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Background: Mild cognitive impairment (MCI) involves cognitive decline beyond typical age-related changes, but without significant daily activity disruption. It can encompass various cognitive domains as the causes of MCI are diverse. MCI as well as frequent comorbid neuropsychiatric conditions like depression and anxiety affect individuals' quality of life. Early interventions are essential, and computerized cognitive training (cCT) is an established treatment method. This paper presents the protocol for the NeuroNation MED Effectiveness Study, evaluating the self-administered mobile cCT intervention ("NeuroNation MED") in individuals with MCI to assess training effects on cognitive domains, health competence, neuropsychiatric symptoms, psychological well-being, and the general application usability., Methods: This study protocol presents a single-blinded multicenter randomized controlled trial that will be carried out in six study centers in Germany and Luxembourg. We included adults with MCI (existing F06.7 ICD-10-GM diagnosis and TICS ≥ 21 and ≤ 32). The intervention group will use a mobile, multi-domain cCT ("NeuroNation MED") for 12 weeks. Meanwhile, the wait list control group will receive standard medical care or no care. The eligibility of volunteers will be determined through a telephone screening. After completion of the baseline examination, patients will be randomly assigned to one of the experimental conditions in a 2:1 ratio. In total, 286 participants will be included in this study. The primary outcome is the change of cognitive performance measured by the index score of the screening module of the Neuropsychological Assessment Battery. Secondary outcomes are changes in the Cognitive Failures Questionnaire, Hospital Anxiety and Depression Scale, Health-49, Health Literacy Questionnaire, among others. All of the primary and secondary outcomes will be assessed at baseline and after the 12-week post-allocation period. Furthermore, the intervention group will undergo an assessment of the System Usability Scale, and the training data of the NeuroNation MED application will be analyzed., Discussion: This study aims to assess the effectiveness of a mobile self-administered cCT in enhancing cognitive abilities among individuals diagnosed with MCI. Should the findings confirm the effectiveness of the NeuroNation MED app, it may confer possible benefits for the care management of patients with MCI, owing to the accessibility, cost-effectiveness, and home-based setting it provides. Specifically, the cCT program could provide patients with personalized cognitive training, educational resources, and relaxation techniques, enabling participants to independently engage in cognitive training sessions at home without further supervision., Trial Registration: German Clinical Trials Register DRKS00025133. Registered on November 5, 2021., (© 2024. The Author(s).)

Purpose: Wearable ultrasound devices can be used to continuously monitor muscle activity. One possible application is to provide real-time feedback during physiotherapy, to show a patient whether an exercise is performed correctly. Algorithms which automatically analyze the data can be of importance to overcome the need for manual assessment and annotations and speed up evaluations especially when considering real-time video sequences. They even could be used to present feedback in an understandable manner to patients in a home-use scenario. The following work investigates three deep learning based segmentation approaches for abdominal muscles in ultrasound videos during a segmental stabilizing exercise. The segmentations are used to automatically classify the contraction state of the muscles., Methods: The first approach employs a simple 2D network, while the remaining two integrate the time information from the videos either via additional tracking or directly into the network architecture. The contraction state is determined by comparing measures such as muscle thickness and center of mass between rest and exercise. A retrospective analysis is conducted but also a real-time scenario is simulated, where classification is performed during exercise., Results: Using the proposed segmentation algorithms, 71% of the muscle states are classified correctly in the retrospective analysis in comparison to 90% accuracy with manual reference segmentation. For the real-time approach the majority of given feedback during exercise is correct when the retrospective analysis had come to the correct result, too., Conclusion: Both retrospective and real-time analysis prove to be feasible. While no substantial differences between the algorithms were observed regarding classification, the networks incorporating the time information showed temporally more consistent segmentations. Limitations of the approaches as well as reasons for failing cases in segmentation, classification and real-time assessment are discussed and requirements regarding image quality and hardware design are derived., (© 2024. The Author(s).)

Background: In addition to sociodemographic factors, action-theoretical constructs, such as technology acceptance and competence play an important role in technology use., Objective: This study aimed to examine the associations between technology use, sociodemographic factors, action-theoretical constructs, and technology interest., Material and Methods: Data were collected from 585 study participants aged over 60 years from 14 surveys conducted between 2014 and 2020. A structural equation model was used to explain the relationships., Results: The structural equation model with covariates of survey year, age, gender, and education (n = 585) yielded the following fit indices: comparative fit index (CFI) = 0.918, Tucker-Lewis index (TLI) = 0.894, Root Mean Square Error of Approximation (RMSEA) = 0.056 [95 % confidence interval: 0.050-0.063], Standardized Root Mean Square Residual (SRMR) = 0.079, χ2 = 3051.936 (p < 0.001), χ2/degrees of freedom (df) = 18.499. The strongest associations with technology use were found for technology acceptance and competence. Additionally, technology competence showed a significant association with technology interest. Gender and technology interest were not related to technology use but it was observed that men had higher levels of technology acceptance, control, competence, and interest., Discussion: Taking technology competence beliefs into account plays a crucial role in understanding the technology usage and interest of older individuals. Additionally, gender-specific differences in the theoretical constructs of action and interest in technology have been revealed in the context of the digital divide., (© 2023. The Author(s).)

Background and Objective: One possible approach to counter singularization and loneliness of older adults is the development and implementation of socially interactive robots. Little is known about the expectations and experiences of older adults with socially interactive humanoid robots., Material and Methods: In a mixed-methods design study, user expectations before interaction and the experience and evaluation of verbal and non-verbal communication after interaction with a robot were assessed. Semi-structured interviews were conducted after the interaction., Results: The majority of older adults expected verbal communication. After the interaction the evaluation of the quality of verbal communication differed. Participants did not expect any form of nonverbal communication. Nonverbal communication was highlighted as particularly positive. Gestures, facial expressions, and body movements were described as confidence building., Conclusion: The robot's ability to communicate nonverbally might positively influence older adults' experience of communication with the robot. In the development of socially interactive robots non-verbal communication should be given more consideration in order to contribute to successful human-robot interaction., (© 2024. The Author(s).)