Evidence map›Paper›PMID 39047284›Full record

ArticleJMIR biomedical engineering2024

Agreement Between Apple Watch and Actical Step Counts in a Community Setting: Cross-Sectional Investigation From the Framingham Heart Study.

Nicole L Spartano, Yuankai Zhang, Chunyu Liu, Ariel Chernofsky, Honghuang Lin, Ludovic Trinquart, Belinda Borrelli, Chathurangi H Pathiravasan, Vik Kheterpal, Christopher Nowak and 4 more

Abstract read
In one paragraph

Article in JMIR biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

14 authors.

Nicole L SpartanoSection of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States.ORCID https://orcid.org/0000-0002-0724-8629
Yuankai ZhangBoston University School of Public Health, Boston, MA, United States.ORCID https://orcid.org/0000-0002-2318-7539
Chunyu LiuBoston University School of Public Health, Boston, MA, United States.ORCID https://orcid.org/0000-0002-9160-0153
Ariel ChernofskyBoston University School of Public Health, Boston, MA, United States.ORCID https://orcid.org/0000-0002-7104-409X
Honghuang LinUniversity of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0003-3043-3942
Ludovic TrinquartInstitute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, United States.ORCID https://orcid.org/0000-0002-3028-4900
Belinda BorrelliBoston University Henry M. Goldman School of Dental Medicine, Center for Behavioral Science Research, Boston, MA, United States.ORCID https://orcid.org/0000-0002-0859-796X
Chathurangi H PathiravasanBoston University School of Public Health, Boston, MA, United States.ORCID https://orcid.org/0000-0003-2170-1247
Vik KheterpalCare Evolution, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-4752-4229
Christopher NowakCare Evolution, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0003-1796-9558
Ramachandran S VasanBoston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States.ORCID https://orcid.org/0000-0001-7357-5970
Emelia J BenjaminBoston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States.ORCID https://orcid.org/0000-0003-4076-2336
David D McManusDepartment of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0002-9343-6203
Joanne M MurabitoBoston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States.ORCID https://orcid.org/0000-0002-0192-7516

Funding

The Center for Advancing Point of Care in Heart, Lung, Blood and Sleep DiseasesU54HL143541 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI BRYAN O BUCHHOLZ, Nathaniel Scott Hafer · 2018 to 2026
$141.0M
FRAMINGHAM HEART STUDY - YEAR 5 EXAM75N92019D00031 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI RAMACHANDRAN, VASAN · 2019 to 2024
$29.8M
IDENTIFICATION OF COMMON GENETIC VARIANTS FOR ATRIAL FIBRILLATION AND PR INTERVALR01HL092577 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI BENJAMIN, EMELIA J., ELLINOR, PATRICK THOMAS · 2009 to 2025
$20.6M
ConProject-006U54HL120163 · NHLBI · AMERICAN HEART ASSOCIATION · PI BHATNAGAR, ARUNI, ROBERTSON, ROSE MARIE · 2018 to 2022
$18.8M
Proteomic Profiling of Precise Exercise Pathophenotypes Across the HFpEF SpectrumR01HL131029 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Gregory Dyer Lewis, Matthew G. Nayor · 2016 to 2026
$7.7M
A phase I trial of AdKCNH2-G628S gene therapy for post-op atrial fibrillationR33HL158541 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI DONAHUE, J KEVIN, MCMANUS, DAVID D. · 2022 to 2023
$3.4M
SUPPORT-AF IVR01HL155343 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI KAPOOR, ALOK, MCMANUS, DAVID D. · 2021 to 2025
$3.4M
CAPSITE: Community Assessment of Pain and Sensitization in the ElderlyR01AG066010 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI BENJAMIN, EMELIA J., EDWARDS, ROBERT R · 2020 to 2025
$3.3M
Systematic Assessment of Geriatric Elements in Atrial Fibrillation (SAGE-AF)R01HL126911 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI MCMANUS, DAVID D., SACZYNSKI, JANE STEELE · 2016 to 2019
$3.2M
Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approachesU01AG068221 · NIA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI DESTEFANO, ANITA L, LIN, HONGHUANG · 2021 to 2024
$2.5M
FHS-NEXT - Framingham Novel EXam using TechnologyR01HL141434 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI BENJAMIN, EMELIA J., MCMANUS, DAVID D. · 2019 to 2022
$2.5M
IMPACT: Integrative Mindfulness-Based Predictive Approach for Chronic low back pain TreatmentUG3NS135168 · NINDS · WORCESTER POLYTECHNIC INSTITUTE · PI AGU, EMMANUEL, KING, JEAN A · 2023 to 2023
$1.6M
NHLBI NIH HHS 75N92019D00031NHLBI NIH HHS R01 HL092577NHLBI NIH HHS R01 HL126911NHLBI NIH HHS R01 HL131029NHLBI NIH HHS R01 HL141434NHLBI NIH HHS R01 HL155343NHLBI NIH HHS R33 HL158541NHLBI NIH HHS U54 HL120163NHLBI NIH HHS U54 HL143541NIA NIH HHS R01 AG047645NIA NIH HHS R01 AG066010NIA NIH HHS U01 AG068221NINDS NIH HHS UG3 NS135168
6 · The paper itself

Abstract

backgroundStep counting is comparable among many research-grade and consumer-grade accelerometers in laboratory settings.

objectiveThe purpose of this study was to compare the agreement between Actical and Apple Watch step-counting in a community setting.

methodsAmong Third Generation Framingham Heart Study participants (N=3486), we examined the agreement of step-counting between those who wore a consumer-grade accelerometer (Apple Watch Series 0) and a research-grade accelerometer (Actical) on the same days. Secondarily, we examined the agreement during each hour when both devices were worn to account for differences in wear time between devices.

resultsWe studied 523 participants (n=3223 person-days, mean age 51.7, SD 8.9 years; women: n=298, 57.0%). Between devices, we observed modest correlation (intraclass correlation [ICC] 0.56, 95% CI 0.54-0.59), poor continuous agreement (29.7%, n=957 of days having steps counts with ≤15% difference), a mean difference of 499 steps per day higher count by Actical, and wide limits of agreement, roughly ±9000 steps per day. However, devices showed stronger agreement in identifying who meets various steps per day thresholds (eg, at 8000 steps per day, kappa coefficient=0.49), for which devices were concordant for 74.8% (n=391) of participants. In secondary analyses, in the hours during which both devices were worn (n=456 participants, n=18,760 person-hours), the correlation was much stronger (ICC 0.86, 95% CI 0.85-0.86), but continuous agreement remained poor (27.3%, n=5115 of hours having step counts with ≤15% difference) between devices and was slightly worse for those with mobility limitations or obesity.

conclusionsOur investigation suggests poor overall agreement between steps counted by the Actical device and those counted by the Apple Watch device, with stronger agreement in discriminating who meets certain step thresholds. The impact of these challenges may be minimized if accelerometers are used by individuals to determine whether they are meeting physical activity guidelines or tracking step counts. It is also possible that some of the limitations of these older accelerometers may be improved in newer devices.

Indexed as

accelerometerActicalApple Watchdigital healthfitness trackerFramingham Heart StudymHealthmobile healthmobile phonephysical activitystep countstrackerwearablewearable devicewearables

Identifiers

PMID39047284
PMCPMC11306942

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.