Health Professions Library Help Guide

Sensors (Basel). 2025 May 8;25(10):2966. doi: 10.3390/s25102966.

ABSTRACT

The dynamic process of eating-including chewing, biting, swallowing, food items, eating time and rate, mass, environment, and other metrics-may characterize behavioral aspects of eating. This article presents a systematic review of the use of sensor technology to measure and monitor eating behavior. The PRISMA 2020 guidelines were followed to review the full texts of 161 scientific manuscripts. The contributions of this review article are twofold: (i) A taxonomy of sensors for quantifying various aspects of eating behavior is established, classifying the types of sensors used (such as acoustic, motion, strain, distance, physiological, cameras, and others). (ii) The accuracy of measurement devices and methods is assessed. The review highlights the advantages and limitations of methods that measure and monitor different eating metrics using a combination of sensor modalities and machine learning algorithms. Furthermore, it emphasizes the importance of testing these methods outside of restricted laboratory conditions, and it highlights the necessity of further research to develop privacy-preserving approaches, such as filtering out non-food-related sounds or images, to ensure user confidentiality and comfort. The review concludes with a discussion of challenges and future trends in the use of sensors for monitoring eating behavior.

PMID:40431762 | PMC:PMC12114971 | DOI:10.3390/s25102966

Int J Med Inform. 2025 May 20;202:105987. doi: 10.1016/j.ijmedinf.2025.105987. Online ahead of print.

ABSTRACT

BACKGROUND: Tobacco use remains a significant global health challenge, contributing substantially to preventable morbidity and mortality. Despite established interventions, outcomes vary due to scalability issues, resource constraints, and limited reach.

OBJECTIVE: To systematically explore current artificial intelligence (AI) applications within tobacco control, highlighting their usefulness, benefits, limitations, and ethical implications.

METHOD: This scoping review followed the Arksey and O'Malley framework and PRISMA-ScR guidelines. Five major databases (PubMed, Scopus, Web of Science, IEEE Xplore, and PsycINFO) were searched for articles published between January 2010 and March 2025. From 1,172 initial records, 57 studies met inclusion criteria after screening.

RESULTS: AI-driven tools, including machine learning and natural language processing, effectively monitor social media for emerging tobacco trends and personalize smoking cessation interventions. Applications were predominantly focused on predictive modelling (using algorithms like XGBoost and SVM to predict e-cigarette use and relapse risk), cessation support (employing chatbots and reinforcement learning to improve accessibility), and social media surveillance (detecting synthetic nicotine promotions and analysing vaping trends). Approximately 22% of studies aligned with WHO FCTC Article 13 (tobacco advertising regulation), while 45% supported Article 14 (cessation services). However, tobacco industry interference remains a critical challenge, with AI technologies exploited to undermine public health initiatives, target vulnerable populations, and manipulate policy discussions. Critical issues including algorithmic bias, privacy concerns, interpretability challenges, and data quality must be addressed to ensure positive impact.

CONCLUSION: AI holds considerable promise for extending tobacco control if implemented ethically, transparently, and collaboratively. Future directions emphasize explainable AI development, integration of real-time intervention systems, global data inclusion, and robust cross-sector collaboration. While the current landscape shows a laudable start, it reflects the need for more diverse skill sets to fully harness AI's extensive prospects for tobacco control and achieving tobacco endgame goals.

PMID:40409168 | DOI:10.1016/j.ijmedinf.2025.105987

J Med Internet Res. 2025 May 23;27:e72229. doi: 10.2196/72229.

ABSTRACT

BACKGROUND: Differentiating bipolar disorder (BD) from unipolar depression (UD) is essential, as these conditions differ greatly in their progression and treatment approaches. Digital phenotyping, which involves using data from smartphones or other digital devices to assess mental health, has emerged as a promising tool for distinguishing between these two disorders.

OBJECTIVE: This systematic review aimed to achieve two goals: (1) to summarize the existing literature on the use of digital phenotyping to directly distinguish between UD and BD and (2) to review studies that use digital phenotyping to classify UD, BD, and healthy control (HC) individuals. Furthermore, the review sought to identify gaps in the current research and propose directions for future studies.

METHODS: We systematically searched the Scopus, IEEE Xplore, PubMed, Embase, Web of Science, and PsycINFO databases up to March 20, 2025. Studies were included if they used portable or wearable digital tools to directly distinguish between UD and BD, or to classify UD, BD, and HC. Original studies published in English, including both journal and conference papers, were included, while reviews, narrative reviews, systematic reviews, and meta-analyses were excluded. Articles were excluded if the diagnosis was not made through a professional medical evaluation or if they relied on electronic health records or clinical data. For each included study, the following information was extracted: demographic characteristics, diagnostic criteria or psychiatric assessments, details of the technological tools and data types, duration of data collection, data preprocessing methods, selected variables or features, machine learning algorithms or statistical tests, validation, and main findings.

RESULTS: We included 21 studies, of which 11 (52%) focused on directly distinguishing between UD and BD, while 10 (48%) classified UD, BD, and HC. The studies were categorized into 4 groups based on the type of digital tool used: 6 (29%) used smartphone apps, 3 (14%) used wearable devices, 11 (52%) analyzed audiovisual recordings, and 1 (5%) used multimodal technologies. Features such as activity levels from smartphone apps or wearable devices emerged as potential markers for directly distinguishing UD and BD. Patients with BD generally exhibited lower activity levels than those with UD. They also tended to show higher activity in the morning and lower in the evening, while patients with UD showed the opposite pattern. Moreover, speech modalities or the integration of multiple modalities achieved better classification performance across UD, BD, and HC groups, although the specific contributing features remained unclear.

CONCLUSIONS: Digital phenotyping shows potential in distinguishing BD from UD, but challenges like data privacy, security concerns, and equitable access must be addressed. Further research should focus on overcoming these challenges and refining digital phenotyping methodologies to ensure broader applicability in clinical settings.

TRIAL REGISTRATION: PROSPERO CRD42024624202; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024624202.

PMID:40408762 | DOI:10.2196/72229