Evidence map›Paper›PMID 38935967›Full record

ArticleJMIR formative research2024

Real-World Evidence From a Digital Health Treatment Program for Female Urinary Incontinence: Observational Study of Outcomes Following User-Centered Product Design.

Evelyn Hall, Laura Keyser, Jessica McKinney, Samantha Pulliam, Milena Weinstein

Erratum issuedAbstract read
In one paragraph

Article in JMIR formative research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Observational
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Evelyn HallTufts University Medical Center, Tufts University School of Medicine, Boston, MA, United States.ORCID https://orcid.org/0000-0001-8338-5572
Laura KeyserDepartment of Physical Therapy and Rehabilitation Science, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-4168-9575
Jessica McKinneyAxena Health, Inc., Auburndale, MA, United States.ORCID https://orcid.org/0000-0001-8442-0771
Samantha PulliamTufts University Medical Center, Tufts University School of Medicine, Boston, MA, United States.ORCID https://orcid.org/0000-0003-4886-6711
Milena WeinsteinMassachusetts General Hospital, Harvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0002-9350-6953

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUrinary incontinence (UI) affects millions of women with substantial health and quality-of-life impacts. Supervised pelvic floor muscle training (PFMT) is the recommended first-line treatment. However, multiple individual and institutional barriers impede women's access to skilled care. Evidence suggests that digital health solutions are acceptable and may be effective in delivering first-line incontinence treatment, although these technologies have not yet been leveraged at scale.

objectiveThe primary objective is to describe the effectiveness and safety of a prescribed digital health treatment program to guide PFMT for UI treatment among real-world users. The secondary objectives are to evaluate patient engagement following an updated user platform and identify the factors predictive of success.

methodsThis retrospective cohort study of women who initiated device use between January 1, 2022, and June 30, 2023, included users aged ≥18 years old with a diagnosis of stress, urgency, or mixed incontinence or a score of >33.3 points on the Urogenital Distress Inventory Short Form (UDI-6). Users are prescribed a 2.5-minute, twice-daily, training program guided by an intravaginal, motion-based device that pairs with a smartphone app. Data collected by the device or app include patient-reported demographics and outcomes, adherence to the twice-daily regimen, and pelvic floor muscle performance parameters, including angle change and hold time. Symptom improvement was assessed by the UDI-6 score change from baseline to the most recent score using paired 2-tailed t tests. Factors associated with meeting the UDI-6 minimum clinically important difference were evaluated by regression analysis.

resultsOf 1419 users, 947 met inclusion criteria and provided data for analysis. The mean baseline UDI-6 score was 46.8 (SD 19.3), and the mean UDI-6 score change was 11.3 (SD 19.9; P<.001). Improvement was reported by 74% (697/947) and was similar across age, BMI, and incontinence subtype. Mean adherence was 89% (mean 12.5, SD 2.1 of 14 possible weekly uses) over 12 weeks. Those who used the device ≥10 times per week were more likely to achieve symptom improvement. In multivariate logistic regression analysis, baseline incontinence symptom severity and maximum angle change during pelvic floor muscle contraction were significantly associated with meeting the UDI-6 minimum clinically important difference. Age, BMI, and UI subtype were not associated.

conclusionsThis study provides real-world evidence to support the effectiveness and safety of a prescribed digital health treatment program for female UI. A digital PFMT program completed with visual guidance from a motion-based device yields significant results when executed ≥10 times per week over a period of 12 weeks. The program demonstrates high user engagement, with 92.9% (880/947) of users adhering to the prescribed training regimen. First-line incontinence treatment, when implemented using this digital program, leads to statistically and clinically substantial symptom improvements across age and BMI categories and incontinence subtypes.

Indexed as

digital healthevidencemobile phonepelvic floor muscle trainingreal-worldurinary incontinenceuser-centered design

Identifiers

PMID38935967
PMCPMC11240060

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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.