Trial reportJMIR formative research2026
Feasibility and Preliminary Effectiveness of the ChulaCancer Mobile Chatbot for Supportive Care of Patients With Breast or Colorectal Cancer Receiving Chemotherapy: Pilot Randomized Controlled Trial.
Trial report in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChemotherapy-related toxicities often lead to unscheduled health care use and diminished quality of life. Digital health interventions, such as chatbots, offer a scalable solution for supportive care; however, evidence regarding their effectiveness in resource-limited, low- and middle-income settings remains limited.
objectiveThis study aimed to evaluate the feasibility, use, and preliminary effectiveness of a closed-loop chatbot (ChulaCancer Chatbot) in reducing unscheduled hospital visits and stabilizing quality of life among patients receiving chemotherapy for breast or colorectal cancer.
methodsThis pilot randomized controlled trial enrolled 40 patients at a single academic center in Thailand, randomized 1:1 to either ChulaCancer chatbot plus usual care or usual care alone. The primary end point was the proportion of unscheduled hospital visits due to chemotherapy-related toxicities within 12 weeks of treatment initiation. Secondary end points included longitudinal quality of life changes (30-item EORTC Quality of Life Questionnaire) measured at baseline, following chemotherapy cycle 2, and following cycle 4. Use metrics were extracted from the chatbot platform. Data were analyzed using the Fisher exact test and linear mixed-effects models.
resultsThe platform recorded 2393 total messages with a 70.5% (503/713) successful response rate for user-initiated queries. Unscheduled hospital visits occurred in 15% (3/20) of the chatbot group compared to 35% (7/20) of the usual care group (P=.24). While infection-related visits were similar between groups, the usual care group recorded multiple visits for low-acuity symptoms (eg, anxiety, headache, and edema) that were absent in the chatbot group. Regarding quality of life, the chatbot group demonstrated a significant mitigation of cancer-related fatigue following cycle 4 compared with the usual care group (P=.02 between groups). Additionally, the chatbot group significantly improved in global health status (P=.04) and avoided the decline in physical functioning observed in the control arm (P=.04).
conclusionsThe integration of a closed-loop chatbot into oncology care is feasible and provides a potential secure triage mechanism that may reduce acute care use for low-acuity concerns. Future large-scale trials incorporating agentic artificial intelligence are warranted to further validate clinical and economic benefits.
trial registrationThai Clinical Trials Registry TCTR20251220014; https://tinyurl.com/5b6k3e63.
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