Evidence map›Paper›PMID 32402392›Full record

ArticleNursing outlook

The potential of digital phenotyping to advance the contributions of mobile health to self-management science.

Kavita Radhakrishnan, Miyong T Kim, Marissa Burgermaster, Richard Allen Brown, Bo Xie, Molly S Bray, Catherine A Fournier

Abstract read
In one paragraph

Article in Nursing outlook. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Trial
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. BMC digital health · 2024
    Article
  8. Article
  9. The Recent Advances of Mobile Healthcare in Cardiology Practice.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2022
    Review
  10. Article
  11. Article
  12. 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

7 authors.

Kavita RadhakrishnanSchool of Nursing, The University of Texas - Austin, Austin, TX. Electronic address: kradhakrishnan@mail.nur.utexas.edu.
Miyong T KimSchool of Nursing, The University of Texas - Austin, Austin, TX.
Marissa BurgermasterDepartment of Population Health, The University of Texas - Austin, Austin, TX; Department of Nutritional Sciences, The University of Texas - Austin, Austin, TX.
Richard Allen BrownSchool of Nursing, The University of Texas - Austin, Austin, TX.
Bo XieSchool of Nursing, The University of Texas - Austin, Austin, TX; School of Information, The University of Texas - Austin, Austin, TX.
Molly S BraySchool of Nutrition, Department of Pediatrics, The University of Texas - Austin, Austin, TX.
Catherine A FournierSchool of Nursing, The University of Texas - Austin, Austin, TX.

Funding

Pilot & Feasibility CoreP30NR015335 · NINR · UNIVERSITY OF TEXAS AT AUSTIN · PI BROWN, SHARON A · 2014 to 2018
$2.3M
NINR NIH HHS P30 NR015335
6 · The paper itself

Abstract

Digital phenotyping consists of moment-by-moment quantification of behavioral data from individual people, typically collected passively from smartphones and other sensors. Within the evolving context of precision health, digital phenotyping can advance the use of mobile health -based self-management tools and interventions by enabling more accurate prediction for prevention and treatment, facilitating supportive strategies, and informing the development of features to motivate self-management behaviors within real-world conditions. This represents an advancement in self-management science: with digital phenotyping, nurse scientists have opportunities to tailor interventions with increased precision. In this paper, we discuss the emergence of digital phenotyping, the historical background of ecological momentary assessment, and the current state of the science of digital phenotyping, with implications for research design, computational requirements, and ethical considerations in self-management science, as well as limitations.

Indexed as

PhenotypePrecision MedicineSelf-ManagementTelemedicineEcological Momentary AssessmentHumansWearable Electronic DevicesDigital phenotypingMhealthPrecision medicineSelf-management interventions

Identifiers

PMID32402392
PMCPMC12927669

What OpenQuestion holds

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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.