Evidence map›Paper›PMID 34994694›Full record

ArticleJMIR diabetes2022

Comparison of Daily Routines Between Middle-aged and Older Participants With and Those Without Diabetes in the Electronic Framingham Heart Study: Cohort Study.

Yuankai Zhang, Chathurangi H Pathiravasan, Michael M Hammond, Hongshan Liu, Honghuang Lin, Mayank Sardana, Ludovic Trinquart, Belinda Borrelli, Emily S Manders, Jelena Kornej and 7 more

Abstract read
In one paragraph

Article in JMIR diabetes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

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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

17 authors.

Yuankai ZhangDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, United States.ORCID https://orcid.org/0000-0002-2318-7539
Chathurangi H PathiravasanDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, United States.ORCID https://orcid.org/0000-0003-2170-1247
Michael M HammondBoston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States.ORCID https://orcid.org/0000-0002-0157-4458
Hongshan LiuBoston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States.ORCID https://orcid.org/0000-0001-9379-6279
Honghuang LinSection of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States.ORCID https://orcid.org/0000-0003-3043-3942
Mayank SardanaCardiology Division, Department of Medicine, University of California San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0001-9465-1047
Ludovic TrinquartDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, United States.ORCID https://orcid.org/0000-0002-3028-4900
Belinda BorrelliCenter for Behavioral Science Research, Henry M. Goldman School of Dental Medicine, Boston University, Boston, MA, United States.ORCID https://orcid.org/0000-0002-0859-796X
Emily S MandersBoston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States.ORCID https://orcid.org/0000-0001-9508-1541
Jelena KornejBoston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States.ORCID https://orcid.org/0000-0003-3551-1145
Nicole L SpartanoSection of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University School of Medicine, Boston, MA, United States.ORCID https://orcid.org/0000-0002-0724-8629
Christopher NowakCare Evolution, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0003-1796-9558
Vik KheterpalCare Evolution, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-4752-4229
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 McManusCardiology Division, Department of Medicine, University of Massachusetts 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
Chunyu LiuDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, United States.ORCID https://orcid.org/0000-0002-9160-0153

Funding

The Center for Advancing Point of Care in Heart, Lung, Blood and Sleep DiseasesU54HL143541 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI Nathaniel Scott Hafer · 2018 to 2026
$141.0M
FRAMINGHAM HEART STUDY - YEAR 5 EXAM75N92019D00031 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · 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
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
Detecting bleeding events using EHRs for prediction in AfibR01HL137794 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI MCMANUS, DAVID D., YU, HONG · 2018 to 2021
$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
Smartwatch Monitoring for Atrial Fibrillation After StrokeR01HL137734 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI CHON, KI H, MCMANUS, DAVID D. · 2018 to 2021
$3.1M
FHS-NEXT - Framingham Novel EXam using TechnologyR01HL141434 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI BENJAMIN, EMELIA J., MCMANUS, DAVID D. · 2019 to 2022
$2.5M
NHLBI NIH HHS 75N92019D00031NHLBI NIH HHS R01 HL092577NHLBI NIH HHS R01 HL126911NHLBI NIH HHS R01 HL137734NHLBI NIH HHS R01 HL137794NHLBI NIH HHS R01 HL141434NHLBI NIH HHS U54 HL120163NHLBI NIH HHS U54 HL143541NIA NIH HHS R01 AG066010
6 · The paper itself

Abstract

backgroundDaily routines (eg, physical activity and sleep patterns) are important for diabetes self-management. Traditional research methods are not optimal for documenting long-term daily routine patterns in participants with glycemic conditions. Mobile health offers an effective approach for collecting users' long-term daily activities and analyzing their daily routine patterns in relation to diabetes status.

objectiveThis study aims to understand how routines function in diabetes self-management. We evaluate the associations of daily routine variables derived from a smartwatch with diabetes status in the electronic Framingham Heart Study (eFHS).

methodsThe eFHS enrolled the Framingham Heart Study participants at health examination 3 between 2016 and 2019. At baseline, diabetes was defined as fasting blood glucose level ≥126 mg/dL or as a self-report of taking a glucose-lowering medication; prediabetes was defined as fasting blood glucose level of 100-125 mg/dL. Using smartwatch data, we calculated the average daily step counts and estimated the wake-up times and bedtimes for the eFHS participants on a given day. We compared the average daily step counts and the intraindividual variability of the wake-up times and bedtimes of the participants with diabetes and prediabetes with those of the referents who were neither diabetic nor prediabetic, adjusting for age, sex, and race or ethnicity.

resultsWe included 796 participants (494/796, 62.1% women; mean age 52.8, SD 8.7 years) who wore a smartwatch for at least 10 hours/day and remained in the study for at least 30 days after enrollment. On average, participants with diabetes (41/796, 5.2%) took 1611 fewer daily steps (95% CI 863-2360; P<.001) and had 12 more minutes (95% CI 6-18; P<.001) in the variation of their estimated wake-up times, 6 more minutes (95% CI 2-9; P=.005) in the variation of their estimated bedtimes compared with the referents (546/796, 68.6%) without diabetes or prediabetes. Participants with prediabetes (209/796, 26.2%) also walked fewer daily steps (P=.04) and had a larger variation in their estimated wake-up times (P=.04) compared with the referents.

conclusionsOn average, participants with diabetes at baseline walked significantly fewer daily steps and had larger variations in their wake-up times and bedtimes than the referent group. These findings suggest that modifying the routines of participants with poor glycemic health may be an important approach to the self-management of diabetes. Future studies should be designed to improve the remote monitoring and self-management of diabetes.

Indexed as

daily physical activitiesdaily routine patterndiabetesdiabetes self-managementmobile healthmobile phonesleepsmartwatchstep counts

Identifiers

PMID34994694
PMCPMC8783285

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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.