Evidence map›Paper›PMID 38755706›Full record

ArticleTrials2024

Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial.

Mohammed S Abusamaan, Jeromie Ballreich, Adrian Dobs, Brian Kane, Nisa Maruthur, John McGready, Kristin Riekert, Amal A Wanigatunga, Mary Alderfer, Defne Alver and 5 more

Registry-linked trialAbstract readClinical Trial Protocol
PubMed Full text at PMC Publisher
In one paragraph

Article in Trials, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05056376 (Effectiveness and Cost-Effectiveness of Fully-Automated Digital vs. Human Coach-Based Diabetes Prevention Programs), which is not on this map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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.

NCT05056376 nacompletednot on this map

Effectiveness and Cost-Effectiveness of Fully-Automated Digital vs. Human Coach-Based Diabetes Prevention Programs

TypeinterventionalSponsorJohns Hopkins UniversityRan2021 to 2024Enrolled368ConditionsPreDiabetes, Hyperglycemia, Glucose, High Blood, OverweightArmsDigital Diabetes Prevention Program (dDPP), Human Coach-based Diabetes Prevention Program (hDPP)
3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Review
  4. Review
  5. Review
  6. Article
  7. Review
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

15 authors.

Mohammed S AbusamaanDivision of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jeromie BallreichDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Adrian DobsDivision of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Brian KaneTower Health Medical Group Family Medicine, Reading, PA, USA.
Nisa MaruthurDivision of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
John McGreadyDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Kristin RiekertDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, USA.
Amal A WanigatungaDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Mary AlderferReading Hospital Tower Health, Reading, PA, USA.
Defne AlverDivision of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Benjamin LalaniDivision of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Benjamin RinghamDivision of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Fatmata VandiDivision of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Daniel ZadeDivision of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Nestoras N MathioudakisDivision of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. nmathio1@jhmi.edu.ORCID http://orcid.org/0000-0002-0210-655X

Funding

Supplement to Effectiveness of Digital Versus In-Person Diabetes Prevention ProgramsR01DK125780 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI MATHIOUDAKIS, NESTORAS N · 2020 to 2023
$3.0M
NIDDK NIH HHS R01 DK125780NIDDK NIH HHS R01DK125780
6 · The paper itself

Abstract

backgroundPrediabetes is a highly prevalent condition that heralds an increased risk of progression to type 2 diabetes, along with associated microvascular and macrovascular complications. The Diabetes Prevention Program (DPP) is an established effective intervention for diabetes prevention. However, participation in this 12-month lifestyle change program has historically been low. Digital DPPs have emerged as a scalable alternative, accessible asynchronously and recognized by the Centers for Disease Control and Prevention (CDC). Yet, most digital programs still incorporate human coaching, potentially limiting scalability. Furthermore, existing effectiveness results of digital DPPs are primarily derived from per protocol, longitudinal non-randomized studies, or comparisons to control groups that do not represent the standard of care DPP. The potential of an AI-powered DPP as an alternative to the DPP is yet to be investigated. We propose a randomized controlled trial (RCT) to directly compare these two approaches.

methodsThis open-label, multicenter, non-inferiority RCT will compare the effectiveness of a fully automated AI-powered digital DPP (ai-DPP) with a standard of care human coach-based DPP (h-DPP). A total of 368 participants with elevated body mass index (BMI) and prediabetes will be randomized equally to the ai-DPP (smartphone app and Bluetooth-enabled body weight scale) or h-DPP (referral to a CDC recognized DPP). The primary endpoint, assessed at 12 months, is the achievement of the CDC's benchmark for type 2 diabetes risk reduction, defined as any of the following: at least 5% weight loss, at least 4% weight loss and at least 150 min per week on average of physical activity, or at least a 0.2-point reduction in hemoglobin A1C. Physical activity will be objectively measured using serial actigraphy at baseline and at 1-month intervals throughout the trial. Secondary endpoints, evaluated at 6 and 12 months, will include changes in A1C, weight, physical activity measures, program engagement, and cost-effectiveness. Participants include adults aged 18-75 years with laboratory confirmed prediabetes, a BMI of ≥ 25 kg/m DISCUSSION: Prediabetes is a significant public health issue, necessitating scalable interventions for the millions affected. Our pragmatic clinical trial is unique in directly comparing a fully automated AI-powered approach without direct human coach interaction. If proven effective, it could be a scalable, cost-effective strategy. This trial will offer vital insights into both AI and human coach-based behavioral change strategies in real-world clinical settings.

trial registrationClinicalTrials.gov NCT05056376. Registered on September 24, 2021, https://clinicaltrials.gov/study/NCT05056376.

Indexed as

Artificial IntelligenceDiabetes Mellitus, Type 2MentoringPrediabetic StateRandomized Controlled Trials as TopicAdultEquivalence Trials as TopicFemaleHumansMaleMiddle AgedMobile ApplicationsMulticenter Studies as TopicPragmatic Clinical Trials as TopicRisk Reduction BehaviorTime FactorsAIArtificial intelligenceDiabetes Prevention ProgramDigitalDPPHealthPhysical activityPrediabetesProtocolRandomized controlled trial

Identifiers

PMID38755706
PMCPMC11100129

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.