Evidence map›Paper›PMID 30061092›Full record

ArticleJournal of medical Internet research2018

Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions.

Tjeerd W Boonstra, Jennifer Nicholas, Quincy Jj Wong, Frances Shaw, Samuel Townsend, Helen Christensen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 6 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

44 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  5. Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019
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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

6 authors.

Tjeerd W BoonstraBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0002-6969-7243
Jennifer NicholasBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0003-1889-1107
Quincy Jj WongBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0001-9393-4301
Frances ShawBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0001-8690-653X
Samuel TownsendBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0001-8453-6387
Helen ChristensenBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0003-0435-2065

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMobile phone sensor technology has great potential in providing behavioral markers of mental health. However, this promise has not yet been brought to fruition.

objectiveThe objective of our study was to examine challenges involved in developing an app to extract behavioral markers of mental health from passive sensor data.

methodsBoth technical challenges and acceptability of passive data collection for mental health research were assessed based on literature review and results obtained from a feasibility study. Socialise, a mobile phone app developed at the Black Dog Institute, was used to collect sensor data (Bluetooth, location, and battery status) and investigate views and experiences of a group of people with lived experience of mental health challenges (N=32).

resultsOn average, sensor data were obtained for 55% (Android) and 45% (iOS) of scheduled scans. Battery life was reduced from 21.3 hours to 18.8 hours when scanning every 5 minutes with a reduction of 2.5 hours or 12%. Despite this relatively small reduction, most participants reported that the app had a noticeable effect on their battery life. In addition to battery life, the purpose of data collection, trust in the organization that collects data, and perceived impact on privacy were identified as main factors for acceptability.

conclusionsBased on the findings of the feasibility study and literature review, we recommend a commitment to open science and transparent reporting and stronger partnerships and communication with users. Sensing technology has the potential to greatly enhance the delivery and impact of mental health care. Realizing this requires all aspects of mobile phone sensor technology to be rigorously assessed.

Indexed as

AdolescentAdultAgedCell PhoneData CollectionFemaleHumansMaleMental HealthMiddle AgedMobile ApplicationsResearch DesignTechnologyYoung Adultdepressionethicsmental healthmobile healthpassive sensingsmartphoneubiquitous computingwearable sensors

Identifiers

PMID30061092
PMCPMC6090171

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.