Discordances in patients' and physicians' perspectives on which contextual factors should be accounted for during smoking cessation.

BACKGROUND: Smoking cessation interventions requires attending to the circumstances and needs of individual patients. We aimed at highlighting the discordances between patients' and physicians' perspectives on contextual factors that should be considered during smoking cessation. METHODS: We identified 36 contextual factors identified that should be considered during smoking cessation using PubMed and interviewing general practitioners. Physicians recruited through social networks campaigns and smoker or former smoker patients from the ComPaRe cohort selected the factors they considered most relevant in two online paired comparison experiment. Bradley Terry Luce models estimated the ability of each factor (i.e. the probability to be preferred). We calculated the Pearson's correlation and the intraclass correlation coefficients for the contextual factor from each perspective and compared the ranking of the 10 contextual factors with the highest abilities. RESULTS: Seven hundred and ninety-three patients' and 795 physicians' perspectives estimated the ability (i.e., importance) of the contextual factors in 11 963 paired comparisons. We found a high correlation between physicians' and patients' perspectives of the contextual factors to be considered for smoking cessation (r = 0.76, P < 0.0001). However, the agreement between the abilities of contextual factors was poor (ICC = 0.42 [-0.10; 0.75]; P = 0.09). Fine-grain analysis of participants' answers revealed many discrepancies. For example, 40% factors ranked in the top 10 most important for physicians were not in patients' top 10 ranking. CONCLUSION: Our results highlight the importance of patient-centered care, the need to engage discussions about patients' values, beyond what is thought to be important, to avoid overlooking their real context.

Evaluation of additional resources used in therapist-assisted transdiagnostic internet-delivered cognitive behaviour therapy.

Background: In internet-delivered cognitive behavioural therapy (ICBT) programs, beyond standardized core ICBT lessons, brief additional resources are sometimes available to clients to address comorbid concerns or offer additional information/strategies. These resources remain understudied in terms of how they are selected and perceived by clients, as well as their relationship to satisfaction and outcomes. Methods: Among clients (N = 793) enrolled in a 5-lesson transdiagnostic ICBT course, we examined client use and perceptions of 18 additional resources at 8 weeks in terms of whether clients found resources informative (yes/no) and or helpful (yes/no). Resources elaborated on cognitive strategies (managing beliefs, risk calculation) or on managing specific problems (agricultural stress, alcohol misuse, anger, assertiveness, chronic conditions, communication, grief, health anxiety, motivation, pain, panic, postpartum depression/anxiety, PTSD, sleep, workplace accomodations, worry). Clients also completed symptom measures and ICBT satisfaction questions at 8 weeks. Results: Approximately 50 % (n = 398) of clients rated the resources and, on average, clients reported that 3.35 (SD = 3.34) resources were informative and 2.35 (SD = 2.52) resources were helpful as measured by direct questions developed for this study. Higher pre-treatment PTSD and GAD scores were related to a greater number of resources perceived as informative and or helpful. Rating more resources as informative and or helpful had a weak but positive association with ICBT satisfaction and depression, anxiety, PTSD and insomnia change scores. Limitations of the study include that 31 % (n = 245) did not respond to questions about use of resources and 18.9 % (n = 150) said they did not review resources. Conclusions: There is considerable use of diverse additional resources in ICBT in routine care. Associations suggest that clients are using resources to personalize treatment to their needs and these resources are associated with treatment satisfaction and outcomes. The correlational associations between symptoms and perceived helpfulness of resources can help inform personalization algorithms to optimize ICBT delivery for clients. Further research on how to match clients with, encourage use of, and maximize benefits of resources would be beneficial.

Towards new realities: implications of personalized online layers in our daily lives.

We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-Computer Interaction we observe high potential, but also challenges. In this article, we explore how an increasing amount of online services and tools not only further facilitates our lives, but also shapes our lives and how we perceive our environments. For this purpose, we adopt the metaphor of personalized 'online layers' and show how these layers are and will be interwoven with the lives that we live in the 'human layer' of the real world.

A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User Expectations.

