Evidence map›Paper›PMID 39965189›Full record

ArticleJMIR formative research2025

Exploring the Effect of an 8-Week AI-Composed Exercise Program on Pain Intensity and Well-Being in Patients With Spinal Pain: Retrospective Cohort Analysis.

Annika Griefahn, Florian Avermann, Christoff Zalpour, Robert Percy Marshall, Inés Cordon Morillas, Kerstin Luedtke

Abstract read
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Annika GriefahnDepartment of Physiotherapy, Institute of Health Sciences, University of Lübeck, Lübeck, Germany.ORCID 0000-0003-0470-2539
Florian AvermannEvidence and Evaluation Department, medicalmotion GmbH, München, Germany.ORCID 0000-0002-9063-2508
Christoff ZalpourFaculty of Business Management and Social Sciences, Hochschule Osnabrück, Albrechtstraße 30, Osnabrück, Germany, 49 541969 ext 2998.ORCID 0000-0002-8692-0136
Robert Percy MarshallMedical Department, RasenBallsport Leipzig GmbH, Leipzig, Germany.ORCID 0000-0003-4247-1006
Inés Cordon MorillasORCID 0000-0002-9703-5108
Kerstin LuedtkeDepartment of Physiotherapy, Institute of Health Sciences, University of Lübeck, Lübeck, Germany.ORCID 0000-0002-7308-5469

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Spinal pain, one of the most common musculoskeletal disorders (MSDs), significantly impacts the quality of life due to chronic pain and disability. Physical activity has shown promise in managing spinal pain, although optimizing adherence to exercise remains a challenge. The digital development of artificial intelligence (AI)-driven applications offers a possibility for guiding and supporting patients with MSDs in their daily lives. Objective: The trial aimed to investigate the effect of an 8-week AI-composed exercise program on pain intensity and well-being in patients with spinal pain. It also examined the relationship between exercise frequency, pain intensity, and well-being. In addition, app usage frequency was examined as a proxy for app engagement. Methods: Data from users who met the inclusion criteria were collected retrospectively from the medicalmotion app between January 1, 2020, and June 30, 2023. The intervention involved the use of the medicalmotion app, which provides 3-5 personalized exercises for each session based on individual user data. The primary outcomes assessed pain intensity and well-being using the numeric rating scale (NRS) and the Likert scale. Data were collected at baseline (t0), 4 weeks (t1), and 8 weeks (t2). The correlation between exercise frequency, pain intensity, and well-being was analyzed as a secondary outcome. In addition, average session length and frequency were measured to determine app engagement. Statistical analysis included ANOVA and Spearman correlation analysis. Results: The study included 379 participants with a mean age of 50.96 (SD 12.22) years. At t2, there was a significant reduction of 1.78 points on the NRS (P<.001). The score on the Likert scale for well-being improved by 3.11 points after 8 weeks. Pain intensity showed a negative correlation with the number of daily exercises performed at t1 and t2. Well-being had a small negative correlation with the average number of exercises performed per day. The average number of exercises performed per day was 3.58. The average session length was approximately 10 minutes, and the average interaction with the app was 49.2% (n=27.6 days) of the 56 available days. Conclusions: Overall, the study demonstrates that an app-based intervention program can substantially reduce pain intensity and increase well-being in patients with spinal pain. This retrospective study showed that an app that digitizes multidisciplinary rehabilitation for the self-management of spinal pain significantly reduced user-reported pain intensity in a preselected population of app users.

Indexed as

Artificial IntelligenceExercise TherapyAdultChronic PainFemaleHumansMaleMiddle AgedMobile ApplicationsPain MeasurementQuality of LifeRetrospective StudiesadultsAIapp engagementapplicationsappsartificial intelligenceexerciseintensitymHealthmobile healthphysical activityquestionnaireretrospective analysisspinal painwell-being

Identifiers

PMID39965189
PMCPMC11856805

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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.