0002-8297-6933
Lucien RochatAddiction Division, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland.ORCID 0000-0002-9992-2720
Robert CourtoisDepartment of Psychology, Tours University, Tours, France.ORCID 0000-0002-3557-5264
Farah Ben BrahimDepartment of Psychology, Tours University, Tours, France.ORCID 0000-0002-0269-0848
Riaz KhanAddiction Psychiatry, Foederatio Medicorum Helveticorum, Geneva, Switzerland.ORCID 0000-0002-4520-7465
Yasser KhazaalLausanne University, Lausanne, Switzerland.ORCID 0000-0002-8549-6599
Université de Tours · FRFoederatio Medicorum Helveticorum · CHStanford University · USUniversité de Picardie Jules Verne · FRUniversity of Lausanne · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProblematic smartphone use, like problematic internet use, is a condition for which treatment is being sought on the web. In the absence of established treatments, smartphone-provided tools that monitor or control smartphone use have become increasingly popular, and their dissemination has largely occurred without oversight from the mental health field.

objectiveWe aimed to assess the popularity and perceived effectiveness of smartphone tools that track and limit smartphone use. We also aimed to explore how a set of variables related to mental health, smartphone use, and smartphone addiction may influence the use of these tools.

methodsFirst, we conducted a web-based survey in a representative sample of 1989 US-based adults using the crowdsourcing platform Prolific. Second, we used machine learning and other statistical tools to identify latent user classes; the association between latent class membership and demographic variables; and any predictors of latent class membership from covariates such as daily average smartphone use, social problems from smartphone use, smartphone addiction, and other psychiatric conditions.

resultsSmartphone tools that monitor and control smartphone use were popular among participants, including parents targeting their children; for example, over two-thirds of the participants used sleep-related tools. Among those who tried a tool, the highest rate of perceived effectiveness was 33.1% (58/175). Participants who experienced problematic smartphone use were more likely to be younger and more likely to be female. Finally, 3 latent user classes were uncovered: nonusers, effective users, and ineffective users. Android operating system users were more likely to be nonusers, whereas younger adults and females were more likely to be effective users. The presence of psychiatric symptoms did not discourage smartphone tool use.

conclusionsIf proven effective, tools that monitor and control smartphone use are likely to be broadly embraced. Our results portend well for the acceptability of mobile interventions in the treatment of smartphone-related psychopathologies and, potentially, non-smartphone-related psychopathologies. Better tools, targeted marketing, and inclusive design, as well as formal efficacy trials, are required to realize their potential.

Indexed as

CrowdsourcingSmartphoneAdultChildFemaleHumansInternet Addiction DisorderMachine LearningMaleSurveys and Questionnairesdigital mental health interventionsinternet addictioninternet gaming disordermachine learningmobile phonesmartphone addictionsmartphone toolssocial mediatelemedicinetelepsychiatry

Identifiers

PMID36264627
PMCPMC9634520
OpenAlexW4306913570

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.