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数字表型:以数据为驱动的精神病学重新定义精神健康。

Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health.

机构信息

Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France.

Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France.

出版信息

J Med Internet Res. 2023 Oct 4;25:e44502. doi: 10.2196/44502.


DOI:10.2196/44502
PMID:37792430
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10585447/
Abstract

The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.

摘要

“数字表型”一词指的是患者与环境相互作用所留下的数字足迹。它具有研究和临床应用的潜力,但通过反对医学的两种截然不同的方法,即一种以疾病为中心、旨在对疾病进行分类和治疗的方法,以及另一种以患者及其个人痛苦和生活经历为中心的方法,对我们的医疗保健观念提出了挑战。在心理健康和精神病学领域,数字表型分析的潜在好处包括为治疗开辟新途径,并使患者能够掌控自己的健康。然而,这是以牺牲心理治疗的基本人为因素为代价的,而这种人为因素对于解决患者的痛苦至关重要。在这篇观点文章中,我们讨论了数字表型分析所带来的进步,并强调了这项技术可能带来的风险,即它可能会使医疗保健专业人员部分地被排除在诊断和治疗过程之外,从而忽略了护理的一个重要方面。最后,我们提出了一些具体建议,以改善当前的数字表型分析技术,以便能够利用它在不疏远患者的情况下重新定义心理健康。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bef/10585447/656c893dbd81/jmir_v25i1e44502_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bef/10585447/a9179fc4c1b4/jmir_v25i1e44502_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bef/10585447/656c893dbd81/jmir_v25i1e44502_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bef/10585447/a9179fc4c1b4/jmir_v25i1e44502_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bef/10585447/656c893dbd81/jmir_v25i1e44502_fig2.jpg

相似文献

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Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health.

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BMC Health Serv Res. 2025-8-28

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Harnessing Digital Phenotyping for Early Self-Detection of Psychological Distress.

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[3]
Telehealth and Pharmacotherapy: The Role of Synchronous and Novel Asynchronous Digital Health Tools in Psychiatry.

Pharmaceut Med. 2025-8-25

[4]
Challenges and standardisation strategies for sensor-based data collection for digital phenotyping.

Commun Med (Lond). 2025-8-19

[5]
Evaluating Generative Pretrained Transformer (GPT) models for suicide risk assessment in synthetic patient journal entries.

BMC Psychiatry. 2025-8-1

[6]
Applications of Artificial Intelligence in Psychiatry and Psychology Education: Scoping Review.

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[7]
How to integrate physiological data from wearables in treatment of personality disorders: a narrative review.

Front Psychiatry. 2025-6-18

[8]
Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned.

Front Psychiatry. 2025-6-13

[9]
Feasibility of Collecting and Linking Digital Phenotyping, Clinical, and Genetics Data for Mental Health Research: Pilot Observational Study.

JMIR Form Res. 2025-6-23

[10]
Innovative Digital Cognitive Behavioral Treatment for Insomnia Disorder in Adults (dCBT-i): Framework Development.

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本文引用的文献

[1]
Digital mental health: challenges and next steps.

BMJ Ment Health. 2023-2

[2]
Machine Learning and the Digital Measurement of Psychological Health.

Annu Rev Clin Psychol. 2023-5-9

[3]
Mood instability and activity/energy instability in patients with bipolar disorder according to day-to-day smartphone-based data - An exploratory post hoc study.

J Affect Disord. 2023-8-1

[4]
Focusing on Digital Research Priorities for Advancing the Access and Quality of Mental Health.

JMIR Ment Health. 2023-4-24

[5]
Restoring the missing person to personalized medicine and precision psychiatry.

Front Neurosci. 2023-2-9

[6]
Digital Phenotyping Data to Predict Symptom Improvement and Mental Health App Personalization in College Students: Prospective Validation of a Predictive Model.

J Med Internet Res. 2023-2-9

[7]
Digital mental health care: five lessons from Act 1 and a preview of Acts 2-5.

NPJ Digit Med. 2023-1-26

[8]
Digital mental health for postpartum women: perils, pitfalls, and promise.

NPJ Digit Med. 2023-1-26

[9]
Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review.

JMIR Ment Health. 2023-1-23

[10]
Smartphone digital phenotyping, surveys, and cognitive assessments for global mental health: Initial data and clinical correlations from an international first episode psychosis study.

Digit Health. 2022-11-8

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