Evidence map›Paper›PMID 37017471›Full record

ArticleJMIR formative research2023

Machine Learning Approaches to Classify Self-Reported Rheumatoid Arthritis Health Scores Using Activity Tracker Data: Longitudinal Observational Study.

Kaushal Rao, William Speier, Yiwen Meng, Jinhan Wang, Nidhi Ramesh, Fenglong Xie, Yujie Su, W Benjamin Nowell, Jeffrey R Curtis, Corey Arnold

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 3 pooled it
3.1field-weighted citation impact, top 9% 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, 3 syntheses or guidelines pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. Article
  6. Article
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

10 authors at 2 institutions in 1 country.

Kaushal RaoDepartment of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-5600-4164
William SpeierDepartment of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-0890-8684
Yiwen MengDepartment of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-8055-4510
Jinhan WangDepartment of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0003-1930-2271
Nidhi RameshDepartment of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-1268-8514
Fenglong XieDivision of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, United States.ORCID https://orcid.org/0000-0002-7365-4801
Yujie SuDivision of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, United States.ORCID https://orcid.org/0000-0003-0273-8714
W Benjamin NowellGlobal Healthy Living Foundation, Upper Nyack, NY, United States.ORCID https://orcid.org/0000-0002-4951-6476
Jeffrey R CurtisDivision of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, United States.ORCID https://orcid.org/0000-0002-8907-8976
Corey ArnoldDepartment of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-4119-8143
University of California, Los Angeles · USUniversity of Alabama at Birmingham · US

Funding

Methods and Health Informatics CoreP30AR072583 · NIAMS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Bertha Hidalgo · 2020 to 2026
$5.7M
mHealth for Heart Failure: Predictive Models of Readmission Risk and Self-care Using Consumer Activity TrackersR01HL141773 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARNOLD, COREY WELLS · 2019 to 2022
$3.0M
NHLBI NIH HHS R01 HL141773NIAMS NIH HHS P30 AR072583
6 · The paper itself

Abstract

backgroundThe increasing use of activity trackers in mobile health studies to passively collect physical data has shown promise in lessening participation burden to provide actively contributed patient-reported outcome (PRO) information.

objectiveThe aim of this study was to develop machine learning models to classify and predict PRO scores using Fitbit data from a cohort of patients with rheumatoid arthritis.

methodsTwo different models were built to classify PRO scores: a random forest classifier model that treated each week of observations independently when making weekly predictions of PRO scores, and a hidden Markov model that additionally took correlations between successive weeks into account. Analyses compared model evaluation metrics for (1) a binary task of distinguishing a normal PRO score from a severe PRO score and (2) a multiclass task of classifying a PRO score state for a given week.

resultsFor both the binary and multiclass tasks, the hidden Markov model significantly (P<.05) outperformed the random forest model for all PRO scores, and the highest area under the curve, Pearson correlation coefficient, and Cohen κ coefficient were 0.750, 0.479, and 0.471, respectively.

conclusionsWhile further validation of our results and evaluation in a real-world setting remains, this study demonstrates the ability of physical activity tracker data to classify health status over time in patients with rheumatoid arthritis and enables the possibility of scheduling preventive clinical interventions as needed. If patient outcomes can be monitored in real time, there is potential to improve clinical care for patients with other chronic conditions.

Indexed as

activity trackerarthritisclassificationdigital healthFitbitmachine learningmHealthmobile healthmobile phonemodelnonclinical monitoringoutcome measurepatient reportedphysical dataPROMISrheumaticrheumatismrheumatoid arthritistrackerwearable

Identifiers

PMID37017471
PMCPMC10337464
OpenAlexW4362572058

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.