User Evaluation of a Chat-Based Instant Messaging Support Health Education Program for Patients With Chronic Kidney Disease: Preliminary Findings of a Formative Study
Bibliographic record
Abstract
Background Artificial intelligence–driven chatbots are increasingly being used in health care, but few chat-based instant messaging support health education programs are designed for patients with chronic kidney disease (CKD) to evaluate their effectiveness. In addition, limited research exists on the usage of chat-based programs among patients with CKD, particularly those that integrate a chatbot aimed at enhancing the communication ability and disease-specific knowledge of patients. Objective The objective of this formative study is to gather the data necessary to develop an intervention program of chat-based instant messaging support health education for patients with CKD. Participants’ user experiences will form the basis for program design improvements. Methods Data were collected from April to November 2020 using a structured questionnaire. A pre-post design was used, and a total of 60 patients consented to join the 3-month program. Among them, 55 successfully completed the study measurements. The System Usability Scale was used for participant evaluations of the usability of the chat-based program. Results Paired t tests revealed significant differences before and after intervention for communicative literacy (t54=3.99; P<.001) and CKD-specific disease knowledge (t54=7.54; P<.001). Within disease knowledge, significant differences were observed in the aspects of CKD basic knowledge (t54=3.46; P=.001), lifestyle (t54=3.83; P=.001), dietary intake (t54=5.51; P<.001), and medication (t54=4.17; P=.001). However, no significant difference was found in the aspect of disease prevention. Subgroup analysis revealed that while the findings among male participants were similar to those of the main sample, this was not the case among female participants. Conclusions The findings reveal that a chat-based instant messaging support health education program may be effective for middle-aged and older patients with CKD. The use of a chat-based program with multiple promoting approaches is promising, and users’ evaluation is satisfactory. Trial Registration ClinicalTrials.gov NCT05665517; https://clinicaltrials.gov/study/NCT05665517
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".