Evidence map›Paper›PMID 35072717›Full record

Trial reportJAMA network open2022

Consumer Willingness to Share Personal Digital Information for Health-Related Uses.

David Grande, Nandita Mitra, Raghuram Iyengar, Raina M Merchant, David A Asch, Meghana Sharma, Carolyn C Cannuscio

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

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

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

  1. Pooled it
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  14. Aligning Federated Learning with Existing Trust Structures in Health Care Systems.International journal of environmental research and public health · 2023
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  19. Automated Workflow for Usability Audits in the PHR Realm.International journal of environmental research and public health · 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

7 authors.

David GrandeDivision of General Internal Medicine, University of Pennsylvania, Philadelphia.
Nandita MitraDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia.
Raghuram IyengarWharton School, University of Pennsylvania, Philadelphia.
Raina M MerchantLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
David A AschDivision of General Internal Medicine, University of Pennsylvania, Philadelphia.
Meghana SharmaDivision of General Internal Medicine, University of Pennsylvania, Philadelphia.
Carolyn C CannuscioLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.

Funding

Digital Phenotyping and Cardiovascular HealthR01HL141844 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI MERCHANT, RAINA · 2019 to 2022
$3.1M
Patient Preferences for Collecting and Repurposing Genetic, Consumer and Health Care InformationR01HG009655 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI GRANDE, DAVID T · 2017 to 2020
$1.9M
NHGRI NIH HHS R01 HG009655NHLBI NIH HHS R01 HL141844
6 · The paper itself

Abstract

Importance: Consumers routinely generate digital information that reflects on their health. Objective: To evaluate the factors associated with consumers' willingness to share their digital health information for research, health care, and commercial uses. Design, Setting, and Participants: This national survey with an embedded conjoint experiment recruited US adults from a nationally representative sample, with oversampling of Black and Hispanic panel members. Participants were randomized to 15 scenarios reflecting use cases for consumer digital information from a total of 324 scenarios. Attributes of the conjoint analysis included 3 uses, 3 users, 9 sources of digital information, and 4 relevant health conditions. The survey was conducted from July 10 to 31, 2020. Main Outcomes and Measures: Participants rated each conjoint profile on a 5-point Likert scale (1-5) measuring their willingness to share their personal digital information (with 5 indicating the most willingness to share). Results reflect mean differences in this scale from a multivariable regression model. Results: Among 6284 potential participants, 3543 (56%) responded. A total of 1862 participants (53%) were female, 759 (21%) identified as Black, 834 (24%) identified as Hispanic, and 1274 (36%) were 60 years or older. In comparison with information from electronic health care records, participants were less willing to share information about their finances (coefficient, -0.56; 95% CI, -0.62 to -0.50), places they visit from public cameras (coefficient, -0.28; 95% CI, -0.33 to -0.22), communication on social media (coefficient, -0.20; 95% CI -0.26 to -0.15), and their search history from internet search engines (coefficient, -0.11; 95% CI, -0.17 to -0.06). They were more willing to share information about their steps from applications on their phone (coefficient, 0.22; 95% CI, 0.17-0.28). Among the conjoint attributes, the source of information (importance weight: 59.1%) was more important than the user (17.3%), use (12.3%), and health condition (11.3%). Four clusters of consumers emerged from the sample with divergent privacy views. While the context of use was important, these 4 groups expressed differences in their overall willingness to share, with 337 participants classified as never share; 1116 classified as averse to sharing (mean rating, 1.64; 95% CI, 1.62-1.65); 1616 classified as uncertain about sharing (mean rating, 2.84; 95% CI, 2.81-2.86); and 474 classified as agreeable to sharing (mean rating, 4.18; 95% CI, 4.16-4.21). Respondents who identified as White and non-Hispanic, had higher income, and were politically conservative were more likely to be in a cluster that was less willing to share (ie, never or averse clusters). Conclusions and Relevance: These findings suggest that although consumers' willingness to share personal digital information for health purposes is associated with the context of use, many have strong underlying privacy views that affect their willingness to share. New protections may be needed to give consumers confidence to be comfortable sharing their personal information.

Indexed as

Consumer BehaviorDisclosureElectronic Health RecordsAdultBlack PeopleConfidentialityFemaleHispanic or LatinoHumansIntentionMaleMiddle AgedSurveys and Questionnaires

Identifiers

PMID35072717
PMCPMC8787615

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.