Evidence map›Paper›PMID 40872413›Full record

ArticleMicromachines2025

Leveraging Wearable Sensors for the Identification and Prediction of Defensive Pessimism Personality Traits.

You Zhou, Dongfen Li, Bowen Deng, Weiqian Liang

Abstract read
In one paragraph

Article in Micromachines, 2025. 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

4 authors.

You ZhouCollege of Computer and Network Security, Chengdu University of Technology, 1#, Dongsanlu, Erxianqiao, Chengdu 610059, China.
Dongfen LiCollege of Computer and Network Security, Chengdu University of Technology, 1#, Dongsanlu, Erxianqiao, Chengdu 610059, China.
Bowen DengCollege of Computer and Network Security, Chengdu University of Technology, 1#, Dongsanlu, Erxianqiao, Chengdu 610059, China.
Weiqian LiangCollege of Computer and Network Security, Chengdu University of Technology, 1#, Dongsanlu, Erxianqiao, Chengdu 610059, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Defensive pessimism, an important emotion regulation and motivation strategy, has increasingly attracted scholarly attention in psychology. Recently, sensor-based methods have begun to supplement or replace traditional questionnaire surveys in personality research. However, current approaches for collecting vital signs data face several challenges, including limited monitoring durations, significant data deviations, and susceptibility to external interference. This paper proposes a novel approach using a NiCr/NiSi alloy film temperature sensor, which has a K-type structure and flexible piezoelectric pressure sensor to identify and predict defensive pessimism personality traits. Experimental results indicate that the Seebeck coefficients for K-, T-, and E-type thermocouples are approximately 41 μV/°C, 39 μV/°C, and 57 μV/°C, respectively, which align closely with national standards and exhibit good consistency across multiple experimental groups. Moreover, radial artery frequency experiments demonstrate a strong linear relationship between pulse rate and the intensity of external stimuli, where stronger stimuli correspond to faster pulse rates. Simulation experiments further reveal a high correlation between radial artery pulse frequency and skin temperature, and a regression model based on the physiological sensor data shows a good fit (

Indexed as

defensive pessimismphysiological signalpressure transducerradial arteryseebeck coefficienttemperature sensorwearable sensors

Identifiers

PMID40872413
PMCPMC12388590

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.