Search the digital library catalog

Abstract: Mental health disorders, such as depression, anxiety, and borderline personality disorder (BPD), are common, often begin early, and can cause profound impairment. Traditional assessments rely heavily on subjective reports and clinical observation, which can be inconsistent and biased. Recent advances in AI offer a promising complement by analyzing objective, observable cues from speech, language, facial expressions, physiological signals, and digital behavior. Explainable AI ensures these patterns remain interpretable and clinically meaningful. A synthesis of 24 recent systematic and scoping reviews shows that depression is linked to self-focused negative language, slowed and monotonous speech, reduced facial expressivity, disrupted sleep and activity, and altered phone or online behavior. Anxiety disorders present with negative language bias, monotone speech with pauses, physiological hyperarousal, and avoidance-related behaviors. BPD exhibits more complex patterns, including impersonal or externally focused language, speech dysregulation, paradoxical facial expressions, autonomic dysregulation, and socially ambivalent behaviors. Some cues, like reduced heart rate variability and flattened speech, appear across conditions, suggesting shared transdiagnostic mechanisms, while BPD’s interpersonal and emotional ambivalence stands out. These findings highlight the potential of observable, digitally measurable cues to complement traditional assessments, enabling earlier detection, ongoing monitoring, and more personalized interventions in psychiatry.
Keywords: observable cues, mood disorders, anxiety disorders, borderline personality disorder, multimodal signals, facial expressions, speech patterns, physiological signals, explainable AI, mental health assessment
Published in DKUM: 05.01.2026; Views: 0; Downloads: 1
Full text (835,67 KB)
This document has many files! More...

Abstract: Aims and Objective: The aim of this study was to explore the feasibility, usability and acceptance of integrating Clinical Decision Support Systems with Socially Assistive Robots into hospital grand rounds. Background: Adopting Clinical Decision Support Systems in healthcare faces challenges such as complexity, poor integration with workflows, and concerns about data privacy and quality. Issues such as too many alerts, confusing errors, and difficulty using the technology in front of patients make adoption challenging and prevent it from fitting into daily workflows. Making Clinical Decision Support System simple, intuitive and user-friendly is essential to enable its use in daily practice to improve patient care and decision-making. Methods: This six-month pilot study had two participant groups, with total of 40 participants: a longitudinal intervention group (n =8) and a single-session evaluation group (n=32). Participants were medical doctors at the University Clinical Center Maribor. The intervention involved implementing a Clinical Decision Support System delivered via a Socially Assistive Robot during hospital grand rounds. We developed a system that employed the HL7 FHIR standard for integrating data from hospital monitors, electronic health records, and patient-reported outcomes into a single dashboard. A Pepper-based SAR provided patient specific recommendations through a voice and SAR tablet enabled interface. Key evaluation metrics were assessed using the System Usability Scale (SUS) and the Unified Theory of Acceptance, Use of Technology (UTAUT2) questionnaire, including Effort Expectancy, Performance Expectancy and open ended questions. The longitudinal group used the system for 6 months and completed the assessments twice, after one week and at the end of the study. The single-session group completed the assessment once, immediately after the experiment. Qualitative data were gathered through open-ended questions. Data analysis included descriptive statistics, paired t-tests, and thematic analysis. Results: System usability was rated highly across both groups, with the longitudinal group reporting consistently excellent scores (M =82.08 at final evaluation) compared to the acceptable scores of the single-session group (M =68.96). Extended exposure improved user engagement, reflected in significant increases in Effort Expectancy and Habit over time. Participants found the system enjoyable to use, and while no significant changes were seen in Performance Expectancy, feedback emphasized its efficiency in saving time and improving access to clinical data, supporting its feasibility and acceptability. Conclusions: This research supports the potential of robotic technologies to transform CDSS into more interactive, efficient, and user-friendly tools for healthcare professionals. The paper also suggests further research directions and technical improvements to maximize the impact of innovative technologies in healthcare.
Keywords: clinical decision support systems, clinical decision-making, hospital grand rounds, patient data integration, perceived quality of care, socially assistive robots, usability and familiarity, user experience questionnaire, workload reduction
Published in DKUM: 30.05.2025; Views: 0; Downloads: 3
Full text (3,02 MB)

Abstract: Cancer survivors face numerous challenges, and digital health interventions can empower them by enhancing self-efficacy and patient activation. This prospective study aimed to assess the impact of a mHealth app on self-efficacy and patient activation in 166 breast and colorectal cancer survivors. Participants received a smart bracelet and used the app to access personalized care plans. Data were collected at baseline and follow-ups, including patient-reported outcomes and clinician feedback. The study demonstrated positive impacts on self-efficacy and patient activation. The overall trial retention rate was 75.3%. Participants reported high levels of activation (PAM levels 1–3: P = 1.0; level 4: P = 0.65) and expressed a willingness to stay informed about their disease (CASE-Cancer factor 1: P = 0.98; factor 2: P = 0.66; factor 3: P = 0.25). Usability of the app improved, with an increase in participants rating the system as having excellent usability (from 14.82% to 22.22%). Additional qualitative analysis revealed positive experiences from both patients and clinicians. This paper contributes significantly to cancer survivorship care by providing personalized care plans tailored to individual needs. The PERSIST platform shows promise in improving patient outcomes and enhancing self-management abilities in cancer survivors. Further research with larger and more diverse populations is needed to establish its effectiveness.
Keywords: cancer survivorship, self-efficacy, satisfaction, patient activation, digital health interventions
Published in DKUM: 25.04.2025; Views: 0; Downloads: 0

Abstract: Background: While anxiety disorders are one of the most prevalent mental diseases, they are often overlooked due to shortcomings of the existing diagnostic procedures, which predominantly rely on self-reporting. Due to recent technological advances, this source of information could be complemented by the so-called observable cues – indicators that are displayed spontaneously through individuals’ physiological responses or behaviour and can be detected by modern devices. However, while there are several individual studies on such cues, this research area lacks a synthesis. In line with this, our scoping review aimed to identify observable cues that offer meaningful insight into individuals’ anxiety and to determine how these cues can be measured. Methods: We followed the PRISMA guidelines for scoping reviews. The search string containing terms related to anxiety and observable cues was entered into four databases (Web of Science, MEDLINE, ERIC, IEEE). While the search – limited to English peer-reviewed records published from 2012 onwards – initially yielded 2311 records, only 33 articles fit our selection criteria and were included in the final synthesis. Results: The scoping review unravelled various categories of observable cues of anxiety, specifically those related to facial expressions, speech and language, breathing, skin, heart, cognitive control, sleep, activity and motion, location data and smartphone use. Moreover, we identified various approaches for measuring these cues, including wearable devices, and analysing smartphone usage and social media activity. Conclusions: Our scoping review points to several physiological and behavioural cues associated with anxiety and highlights how these can be measured. These novel insights may be helpful for healthcare practitioners and fuel future research and technology development. However, as many cues were investigated only in a single study, more evidence is needed to generalise these findings and implement them into practice with greater confidence.
Keywords: anxiety, observable cues, digital biomarkers, scoping review, physiological cues, behavioural cues
Published in DKUM: 07.02.2025; Views: 0; Downloads: 7
Full text (910,98 KB)

Keywords: quality of life, surveys and questionnaires, adult oncology, colorectal cancer survivors, Delphi study, scoping review, expert consensus
Published in DKUM: 15.01.2025; Views: 0; Downloads: 9
Full text (539,75 KB)