# Effects of an Internet of Things-Based Medication Assistance System on Real-World ART Adherence and Treatment Response in People Living with HIV

## TL;DR

This study explores how an IoT-based medication system affects HIV treatment adherence and outcomes in real-world settings.

## Contribution

The study introduces an IoT-based medication assistance system for monitoring ART adherence and evaluates its feasibility and error patterns.

## Key findings

- The IoT device showed a median adherence rate of 87.4% and a deviation error rate of 4.4%.
- Error rates decreased significantly over time, indicating improved device proficiency.
- The system may help identify discrepancies between clinical evaluations and actual adherence patterns.

## Abstract

Background/Objectives: The study primarily examined whether an IoT-based medication assistance system enhances ART adherence relative to standard care, and secondarily evaluated device feasibility and error patterns over time. Methods: This prospective study was conducted between June 2022 and October 2023 at a tertiary hospital in South Korea. Adults (≥19 years) living with HIV and prescribed ART were included; those with comorbid hepatitis B or C were excluded. People living with HIV who agreed to use the IoT-based InPHRPILL system (Sofnet Inc., Seoul, Republic of Korea) were assigned to the intervention group, whereas those who declined were assigned to the control group. Viral suppression, CD4+ cell counts, and adherence rates were measured. Additional analyses evaluated 12-month longitudinal adherence using pill-count data in both groups, and device-measured adherence and device-associated error rates in the intervention group. Results: Thirty-five participants (12 in the intervention group and 23 in the control group) were included. The intervention group demonstrated marginally shorter durations since HIV diagnosis and ART initiation at study enrollment, as well as slightly higher baseline HIV-RNA levels; however, these differences did not reach statistical significance. The median pill-counting and IoT device adherence rates were 100% and 87.4%, respectively (median deviation error rate = 4.4%). Poisson regression revealed significantly reduced error rates over time (β = −0.06493, p < 0.01), suggesting improved device use proficiency. Conclusions: IoT-based medication assistance systems may provide objective, real-time monitoring of ART adherence and facilitate identification of discrepancies between clinical evaluations and actual adherence patterns. Larger studies targeting individuals with suboptimal adherence are warranted to determine whether such systems can enhance adherence outcomes.