Evidence map›Paper›PMID 30950799›Full record

ArticleJMIR mHealth and uHealth2019

The Role of Data Type and Recipient in Individuals' Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study.

Jennifer Nicholas, Katie Shilton, Stephen M Schueller, Elizabeth L Gray, Mary J Kwasny, David C Mohr

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

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

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  7. Article
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  11. Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024
    Article
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  13. Article
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  15. Observational
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  17. Contributing to Accessibility Datasets: Reflections on Sharing Study Data by Blind People.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2023
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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.

Jennifer NicholasCenter for Behavioral Intervention Technologies, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.ORCID 0000-0003-1889-1107
Katie ShiltonCollege of Information Studies, University of Maryland, College Park, MD, United States.ORCID 0000-0003-1816-6140
Stephen M SchuellerCenter for Behavioral Intervention Technologies, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.ORCID 0000-0002-1003-0399
Elizabeth L GrayBiostatistics Collaboration Center, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.ORCID 0000-0001-9298-6927
Mary J KwasnyBiostatistics Collaboration Center, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.ORCID 0000-0003-3287-1903
David C MohrCenter for Behavioral Intervention Technologies, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.ORCID 0000-0002-5443-7596

Funding

Northwestern University Clinical and Translational Science Institute (NUCATS)UL1TR001422 · NCATS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI D'AQUILA, RICHARD · 2015 to 2023
$56.8M
Technology Assisted intervention for the Treatment and Prevention of DepressionP20MH090318 · NIMH · NORTHWESTERN UNIVERSITY AT CHICAGO · PI MOHR, DAVID CURTIS · 2011 to 2015
$4.7M
LifeSense: Transforming Behavioral Assessment of Depression Using Personal Sensing TechnologyR01MH111610 · NIMH · NORTHWESTERN UNIVERSITY AT CHICAGO · PI KORDING, KONRAD P., MOHR, DAVID CURTIS · 2017 to 2020
$3.2M
NCATS NIH HHS UL1 TR001422NIMH NIH HHS P20 MH090318NIMH NIH HHS R01 MH111610
6 · The paper itself

Abstract

backgroundThe growing field of personal sensing harnesses sensor data collected from individuals' smartphones to understand their behaviors and experiences. Such data could be a powerful tool within mental health care. However, it is important to note that the nature of these data differs from the information usually available to, or discussed with, health care professionals. To design digital mental health tools that are acceptable to users, understanding how personal sensing data can be used and shared is critical.

objectiveThis study aimed to investigate individuals' perspectives about sharing different types of sensor data beyond the research context, specifically with doctors, electronic health record (EHR) systems, and family members.

methodsA questionnaire assessed participants' comfort with sharing six types of sensed data: physical activity, mood, sleep, communication logs, location, and social activity. Participants were asked about their comfort with sharing these data with three different recipients: doctors, EHR systems, and family members. A series of principal component analyses (one for each data recipient) was performed to identify clusters of sensor data types according to participants' comfort with sharing them. Relationships between recipients and sensor clusters were then explored using generalized estimating equation logistic regression models.

resultsA total of 211 participants completed the questionnaire. The majority were female (171/211, 81.0%), and the mean age was 38 years (SD 10.32). Principal component analyses consistently identified two clusters of sensed data across the three data recipients: "health information," including sleep, mood, and physical activity, and "personal data," including communication logs, location, and social activity. Overall, participants were significantly more comfortable sharing any type of sensed data with their doctor than with the EHR system or family members (P<.001) and more comfortable sharing "health information" than "personal data" (P<.001). Participant characteristics such as age or presence of depression or anxiety did not influence participants' comfort with sharing sensed data.

conclusionsThe comfort level in sharing sensed data was dependent on both data type and recipient, but not individual characteristics. Given the identified differences in comfort with sensed data sharing, contextual factors of data type and recipient appear to be critically important as we design systems that harness sensor data for mental health treatment and support.

Indexed as

AdolescentAdultAgedConfidentialityCross-Sectional StudiesFemaleHumansInformation Storage and RetrievalMaleMental Health ServicesMiddle AgedPrivacyPsychometricsSmartphoneSurveys and Questionnairesanxietydepressiondigital mental healthmHealthmobile phonepersonal sensingprivacy

Identifiers

PMID30950799
PMCPMC6473465

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.