Evidence mapPaperPMID 37616049Full record

ArticleJMIR mHealth and uHealth2023

The Impact of a Digital Weight Loss Intervention on Health Care Resource Utilization and Costs Compared Between Users and Nonusers With Overweight and Obesity: Retrospective Analysis Study.

Ellen Siobhan Mitchell, Alexander Fabry, Annabell Suh Ho, Christine N May, Matthew Baldwin, Paige Blanco, Kyle Smith, Andreas Michaelides, Mostafa Shokoohi, Michael West and 3 more

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 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.

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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Inequalities in Exclusively Mobile Interventions Targeting Weight-Related Behaviors: Systematic Review of Observational Studies.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026
    Pooled it
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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

13 authors.

Ellen Siobhan MitchellAcademic Research, Noom, Inc, New York City, NY, United States.ORCID 0000-0001-7851-6283
Alexander FabryAcademic Research, Noom, Inc, New York City, NY, United States.ORCID 0009-0005-5150-449X
Annabell Suh HoAcademic Research, Noom, Inc, New York City, NY, United States.ORCID 0000-0002-0972-0181
Christine N MayAcademic Research, Noom, Inc, New York City, NY, United States.ORCID 0000-0002-4367-5310
Matthew BaldwinAcademic Research, Noom, Inc, New York City, NY, United States.ORCID 0000-0002-7355-478X
Paige BlancoAcademic Research, Noom, Inc, New York City, NY, United States.ORCID 0009-0005-8987-5229
Kyle SmithAcademic Research, Noom, Inc, New York City, NY, United States.ORCID 0009-0004-6274-1864
Andreas MichaelidesAcademic Research, Noom, Inc, New York City, NY, United States.ORCID 0000-0003-4983-5001
Mostafa ShokoohiEversana, Burlington, ON, Canada.ORCID 0000-0002-3810-752X
Michael WestEversana, Sydney, NS, Canada.ORCID 0009-0000-8952-3702
Kim GoteraEversana, Burlington, ON, Canada.ORCID 0009-0001-2740-1643
Omnya El MassadEversana, Burlington, ON, Canada.ORCID 0009-0009-0347-7801
Anna ZhouEversana, Burlington, ON, Canada.ORCID 0000-0001-5451-4670

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Noom Weight program is a smartphone-based weight management program that uses cognitive behavioral therapy techniques to motivate users to achieve weight loss through a comprehensive lifestyle intervention.

objectiveThis retrospective database analysis aimed to evaluate the impact of Noom Weight use on health care resource utilization (HRU) and health care costs among individuals with overweight and obesity.

methodsElectronic health record data, insurance claims data, and Noom Weight program data were used to conduct the analysis. The study included 43,047 Noom Weight users and 14,555 non-Noom Weight users aged between 18 and 80 years with a BMI of ≥25 kg/m² and residing in the United States. The index date was defined as the first day of a 3-month treatment window during which Noom Weight was used at least once per week on average. Inverse probability treatment weighting was used to balance sociodemographic covariates between the 2 cohorts. HRU and costs for inpatient visits, outpatient visits, telehealth visits, surgeries, and prescriptions were analyzed.

resultsWithin 12 months after the index date, Noom Weight users had less inpatient costs (mean difference [MD] -US $20.10, 95% CI -US $30.08 to -US $10.12), less outpatient costs (MD -US $124.33, 95% CI -US $159.76 to -US $88.89), less overall prescription costs (MD -US $313.82, 95% CI -US $565.42 to -US $62.21), and less overall health care costs (MD -US $450.39, 95% CI -US $706.28 to -US $194.50) per user than non-Noom Weight users. In terms of HRU, Noom Weight users had fewer inpatient visits (MD -0.03, 95% CI -0.04 to -0.03), fewer outpatient visits (MD -0.78, 95% CI -0.93 to -0.62), fewer surgeries (MD -0.01, 95% CI -0.01 to 0.00), and fewer prescriptions (MD -1.39, 95% CI -1.76 to -1.03) per user than non-Noom Weight users. Among a subset of individuals with 24-month follow-up data, Noom Weight users incurred lower overall prescription costs (MD -US $1139.52, 95% CI -US $1972.21 to -US $306.83) and lower overall health care costs (MD -US $1219.06, 95% CI -US $2061.56 to -US $376.55) per user than non-Noom Weight users. The key differences were associated with reduced prescription use.

conclusionsNoom Weight use is associated with lower HRU and costs than non-Noom Weight use, with potential cost savings of up to US $1219.06 per user at 24 months after the index date. These findings suggest that Noom Weight could be a cost-effective weight management program for individuals with overweight and obesity. This study provides valuable evidence for health care providers and payers in evaluating the potential benefits of digital weight loss interventions such as Noom Weight.

Indexed as

OverweightTelemedicineAdolescentAdultAgedAged, 80 and overHumansMiddle AgedObesityPatient Acceptance of Health CareRetrospective StudiesYoung Adultcostsdigital weight loss interventionEHRelectronic health recordhealth care resource utilizationinsurance claimsinverse probability of treatment weightingIPTWmHealthmobile healthmobile phoneNoom Weightobesityoverweight

Identifiers

PMID37616049
PMCPMC10485704

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.