Evidence map›Paper›PMID 30158102›Full record

ArticleJMIR mental health2018

Patient Willingness to Consent to Mobile Phone Data Collection for Mental Health Apps: Structured Questionnaire.

Daniel Di Matteo, Alexa Fine, Kathryn Fotinos, Jonathan Rose, Martin Katzman

Open access · goldAbstract read
In one paragraph

Article in JMIR mental health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 pooled it
5.6field-weighted citation impact, top 4% of its field
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

35 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.

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  17. Global Collaboration Around Digital Mental Health: The LAMP Consortium.Journal of technology in behavioral science · 2022
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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

5 authors at 2 institutions in 1 country.

Daniel Di MatteoThe Centre for Automation of Medicine, The Edward S Rogers Sr Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0000-0001-6082-267X
Alexa FineSTART Clinic for Mood and Anxiety Disorders, Toronto, ON, Canada.ORCID http://orcid.org/0000-0002-9813-1345
Kathryn FotinosSTART Clinic for Mood and Anxiety Disorders, Toronto, ON, Canada.ORCID http://orcid.org/0000-0001-8268-8360
Jonathan RoseThe Centre for Automation of Medicine, The Edward S Rogers Sr Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0000-0002-3551-2175
Martin KatzmanSTART Clinic for Mood and Anxiety Disorders, Toronto, ON, Canada.ORCID http://orcid.org/0000-0002-6169-2595
START Clinic · CAUniversity of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt has become possible to use data from a patient's mobile phone as an adjunct or alternative to the traditional self-report and interview methods of symptom assessment in psychiatry. Mobile data-based assessment is possible because of the large amounts of diverse information available from a modern mobile phone, including geolocation, screen activity, physical motion, and communication activity. This data may offer much more fine-grained insight into mental state than traditional methods, and so we are motivated to pursue research in this direction. However, passive data retrieval could be an unwelcome invasion of privacy, and some may not consent to such observation. It is therefore important to measure patients' willingness to consent to such observation if this approach is to be considered for general use.

objectiveThe aim of this study was to measure the ownership rates of mobile phones within the patient population, measure the patient population's willingness to have their mobile phone used as an experimental assessment tool for their mental health disorder, and, finally, to determine how likely patients would be to provide consent for each individual source of mobile phone-collectible data across the variety of potential data sources.

methodsNew patients referred to a tertiary care mood and anxiety disorder clinic from August 2016 to October 2017 completed a survey designed to measure their mobile phone ownership, use, and willingness to install a mental health monitoring app and provide relevant data through the app.

resultsOf the 82 respondents, 70 (85%) reported owning an internet-connected mobile phone. When asked about installing a hypothetical mobile phone app to assess their mental health disorder, 41% (33/80) responded with complete willingness to install with another 43% (34/80) indicating potential willingness to install such an app. Willingness to give permissions for specific types of data varied by data source, with respondents least willing to consent to audio recording and analysis (19% [15/80] willing respondents, 31% [25/80] potentially willing) and most willing to consent to observation of the mobile phone screen being on or off (46% [36/79] willing respondents and 23% [18/79] potentially willing).

conclusionsThe patients surveyed had a high incidence of ownership of internet-connected mobile phones, which suggests some plausibility for the general approach of mental health state inference through mobile phone data. Patients were also relatively willing to consent to data collection from sources that were less personal but expressed less willingness for the most personal communication and location data.

Indexed as

consentdigital privacymobile appsmobile phonemobile phone sensingmood and anxiety disorderspassive sensingpsychiatric assessment

Identifiers

PMID30158102
PMCPMC6135964
OpenAlexW2809343546

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.