Autonomous vehicles are rapidly advancing and have the potential to revolutionize transportation in the future. This paper primarily focuses on vehicle motion trajectory planning algorithms, examining the methods for estimating collision risks based on sensed environmental information and approaches for achieving user-aligned trajectory planning results. It investigates the different categories of planning algorithms within the scope of local trajectory planning applications for autonomous driving, discussing and differentiating their properties in detail through a review of the recent studies. The risk estimation methods are classified and introduced based on their descriptions of the sensed collision risks in traffic environments and their integration with trajectory planning algorithms. Additionally, various user experience-oriented methods, which utilize human data to enhance the trajectory planning performance and generate human-like trajectories, are explored. The paper provides comparative analyses of these algorithms and methods from different perspectives, revealing the interconnections between these topics. The current challenges and future prospects of the trajectory planning tasks in autonomous vehicles are also discussed.

Influence of personality traits on generation Z consumers' click-through intentions towards personalized advertisements: A mixed-methods study.

Personalized social media advertisements (PSMAs) are developed by using consumers' personal information like names, demographic details, location, past buying history, and lifestyle interests to quickly grab consumers' attention within the cluttered space of digital advertisements. Generation Z consumers are highly connected to social media. Hence, this study attempts to understand how Generation Z consumers with different personality traits perceive personalized advertisements (PAs) on Facebook, and subsequently, how their perceived personalization influences their intention to click on PAs based on perceived usefulness and privacy concerns associated with those advertisements. The theoretical underpinning of this research is based on the self-congruency theory and privacy-calculus theory. An explanatory sequential mixed-methods design has been adopted, where a quantitative analysis (Study 1) is followed by a qualitative approach (Study 2). For Study 1, responses were collected from 324 Generation Z consumers through a structured questionnaire and the data was analyzed using the structural equation modeling technique to measure relationships among the constructs. Further, in Study 2, in-depth interviews were conducted with 15 Generation Z consumers, a purposively selected subset of informants from Study 1, to explore the potential causes of those relationships observed in Study 1. It has been found from the study that consumers' perception of PSMAs varies based on their personality traits. Consumers with dominant extraversion, conscientiousness, and neurotic personality traits perceive PSMAs positively whereas the openness to experience and agreeableness dominant consumers perceive those negatively. Positive perception of PSMAs among consumers increases the perceived usefulness of communications and subsequently improves the click-through intentions of the consumers. Generation Z Consumers' perception of PSMAs does not have any influence on their privacy concerns. Consumers' high privacy concerns reduce the click-through intention rate toward PSMAs. The study will help digital marketing managers to strategically deliver PSMAs, thereby enhancing the efficiency of their advertisements.

Does personality matter: examining the value of personality insights for personalized nudges that encourage the selection of learning resources.

Nudging is a mechanism aimed at influencing people's behavior while maintaining the individual's freedom of choice. Nudges have been adopted in learning contexts where individuals are responsible for shaping their learning and, at the same time, receive guidance from the system. Not everyone responds to nudges in the same way. While social science research indicates that individual differences play a crucial role in peoples' nudgeability, there has been little research examining computational approaches that explore how individual differences affect user responses to nudges (especially in a learning context). Two studies were conducted to explore how individual differences, specifically focusing on personality, can affect nudge response in the context of healthcare education, where individuals use resources as a part of their informal learning and professional development. Different nudges, designed based on personality characteristics, were provided to draw individual users' attention to educational resources to encourage user engagement. The findings indicate that personality insights can be a predictor for nudge selection, suggesting that different nudges may be more effective when recommending learning resources to people with different personality characteristics.

Identifying Person-Specific Drivers of Depression in Adolescents: Protocol for a Smartphone-Based Ecological Momentary Assessment and Passive Sensing Study.

