Evidence map›Paper›PMID 39990544›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Self-reported and tracker-estimated physical activity outcomes in women with chronic pelvic pain disorders: A longitudinal evaluation of construct validity.

Tanisee Nagaldinne, Samia Shahnawaz, Suzanne R Bakken, Noemie Elhadad, Emma N Horan, Carol Ewing-Garber, Jovita Rodrigues, Matteo Danieletto, Kyle Landell, Ipek Ensari

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Tanisee NagaldinneWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10027.
Samia ShahnawazWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10027.
Suzanne R BakkenColumbia University School of Nursing, New York, NY, 10031.
Noemie ElhadadDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10031.
Emma N HoranDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10031.
Carol Ewing-GarberDepartment of Biobehavioral Sciences, Teachers College Columbia University, New York, NY, 10027.
Jovita RodriguesWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10027.
Matteo DanielettoWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10027.
Kyle LandellWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10027.
Ipek EnsariWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10027.

Funding

Evaluation techniques for mHealth outcome measures using patient generated health dataR01HD108263 · NICHD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Ipek Ensari · 2022 to 2026
$1.7M
NICHD NIH HHS R01 HD108263
6 · The paper itself

Abstract

Objective: This study aims to evaluate the short form International Physical Activity Questionnaire (IPAQ) for use in women with chronic pelvic pain disorders (CPPDs) by comparing its scores against objectively-estimated physical activity (PA) outcomes. We investigated IPAQ components that are most consistently predictive of habitual PA behavior. Method: The study sample included 966 weeks of data from 112 women with CPPDs who enrolled in a 14-week mHealth-based self-tracking study. Participants wore Fitbit devices and completed the IPAQ every week. We compared the IPAQ-reported minutes of walking, total activity, sitting, light-, moderate-, and vigorous intensity PA for concordance and divergence against their corresponding Fitbit estimates. We used linear mixed-effects regression models (MLMs) for all analyses and quantified the between-participant variance in the magnitude of agreement between the two methods via random slope terms. We further evaluated temporal consistency in scores using intraclass correlation coefficients (ICCs). Results: IPAQ-reported walking minutes were strongly associated with Fitbit step counts (B = 3952.36; p = 0.006), minutes of moderate PA (B = 15.498; p = 0.0113), and moderate-to-vigorous PA (MVPA; B = 28.973; p = 0.007). IPAQ total activity minutes were associated with Fitbit minutes of vigorous PA (B = 15.183; p = 0.007) and MVPA (B = 25.658; p = 0.010). IPAQ moderate activity minutes were predictive of Fitbit vigorous PA minutes (B = 9.060; SE = 3.719; p = 0.0151). There was substantial between-individual variance in these point estimates based on the significant random-effect terms, and average weekly PA level was a significant moderator of the association between IPAQ-reported and Fitbit-estimated scores for these variables. IPAQ-reported sitting minutes were inversely associated with Fitbit step counts (B = -3125.61; p = 0.004), and minutes of MVPA (B = -21.848; p = 0.007), vigorous AP (B = -10.854; p = 0.042), and moderate PA (B = -10.985; p = 0.004). Conclusion: These findings provide support for using IPAQ-reported walking and total activity minutes to monitor several PA domains in women with CPPDs, given their concordance with several tracker-estimated PA outcomes. However, the item on "sitting time" may not be a suitable for assessing sedentary time.

Identifiers

PMID39990544
PMCPMC11844612

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.