Background And Objectives: Substance use disorders (SUDs) are chronic relapsing diseases characterized by significant morbidity and mortality. Phenomenologically, patients with SUDs present with a repeating cycle of intoxication, withdrawal, and craving, significantly impacting their diagnosis and treatment. There is a need for better identification and monitoring of these disease states. Remote monitoring chronic illness with wearable devices offers a passive, unobtrusive, constant physiological data assessment. We evaluate the current evidence base for remote monitoring of nonalcohol, nonnicotine SUDs.

Methods: We performed a systematic, comprehensive literature review and screened 1942 papers.

Results: We found 15 studies that focused mainly on the intoxication stage of SUD. These studies used wearable sensors measuring several physiological parameters (ECG, HR, O , Accelerometer, EDA, temperature) and implemented study-specific algorithms to evaluate the data.

Discussion And Conclusions: Studies were extracted, organized, and analyzed based on the three SUD disease states. The sample sizes were relatively small, focused primarily on the intoxication stage, had low monitoring compliance, and required significant computational power preventing "real-time" results. Cardiovascular data was the most consistently valuable data in the predictive algorithms. This review demonstrates that there is currently insufficient evidence to support remote monitoring of SUDs through wearable devices.

Scientific Significance: This is the first systematic review to show the available data on wearable remote monitoring of SUD symptoms in each stage of the disease cycle. This clinically relevant approach demonstrates what we know and do not know about the remote monitoring of SUDs within disease states.

Download full-text PDF

Source
http://dx.doi.org/10.1111/ajad.13341DOI Listing

Publication Analysis

Top Keywords

remote monitoring
24
disease states
12
systematic review
8
wearable remote
8
monitoring
8
focused intoxication
8
intoxication stage
8
monitoring suds
8
remote
6
wearable
5

Similar Publications

Introduction: Screening diabetic retinopathy (DR) for timely management can reduce global blindness. Many existing DR screening programs worldwide are non-digital, standalone, and deployed with grading retinal photographs by trained personnel. To integrate the screening programs, with or without artificial intelligence (AI), into hospital information systems to improve their effectiveness, the non-digital workflow must be transformed into digital.

View Article and Find Full Text PDF

This research was carried out to assess the concentrations of carbon monoxide (CO) and formaldehyde (HCHO) in Edo State, Southern Nigeria, using remote sensing data. A secondary data collection method was used for the assessment, and the levels of CO and HCHO were extracted annually from Google Earth Engine using information from Sentinel-5-P satellite data (COPERNISCUS/S5P/NRTI/L3_) and processed using ArcMap, Google Earth Engine, and Microsoft Excel to determine the levels of CO and HCHO in the study area from 2018 to 2023. The geometry of the study location is highlighted, saved and run, and a raster imagery file of the study area is generated after the task has been completed with a 'projection and extent' in the Geographic Tagged Image File Format (.

View Article and Find Full Text PDF

The Impact of Artificial Intelligence and Machine Learning in Organ Retrieval and Transplantation: A Comprehensive Review.

Curr Res Transl Med

January 2025

Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.

This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.

View Article and Find Full Text PDF

Objectives: We compared the effectiveness of audio-based care, as a replacement or a supplement to usual care, for managing diabetes.

Background: Diabetes is a chronic condition afflicting many in the United States. The impact of audio-based care on the health of individuals with diabetes is unclear, particularly for those at risk for disparities-many of whom may only be able to access telehealth services through telephone.

View Article and Find Full Text PDF

Objectives: To study the rates of abnormal placentae and associated adverse perinatal outcomes in pregnant women who had COVID 19 infection during pregnancy, remote from delivery. To study the histopathological findings associated with these abnormal placentae.

Methods: A prospective cohort study was carried out, recruiting pregnant women with singleton gestation, who had COVID 19 infection during their pregnancy, remote from delivery between August 2021 to July 2022.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!