Evidence map›Paper›PMID 40991940›Full record

ArticleJMIR mHealth and uHealth2025

Relationship Between Activity Tracker Metrics and the Physical Activity Index and Their Association With Cardiometabolic Phenotypes, Subclinical Atherosclerosis, and Cardiac Remodeling: Cross-Sectional Study.

Weiting Huang, Mark Kei Fong Wong, Enver De Wei Loh, Tracy Koh, Alex Weixian Tan, Xiayan Shen, Onur Varli, Siew Ching Kong, Calvin Woon Loong Chin, Swee Yaw Tan and 3 more

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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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.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

13 authors.

Weiting HuangNational Heart Centre Singapore, Singapore, Singapore.ORCID 0000-0003-3453-2374
Mark Kei Fong WongNanyang Technological University, Singapore, Singapore.ORCID 0000-0003-4146-9588
Enver De Wei LohNanyang Technological University, Singapore, Singapore.ORCID 0000-0002-9205-1854
Tracy KohNational Heart Centre Singapore, Singapore, Singapore.ORCID 0009-0004-7868-5393
Alex Weixian TanNational Heart Centre Singapore, Singapore, Singapore.ORCID 0009-0004-1982-1834
Xiayan ShenNational Heart Centre Singapore, Singapore, Singapore.ORCID 0000-0001-7248-0179
Onur VarliNational Heart Centre Singapore, Singapore, Singapore.ORCID 0009-0005-8565-7179
Siew Ching KongNational Heart Centre Singapore, Singapore, Singapore.ORCID 0009-0003-6871-7139
Calvin Woon Loong ChinNational Heart Centre Singapore, Singapore, Singapore.ORCID 0000-0002-9867-4390
Swee Yaw TanNational Heart Centre Singapore, Singapore, Singapore.ORCID 0000-0002-2780-3247
Jonathan Jiunn Liang YapNational Heart Centre Singapore, Singapore, Singapore.ORCID 0000-0002-5227-8536
Eddie Yin Kwee NgNanyang Technological University, Singapore, Singapore.ORCID 0000-0002-5701-1080
Khung Keong YeoNational Heart Centre Singapore, Singapore, Singapore.ORCID 0000-0002-5457-4881

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundConsumer wearable technology quantifies physical activity; however, the association between these metrics and cardiometabolic health requires further elucidation.

objectiveThis study identified latent factors derived from Fitbit heart rate metrics and their relationship with cross-sectional cardiovascular phenotypes.

methodsThis cross-sectional analysis included 457 participants from the SingHEART study, a multiethnic, population-based study of Asian individuals aged 21 to 69 years recruited in Singapore. Participants wore the Fitbit Charge HR for 7 days, and data on physical activity metrics, self-reported physical activity index (PAI), blood tests, coronary artery calcium scores, and cardiac magnetic resonance imaging were collected. Exploratory factor analysis identified latent factors from Fitbit metrics, and multivariate regression analysis assessed associations with blood and cardiovascular imaging phenotypes.

resultsHigher levels of self-reported PAI were significantly associated with a higher number of calories burned (P=.008), number of steps and floors climbed, distance, number of activity calories, and number of very active minutes (P<.001). However, there was no association between PAI and other Fitbit metrics. Using exploratory factor analysis, we identified three latent factors measured by Fitbit metrics: (1) elevated metabolic equivalents of task (METs; calories burned per day, minutes per day spent fairly active in 3-6 METs and very active in ≥6 METs, and activity calories), (2) total activity (steps per day, distance in kilometers per day, and number of floors per day), and (3) others, all with a Cronbach α of >0.7. Higher total activity was associated with increased high-density lipoprotein levels (β=0.06; P<.001), decreased triglyceride levels (β=-0.10; P=.006), and lower BMI (β=-0.63; P<.001) after adjustment for age, gender, systolic blood pressure, total cholesterol, and family history of heart disease. The interaction between total activity and elevated METs was associated with lower fasting glucose (β=-0.07; P=.004). Elevated METs were associated with higher log(coronary artery calcium+1) and higher BMI (P<.001). Total activity was significantly associated with higher indexed biventricular systolic (P=.01 for left and P=.006 for right) and diastolic volumes (P<.001) and higher indexed left ventricular mass (P=.005).

conclusionsWe identified 3 groups of wearable metrics with distinct characteristics. While total activity had a significant relationship with self-reported PAI, most metrics of elevated METs did not. Total activity had a consistent and favorable association with lipid and glucose profiles and a dose-dependent association with cardiac remodeling. Elevated METs alone did not appear to have a significant association with favorable cardiovascular profiles. This study suggests that the total activity metrics are robust and dependable when interpreting an individual's activity levels, with construct validity according to self-reported PAI and a positive association with lipid and glucose profiles, and demonstrate dose-dependent associations with cardiac remodeling after adjustment for demographics and risk factors. Findings related to elevated METs may be due to the Hawthorne effect and require further studies.

Indexed as

AtherosclerosisExerciseFitness TrackersVentricular RemodelingAdultAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedPhenotypeSingaporecardiovascular healthdigital healthmetabolic healthpsychometric propertieswearables

Identifiers

PMID40991940
PMCPMC12508664

What OpenQuestion holds

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LicenceCC BY
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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.