Evidence map›Paper›PMID 42809087›Full record

ArticleIntegrative psychological & behavioral science2026

It Knows My Emotions Better Than I Do: Digital Phenotyping and the Contest Over Symptom Interpretation.

Xufeng Zhang, Han Li, Shenghui Bao

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Article in Integrative psychological & behavioral science, 2026. 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
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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

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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

3 authors.

Xufeng ZhangResp AI Research Lab, CIIOE, Xiamen, 361000, China.
Han LiResp AI Research Lab, CIIOE, Xiamen, 361000, China. hanli@respai.cn.
Shenghui BaoResp AI Research Lab, CIIOE, Xiamen, 361000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital phenotyping is increasingly presented as a means of detecting mental distress through smartphones, wearables, and other sensor-rich personal devices. In this article, we argue that the central philosophical and clinical issue raised by digital phenotyping extends beyond predictive accuracy to interpretive authority, which concerns who gets to name what a feeling, behavioral pattern, or physiological fluctuation means. Psychiatry has always involved uncertainty, proxy reasoning, and the negotiation of testimony, but digital phenotyping intensifies these dynamics by translating lived states into behavioral and physiological signals that are then modeled as indicators of psychopathology. This translation promises earlier detection and more continuous monitoring, yet it also risks displacing first-person understanding, narrowing the available interpretive vocabulary, and recoding distress through pattern recognition in ways that can obscure meaning. Drawing on work in digital phenotyping, philosophy of technology, phenomenological psychopathology, and epistemic injustice, we develop a theoretical account of how digital systems may compete with patients and clinicians for the right to interpret symptoms. We show that the most consequential conflicts concern credibility, categories, temporality, and subject formation. The result is a new diagnostic tool embedded in a new social arrangement of knowledge in which persons may come to mistrust their own experience unless it is confirmed by data. We conclude by proposing a dialogical and clinically modest model for digital mental health, one that treats algorithmic outputs as revisable prompts within a larger interpretive practice while preserving space for human interpretation.

Indexed as

EmotionsMental DisordersDigital HealthHumansPhenotypeDigital phenotypingEpistemic injusticeMental healthPassive sensingSymptom interpretation

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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.