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Record W4400487625 · doi:10.2196/49703

Usability and Preliminary Efficacy of an Adaptive Supportive Care System for Patients With Cancer: Pilot Randomized Controlled Trial

2024· article· en· W4400487625 on OpenAlexvenueno aff
Sharon H. Baik, Karen Clark, Marisol Sanchez, Matthew Loscalzo, Ashley Celis, Marianne Razavi, Dershung Yang, William Dale, Niina Haas

Bibliographic record

VenueJMIR Cancer · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsUsabilityRandomized controlled trialMedicineMedical physicsOncologyComputer scienceHuman–computer interactionInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Using an iterative user-centered design process, our team developed a patient-centered adaptive supportive care system, PatientCareAnywhere, that provides comprehensive biopsychosocial screening and supportive cancer care to patients across the continuum of care adaptively. The overarching goal of PatientCareAnywhere is to improve health-related quality of life (HRQOL) and self-efficacy of patients with cancer by empowering them with self-management skills and bringing cancer care support directly to them at home. Such support is adaptive to the patient's needs and health status and coordinated across multiple sources in the forms of referrals, education, engagement of community resources, and secure social communication. OBJECTIVE: This study aims to assess the usability of the new web-based PatientCareAnywhere system and examine the preliminary efficacy of PatientCareAnywhere to improve patient-reported outcomes compared with usual care. METHODS: For phase 1, usability testing participants included patients with cancer (n=4) and caregivers (n=7) who evaluated the software prototype and provided qualitative (eg, interviews) and quantitative (eg, System Usability Scale) feedback. For phase 2, participants in the 3-month pilot randomized controlled trial were randomized to receive the PatientCareAnywhere intervention (n=36) or usual care control condition (n=36). HRQOL and cancer-relevant self-efficacy were assessed at baseline (preintervention assessment) and 12 weeks from baseline (postintervention assessment); mean differences between pre- and postintervention scores were compared between the 2 groups. RESULTS: Participants were highly satisfied with the prototype and reported above-average acceptable usability, with a mean System Usability Scale score of 84.09 (SD 10.02). Qualitative data supported the overall usability and perceived usefulness of the intervention, with a few design features (eg, "help request" function) added based on participant feedback. With regard to the randomized controlled trial, patients in the intervention group reported significant improvements in HRQOL from pre- to postintervention scores (mean difference 6.08, SD 15.26) compared with the control group (mean difference -2.95, SD 10.63; P=.01). In contrast, there was no significant between-group difference in self-efficacy (P=.09). CONCLUSIONS: Overall, PatientCareAnywhere represents a user-friendly, functional, and acceptable supportive care intervention with preliminary efficacy to improve HRQOL among patients diagnosed with cancer. Future studies are needed to further establish the efficacy of PatientCareAnywhere as well as explore strategies to enhance user engagement and investigate the optimal intensity, frequency, and use of the intervention to improve patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT02408406; https://clinicaltrials.gov/study/NCT02408406.

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)0.013
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.307
Teacher spread0.293 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

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