BACKGROUND: Adolescence is marked by an increasing risk of depression and is an optimal window for prevention and early intervention. Personalizing interventions may be one way to maximize therapeutic benefit, especially given the marked heterogeneity in depressive presentations. However, empirical evidence that can guide personalized intervention for youth is lacking. Identifying person-specific symptom drivers during adolescence could improve outcomes by accounting for both developmental and individual differences. OBJECTIVE: This study leverages adolescents' everyday smartphone use to investigate person-specific drivers of depression and validate smartphone-based mobile sensing data against established ambulatory methods. We describe the methods of this study and provide an update on its status. After data collection is completed, we will address three specific aims: (1) identify idiographic drivers of dynamic variability in depressive symptoms, (2) test the validity of mobile sensing against ecological momentary assessment (EMA) and actigraphy for identifying these drivers, and (3) explore adolescent baseline characteristics as predictors of these drivers. METHODS: A total of 50 adolescents with elevated symptoms of depression will participate in 28 days of (1) smartphone-based EMA assessing depressive symptoms, processes, affect, and sleep; (2) mobile sensing of mobility, physical activity, sleep, natural language use in typed interpersonal communication, screen-on time, and call frequency and duration using the Effortless Assessment of Risk States smartphone app; and (3) wrist actigraphy of physical activity and sleep. Adolescents and caregivers will complete developmental and clinical measures at baseline, as well as user feedback interviews at follow-up. Idiographic, within-subject networks of EMA symptoms will be modeled to identify each adolescent's person-specific drivers of depression. Correlations among EMA, mobile sensor, and actigraph measures of sleep, physical, and social activity will be used to assess the validity of mobile sensing for identifying person-specific drivers. Data-driven analyses of mobile sensor variables predicting core depressive symptoms (self-reported mood and anhedonia) will also be used to assess the validity of mobile sensing for identifying drivers. Finally, between-subject baseline characteristics will be explored as predictors of person-specific drivers. RESULTS: As of October 2023, 84 families were screened as eligible, of whom 70% (n=59) provided informed consent and 46% (n=39) met all inclusion criteria after completing baseline assessment. Of the 39 included families, 85% (n=33) completed the 28-day smartphone and actigraph data collection period and follow-up study visit. CONCLUSIONS: This study leverages depressed adolescents' everyday smartphone use to identify person-specific drivers of adolescent depression and to assess the validity of mobile sensing for identifying these drivers. The findings are expected to offer novel insights into the structure and dynamics of depressive symptomatology during a sensitive period of development and to inform future development of a scalable, low-burden smartphone-based tool that can guide personalized treatment decisions for depressed adolescents. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/43931.

Evaluation of enhanced external counterpulsation for diabetic foot based on a patient-specific 0D-1D cardiovascular system model.

BACKGROUND AND OBJECTIVE: Diabetic foot (DF) complications often lead to severe vascular issues. This study investigated the effectiveness of enhanced external counterpulsation (EECP) and its derived innovative compression strategies in addressing poor perfusion in DF. Although developing non-invasive and efficient treatment methods for DF is critical, the hemodynamic alterations during EECP remain underexplored despite promising outcomes in microcirculation. This research sought to address this gap by developing a patient-specific 0D-1D model based on clinical ultrasound data to identify potentially superior compression strategies that could substantially enhance blood flow in patients with DF complications. METHODS: Data were gathered from 10 patients with DF utilizing ultrasound for blood flow rate and computed tomography angiography (CTA) to identify lower limb conditions. Clinical measurements during standard EECP, with varying cuff pressures, facilitated the creation of a patient-specific 0D-1D model through a two-step parameter estimation process. The accuracy of this model was verified via comparison with the clinical measurements. Four compression strategies were proposed and rigorously evaluated using this model: EECP-Simp-I (removing hip cuffs), EECP-Simp-II (further removing the cuffs around the lower leg), EECP-Impr-I (removing all cuffs around the affected side), and EECP-Impr-II (building a loop circulation from the healthy side to the affected side). RESULTS: The predicted results under the rest and standard EECP states were generally closely aligned with clinical measurements. The patient-specific 0D-1D model demonstrated that EECP-Simp-I and EECP-Impr-I contributed similar enhancement to perfusion in the dorsal artery (DA) and were comparable to standard EECP, while EECP-Simp-II had the least effect and EECP-Impr-II displayed the most significant enhancement. Pressure at the aortic root (AO) remained consistent across strategies. CONCLUSIONS: EECP-Simp-I is recommended for patients with DF, emphasizing device simplification. However, EECP-Simp-II is discouraged as it significantly diminished blood perfusion in this study, except in cases of limb fragility. EECP-Impr-II showed superior enhancement of blood perfusion in DA to all other strategies but required a more complex EECP device. Despite increased AO pressure in all the proposed compression strategies, safety could be guaranteed as the pressue remained within a safe range.

Changes in the Frequency of Actions Associated With Mental Health During Online Treatment: Analysis of Demographic and Clinical Factors.

