Evidence map›Paper›PMID 33760846›Full record

Observational studyPloS one2021

Activity data from wearables as an indicator of functional capacity in patients with cardiovascular disease.

Neil Rens, Neil Gandhi, Jonathan Mak, Jeddeo Paul, Drew Bent, Stephanie Liu, Dasha Savage, Helle Nielsen-Bowles, Doran Triggs, Ghausia Ata and 3 more

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.4field-weighted citation impact, top 12% of its field
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

15 citing papers in PubMed, 27 citations in OpenAlex.

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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

13 authors at 1 institution in 1 country.

Neil RensDivision of Vascular Surgery, Department of Surgery, Stanford University, Stanford, California, United States of America.ORCID 0000-0002-4236-2814
Neil GandhiDivision of Vascular Surgery, Department of Surgery, Stanford University, Stanford, California, United States of America.
Jonathan MakDivision of Vascular Surgery, Department of Surgery, Stanford University, Stanford, California, United States of America.ORCID 0000-0001-9111-2566
Jeddeo PaulDivision of Vascular Surgery, Department of Surgery, Stanford University, Stanford, California, United States of America.
Drew BentGraduate School of Business, Stanford University, Stanford, California, United States of America.
Stephanie LiuGraduate School of Business, Stanford University, Stanford, California, United States of America.
Dasha SavageDivision of Vascular Surgery, Department of Surgery, Stanford University, Stanford, California, United States of America.
Helle Nielsen-BowlesDivision of Vascular Surgery, Department of Surgery, Stanford University, Stanford, California, United States of America.
Doran TriggsDivision of Vascular Surgery, Department of Surgery, Stanford University, Stanford, California, United States of America.
Ghausia AtaDivision of Vascular Surgery, Department of Surgery, Stanford University, Stanford, California, United States of America.
Julia TalgoDivision of Vascular Surgery, Department of Surgery, Stanford University, Stanford, California, United States of America.ORCID 0000-0002-7336-5524
Santiago GutierrezDivision of Vascular Surgery, Department of Surgery, Stanford University, Stanford, California, United States of America.
Oliver AalamiDivision of Vascular Surgery, Department of Surgery, Stanford University, Stanford, California, United States of America.
Stanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSmartphone and wearable-based activity data provide an opportunity to remotely monitor functional capacity in patients. In this study, we assessed the ability of a home-based 6-minute walk test (6MWT) as well as passively collected activity data to supplement or even replace the in-clinic 6MWTs in patients with cardiovascular disease.

methodsWe enrolled 110 participants who were scheduled for vascular or cardiac procedures. Each participant was supplied with an iPhone and an Apple Watch running the VascTrac research app and was followed for 6 months. Supervised 6MWTs were performed during clinic visits at scheduled intervals. Weekly at-home 6MWTs were performed via the VascTrac app. The app passively collected activity data such as daily step counts. Logistic regression with forward feature selection was used to assess at-home 6MWT and passive data as predictors for "frailty" as measured by the gold-standard supervised 6MWT. Frailty was defined as walking <300m on an in-clinic 6MWT.

resultsUnder a supervised in-clinic setting, the smartphone and Apple Watch with the VascTrac app were able to accurately assess 'frailty' with sensitivity of 90% and specificity of 85%. Outside the clinic in an unsupervised setting, the home-based 6MWT is 83% sensitive and 60% specific in assessing "frailty." Passive data collected at home were nearly as accurate at predicting frailty on a clinic-based 6MWT as was a home-based 6MWT, with area under curve (AUC) of 0.643 and 0.704, respectively.

conclusionsIn this longitudinal observational study, passive activity data acquired by an iPhone and Apple Watch were an accurate predictor of in-clinic 6MWT performance. This finding suggests that frailty and functional capacity could be monitored and evaluated remotely in patients with cardiovascular disease, enabling safer and higher resolution monitoring of patients.

Indexed as

Remote Sensing TechnologyWearable Electronic DevicesAccelerometryAgedCardiovascular DiseasesFemaleFrailtyHumansLongitudinal StudiesMaleMiddle AgedMobile ApplicationsMonitoring, AmbulatoryQuality of LifeSmartphone

Identifiers

PMID33760846
PMCPMC7990307
OpenAlexW3139106521

What OpenQuestion holds

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LicenceCC0
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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.