Evidence map›Paper›PMID 29793899›Full record

ReviewJMIR mHealth and uHealth2018

Attention and Cognitive Bias Modification Apps: Review of the Literature and of Commercially Available Apps.

Melvyn Zhang, JiangBo Ying, Guo Song, Daniel Ss Fung, Helen Smith

Abstract readReview
In one paragraph

Review in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 2 pooled it
–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

30 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  10. Application of Digital Medicine in Addiction.Journal of Shanghai Jiaotong University (science) · 2022
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  16. Challenges When Evaluating Cognitive Bias Modification Interventions for Substance Use Disorder.International journal of environmental research and public health · 2020
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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

5 authors.

Melvyn ZhangNational Addictions Management Service, Institute of Mental Health, Singapore, Singapore.ORCID http://orcid.org/0000-0001-8385-2345
JiangBo YingNational Addictions Management Service, Institute of Mental Health, Singapore, Singapore.ORCID http://orcid.org/0000-0001-8503-8240
Guo SongNational Addictions Management Service, Institute of Mental Health, Singapore, Singapore.ORCID http://orcid.org/0000-0002-6672-404X
Daniel Ss FungMedical Board / Department of Development Psychiatry, Institute of Mental Health, Singapore, Singapore.ORCID http://orcid.org/0000-0003-0718-9363
Helen SmithDepartment of Family Medicine and Primary Care, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0003-1883-6124

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAutomatic processes, such as attentional biases or interpretative biases, have been purported to be responsible for several psychiatric disorders. Recent reviews have highlighted that cognitive biases may be modifiable. Advances in eHealth and mHealth have been harnessed for the delivery of cognitive bias modification. While several studies have evaluated mHealth-based bias modification intervention, no review, to our knowledge, has synthesized the evidence for it. In addition, no review has looked at commercial apps and their functionalities and methods of bias modification. A review is essential in determining whether scientifically validated apps are available commercially and the proportion of commercial apps that have been evaluated scientifically.

objectiveThe objective of this review was primarily to determine the proportion of attention or cognitive bias modification apps that have been evaluated scientifically and secondarily to determine whether the scientifically evaluated apps were commercially available. We also sought to identify commercially available bias modification apps and determine the functionalities of these apps, the methods used for attention or cognitive bias modification, and whether these apps had been evaluated scientifically.

methodsTo identify apps in the published literature, we searched PubMed, MEDLINE, PsycINFO, and Scopus for studies published from 2000 to April 17, 2018. The search terms used were "attention bias" OR "cognitive bias" AND "smartphone" OR "smartphone application" OR "smartphone app" OR "mobile phones" OR "mobile application" OR mobile app" OR "personal digital assistant." To identify commercial apps, we conducted a manual cross-sectional search between September 15 and 25, 2017 in the Apple iTunes and Google Play app stores. The search terms used to identify the apps were "attention bias" and "cognitive bias." We also conducted a manual search on the apps with published evaluations.

resultsThe effectiveness of bias modification was reported in 7 of 8 trials that we identified in the published literature. Only 1 of the 8 previously evaluated apps was commercially available. The 17 commercial apps we identified tended to use either an attention visual search or gamified task. Only 1 commercial app had been evaluated in the published literature.

conclusionsThis is perhaps the first review to synthesize the evidence for published mHealth attention bias apps. Our review demonstrated that evidence for mHealth attention bias apps is inconclusive, and quite a few commercial apps have not been validated scientifically.

Indexed as

attention biascognitive biasmHealthmobile applicationspsychiatrysmartphonetelemedicine

Identifiers

PMID29793899
PMCPMC5992457

What OpenQuestion holds

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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.