BACKGROUND: Specific daily actions (eg, goal setting, meaningful activities) are associated with mental health. Performing specific daily actions at a higher frequency is associated with significantly lower baseline symptoms of depression and anxiety, as well as better psychological treatment outcomes for depression and anxiety. OBJECTIVE: This study explored how the frequency of specific daily actions associated with mental health may differ prior to, during, and following treatment according to demographic and clinical characteristics. METHODS: Using a sample of 448 patients from an Australian national digital psychology service, we examined baseline differences in daily action frequency and changes in daily action frequency during a digital psychological treatment according to demographic and clinical subgroups. A total of 5 specific types of daily actions were measured using the Things You Do Questionnaire: healthy thinking, meaningful activities, goals and plans, healthy habits, and social connections. RESULTS: The frequency of daily actions differed according to employment status (largest P=.005) and educational level (largest P=.004). Daily action frequency was lower in those participants with more severe or chronic depression or anxiety symptoms (largest P=.004). Participants reported larger increases in how often they did these daily actions from baseline to midtreatment compared to mid- to posttreatment. Depression duration (P=.01) and severity (P<.001) were associated with differences in how daily action frequency changed during treatment. CONCLUSIONS: The findings of this study support continued research exploring the relationship between daily actions and mental health, how this relationship might differ between individuals, and the clinical potential of supporting individuals to increase the frequency of daily actions to improve mental health.

Challenges and solutions in 3D printing for oncology: a narrative review.

BACKGROUND AND OBJECTIVE: Oncology is increasingly adopting three-dimensional (3D) printing, a method of creating objects through additive manufacturing using various techniques and materials. This technology, divided into conventional 3D printing (using non-biological materials like thermoplastics or titanium) and bioprinting (involving living cells and tissues), has shown potential in surgical planning, implant creation, and radiotherapy. However, despite promising preclinical and clinical applications, its clinical integration faces challenges such as a lack of strong evidence, standardized guidelines, and detailed data on costs and scalability. This study reviews the current use of 3D printing in oncology, aiming to differentiate between practical and experimental applications, thereby guiding clinicians interested in incorporating this technology. METHODS: A literature search was conducted to gather comments, reviews, and preclinical and clinical studies focusing on the use of 3D printing in oncology, with publications dated before December 1, 2023. The search for pertinent studies involved utilizing PubMed and Google Scholar Review. The selection process for articles was based on a unanimous consensus among all authors. We excluded topics related to bioprinting and the technical nuances of 3D printing. KEY CONTENT AND FINDINGS: The review comprehensively describes the utilization of 3D printing in radiation oncology, surgical oncology, orthopedic oncology, medical oncology, hyperthermia, and patients' education. However, 3D printing faces several limitations that are related to unpredictable costs, difficult scalability, very complex regulations and lack of standardization. CONCLUSIONS: 3D printing is increasingly useful in oncology for diagnostics and treatment, yet remains experimental and case-based. Despite growing literature, it focuses mostly on pre-clinical studies and case reports, with few clinical studies involving small samples. Thus, extensive research is needed to fully evaluate its efficacy and application in larger patient groups.

Approaches to Tailoring Between-Session Mental Health Therapy Activities.

Mental health activities conducted by patients between therapy sessions (or "therapy homework") are a component of addressing anxiety and depression. However, to be effective, therapy homework must be tailored to the client's needs to address the numerous barriers they encounter in everyday life. In this study, we analyze how therapists and clients tailor therapy homework to their client's needs. We interviewed 13 therapists and 14 clients about their experiences tailoring and engaging in therapy homework. We identify criteria for tailoring homework, such as client skills, discomfort, and external barriers. We present how homework gets adapted, such as through changes in difficulty or by identifying alternatives. We discuss how technologies can better use client information for personalizing mental health interventions, such as adapting to client barriers, adjusting homework to these barriers, and creating a safer environment to support discomfort.

Parent Explanatory Model Personalization as a Method of Reducing Risk for Poor Engagement and Outcomes in PCIT among Culturally Diverse Families.

