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Record W2971916259 · doi:10.2196/13560

Development of a Web-Based Monitoring System for Power Tilt-in-Space Wheelchairs: Formative Evaluation

2019· article· en· W2971916259 on OpenAlexafffundvenue
Charles Campeau-Vallerand, François Michaud, François Routhier, Philippe S. Archambault, Dominic Létourneau, Dominique Gélinas-Bronsard, Claudine Auger

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsMcGill UniversityCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleUniversité de MontréalUniversité LavalInstitut interdisciplinaire d'innovation technologiqueUniversité de SherbrookeCentre for Interdisciplinary Research in Rehabilitation
FundersUniversité de MontréalUniversité de Sherbrooke
KeywordsWheelchairTilt sensoreHealthFormative assessmentIntervention (counseling)Psychological interventionTilt (camera)MedicineComputer scienceApplied psychologyHuman–computer interactionPsychologyHealth careEngineeringNursingWorld Wide WebTelecommunications

Abstract

fetched live from OpenAlex

BACKGROUND: In order to prevent pressure ulcers, wheelchair users are advised to regularly change position to redistribute or eliminate pressure between the buttocks region and the seat of the wheelchair. A power tilt-in-space wheelchair (allowing simultaneous pivoting of the seat and the backrest of the wheelchair toward the back or front) meets many clinical purposes, including pressure management, increased postural control, and pain management. However, there is a significant gap between the use of tilt as recommended by clinicians and its actual usage. A Web-based electronic health (eHealth) intervention, including a goal setting, monitoring, reminder, and feedback system of the use of power tilt-in-space wheelchairs was developed. The intervention incorporates behavior change principles to promote optimal use of tilt and to improve clinical postprocurement follow-up. OBJECTIVE: This study aimed to conduct a formative evaluation of the intervention prototype to pinpoint the functionalities needed by end users, namely, power wheelchair users and clinicians. METHODS: On the basis of an evaluation framework for Web-based eHealth interventions, semistructured interviews were conducted with power wheelchair users and clinicians. A content analysis was performed with a mix of emerging and a priori concepts. RESULTS: A total of 5 users of power tilt-in-space wheelchairs and 5 clinicians who had experience in the field of mobility aids aged 23 to 55 years were recruited. Participants found the Web interface and the physical components easy to use. They also appreciated the reminder feature that encourages the use of the tilt-in-space and the customization of performance goals. Participants requested improvements to the visual design and learnability of the Web interface, the customization of reminders, feedback about specific tilt parameters, and the bidirectionality of the interaction between the user and the clinician. They thought the current version of the intervention prototype could promote optimal use of the tilt and improve clinical postprocurement follow-up. CONCLUSIONS: On the basis of the needs identified by power wheelchair users and clinicians regarding the prototype of a power tilt-in-space wheelchair monitoring system, 3 main directions were defined for future development of the intervention. Further research with new wheelchair users, manual tilt-in-space wheelchairs, various age groups, and family caregivers is recommended to continue the formative evaluation of the prototype.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.038
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.059
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.392
Teacher spread0.355 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations27
Published2019
Admission routes3
Has abstractyes

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