Scoping Review of Technological Solutions for Community Dwelling Older Adults and Implications for Instrumental Activities of Daily Living.

Aging in place is not without its challenges, with physical, psychological, social, and economic burdens on caregivers and seniors. To address these challenges and promote active aging, technological advancements offer a range of digital tools, applications, and devices, enabling community dwelling older adults to live independently and safely. Despite these opportunities, the acceptance of technology among the older adults remains low, often due to a mismatch between technology development and the actual needs and goals of seniors. The aim of this review is to identify recent technological solutions that monitor the health and well-being of aging adults, particularly within the context of instrumental activities of daily living (IADL). A scoping review identified 52 studies that meet specific inclusion criteria. The outcomes were classified based on social connectedness, autonomy, mental health, physical health, and safety. Our review revealed that a predominant majority (82%) of the studies were observational in design and primarily focused on health-related IADLs (59%) and communication-related IADLs (31%). Additionally, the study highlighted the crucial role of involving older adults in study design processes, with only 8 out of the 52 studies incorporating this approach. Our review also established the interview method as the most favoured technology evaluation tool for older adults' studies. The metrics of 'usability' and 'acceptance' emerged as the most frequently employed measures for technology assessment. This study contributes to the existing literature by shedding light on the implications of technological solutions for community dwelling older adults, emphasizing the types of technologies employed and their evaluation results.

Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST).

The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app's usability as above satisfactory (i.e., 7.9 on 1-10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1-10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care.

Measuring young individuals' responses to climate change: validation of the Slovenian versions of the climate anxiety scale and the climate change worry scale.

Introduction: While increasing awareness of climate change is needed to address this threat to the natural environment and humanity, it may simultaneously negatively impact mental health. Previous studies suggest that climate-specific mental health phenomena, such as climate anxiety and worry, tend to be especially pronounced in youth. To properly understand and address these issues, we need valid measures that can also be used in non-Anglophone samples. Therefore, in the present paper, we aimed to validate Slovenian versions of the Climate Anxiety Scale (CAS) and the Climate Change Worry Scale (CCWS) among Slovenian youth. Method: We conducted an online survey in which 442 young individuals (18-24 years) from Slovenia filled out the two central questionnaires and additional instruments capturing other relevant constructs (e.g., general anxiety, neuroticism, and behavioral engagement). Results: The confirmatory factor analyses results supported the hypothesized factorial structure of the CAS (two factors) and the CCWS (one factor). Both scales also demonstrated great internal reliability. Moreover, the analyses exploring both constructs' nomological networks showed moderate positive associations with similar measures, such as anxiety and stress (convergent validity), and very weak associations with measures they should not be particularly related to, such as narcissism (discriminant validity). Lastly, we found that the CAS and, even more so, the CCWS have unique predictive value in explaining outcomes such as perceived threat, support for climate policies, and behavioral engagement (incremental validity). Discussion: Overall, Slovenian versions of the CAS and the CCWS seem to be valid, reliable, and appropriate for future studies tackling young individuals' responses to climate change. Limitations of the study and areas for future research are discussed.

A protocol on the effects of interactive digital assistance on engagement and perceived quality of care of surgery patients and self-efficacy and workload of staff.

Introduction: The workforce shortage in the healthcare context is a growing issue that exerts detrimental effects on employees (e.g., higher workload) and patients (e.g., suboptimal patient care). Since traditional approaches alone may not be enough to solve this problem, there is a need for complementary innovative digital health solutions, such as socially assistive robots. Hence, the proposed study aims to investigate the effects of gamified nursing education and physiotherapy delivered by a socially assistive robot on patient- (engagement, perceived quality of care) and employee-related outcomes (perceived self-efficacy, workload). Methods and analysis: Approximately 90 vascular and thoracic surgery patients will receive either standard care or standard care with additional robot interactions over the course of 3-5 days. Additionally, approximately 34 nursing and physiotherapeutic employees will fill out self-report questionnaires after weeks of not using a social robot and weeks of using a social robot. The main hypotheses will be tested with mixed-design analyses of variance and paired-samples t-tests. Discussion: While the proposed study has some limitations, the results will provide high-quality and comprehensive evidence on the effectiveness of socially assistive robots in healthcare. Ethics and dissemination: The study was approved by the Medical Ethics Commission of the University Medical Center and registered in the ISRCTN registry (ISRCTN96689284). The study findings will be summarized in international peer-reviewed scientific journals and meetings and communicated to relevant stakeholders.

