Smartphone Instrumented 30-Second Chair Stand Recovers Body Composition and Strength Biomarkers.

Colin Barry, Edward Jay Wang, David Wing

Abstract readPreprint

In one paragraph

Article in Research square, 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

3 authors.

Colin BarryElectrical and Computer Engineering Department University of California San Diego, La Jolla, CA, USA.

Edward Jay WangElectrical and Computer Engineering Department University of California San Diego, La Jolla, CA, USA.

David WingExercise and Physical Activity Resource Center, University of California San Diego, La Jolla, CA, USA.

Funding

Preoperative Risk Prediction of Postoperative Complications for Elective Cardiac Surgery using At-home Smartphone DynamometryR21AG084975 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Edward Jay Wang · 2024 to 2026

$637k

NIA NIH HHS R21 AG084975

6 · The paper itself

Abstract

The 30-second chair stand test is widely used in geriatric assessment, yet clinical practice only records the repetition count, discarding the movement information within each cycle. This manuscript reveals that a smartphone held at shoulder level during the test can not only count stand-sit cycles, but also recover information related to body composition and strength. In a study of 78 adults, movement features extracted from the smartphone inertial measurement unit (IMU) signal during a chair stand test were used in simple regression models to predict clinical metrics from a dynamometer and dual-energy X-ray absorptiometry (DXA) scanner. Combined with height and weight to compute force and inverse kinematics, the smartphone-derived features predict dominant grip strength with Pearson

Identifiers

PMID42427834

DOI10.21203/rs.3.rs-9931907/v1

PMCPMC13345531

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.