Background/Objectives: Evidence supports the efficacy of Behavioral Parent Training (BPT) interventions such as Parent-Child Interaction Therapy (PCIT) for treating child behavior problems; however, treatment engagement and outcomes vary across ethnic groups. Risk for poor treatment engagement and outcomes may be attributed in part to misalignment between parent explanatory model components (PEMs) and the traditional BPT model, including treatment expectations, etiological explanations, parenting styles, and family support for treatment. The present study aims to examine whether personalized treatment adaptations addressing these PEM-BPT misalignments reduce risk for poor treatment engagement and outcomes. Methods: The authors previously utilized the PersIn framework to develop a personalized version of PCIT (MY PCIT) that assesses these PEMs in order to identify families at risk for poor treatment engagement and outcomes. Families were identified as high risk (due to PEM-BPT misalignment) and low risk (meaning those without identified PEM-BPT misalignment) for specific PEMs. Families at elevated risk then received tailored treatment materials designed to improve alignment between the parental explanatory model and the PCIT treatment explanatory model. A recent pilot trial of MY PCIT demonstrated positive treatment outcomes; however, the extent to which adaptations were successful in reducing the underlying risk factors has not yet been examined. Results: Findings demonstrate that the personalization approach was effective in reducing indicators of risk, and that families who were initially at high and low risk during pre-treatment reported similar levels of treatment engagement and outcomes by post-treatment. Conclusions: The findings suggest that this personalized approach has the potential to reduce risk associated with poor treatment engagement and outcomes for culturally diverse families.

Electroforming of Personalized Multi-Level and Free-Form Metal Parts Utilizing Fused Deposition Modeling-Manufactured Molds.

Adapting to the growing demand for personalized, small-batch manufacturing, this study explores the development of additively manufactured molds for electroforming personalized metal parts. The approach integrates novel multi-level mold design and fabrication techniques, along with the experimental procedures for the electroforming process. This work outlines design considerations and guidelines for effective electroforming in additively manufactured molds, successfully demonstrating the production of composite metal components with multi-level and free-form geometries. By emphasizing cost efficiency and part quality, particularly for limited-thickness metal components, the developed technique offers distinct advantages over existing metal additive manufacturing methods. This approach establishes itself as a flexible and durable method for metal additive manufacturing, expanding the scope of electroforming beyond traditional constraints such as thin-walled hollow structures, 2D components, and nanoscale applications.

A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study.

BACKGROUND: There are a wide range of potential adverse health effects, ranging from headaches to cardiovascular disease, associated with long-term negative emotions and chronic stress. Because many indicators of stress are imperceptible to observers, the early detection of stress remains a pressing medical need, as it can enable early intervention. Physiological signals offer a noninvasive method for monitoring affective states and are recorded by a growing number of commercially available wearables. OBJECTIVE: We aim to study the differences between personalized and generalized machine learning models for 3-class emotion classification (neutral, stress, and amusement) using wearable biosignal data. METHODS: We developed a neural network for the 3-class emotion classification problem using data from the Wearable Stress and Affect Detection (WESAD) data set, a multimodal data set with physiological signals from 15 participants. We compared the results between a participant-exclusive generalized, a participant-inclusive generalized, and a personalized deep learning model. RESULTS: For the 3-class classification problem, our personalized model achieved an average accuracy of 95.06% and an F1-score of 91.71%; our participant-inclusive generalized model achieved an average accuracy of 66.95% and an F1-score of 42.50%; and our participant-exclusive generalized model achieved an average accuracy of 67.65% and an F1-score of 43.05%. CONCLUSIONS: Our results emphasize the need for increased research in personalized emotion recognition models given that they outperform generalized models in certain contexts. We also demonstrate that personalized machine learning models for emotion classification are viable and can achieve high performance.

Use of hospital big data to optimize and personalize laboratory test interpretation with an application.

BACKGROUND AND AIMS: In laboratory medicine, test results are generally interpreted with 95% reference intervals but correlations between laboratory tests are usually ignored. We aimed to use hospital big data to optimize and personalize laboratory data interpretation, focusing on platelet count. MATERIAL AND METHODS: Laboratory tests were extracted from the hospital database and exploited by an algorithmic stepwise procedure. For any given laboratory test Y, an "optimized and personalized reference population" was defined by keeping only patients whose laboratory values for all Y-correlated tests fell within their own usual reference intervals, and by partitioning groups by individual-specific variables like sex and age category. The method was applied to platelet count. RESULTS: Laboratory data were recorded for 28,082 individuals. At the end of the algorithmic process, seven correlated laboratory tests were chosen, resulting in a reference sample of 159 platelet counts. A new 95 % reference interval was constructed [152-334 × 109/L], notably reduced (27.2 %) compared to conventional reference values [150-400 × 109/L]. The reference interval was validated on a sample of 2,129 patients from another downtown laboratory, emphasizing the potential transference of the hospital-derived reference limits. CONCLUSION: This method offers new perspectives in laboratory data interpretation, especially in patient screening and longitudinal follow-up.

