Evidence map›Paper›PMID 32060724›Full record

ArticleWiener klinische Wochenschrift2020

Prescription of individual therapeutic exercises via smartphone app for patients suffering from non-specific back pain : A qualitative feasibility and quantitative pilot study.

Timothy Hasenöhrl, Thomas Windschnurer, Ronald Dorotka, Clemens Ambrozy, Richard Crevenna

Registry-linked trialAbstract read
In one paragraph

Article in Wiener klinische Wochenschrift, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06896812 (A Pilot Study on App-based Treatment Combining Physical Exercise, Graded Activity, and Pain Journaling for Patients with Spinal Complaints), which is not on this map. Cited by 14 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 3 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.

NCT06896812 nanot yet recruitingnot on this mapstarted 2025, after this paper: background citation

A Pilot Study on App-based Treatment Combining Physical Exercise, Graded Activity, and Pain Journaling for Patients with Spinal Complaints

TypeinterventionalSponsorZuyderland Medisch CentrumRan2025 to 2025Enrolled30ConditionsBack Pain, Spinal Disease, Spinal ConditionArmsThe app-based treatment consists of a combination of physical exercise, graded activity, and pain journaling.
3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Article
  6. Article
  7. Article
  8. App-Based Rehabilitation in Back Pain, a Systematic Review.Journal of personalized medicine · 2022
    Review
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. 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

5 authors.

Timothy HasenöhrlDepartment of Physical Medicine, Rehabilitation and Occupational Medicine, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Thomas WindschnurerDepartment of Physical Medicine, Rehabilitation and Occupational Medicine, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Ronald DorotkaOrthopaedic City Center-medCLINIC, Dominikanerbastei 3, 1010, Vienna, Austria.
Clemens AmbrozyDepartment of Physical Medicine, Rehabilitation and Occupational Medicine, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Richard CrevennaDepartment of Physical Medicine, Rehabilitation and Occupational Medicine, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria. richard.crevenna@meduniwien.ac.at.ORCID http://orcid.org/0000-0002-9177-0101

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe purpose of this qualitative study was the assessment of the feasibility and acceptance of orthopedists prescribing individualized therapeutic exercises via a smartphone app to patients suffering from non-specific back pain.

methodsA total of 27 patients (mean age 44.8 ± 13.2 years) diagnosed with acute non-specific back pain were prescribed individually chosen therapy exercises via a smartphone app. Before the patients started and after 4 weeks of exercising all participants went through an assessment protocol consisting of questionnaires (Oswestry Disability Index [ODI], Short Form-36 [SF-36], International Physical Activity Questionnaire [IPAQ], Work Ability Index [WAI], Visual Analogue Scale [VAS] back pain, sociodemographic parameters), assessment of functional parameters (handgrip strength, timed up and go test). With 16 randomly chosen patients semi-structured interviews were undertaken at the end of the intervention period. Interview transcripts were analyzed using thematic analysis. Power analysis and a priori sample size calculations were undertaken with the quantitative data.

resultsFrom the interviews four thematic categories emerged: prior exercise experience, evaluation of exercise intensity, communication with physician via smartphone app, and variability of exercise location. Quantitative analysis of secondary data showed significant improvements in back pain (ODI) as well as quality of life domains "physical functioning", "bodily pain" and "vitality" (SF-36) of which "bodily pain" was sufficiently powered with the current sample size.

conclusionThe prescription of therapeutic exercises via smartphone app to patients suffering from non-specific back pain is feasible and well-accepted in patients at all ages. Pilot data additionally pointed towards efficacy of the intervention.

Indexed as

SmartphoneAdultBack PainExercise TherapyFeasibility StudiesFemaleHand StrengthHumansMaleMiddle AgedPilot ProjectsPostural BalanceQuality of LifeTime and Motion StudiesAcceptanceCommunicationE-healthIndividualisationMobile health

Identifiers

PMID32060724
PMCPMC7080705

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.