Evidence map›Paper›PMID 34444160›Full record

ArticleInternational journal of environmental research and public health2021

Instrumental Validity of the Motion Detection Accuracy of a Smartphone-Based Training Game.

Bernhard Guggenberger, Andreas J Jocham, Birgit Jocham, Alexander Nischelwitzer, Helmut Ritschl

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

  1. Pooled it
  2. Article
  3. 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.

Bernhard GuggenbergerDepartment of Health Studies, University of Applied Sciences JOANNEUM, 8020 Graz, Austria.ORCID 0000-0002-2826-2999
Andreas J JochamDepartment of Health Studies, University of Applied Sciences JOANNEUM, 8020 Graz, Austria.
Birgit JochamDepartment of Health Studies, University of Applied Sciences JOANNEUM, 8020 Graz, Austria.
Alexander NischelwitzerDepartment of Applied Computer Sciences, University of Applied Sciences JOANNEUM, 8020 Graz, Austria.
Helmut RitschlDepartment of Health Studies, University of Applied Sciences JOANNEUM, 8020 Graz, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Demographic changes associated with an expanding and aging population will lead to an increasing number of orthopedic surgeries, such as joint replacements. To support patients' home exercise programs after total hip replacement and completing subsequent inpatient rehabilitation, a low-cost, smartphone-based augmented reality training game (TG) was developed. To evaluate its motion detection accuracy, data from 30 healthy participants were recorded while using the TG. A 3D motion analysis system served as reference. The TG showed differences of 18.03 mm to 24.98 mm along the anatomical axes. Surveying the main movement direction of the implemented exercises (squats, step-ups, side-steps), differences between 10.13 mm to 24.59 mm were measured. In summary, the accuracy of the TG's motion detection is sufficient for use in exergames and to quantify progress in patients' performance. Considering the findings of this study, the presented exer-game approach has potential as a low-cost, easily accessible support for patients in their home exercise program.

Indexed as

Arthroplasty, Replacement, HipTelerehabilitationAgedExercise TherapyHumansSmartphoneaccuracyaugmented realitylow-costphysiotherapysmartphonetelerehabilitationtraining game

Identifiers

PMID34444160
PMCPMC8394475

What OpenQuestion holds

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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.