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Record W3007738972 · doi:10.2196/17088

Development and Usability Testing of a Web-Based and Therapist-Assisted Coping Skills Program for Managing Psychosocial Problems in Individuals With Hand and Upper Limb Injuries: Mixed Methods Study

2020· article· en· W3007738972 on OpenAlexaffvenue
Folarin Omoniyi Babatunde, Joy C. MacDermid, Ruby Grewal, Luciana Macedo, Mike Szekeres

Bibliographic record

VenueJMIR Human Factors · 2020
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsSt Joseph's Health CareWestern UniversityMcMaster University
Fundersnot available
KeywordsUsabilityPsychological interventionPsychosocialCoping (psychology)Applied psychologyPsychologyThink aloud protocolClinical psychologyMedicineComputer scienceNursingPsychotherapist

Abstract

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BACKGROUND: Ineffective coping has been linked to prolonged pain, distress, anxiety, and depression after a hand and upper limb injury. Evidence shows that interventions based on cognitive behavioral therapy (CBT) may be effective in improving treatment outcomes, but traditional psychological interventions are resource intensive and unrealistic in busy hand therapy practices. Developing web-based, evidence-based psychological interventions specifically for hand therapy may be feasible in clinical practice and at home with reduced training and travel costs. Hand Therapy Online Coping Skills (HOCOS) is a program developed to supplement traditional hand therapy with therapist-assisted coping skills training based on principles from CBT and the Technology Acceptance Model. OBJECTIVE: This study aimed to describe the development and assess the usability of HOCOS to support hand therapists in the management of psychosocial problems. METHODS: The ADDIE model (Analysis, Design, Development, Implementation, and Evaluation) of system design was applied to create HOCOS. The usability testing of HOCOS involved a 2-stage process. In the first step, heuristic testing with information and communications technology (ICT) experts was completed using two sets of heuristics: Monkman heuristics and the Health Literacy Online (HLO) checklist. The second step involved user testing with hand therapists performing a series of online and face-to-face activities, completing 12 tasks on the website using the think-aloud protocol, completing the system usability scale (SUS) questionnaire, and a semistructured feedback interview in 2 iterative cycles. Descriptive statistics and content analyses were used to organize the data. RESULTS: In total, 4 ICT experts and 12 therapists completed usability testing. The heuristic evaluation revealed 15 of 35 violations on the HLO checklist and 5 of 11 violations on the Monkman heuristics. Initially, hand therapists found 5 tasks to be difficult but were able to complete all 12 tasks after the second cycle of testing. The cognitive interview findings were organized into 6 themes: task performance, navigation, design esthetics, content, functionality and features, and desire for future use. Usability issues identified were addressed in two iterative cycles. There was good agreement on all items of the SUS. Overall, therapists found that HOCOS was a detailed and helpful learning resource for therapists and patients. CONCLUSIONS: We describe the development and usability testing of HOCOS; a new web-based psychosocial intervention for individuals with a hand and upper limb injuries. HOCOS targets psychosocial problems linked to prolonged pain and disability by increasing access to therapist-guided coping skills training. We actively involved target users in the development and usability evaluation of the website. The final website was modified to meet the needs and preferences of the participants.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.085
GPT teacher head0.446
Teacher spread0.360 · 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 designObservational
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

Citations13
Published2020
Admission routes2
Has abstractyes

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