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April 10, 2026Sensors2 citationsOpen Access

AI-Guided Remission: Protocol for a Conversational Agent (Chatbot) for Dosing Activity and Footwear Progression After Diabetic Limb Reconstruction

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LFLucian FeraruDKDavid C. KlonoffBNBijan Najafi

Key Points

  • The aim is to design a chatbot that assists patients during the remission phase post-diabetic limb reconstruction to enhance self-care.
  • Develop a conversational agent that turns clinical guidelines into daily self-care actions.
  • Integrate automatic data capture from devices like smartphones and smartwatches with manual patient entries.
  • Dosing walking like a drug based on tissue response and guiding footwear progression with escalation pathways.
  • Conduct a single-arm feasibility pilot study to evaluate engagement, safety, and implementation fidelity.
  • No clinical outcomes are reported as this is a protocol study and enrollment hasn't started.
  • Highlights the planned sensing-to-decision workflow and internal verification procedures.
  • Sets the groundwork for future evaluations of clinical efficacy.

Abstract

Background: Diabetic foot ulcers recur frequently after healing. The first three months carry the highest risk. Remission is a vulnerable phase that demands precise self-care and timely feedback. Evidence supports thermometry and protective footwear with gradual return to activity, yet adherence at home is inconsistent. Objective: To describe the design and planned evaluation of a conversational agent (chatbot) that guides patients through the remission phase following diabetic limb reconstruction. Methods: This protocol describes a conversational agent (chatbot) that turns remission guidance into daily actions, grounded in clinical expertise and established care guidelines. Walking is dosed like a drug, with careful titration based on tissue response. The agent integrates automatic data capture (smartphone step counts, skin temperature, shoe step streams, smartwatch step streams, Bluetooth thermometry when available, and app session timestamps) with manual patient entries (shoe wear time, skin redness persistence, and symptom checks). It doses walking activity, guides footwear break-in, prompts photo-confirmed concerns, following clinician-informed rules and escalation pathways. We define data quality checks for missingness and physiologic plausibility, and the agent reinforces reducing weight-bearing activity when risk signals appear. We outline device drift. The study is designed as a single-arm feasibility pilot (n = 30) to assess engagement, safety, and implementation fidelity. Results: No clinical outcome results are reported because this is a protocol study and enrollment has not yet begun. This study presents the prespecified sensing-to-decision workflow, escalation logic, and pilot endpoints, along with internal technical verification procedures (e.g., message delivery reliability, data completeness checks, and rule-engine consistency testing). Conclusions: A remission chatbot is a plausible method to extend specialist support into the home, reflecting integration of clinical expertise with digital health tools. This protocol defines how feasibility, safety, and usability will be evaluated. Clinical efficacy should be confirmed in future studies.

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Cite This Study

Feraru et al. (2026) studied this question.

synapsesocial.com/papers/69d895be6c1944d70ce06c85https://doi.org/10.3390/s26082299
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