Evidence map›Paper›PMID 37861744›Full record

ArticleDiscover mental health2023

Digital footprints as a new translational approach for mental health care: a commentary.

Julio Licinio, Ma-Li Wong

Abstract read
In one paragraph

Article in Discover mental health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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

2 citing papers in PubMed.

  1. Artificial intelligence and eating disorders: a commentary.Eating and weight disorders : EWD · 2023
    Article
  2. Review
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

2 authors.

Julio LicinioPrecision Medicine Laboratory in Psychiatry (PMLP), Institute for Human Performance, State University of New York, Upstate Medical University, 505 Irving Ave 3302, Syracuse, NY, 13210, USA. juliolicinio@gmail.com.
Ma-Li WongPrecision Medicine Laboratory in Psychiatry (PMLP), Institute for Human Performance, State University of New York, Upstate Medical University, 505 Irving Ave 3302, Syracuse, NY, 13210, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is a crisis in mental health care, with more people suffering from psychiatric disorders than resources that are available for treatment, even though spending is substantial. Millions who suffer from addiction, psychosis, depression and suicidality are either untreated or inadequately treated and organized psychiatry is unable to reach them. Possibly as reflection of under-treatment of psychiatric disorders, the rates of suicide have risen: from 1999 through 2014, the age-adjusted suicide rate in the US increased 24%, from 10.5 to 13.0 per 100,000. Assessment of psychiatric symptoms in ongoing outpatient settings is costly, inadequate and unable to detect clinical changes over time. One's digital phenotype is assessed through footprints left over as result of our interface with technology, including automated assessments of quantity and quality of social media activity, patterns and speed of device usage, and physiological data that is automatically collected, such as location, quantity and type of movement, heart rate, and sleep patterns. The use of digital footprints has been advocated for large-scale data collection that can facilitate psychiatric research in naturalistic settings. We highlight recent papers in Discover Mental Health addressing digital approaches to mental health and we also advance here the concept that digital footprints are ready for clinical use. However, before that happens there needs to be discussion on the appropriate boundaries between care that is driven by signals from digital footprints and the rights to privacy and self-determination.

Identifiers

PMID37861744
PMCPMC10501006

What OpenQuestion holds

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LicenceCC BY
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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.