Understanding preferences of patients with obesity for metabolic bariatric surgery: a comprehensive adaptive choice-based conjoint analysis.

BACKGROUND: Patient preferences toward metabolic bariatric surgery (MBS) remain inadequately explored. OBJECTIVE: This study aims to identify and analyze the key factors influencing the decision-making process of patients considering MBS. SETTING: The research was conducted at the metabolic bariatric surgery clinic of the Medical Research Institute Hospital, Alexandria University, Egypt. METHOD: Patients with obesity were recruited at the clinic before MBS. The surgical profiles were characterized by attributes including treatment method, recovery and reversibility, treatment tenure, expected weight loss, impact on associated medical problems, risk of complication, side effects, dietary changes, and out-of-pocket costs. Patients engaged in an online survey comprising sociodemographic data, Build Your Own (BYO) section, screening section, and choice tournament section. Adaptive choice-based conjoint analysis was employed to discern the preferences. RESULTS: Of the 299 respondents, the surgical profiles with the highest preference involved a loss of 80% of excess weight without any recurrence (14.67 [95% CI, 14.10-15.23]), 0% risk of complication (13.74 [95% CI, 13.03-14.45]), and absence of adverse effects (11.32 [95% CI, 10.73-11.91]). K-mean cluster analysis identified 2 distinct groups: "patients prioritize weight loss" group prioritized excess weight loss, surgery availability, and diet change, whereas "patients prioritize avoidance of complications" group focused on the risk of complication, adverse effects, and the surgery mechanism. CONCLUSIONS: MBS candidates predominantly value weight loss without recurrence, followed by minimization of complication risks and adverse effects, within 3 years postsurgery. Conversely, initial out-of-pocket costs and resolution of medical conditions were deemed the least influential attributes.

PEMOCS: theory derivation of a concept for PErsonalized MOtor-Cognitive exergame training in chronic Stroke-a methodological paper with an application example.

Background: Coping with residual cognitive and gait impairments is a prominent unmet need in community-dwelling chronic stroke survivors. Motor-cognitive exergames may be promising to address this unmet need. However, many studies have so far implemented motor-cognitive exergame interventions in an unstructured manner and suitable application protocols remain yet unclear. We, therefore, aimed to summarize existing literature on this topic, and developed a training concept for motor-cognitive exergame interventions in chronic stroke. Methods: The development of the training concept for personalized motor-cognitive exergame training for stroke (PEMOCS) followed Theory Derivation procedures. This comprised (1.1) a thorough (narrative) literature search on long-term stroke rehabilitation; (1.2) a wider literature search beyond the topic of interest to identify analogies, and to induce creativity; (2) the identification of parent theories; (3) the adoption of suitable content or structure of the main parent theory; and (4) the induction of modifications to adapt it to the new field of interest. We also considered several aspects of the "Framework for Developing and Evaluating Complex Interventions" by the Medical Research Council. Specifically, a feasibility study was conducted, and refining actions based on the findings were performed. Results: A training concept for improving cognitive functions and gait in community-dwelling chronic stroke survivors should consider the principles for neuroplasticity, (motor) skill learning, and training. We suggest using a step-based exergame training for at least 12 weeks, 2-3 times a week for approximately 45 min. Gentile's Taxonomy for Motor Learning was identified as suitable fundament for the personalized progression and variability rules, and extended by a third cognitive dimension. Concepts and models from related fields inspired further additions and modifications to the concept. Conclusion: We propose the PEMOCS concept for improving cognitive functioning and gait in community-dwelling chronic stroke survivors, which serves as a guide for structuring and implementing motor-cognitive exergame interventions. Future research should focus on developing objective performance parameters that enable personalized progression independent of the chosen exergame type.

The clinical utility and relevance in clinical practice of DSM-5 specifiers for major depressive disorder: A Delphi expert consensus study.

