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Record W4410066905 · doi:10.2196/67254

Smart Technology Facilitated Patient-Centered Venous Thromboembolism Management (the SmaVTE Study): Protocol for a Randomized Controlled Trial

2025· article· en· W4410066905 on OpenAlexvenueno aff
Zhigeng Jin, Z. Zhang, Hao Wang, Ying Yang, Lina Ren, Hui Zhang, Zhenguo Zhai, Yutao Guo

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintRandomized controlled trialVenous thromboembolismProtocol (science)MedicinePhysical therapyAlternative medicineComputer scienceSurgeryWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Venous thromboembolism (VTE) is a significant public health issue, with a rising global incidence despite extensive research efforts. Patient-centered care, which tailors treatment to individual needs, has shown potential in enhancing outcomes. The integration of smart technologies with psychological frameworks such as the health belief model and the knowledge-attitude-practice (KAP) model may further improve patient engagement and adherence. To address this, we have developed a smart technique-assisted patient-centered care mobile health app for managing VTE (mVTEA), which integrates psychological frameworks to improve patient outcomes in VTE management. OBJECTIVE: This study aims to investigate the impact of the mVTEA app on the knowledge, attitudes, and practices of VTE in patients with or at high risk of VTE. METHODS: The SmaVTE (smart technology facilitated patient-centered venous thromboembolism management) study is a 2-armed, single-center, parallel-group, randomized controlled trial. A total of 256 hospitalized patients with or at high risk of VTE will be recruited on the day of their discharge from August 2024 to June 2025. Participants will be randomly allocated to either the mVTEA management group or the routine management group in a 1:1 ratio. The mVTEA management group (n=128) will receive patient-centered VTE management facilitated by the mVTEA app after discharge. The routine management group (n=128) will be administered conventional postdischarge management according to local clinical practice. The KAP of patients will be assessed by a structured KAP questionnaire on VTE. The primary outcome is the difference in patients' KAP on VTE at 3-month follow-up between the 2 groups. Secondary outcomes include scores on each domain of the questionnaire, quality of life, VTE events, chronic thromboembolic pulmonary hypertension, chronic thromboembolic pulmonary disease, postpulmonary embolism syndrome, major bleeding events, VTE-related hospitalizations or rehospitalizations, deaths, and new-onset atrial fibrillation or atrial flutter at 3-month follow-up. RESULTS: Participants are currently being recruited. The first participant was enrolled in August 2024, which marked the official start of the study. The recruitment process is expected to be completed in June 2025. As of the submission of the paper, 185 patients had been enrolled in this clinical trial. At present, all included patients are being followed up according to the outlined schedule. CONCLUSIONS: The SmaVTE study offers a pioneering approach to VTE prevention and treatment by combining smart technology with patient-centered care and established theoretical frameworks. The findings could significantly impact clinical practice and inspire further research into the integration of smart technologies with behavioral science theories. TRIAL REGISTRATION: ClinicalTrials.gov NCT06350331; https://clinicaltrials.gov/study/NCT06350331. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67254.

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)0.033
metaresearch head score (Gemma)0.032
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.083
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.032
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0120.005
Bibliometrics0.0020.003
Science and technology studies0.0030.004
Scholarly communication0.0050.004
Open science0.0030.002
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0830.012

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 head0.106
GPT teacher head0.507
Teacher spread0.401 · 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
GenreProtocol

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
Published2025
Admission routes1
Has abstractyes

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