Scoping Review on the Multimodal Classification of Depression and Experimental Study on Existing Multimodal Models.

Depression is a prevalent comorbidity in patients with severe physical disorders, such as cancer, stroke, and coronary diseases. Although it can significantly impact the course of the primary disease, the signs of depression are often underestimated and overlooked. The aim of this paper was to review algorithms for the automatic, uniform, and multimodal classification of signs of depression from human conversations and to evaluate their accuracy. For the scoping review, the PRISMA guidelines for scoping reviews were followed. In the scoping review, the search yielded 1095 papers, out of which 20 papers (8.26%) included more than two modalities, and 3 of those papers provided codes. Within the scope of this review, supported vector machine (SVM), random forest (RF), and long short-term memory network (LSTM; with gated and non-gated recurrent units) models, as well as different combinations of features, were identified as the most widely researched techniques. We tested the models using the DAIC-WOZ dataset (original training dataset) and using the SymptomMedia dataset to further assess their reliability and dependency on the nature of the training datasets. The best performance was obtained by the LSTM with gated recurrent units (F1-score of 0.64 for the DAIC-WOZ dataset). However, with a drop to an F1-score of 0.56 for the SymptomMedia dataset, the method also appears to be the most data-dependent.

A randomized controlled trial for evaluating the impact of integrating a computerized clinical decision support system and a socially assistive humanoid robot into grand rounds during pre/post-operative care.

Although clinical decision support systems (CDSSs) are increasingly emphasized as one of the possible levers for improving care, they are still not widely used due to different barriers, such as doubts about systems' performance, their complexity and poor design, practitioners' lack of time to use them, poor computer skills, reluctance to use them in front of patients, and deficient integration into existing workflows. While several studies on CDSS exist, there is a need for additional high-quality studies using large samples and examining the differences between outcomes following a decision based on CDSS support and those following decisions without this kind of information. Even less is known about the effectiveness of a CDSS that is delivered during a grand round routine and with the help of socially assistive humanoid robots (SAHRs). In this study, 200 patients will be randomized into a Control Group (i.e. standard care) and an Intervention Group (i.e. standard care and novel CDSS delivered via a SAHR). Health care quality and Quality of Life measures will be compared between the two groups. Additionally, approximately 22 clinicians, who are also active researchers at the University Clinical Center Maribor, will evaluate the acceptability and clinical usability of the system. The results of the proposed study will provide high-quality evidence on the effectiveness of CDSS systems and SAHR in the grand round routine.

Establishing an Expert Consensus on Key Indicators of the Quality of Life among Breast Cancer Survivors: A Modified Delphi Study.

(1) Background: The needs of cancer survivors are often not reflected in practice. One of the main barriers of the use of patient-reported outcomes is associated with data collection and the interpretation of patient-reported outcomes (PROs) due to a multitude of instruments and measuring approaches. The aim of the study was to establish an expert consensus on the relevance and key indicators of quality of life in the clinical practice of breast cancer survivors. (2) Methods: Potential indicators of the quality of life of breast cancer survivors were extracted from the established quality of life models, depicting survivors' perspectives. The specific domains and subdomains of quality of life were evaluated in a two-stage online Delphi process, including an international and multidisciplinary panel of experts. (3) Results: The first round of the Delphi process was completed by 57 and the second by 37 participants. A consensus was reached for the Physical and Psychological domains, and on eleven subdomains of quality of life. The results were further supported by the additional ranking of importance of the subdomains in the second round. (4) Conclusions: The current findings can serve to optimize the use of instruments and address the challenges related to data collection and interpretation as the facilitators of the adaption in routine practice.

