Evidence map›Paper›PMID 27234480›Full record

Trial reportJournal of medical Internet research2016

The Electronic CardioMetabolic Program (eCMP) for Patients With Cardiometabolic Risk: A Randomized Controlled Trial.

Kristen M J Azar, Suneil Koliwad, Tak Poon, Lan Xiao, Nan Lv, Robert Griggs, Jun Ma

2 registry-linked trialsAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of medical Internet research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 22 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 6 pooled it
–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.

NCT02246400 nacompletednot on this map

CardioMetabolic Program Meets eHealth: Redesign and Evaluation for Optimal Reach and Impact

TypeinterventionalSponsorPalo Alto Medical FoundationRan2014 to 2014Enrolled74ConditionsObesity, Metabolic Syndrome, Pre-diabetes, Type 2 DiabetesArmseCMP
NCT07144774 nacompletednot on this mapstarted 2024, after this paper: background citation

The Effectiveness of Wearable Devices on Health Promotion in Individuals With Diabetes Mellitus

TypeinterventionalSponsorPacific UniversityRan2024 to 2024Enrolled9ConditionsDiabetes (DM)ArmsFitbit wrist-worn Inspire 2® activity tracker (Fitbit Inc., San Francisco, CA, USA)
3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  17. Digital Health-Supported Lifestyle Change Programs to Prevent Type 2 Diabetes.Diabetes spectrum : a publication of the American Diabetes Association · 2018
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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

7 authors.

Funding

Research BaseP30DK063720 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI VAISSE, CHRISTIAN · 2003 to 2019
$21.8M
NIDDK NIH HHS P30 DK063720
6 · The paper itself

Abstract

backgroundEffective lifestyle interventions targeting high-risk adults that are both practical for use in ambulatory care settings and scalable at a population management level are needed.

objectiveOur aim was to examine the potential effectiveness, feasibility, and acceptability of delivering an evidence-based Electronic Cardio-Metabolic Program (eCMP) for improving health-related quality of life, improving health behaviors, and reducing cardiometabolic risk factors in ambulatory care high-risk adults.

methodsWe conducted a randomized, wait-list controlled trial with 74 adults aged ≥18 years recruited from a large multispecialty health care organization. Inclusion criteria were (1) BMI ≥35 kg/m(2) and prediabetes, previous gestational diabetes and/or metabolic syndrome, or (2) BMI ≥30 kg/m(2) and type 2 diabetes and/or cardiovascular disease. Participants had a mean age of 59.7 years (SD 11.2), BMI 37.1 kg/m(2) (SD 5.4) and were 59.5% female, 82.4% white. Participants were randomized to participate in eCMP immediately (n=37) or 3 months later (n=37). eCMP is a 6-month program utilizing video conferencing, online tools, and pre-recorded didactic videos to deliver evidence-based curricula. Blinded outcome assessments were conducted at 3 and 6 months postbaseline. Data were collected and analyzed between 2014 and 2015. The primary outcome was health-related quality of life. Secondary outcomes included biometric cardiometabolic risk factors (eg, body weight), self-reported diet and physical activity, mental health status, retention, session attendance, and participant satisfaction.

resultsChange in quality of life was not significant in both immediate and delayed participants. Both groups significantly lost weight and reduced waist circumference at 6 months, with some cardiometabolic factors trending accordingly. Significant reduction in self-reported anxiety and perceived stress was seen in the immediate intervention group at 6 months. Retention rate was 93% at 3 months and 86% at 6 months post-baseline. Overall eCMP attendance was high with 59.5-83.8% of immediate and delayed intervention participants attending 50% of the virtual stress management and behavioral lifestyle sessions and 37.8-62.2% attending at least 4 out of 7 in-person physical activity sessions. The intervention received high ratings for satisfaction.

conclusionsThe technology-assisted eCMP is a feasible and well-accepted intervention and may significantly decrease cardiometabolic risk among high-risk individuals.

trial registrationClinicaltrials.gov NCT02246400; https://clinicaltrials.gov/ct2/show/NCT02246400 (Archived by WebCite at http://www.webcitation.org/6h6mWWokP).

Indexed as

Body WeightCardiovascular DiseasesFemaleHealth BehaviorHumansLife StyleMaleMetabolic SyndromeMiddle AgedPilot ProjectsQuality of LifeRisk Factorsbehavior changecardiovascular diseasehealth technologylifestylemetabolic syndromeprevention

Identifiers

PMID27234480
PMCPMC4902854

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.