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e-Addictology: An Overview of New Technologies for Assessing and Intervening in Addictive Behaviors

Overview of attention for article published in Frontiers in Psychiatry, March 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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1 blog
policy
1 policy source
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32 X users
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1 Facebook page

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334 Mendeley
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Title
e-Addictology: An Overview of New Technologies for Assessing and Intervening in Addictive Behaviors
Published in
Frontiers in Psychiatry, March 2018
DOI 10.3389/fpsyt.2018.00051
Pubmed ID
Authors

Florian Ferreri, Alexis Bourla, Stephane Mouchabac, Laurent Karila

Abstract

New technologies can profoundly change the way we understand psychiatric pathologies and addictive disorders. New concepts are emerging with the development of more accurate means of collecting live data, computerized questionnaires, and the use of passive data. Digital phenotyping, a paradigmatic example, refers to the use of computerized measurement tools to capture the characteristics of different psychiatric disorders. Similarly, machine learning-a form of artificial intelligence-can improve the classification of patients based on patterns that clinicians have not always considered in the past. Remote or automated interventions (web-based or smartphone-based apps), as well as virtual reality and neurofeedback, are already available or under development. These recent changes have the potential to disrupt practices, as well as practitioners' beliefs, ethics and representations, and may even call into question their professional culture. However, the impact of new technologies on health professionals' practice in addictive disorder care has yet to be determined. In the present paper, we therefore present an overview of new technology in the field of addiction medicine. Using the keywords [e-health], [m-health], [computer], [mobile], [smartphone], [wearable], [digital], [machine learning], [ecological momentary assessment], [biofeedback] and [virtual reality], we searched the PubMed database for the most representative articles in the field of assessment and interventions in substance use disorders. We screened 595 abstracts and analyzed 92 articles, dividing them into seven categories: e-health program and web-based interventions, machine learning, computerized adaptive testing, wearable devices and digital phenotyping, ecological momentary assessment, biofeedback, and virtual reality. This overview shows that new technologies can improve assessment and interventions in the field of addictive disorders. The precise role of connected devices, artificial intelligence and remote monitoring remains to be defined. If they are to be used effectively, these tools must be explained and adapted to the different profiles of physicians and patients. The involvement of patients, caregivers and other health professionals is essential to their design and assessment.

X Demographics

X Demographics

The data shown below were collected from the profiles of 32 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 334 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 334 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 44 13%
Student > Ph. D. Student 39 12%
Student > Master 39 12%
Student > Bachelor 29 9%
Student > Doctoral Student 22 7%
Other 68 20%
Unknown 93 28%
Readers by discipline Count As %
Psychology 62 19%
Medicine and Dentistry 41 12%
Computer Science 35 10%
Social Sciences 18 5%
Neuroscience 12 4%
Other 50 15%
Unknown 116 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 33. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 September 2020.
All research outputs
#1,108,065
of 23,905,714 outputs
Outputs from Frontiers in Psychiatry
#593
of 10,998 outputs
Outputs of similar age
#26,113
of 334,004 outputs
Outputs of similar age from Frontiers in Psychiatry
#21
of 137 outputs
Altmetric has tracked 23,905,714 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,998 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 334,004 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.