Study protocol: a survey exploring patients' and healthcare professionals' expectations, attitudes and ethical acceptability regarding the integration of socially assistive humanoid robots in nursing.

INTRODUCTION: Population ageing, the rise of chronic diseases and the emergence of new viruses are some of the factors that contribute to an increasing share of gross domestic product dedicated to health spending. COVID-19 has shown that nursing staff represents the critical part of hospitalisation. Technological developments in robotics and artificial intelligence can significantly reduce costs and lead to improvements in many hospital processes. The proposed study aims to assess expectations, attitudes and ethical acceptability regarding the integration of socially assistive humanoid robots into hospitalised care workflow from patients' and healthcare professionals' perspectives and to compare them with the results of similar studies. METHODS/DESIGN: The study is designed as a cross-sectional survey, which will include three previously validated questionnaires, the Technology-Specific Expectation Scale (TSES), the Ethical Acceptability Scale (EAS) and the Negative Attitudes towards Robots Scale (NARS). The employees of a regional clinical centre will be asked to participate via an electronic survey and respond to TSES and EAS questionaries. Patients will respond to TSES and NARS questionaries. The survey will be conducted online. ETHICS AND DISSEMINATION: Ethical approval for the study was obtained by the Medical Ethics Commission of the University Medical Center Maribor. Results will be published in a relevant scientific journal and communicated to participants and relevant institutions through dissemination activities and the ecosystem of the Horizon 2020 funded project HosmartAI (grant no. 101016834). ETHICAL APPROVAL DATE: 06 May 2021. ESTIMATED START OF THE STUDY: December 2021.

Aging Adults' Motivation to Use Embodied Conversational Agents in Instrumental Activities of Daily Living: Results of Latent Profile Analysis.

The rapidly increasing share of ageing adults in the population drives the need and interest in assistive technology, as it has the potential to support ageing individuals in living independently and safely. However, technological development rarely reflects how needs, preferences, and interests develop in different ways while ageing. It often follows the strategy of "what is possible" rather than "what is needed" and "what preferred". As part of personalized assistive technology, embodied conversational agents (ECAs) can offer mechanisms to adapt the technological advances with the stakeholders' expectations. The present study explored the motivation among ageing adults regarding technology use in multiple domains of activities of daily living. Participants responded to the questionnaire on the perceived importance of instrumental activities of daily living and acceptance of the idea of using ECAs to support them. Latent profile analysis revealed four profiles regarding the motivation to use ECAs (i.e., a low motivation profile, two selective motivation profiles with an emphasis on physical and psychological well-being, and a high motivation profile). Profiles were compared in terms of their acceptance of ECA usage in various life domains. The results increase the knowledge needed in the development of assistive technology adapted to the expectations of ageing adults.

Language, Speech, and Facial Expression Features for Artificial Intelligence-Based Detection of Cancer Survivors' Depression: Scoping Meta-Review.

BACKGROUND: Cancer survivors often experience disorders from the depressive spectrum that remain largely unrecognized and overlooked. Even though screening for depression is recognized as essential, several barriers prevent its successful implementation. It is possible that better screening options can be developed. New possibilities have been opening up with advances in artificial intelligence and increasing knowledge on the connection of observable cues and psychological states. OBJECTIVE: The aim of this scoping meta-review was to identify observable features of depression that can be intercepted using artificial intelligence in order to provide a stepping stone toward better recognition of depression among cancer survivors. METHODS: We followed a methodological framework for scoping reviews. We searched SCOPUS and Web of Science for relevant papers on the topic, and data were extracted from the papers that met inclusion criteria. We used thematic analysis within 3 predefined categories of depression cues (ie, language, speech, and facial expression cues) to analyze the papers. RESULTS: The search yielded 1023 papers, of which 9 met the inclusion criteria. Analysis of their findings resulted in several well-supported cues of depression in language, speech, and facial expression domains, which provides a comprehensive list of observable features that are potentially suited to be intercepted by artificial intelligence for early detection of depression. CONCLUSIONS: This review provides a synthesis of behavioral features of depression while translating this knowledge into the context of artificial intelligence-supported screening for depression in cancer survivors.

