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Record W4297817182 · doi:10.2196/28851

A Psychological Support Intervention to Help Injured Athletes “Get Back in the Game”: Design and Development Study

2022· article· en· W4297817182 on OpenAlexaffvenue
Clare L. Ardern, Nicholas Hooper, Paul O’Halloran, Kate E. Webster, Joanna Kvist

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAthletesPsychological interventionPhysical therapyRehabilitationThematic analysisMedicinePopulationAnterior cruciate ligamentRandomized controlled trialIntervention (counseling)UsabilityPsychologyApplied psychologyPhysical medicine and rehabilitationQualitative researchNursingSurgeryComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: After a serious knee injury, up to half of athletes do not return to competitive sport, despite recovering sufficient physical function. Athletes often desire psychological support for the return to sport, but rehabilitation clinicians feel ill-equipped to deliver adequate support. OBJECTIVE: We aimed to design and develop an internet-delivered psychological support program for athletes recovering from knee ligament surgery. METHODS: Our work for developing and designing the Back in the Game intervention was guided by a blend of theory-, evidence-, and target population-based strategies for developing complex interventions. We systematically searched for qualitative evidence related to athletes' experiences with, perspectives on, and needs for recovery and return to sport after anterior cruciate ligament (ACL) injury. Two reviewers coded and synthesized the results via thematic meta-synthesis. We systematically searched for randomized controlled trials reporting on psychological support interventions for improving ACL rehabilitation outcomes in athletes. One reviewer extracted the data, including effect estimates; a second reviewer checked the data for accuracy. The results were synthesized descriptively. We conducted feasibility testing in two phases-(1) technical assessment and (2) feasibility and usability testing. For phase 1, we recruited clinicians and people with lived experience of ACL injury. For phase 2, we recruited patients aged between 15 and 30 years who were within 8 weeks of ACL reconstruction surgery. Participants completed a 10-week version of the intervention and semistructured interviews for evaluating acceptability, demand, practicality, and integration. This project was approved by the Swedish Ethical Review Authority (approval number: 2018/45-31). RESULTS: The following three analytic themes emerged from the meta-synthesis (studies: n=16; participants: n=164): (1) tools or strategies for supporting rehabilitation progress, (2) barriers and facilitators for the physical readiness to return to sport, and (3) barriers and facilitators for the psychological readiness to return to sport. Coping strategies, relaxation, and goal setting may have a positive effect on rehabilitation outcomes after ACL reconstruction (randomized controlled trials: n=7; participants: n=430). There were no trials of psychological support interventions for improving the return to sport. Eleven people completed phase 1 of feasibility testing (technical assessment) and identified 4 types of software errors, which we fixed. Six participants completed the feasibility and usability testing phase. Their feedback suggested that the intervention was easy to access and addressed the needs of athletes who want to return to sport after ACL reconstruction. We refined the intervention to include more multimedia content and support access to and the use of the intervention features. CONCLUSIONS: The Back in the Game intervention is a 24-week, internet-delivered, self-guided program that comprises 7 modules that complement usual rehabilitation, changes focus as rehabilitation progresses, is easy to access and use, and includes different psychological support strategies.

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)0.004
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.114
GPT teacher head0.465
Teacher spread0.351 · 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 designOther design
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

Citations26
Published2022
Admission routes2
Has abstractyes

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