Evidence map›Paper›PMID 33068424›Full record

ReviewJournal of pediatric psychology2020

The Needle in the Haystack: Identifying Credible Mobile Health Apps for Pediatric Populations during a Pandemic and beyond.

Alexandra M Psihogios, Colleen Stiles-Shields, Martha Neary

Registry-linked trialAbstract readCase ReportsReview
In one paragraph

Review in Journal of pediatric psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07219095 (Use of a Mental Health App in the Recovery of Adolescent Dancers From a Lower Extremity Injury), which is not on this map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 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.

NCT07219095 narecruitingnot on this mapstarted 2025, after this paper: background citation

Use of a Mental Health App in the Recovery of Adolescent Dancers From a Lower Extremity Injury: A Randomized Controlled Trial

TypeinterventionalSponsorUniversity of California, San DiegoRan2025 to 2029Enrolled50ConditionsAnkle Injury, Foot Injury, Lower Extremity InjuryArmsMental Health App
3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Reinforcement Learning on Dyads to Enhance Medication Adherence.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2025
    Article
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  16. Utility, barriers and facilitators to the use of connected health to support families impacted by paediatric cancer: a qualitative analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2022
    Article
  17. Review
  18. Article
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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

3 authors.

Alexandra M PsihogiosDivision of Oncology and Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children's Hospital of Philadelphia, Philadelphia, PA.
Colleen Stiles-ShieldsDepartment of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL.
Martha NearyDepartment of Psychological Science, University of California, Irvine, CA.

Funding

Technology Enabled Services for Coordinated Care of Depression in Healthcare SettingsP50MH119029 · NIMH · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Jessica Lee Schleider · 2020 to 2026
$10.7M
Using Real Time Mobile Health Approaches to Understand and Promote Oral Chemotherapy Adherence in Adolescents and Young Adults with LeukemiaK08CA241335 · NCI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI PSIHOGIOS, ALEXANDRA · 2019 to 2024
$737k
NCI NIH HHS K08 CA241335NIMH NIH HHS P50 MH119029
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has ignited wider clinical adoption of digital health tools, including mobile health apps (mHealth apps), to address mental and behavioral health concerns at a distance. While mHealth apps offer many compelling benefits, identifying effective apps in the crowded and largely unregulated marketplace is laborious. Consumer demand and industry productivity are increasing, although research is slower, making it challenging for providers to determine the most credible and safe apps for patients in need. OBJECTIVES/

methodsThis commentary offers a practical, empirically guided framework and associated resources for selecting appropriate mHealth apps for pediatric populations during the pandemic and beyond.

resultsIn the first stage, Narrow the target problem, end user, and contender apps. Beginning the search with continuously updated websites that contain expert app ratings can help expedite this process (e.g., Psyberguide). Second, Explore each contender app's: (a) scientific and theoretical support (e.g., are app components consistent with health behavior change theories?), (b) privacy policies, and (c) user experience (e.g., through crowdsourcing feedback about app usability and appeal via social media). Third, use clinical expertise and stakeholder feedback to Contextualize whether the selected app is a good fit for a particular patient and/or caregiver (e.g., by considering age, race/ethnicity, ability, gender, sexual orientation, technology access), including conducting a brief self-pilot of the app.

conclusionYouth are increasingly turning to technology for support, especially during the pandemic, and pediatric psychologists must be primed to recommend the most credible tools. We offer additional recommendations for rapidly disseminating evidence-based apps to the public.

Indexed as

BetacoronavirusAdolescentChildCoronavirus InfectionsCOVID-19HumansLongitudinal StudiesMaleMental DisordersMobile ApplicationsPandemicsPneumonia, ViralQuarantineSARS-CoV-2Telemedicinechronic illnesscomputer applications/eHealthhealth behaviormental healthprofessional and training issues

Identifiers

PMID33068424
PMCPMC7588098

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.