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Record W3003393495 · doi:10.2196/14282

Improving Maximal Strength in the Initial Postoperative Phase After Anterior Cruciate Ligament Reconstruction Surgery: Randomized Controlled Trial of an App-Based Serious Gaming Approach

2020· article· en· W3003393495 on OpenAlexvenueno aff
Jan‐Dierk Clausen, Niclas Nahen, Hauke Horstmann, Florian Lasch, Werner Krutsch, Christian Krettek, Thomas Sanjay Weber-Spickschen

Bibliographic record

VenueJMIR Serious Games · 2020
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnterior cruciate ligament reconstructionAnterior cruciate ligamentRandomized controlled trialPrehabilitationRehabilitationOrthopedic surgeryPhysical therapySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Anterior cruciate ligament reconstruction surgery is one of the most common orthopedic procedures. One of the main factors that influence the outcome is regaining strength in the postoperative phase. Because anterior cruciate ligament reconstruction surgeries are often performed in young patients, we combined the concept of prehabilitation with an app-based serious gaming approach to improve maximal strength postoperatively. OBJECTIVE: Our objective was to conduct a prospective randomized trial to evaluate whether an app-based active muscle training program (GenuSport Knee Trainer) can improve postoperative strength by starting rehabilitation immediately after primary anterior cruciate ligament reconstruction surgery. METHODS: We designed a pilot study in which we randomly assigned patients receiving primary anterior cruciate ligament reconstruction to either the serious gaming training (intervention) group or a conventional rehabilitation (control) group. Except for the serious gaming-based training, both groups followed the same postoperative treatment protocol. Outcome parameters were absolute and relative change in maximal strength, as well as the International Knee Documentation Committee Subjective Knee evaluation form, Knee Injury and Osteoarthritis Outcome Score, and Lysholm Knee Score. RESULTS: In total 26 patients agreed to participate (14 patients in the intervention group and 12 patients in the control group, 1 of whom was lost to follow-up). We noted a difference in absolute maximum strength between the exergaming intervention and the control groups. Mean maximum strength preoperatively was 155.1 (SD 79.2) N in the intervention group (n=14) and 157.0 (SD 40.8) N in the control group (n=11). Postoperative mean maximum strength was 212.8 (SD 78.5) N in the intervention group and 154.5 (SD 27.1) N in the control group. Mean absolute change in maximum strength was 57.7 (SD 95.2) N in the intervention group and -4.8 (22.2) N in the control group. The analysis of covariance model with absolute change as the dependent variable and treatment group and baseline maximum strength as covariates showed a relevant difference in relative change between treatment groups (intervention - control) of 59.7 N (95% CI 10.1-109.3; P=.02). Similarly to the absolute increase, the relative change in maximum strength was relevantly higher in the exergaming group. The mean relative change in maximum strength was 1.7 (SD 1.17) in the intervention group and 1 (SD 0.13) in the control group. No adverse events or problems were reported during the study period. CONCLUSIONS: Implementation of an app-based active muscle training program in the early postoperative therapy scheme was associated with an improvement in maximal strength. Therefore, we considered the use of GenuSport training after anterior cruciate ligament reconstruction to be a helpful complement to rehabilitation after anterior cruciate ligament reconstruction surgery to improve strength in the early postoperative phase. To our knowledge this was the first study to analyze immediate postoperative serious gaming-based training with the GenuSport device based on strength improvement.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.295
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2020
Admission routes1
Has abstractyes

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