Evidence map›Paper›PMID 39575316›Full record

Article... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks2023

Understanding Privacy Risks versus Predictive Benefits in Wearable Sensor-Based Digital Phenotyping: A Quantitative Cost-Benefit Analysis.

Zhiyuan Wang, Mark Rucker, Emma R Toner, Maria A Larrazabal, Mehdi Boukhechba, Bethany A Teachman, Laura E Barnes

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Zhiyuan WangDepartment of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia.
Mark RuckerDepartment of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia.
Emma R TonerDepartment of Psychology, University of Virginia, Charlottesville, Virginia.
Maria A LarrazabalDepartment of Psychology, University of Virginia, Charlottesville, Virginia.
Mehdi BoukhechbaJanssen Pharmaceutical Companies, Johnson & Johnson, Titusville, New Jersey.
Bethany A TeachmanDepartment of Psychology, University of Virginia, Charlottesville, Virginia.
Laura E BarnesDepartment of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia.

Funding

SCH: INT: Context-Aware Micro-Interventions for Social AnxietyR01MH132138 · NIMH · UNIVERSITY OF VIRGINIA · PI BARNES, LAURA ELIZABETH, TEACHMAN, BETHANY A · 2022 to 2025
$1.3M
NIMH NIH HHS R01 MH132138
6 · The paper itself

Abstract

Wearable devices with embedded sensors can provide personalized healthcare and wellness benefits in digital phenotyping and adaptive interventions. However, the collection, storage, and transmission of biometric data (including processed features rather than raw signals) from these devices pose significant privacy concerns. This quantitative, data-driven study examines the privacy risks associated with wearable-based digital phenotyping practices, with a focus on user

Identifiers

PMID39575316
PMCPMC11581184

What OpenQuestion holds

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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.