Patients-centered SurvivorShIp care plan after Cancer treatments based on Big Data and Artificial Intelligence technologies (PERSIST): a multicenter study protocol to evaluate efficacy of digital tools supporting cancer survivors.

BACKGROUND: It is encouraging to see a substantial increase in individuals surviving cancer. Even more so since most of them will have a positive effect on society by returning to work. However, many cancer survivors have unmet needs, especially when it comes to improving their quality of life (QoL). Only few survivors are able to meet all of the recommendations regarding well-being and there is a body of evidence that cancer survivors' needs often remain neglected from health policy and national cancer control plans. This increases the impact of inequalities in cancer care and adds a dangerous component to it. The inequalities affect the individual survivor, their career, along with their relatives and society as a whole. The current study will evaluate the impact of the use of big data analytics and artificial intelligence on the self-efficacy of participants following intervention supported by digital tools. The secondary endpoints include evaluation of the impact of patient trajectories (from retrospective data) and patient gathered health data on prediction and improved intervention against possible secondary disease or negative outcomes (e.g. late toxicities, fatal events). METHODS/DESIGN: The study is designed as a single-case experimental prospective study where each individual serves as its own control group with basal measurements obtained at the recruitment and subsequent measurements performed every 6 months during follow ups. The measurement will involve CASE-cancer, Patient Activation Measure and System Usability Scale. The study will involve 160 survivors (80 survivors of Breast Cancer and 80 survivors of Colorectal Cancer) from four countries, Belgium, Latvia, Slovenia, and Spain. The intervention will be implemented via a digital tool (mHealthApplication), collecting objective biomarkers (vital signs) and subjective biomarkers (PROs) with the support of a (embodied) conversational agent. Additionally, the Clinical Decision Support system (CDSS), including visualization of cohorts and trajectories will enable oncologists to personalize treatment for an efficient care plan and follow-up management. DISCUSSION: We expect that cancer survivors will significantly increase their self-efficacy following the personalized intervention supported by the m-HealthApplication compared to control measurements at recruitment. We expect to observe improvement in healthy habits, disease self-management and self-perceived QoL. Trial registration ISRCTN97617326. https://doi.org/10.1186/ISRCTN97617326 . Original Registration Date: 26/03/2021.

The Role of Attitudes to, and the Frequency of, Domestic Violence Encounters in the Healthcare Professionals' Handling of Domestic Violence Cases.

BACKGROUND: Domestic violence is recognized as a public health problem with a high prevalence in the general population. Healthcare professionals play an important role in the recognition and treatment of domestic violence. Hence, conducting research on factors that facilitate or inhibit appropriate actions by healthcare professionals is of the upmost importance. The objective of the study was to examine the relationship between healthcare professionals' attitudes toward the acceptability of domestic violence and their responses when dealing with victims of domestic violence. METHODS: The sample consisted of 322 healthcare professionals (physicians, dentists, nursing staff and other healthcare workers; 85.2% female), who completed a questionnaire, assessing their attitudes towards domestic violence, experience, behaviour and perceived barriers in recognizing and treating domestic violence in the health care sector. The study was cross-sectional and used availability sampling. RESULTS: The results showed no significant differences in domestic violence acceptability attitudes when comparing groups of healthcare professionals who reported low or high frequency of domestic violence cases encounters. Furthermore, we found that domestic violence acceptability attitudes were negatively associated with action taking when the frequency of encounters with domestic violence cases was high and medium. However, the attitudes were not associated with action taking when the frequency of encounters with domestic violence cases was low. CONCLUSIONS: The results highlight the important role of attitudes in action taking of healthcare professionals when it comes to domestic violence. This indicates the need for educational interventions that specifically target healthcare professionals' attitudes towards domestic violence.