Evidence map›Paper›PMID 41934783›Full record

ArticleClinics (Sao Paulo, Brazil)2026

A novel machine learning model of smartphone-based 1-minute sit-to-stand test for prediction of six-minute walk test distance in patients with COPD.

Simin Xie, Xiao Ge, Lin Huang, Wenyu Zhu, Yue Yang, Min Zhou, Jieming Qu, Yi Guo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinics (Sao Paulo, Brazil), 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
–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

8 authors.

Simin XieDepartment of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiao GeDepartment of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lin HuangDepartment of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Wenyu ZhuLuca Healthcare (Shanghai) Co., Ltd, China.
Yue YangLuca Healthcare (Shanghai) Co., Ltd, China.
Min ZhouDepartment of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, China.
Jieming QuDepartment of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, China.
Yi GuoDepartment of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, China. Electronic address: guoyi621@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRegular assessment of exercise tolerance is essential for managing COPD, nevertheless, the standard 6-Minute Walk Test (6MWT) is difficult to perform outside clinical settings. This study aimed to develop and validate a smartphone-based digital 1-Minute Sit-to-Stand Test (1MSTST) to estimate 6-Minute Walking Distance (6MWD) by integrating data from the phone's Inertial Motion Unit (IMU) with advanced machine learning algorithms, offering a convenient alternative for remote functional assessment.

methodsThe enrolled COPD patients completed the smartphone-based digital 1MSTST and 6MWT with a minimum 15-minute rest period between the two tests. Accelerometer and gyroscope data were recorded by a smartphone throughout the 1MSTST. Systolic and Diastolic Blood Pressure (SBP, DBP), Heart Rate (HR) and Pulse Oxygen Saturation (SpO

resultsA total of 66 patients with stable COPD were enrolled to build the predictive model for 6MWD. The change of HR and SBP after 1MSTST was higher than that of 6MWT (paired t-test, ΔHR: p < 0.0001, ΔSBP: p < 0.0001) with no significant difference in the change of DBP and SpO

conclusionsThe smartphone-based digital 1MSTST, combined with machine learning, can accurately estimate 6MWD. The significance of this study lies in proposing a novel assessment paradigm that may serve as a practical tool for remote monitoring of exercise capacity in COPD management.

Indexed as

1-Minute Sit To Stand Test (1MSTST)6-Minute Walk Test (6MWT)Chronic Obstructive Pulmonary Disease (COPD)Machine Learning ModelSmartphone

Identifiers

PMID41934783
PMCPMC13085062

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.