Evidence map›Paper›PMID 36156872›Full record

ArticleDigital biomarkers

Smartphone-Based Gait Cadence to Identify Older Adults with Decreased Functional Capacity.

Daniel S Rubin, Sylvia L Ranjeva, Jacek K Urbanek, Marta Karas, Maria Lucia L Madariaga, Megan Huisingh-Scheetz

Abstract read
In one paragraph

Article in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Trial
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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

6 authors.

Daniel S RubinDepartment of Anesthesia and Critical Care, The University of Chicago, Chicago, Illinois, USA.
Sylvia L RanjevaDepartment of Anesthesia, Massachusetts General Hospital, Harvard, Boston, Massachusetts, USA.
Jacek K UrbanekDepartment of Medicine, Division of Geriatric Medicine and Gerontology, Johns Hopkins University, Baltimore, Maryland, USA.
Marta KarasJohns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Maria Lucia L MadariagaDepartment of Surgery, Section of Cardiac and Thoracic Surgery, University of Chicago, Chicago, Illinois, USA.
Megan Huisingh-ScheetzDepartment of Medicine, Section of Geriatrics, University of Chicago, Chicago, Illinois, USA.

Funding

Early Frailty Biomarkers: Patterns of Activity and Energy ExpenditureK23AG049106 · NIA · UNIVERSITY OF CHICAGO · PI HUISINGH-SCHEETZ, MEGAN J · 2016 to 2021
$924k
NIA NIH HHS K23 AG049106
6 · The paper itself

Abstract

Background: Functional capacity assessment is a critical step in the preoperative evaluation to identify patients at increased risk of cardiac complications and disability after major noncardiac surgery. Smartphones offer the potential to objectively measure functional capacity but are limited by inaccuracy in patients with poor functional capacity. Open-source methods exist to analyze accelerometer data to estimate gait cadence (steps/min), which is directly associated with activity intensity. Here, we used an updated Step Test smartphone application with an open-source method to analyze accelerometer data to estimate gait cadence and functional capacity in older adults. Methods: We performed a prospective observational cohort study within the Frailty, Activity, Body Composition and Energy Expenditure in Aging study at the University of Chicago. Participants completed the Duke Activity Status Index (DASI) and performed an in-clinic 6-min walk test (6MWT) while using the Step Test application on a study smartphone. Gait cadence was measured from the raw accelerometer data using an adaptive empirical pattern transformation method, which has been previously validated. A 6MWT distance of 370 m was used as an objective threshold to identify patients at high risk. We performed multivariable logistic regression to predict walking distance using a priori explanatory variables. Results: Sixty patients were enrolled in the study. Thirty-seven patients completed the protocol and were included in the final data analysis. The median (IQR) age of the overall cohort was 71 (69-74) years, with a body mass index of 31 (27-32). There were no differences in any clinical characteristics or functional measures between participants that were able to walk 370 m during the 6MWT and those that could not walk that distance. Median (IQR) gait cadence for the entire cohort was 110 (102-114) steps/min during the 6MWT. Median (IQR) gait cadence was higher in participants that walked more than 370 m during the 6MWT 112 (108-118) versus 106 (96-114) steps/min; Conclusions: Our pilot study demonstrates the feasibility of using gait cadence as a measure to estimate functional capacity. Our study was limited by a smaller than expected sample size due to COVID-19, and thus, a prospective study with preoperative patients that measures outcomes is necessary to validate our findings.

Indexed as

GaitMobile technologyRaw dataTriaxial accelerometerWearable physical activity monitoring

Identifiers

PMID36156872
PMCPMC9386413

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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.