Evidence map›Paper›PMID 30635262›Full record

ArticleEvidence-based mental health2019

Understanding the quality, effectiveness and attributes of top-rated smartphone health apps.

Hannah Wisniewski, Gang Liu, Philip Henson, Aditya Vaidyam, Narissa Karima Hajratalli, Jukka-Pekka Onnela, John Torous

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Evidence-based mental health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing 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

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

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  10. A shot in the dark: the impact of online visibility on the search for an effective sleep app.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024
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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

7 authors.

Hannah WisniewskiDepartment of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Boston, Massachusetts, USA.
Gang LiuDepartment of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA.
Philip HensonDepartment of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Boston, Massachusetts, USA.
Aditya VaidyamDepartment of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Boston, Massachusetts, USA.
Narissa Karima HajratalliCenter for Neural Science, New York University, New York, USA.
Jukka-Pekka OnnelaDepartment of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA.
John TorousDepartment of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Boston, Massachusetts, USA.

Funding

Using mobile phones for social and behavioral sensing of mood disorder patientsDP2MH103909 · NIMH · HARVARD SCHOOL OF PUBLIC HEALTH · PI ONNELA, JUKKA-PEKKA · 2013 to 2013
$2.4M
Digital Phenotyping for Computational Models of Relapse Prediction in Early Course PsychosisK23MH116130 · NIMH · BETH ISRAEL DEACONESS MEDICAL CENTER · PI TOROUS, JOHN · 2018 to 2021
$872k
NIMH NIH HHS DP2 MH103909NIMH NIH HHS K23 MH116130
6 · The paper itself

Abstract

objectiveThis study aimed to understand the attributes of popular apps for mental health and comorbid medical conditions, and how these qualities relate to consumer ratings, app quality and classification by the WHO health app classification framework.

methodsWe selected the 10 apps from the Apple iTunes store and the US Android Google Play store on 20 July 2018 from six disease states: depression, anxiety, schizophrenia, addiction, diabetes and hypertension. Each app was downloaded by two authors who provided information on the apps' attributes, functionality, interventions, popularity, scientific backing and WHO app classification rating.

resultsA total of 120 apps were examined. Although none of these apps had Food and Drug Administration marketing approval, nearly 50% made claims that appeared medical. Most apps offered a similar type of services with 87.5% assigned WHO classification 1.4.2 'self-monitoring of health or diagnostic data by a client' or 1.6.1 'client look-up of health information'. The 'last updated' attribute was highly correlated with a quality rating of the app although no apps features (eg, uses Global Positioning System, reminders and so on) were.

conclusionDue to the heterogeneity of the apps, we were unable to define a core set of features that would accurately assess app quality. The number of apps making unsupported claims combined with the number of apps offering questionable content warrants a cautious approach by both patients and clinicians in selecting safe and effective ones. CLINICAL IMPLICATIONS: 'Days since last updated' offers a useful and easy clinical screening test for health apps, regardless of the condition being examined.

Indexed as

Patient SafetySmartphoneHumansMental DisordersMobile ApplicationsTelemedicineanxiety disorders

Identifiers

PMID30635262
PMCPMC7061529

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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.