Evidence map›Paper›PMID 35265918›Full record

ArticleCardiovascular digital health journal2021

Heart rate trajectories in patients recovering from acute myocardial infarction: A longitudinal analysis of Apple Watch heart rate recordings.

Daniel Weng, Jie Ding, Apurva Sharma, Lisa Yanek, Helen Xun, Erin M Spaulding, Ngozi Osuji, Pauline P Huynh, Oluseye Ogunmoroti, Matthias A Lee and 3 more

Open access · goldAbstract read
In one paragraph

Article in Cardiovascular digital health journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 27% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Review
  6. Review
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 2 institutions in 1 country.

Daniel WengJohns Hopkins University School of Medicine, Baltimore, Maryland.
Jie DingCiccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Apurva SharmaDivision of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Lisa YanekDepartment of Medicine, Johns Hopkins University School of Medicine, Biostatistics, Epidemiology, and Data Management Core Faculty, Baltimore, Maryland.
Helen XunJohns Hopkins University School of Medicine, Baltimore, Maryland.
Erin M SpauldingJohns Hopkins University School of Nursing, Baltimore, Maryland.
Ngozi OsujiCiccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Pauline P HuynhJohns Hopkins University School of Medicine, Baltimore, Maryland.
Oluseye OgunmorotiDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Matthias A LeeJohns Hopkins University Whiting School of Engineering, Baltimore, Maryland.
Ryan DemoJohns Hopkins University Whiting School of Engineering, Baltimore, Maryland.
Francoise A MarvelCiccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Seth S MartinCiccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Johns Hopkins University · USJohns Hopkins Medicine · US

Funding

CARDIOVASCULAR EPIDEMIOLOGY INSTITUTIONAL TRAININGT32HL007024 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Elizabeth Selvin · 1985 to 2026
$17.5M
Interdisciplinary Training in Cardiovascular Health ResearchT32NR012704 · NINR · JOHNS HOPKINS UNIVERSITY · PI ALLEN, JERILYN K · 2011 to 2020
$1.8M
The Process of User Engagement with mHealthF31NR017328 · NINR · JOHNS HOPKINS UNIVERSITY · PI SPAULDING, ERIN MURDOCK · 2017 to 2019
$129k
NHLBI NIH HHS T32 HL007024NINR NIH HHS F31 NR017328NINR NIH HHS T32 NR012704
6 · The paper itself

Abstract

Background: Using mobile health, vital signs such as heart rate (HR) can be used to assess a patient's recovery process from acute events including acute myocardial infarction (AMI). Objective: We aimed to characterize clinical correlates associated with HR change in the subacute period among patients recovering from AMI. Methods: HR measurements were collected from 91 patients (4447 HR recordings) enrolled in the MiCORE study using the Apple Watch and Corrie smartphone application. Mixed regression models were used to estimate the associations of patient-level characteristics during hospital admission with HR changes over 30 days postdischarge. Results: The mean daily HR at admission was 78.0 beats per minute (bpm) (95% confidence interval 76.1 to 79.8), declining 0.2 bpm/day (-0.3 to -0.1) under a linear model of HR change. History of coronary artery bypass graft, history of depression, or being discharged on anticoagulants was associated with a higher admission HR. Having a history of hypertension, type 2 diabetes mellitus (T2DM), or hyperlipidemia was associated with a slower decrease in HR over time, but not with HR during admission. Conclusion: While a declining HR was observed in AMI patients over 30 days postdischarge, patients with hypertension, T2DM, or hyperlipidemia showed a slower decrease in HR relative to their counterparts. This study demonstrates the feasibility of using wearables to model the recovery process of patients with AMI and represents a first step in helping pinpoint patients vulnerable to decompensation.

Indexed as

Apple WatchBiometricsCardiac rehabDigital healthHeart attackHeart rateMobile healthMyocardial infarctionRemote monitoringWearable

Identifiers

PMID35265918
PMCPMC8890343
OpenAlexW3162423070

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.