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Record W2133622904 · doi:10.2196/jmir.2350

Development and Testing of a Multidimensional iPhone Pain Assessment Application for Adolescents with Cancer

2013· article· en· W2133622904 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Medical Internet Research · 2013
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of OttawaUniversity of TorontoBC Children's HospitalSickKids FoundationHospital for Sick ChildrenMcMaster University
Fundersnot available
KeywordsUsabilityPsychological painCancer painThematic analysisQualitative researchPsychologyPain assessmentMoodFidelityApplied psychologyMedicinePhysical therapyClinical psychologyComputer scienceCancerPain managementHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: Pain is one of the most common and distressing symptoms reported by adolescents with cancer. Despite advancements in pain assessment and management research, pain due to cancer and/or its treatments continues to be poorly managed. Our research group has developed a native iPhone application (app) called Pain Squad to tackle the problem of poorly managed pain in the adolescent with cancer group. The app functions as an electronic pain diary and is unique in its ability to collect data on pain intensity, duration, location, and the impact pain has on an adolescent's life (ie, relationships, school work, sleep, mood). It also evaluates medications and other physical and psychological pain management strategies used. Users are prompted twice daily at configurable times to complete 20 questions characterizing their pain and the app transmits results to a database for aggregate reporting through a Web interface. Each diary entry represents a pain case filed by an adolescent with cancer and a reward system (ie, moving up through law-enforcement team ranks, built-in videotaped acknowledgements from fictitious officers) encourages consistent use of the diary. OBJECTIVE: Our objective was to design, develop, and test the usability, feasibility, compliance, and satisfaction of a game-based smartphone pain assessment tool for adolescents with cancer. METHODS: We used both low- and high-fidelity qualitative usability testing with qualitative semi-structured, audio-taped interviews and iterative cycles to design and refine the iPhone based Pain Squad app. Qualitative thematic analysis of interviews using constant comparative methodology captured emergent themes related to app usability. Content validity was assessed using question importance-rating surveys completed by participants. Compliance and satisfaction data were collected following a 2-week feasibility trial where users were alarmed to record their pain twice daily on the app. RESULTS: Thematic analysis of usability interviews showed the app to be appealing overall to adolescents. Analyses of both low- and high-fidelity testing resulted in minor revisions to the app to refine the theme and improve its usability. Adolescents resoundingly endorsed the game-based nature of the app and its virtual reward system. The importance of app pain diary questions was established by content validity analysis. Compliance with the app, assessed during feasibility testing, was high (mean 81%, SD 22%) and adolescents from this phase of the study found the app likeable, easy to use, and not bothersome to complete. CONCLUSIONS: A multifaceted usability approach demonstrated how the Pain Squad app could be made more appealing to children and adolescents with cancer. The game-based nature and built-in reward system of the app was appealing to adolescents and may have resulted in the high compliance rates and satisfaction ratings observed during clinical feasibility testing.

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.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
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 head0.072
GPT teacher head0.433
Teacher spread0.361 · 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