Major depressive disorder (MDD) is a heterogeneous syndrome, associated with different levels of severity and impairment on the personal functioning for each patient. Classification systems in psychiatry, including ICD-11 and DSM-5, are used by clinicians in order to simplify the complexity of clinical manifestations. In particular, the DSM-5 introduced specifiers, subtypes, severity ratings, and cross-cutting symptom assessments allowing clinicians to better describe the specific clinical features of each patient. However, the use of DSM-5 specifiers for major depressive disorder in ordinary clinical practice is quite heterogeneous. The present study, using a Delphi method, aims to evaluate the consensus of a representative group of expert psychiatrists on a series of statements regarding the clinical utility and relevance of DSM-5 specifiers for major depressive disorder in ordinary clinical practice. Experts reached an almost perfect agreement on statements related to the use and clinical utility of DSM-5 specifiers in ordinary clinical practice. In particular, a complete consensus was found regarding the clinical utility for ordinary clinical practice of using DSM-5 specifiers. The use of specifiers is considered a first step toward a "dimensional" approach to the diagnosis of mental disorders.

Resilient Stress Reactivity Profiles Predict Mental Health Gains from Online Contemplative Training: A Randomized Clinical Trial.

Low-dose app-based contemplative interventions for mental health are increasingly popular, but heterogeneity in intervention responses indicates that a personalized approach is needed. We examined whether different longitudinal resilience-vulnerability trajectories, derived over the course of the COVID-19 pandemic, predicted differences in diverse mental health outcomes after mindfulness and socio-emotional dyadic online interventions. The CovSocial project comprised a longitudinal assessment (phase 1) and an open-label efficacy trial (phase 2). A community sample of 253 participants received 12 min daily app-based socio-emotional dyadic or mindfulness-based interventions, with weekly online coaching for 10 weeks. Before and after the intervention, participants completed validated self-report questionnaires assessing mental health. Stress reactivity profiles were derived from seven repeated assessments during the COVID-19 pandemic (January 2020 to March/April 2021) and were categorized into resilient (more plasticity) or vulnerable (less plasticity) stress recovery profiles. After both interventions, only individuals with resilient stress reactivity profiles showed significant improvements in depression symptomatology, trait anxiety, emotion regulation, and stress recovery. Those with vulnerable profiles did not show significant improvements in any outcome. Limitations of this study include the relatively small sample size and potential biases associated with participant dropout. Brief app-based mental interventions may be more beneficial for those with greater levels of stress resiliency and plasticity in response to stressors. More vulnerable individuals might require more intense and personalized intervention formats.

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study.

BACKGROUND: Understanding a student's depressive symptoms could facilitate significantly more precise diagnosis and treatment. However, few studies have focused on depressive symptom prediction through unobtrusive systems, and these studies are limited by small sample sizes, low performance, and the requirement for higher resources. In addition, research has not explored whether statistically significant rhythms based on different app usage behavioral markers (eg, app usage sessions) exist that could be useful in finding subtle differences to predict with higher accuracy like the models based on rhythms of physiological data. OBJECTIVE: The main objective of this study is to explore whether there exist statistically significant rhythms in resource-insensitive app usage behavioral markers and predict depressive symptoms through these marker-based rhythmic features. Another objective of this study is to understand whether there is a potential link between rhythmic features and depressive symptoms. METHODS: Through a countrywide study, we collected 2952 students' raw app usage behavioral data and responses to the 9 depressive symptoms in the 9-item Patient Health Questionnaire (PHQ-9). The behavioral data were retrieved through our developed app, which was previously used in our pilot studies in Bangladesh on different research problems. To explore whether there is a rhythm based on app usage data, we will conduct a zero-amplitude test. In addition, we will develop a cosinor model for each participant to extract rhythmic parameters (eg, acrophase). In addition, to obtain a comprehensive picture of the rhythms, we will explore nonparametric rhythmic features (eg, interdaily stability). Furthermore, we will conduct regression analysis to understand the association of rhythmic features with depressive symptoms. Finally, we will develop a personalized multitask learning (MTL) framework to predict symptoms through rhythmic features. RESULTS: After applying inclusion criteria (eg, having app usage data of at least 2 days to explore rhythmicity), we kept the data of 2902 (98.31%) students for analysis, with 24.48 million app usage events, and 7 days' app usage of 2849 (98.17%) students. The students are from all 8 divisions of Bangladesh, both public and private universities (19 different universities and 52 different departments). We are analyzing the data and will publish the findings in a peer-reviewed publication. CONCLUSIONS: Having an in-depth understanding of app usage rhythms and their connection with depressive symptoms through a countrywide study can significantly help health care professionals and researchers better understand depressed students and may create possibilities for using app usage-based rhythms for intervention. In addition, the MTL framework based on app usage rhythmic features may more accurately predict depressive symptoms due to the rhythms' capability to find subtle differences. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51540.