Evidence map›Paper›PMID 38345837›Full record

ArticleJMIR human factors2024

Interest in mHealth Among Patients With Low Back Pain: Cross-Sectional Study.

Jonas Ammundsen Ipsen, Louise Fleng Sandal, Natalie Hong Siu Chang, Berit Schiøttz-Christensen, Karen Søgaard, Anders Hansen

Open access · goldAbstract read
In one paragraph

Article in JMIR human factors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.3field-weighted citation impact, top 21% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Observational
  2. Article
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 at 1 institution in 1 country.

Jonas Ammundsen IpsenDepartment of Regional Health Research, University of Southern Denmark, Odense, Denmark.ORCID 0000-0002-1650-0531
Louise Fleng SandalDepartment of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark.ORCID 0000-0001-8436-1046
Natalie Hong Siu ChangDepartment of Regional Health Research, University of Southern Denmark, Odense, Denmark.ORCID 0000-0002-5772-0127
Berit Schiøttz-ChristensenResearch Unit of General Practice, University of Southern Denmark, Odense, Denmark.ORCID 0000-0003-3866-856X
Karen SøgaardDepartment of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark.ORCID 0000-0003-3968-6364
Anders HansenDepartment of Regional Health Research, University of Southern Denmark, Odense, Denmark.ORCID 0000-0002-5400-8847
University of Southern Denmark · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDigitally supported self-management tailored to an individual's need, in addition to usual care, may reduce pain-related disability compared to usual care alone, and patients with low back pain (LBP) using mobile health (mHealth) solutions express positive experiences. Hence, implementing mHealth solutions designed to support self-management is desirable from a clinical and patient perspective. Easily accessible mHealth solutions that can support the self-management of patients with LBP are available, but interest may be subgroup specific. Understanding the characteristics and preferences of patients with LBP labeled as interested may help to reach relevant LBP patient groups and inform the development and implementation of effective interventions with mHealth for patients with LBP.

objectiveThis study aims to explore the proportion of patients with LBP labeled as interested in testing an mHealth solution designed to support self-management in addition to usual care and to assess how these patients differ from those who were labeled as not interested.

methodsThis exploratory cross-sectional study analyzed demographic and patient-reported outcomes from the SpineData registry, a Danish registry of patients with LBP in an outpatient setting. Between February and December 2019, the SpineData registry was used to assess the preliminary eligibility of patients for a clinical trial (selfBACK). Patients were labeled as interested or uninterested depending on if they responded to an invitation to be tested for eligibility for the trial Outcomes were selected from the International Classification of Functioning core set of LBP using a clinical approach. Associations were assessed in a backward selection process, and the proportion of variance explained was assessed with pseudo-R

resultsThis study included 843 patients, with 181 (21%) individuals labeled as interested in participating in the selfBACK trial. Notably, the cohort labeled as interested differed from their uninterested counterparts in two key aspects: age (36-65 years: 116/181, 64.1% vs 347/662, 52.4%; P=.003) and smoking status (smokers: 22/181, 12.5% vs 174/662, 26.6%; P<.001). Those aged 36-65 years had higher odds of being labeled as interested compared to individuals aged 18-35 years (odds ratio [OR] 0.43, 95% CI 0.26-0.71) and those 65 years or older (OR 0.77, 95% CI 0.53-1.15). Nevertheless, age accounted for only a modest proportion of variance (R

conclusionsOur investigation revealed that 181 (21%) individuals with LBP invited to participate in the mHealth solution trial for self-management expressed interest. Generally, the characteristics of those labeled as interested and uninterested were comparable. Of note, patients aged 36-65 years had a higher frequency of being labeled as interested compared to their younger and older counterparts.

Indexed as

Low Back PainPersons with DisabilitiesTelemedicineCross-Sectional StudiesEligibility DeterminationHumanscharacteristicslow back painmHealth solutionsmobile healthpatient interestrepresentativenesstransferability

Identifiers

PMID38345837
PMCPMC10897794
OpenAlexW4391073282

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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.