Evidence map›Paper›PMID 29298748›Full record

ArticleJournal of medical Internet research2018

From eHealth to iHealth: Transition to Participatory and Personalized Medicine in Mental Health.

Sofian Berrouiguet, Mercedes M Perez-Rodriguez, Mark Larsen, Enrique Baca-García, Philippe Courtet, Maria Oquendo

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 4 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

48 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
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  6. Article
  7. Review
  8. Article
  9. Article
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  11. Article
  12. eHealth implementationFrontiers in digital health · 2024
    Article
  13. Article
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  15. Article
  16. Article
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  18. Autonomic complexity dynamically indexes affect regulation in everyday life.Journal of psychopathology and clinical science · 2023
    Article
  19. Article
  20. 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

6 authors.

Sofian Berrouiguet *Lab-STICC, IMT Atlantique, Université Bretagne Loire, Brest, France.ORCID 0000-0001-9148-6218
Mercedes M Perez-Rodriguez *Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States.ORCID 0000-0001-5137-1993
Mark LarsenBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0002-0272-2053
Enrique Baca-GarcíaDepartment of Psychiatry, Fundación Jimenez Diaz Hospital, Autónoma University, Centro de Investigacion en Red Salud Mental, Madrid, Spain.ORCID 0000-0002-6963-6555
Philippe CourtetDepartment of Emergency Psychiatry, University Hospital of Montpellier, University of Montpellier, Montpellier, France.ORCID 0000-0002-6519-8586
Maria OquendoPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.ORCID 0000-0002-7901-5082

Funding

MSHS Translational Science HubKL2TR001435 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI MOSKOWITZ, ALAN J · 2015 to 2019
$3.8M
NCATS NIH HHS KL2 TR001435
6 · The paper itself

Abstract

Clinical assessment in psychiatry is commonly based on findings from brief, regularly scheduled in-person appointments. Although critically important, this approach reduces assessment to cross-sectional observations that miss essential information about disease course. The mental health provider makes all medical decisions based on this limited information. Thanks to recent technological advances such as mobile phones and other personal devices, electronic health (eHealth) data collection strategies now can provide access to real-time patient self-report data during the interval between visits. Since mobile phones are generally kept on at all times and carried everywhere, they are an ideal platform for the broad implementation of ecological momentary assessment technology. Integration of these tools into medical practice has heralded the eHealth era. Intelligent health (iHealth) further builds on and expands eHealth by adding novel built-in data analysis approaches based on (1) incorporation of new technologies into clinical practice to enhance real-time self-monitoring, (2) extension of assessment to the patient's environment including caregivers, and (3) data processing using data mining to support medical decision making and personalized medicine. This will shift mental health care from a reactive to a proactive and personalized discipline.

Indexed as

Cell PhoneData MiningDecision MakingHumansMental HealthPrecision MedicineTelemedicinedata miningdecision makingmental healthmobile phoneWeb app

Identifiers

PMID29298748
PMCPMC5772066

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.