Purpose: This study explores how older adults with chronic back pain (CBP) evaluate different user interface (UI) designs and gamification elements for an ultrasound-based wearable providing real-time biofeedback during segmental stabilization exercises (SSE). The aim is to identify design preferences and motivational factors to enhance usability, engagement, and adherence in this specific population. Methods: We conducted a mixed-methods study with 15 older adults (aged ≥ 65) experiencing CBP. Participants interacted with three UI mockups (simple, anatomical, and playful) via a Wizard-of-Oz simulation and evaluated additional motivational elements (e.g., points, badges, progress charts). Semi-structured interviews and the Technology Usage Inventory (TUI) subscales were used to assess usability, acceptance, and intention to use. Results: Participants preferred the simple and anatomical UI designs, citing clarity, professionalism, and ease of interpretation. The playful design was viewed as less appropriate due to perceived infantilization. Game elements such as progress tracking, points, and levels were positively received, while competitive features like leaderboards were viewed critically. Most participants expressed interest in integrating pain education, favoring multimedia formats. Conclusions: Digital health tools for older adults must prioritize intuitive, medically reliable interfaces and allow personalization of motivational and educational components. The findings highlight the need for age-appropriate UI design and suggest that well-balanced gamification and educational features may enhance perceived acceptance and have the potential to support long-term use, which should be evaluated in longitudinal studies.

BACKGROUND:Digital transformation in healthcare requires professionals who are prepared to integrate technological innovations into clinical practice. Beyond structural implementation, successful adoption depends on individual technology affinity and everyday digital engagement. However, evidence on how routine digital tool use relates to technology attitudes among healthcare professionals remains limited. This study examined the association between daily digital tool usage and technology affinity in German healthcare professionals and explored implications for education and training. METHODS:An exploratory quantitative cross-sectional survey was conducted in accordance with the STROBE reporting guidelines. An anonymous online questionnaire was administered between June 2023 and April 2024 among healthcare professionals in Germany. Technology affinity was measured using the validated TAEG scale, assessing enthusiasm for technology, perceived competence, perceived benefits, and perceived drawbacks. Statistical analyses comprised descriptive statistics and group comparisons. To examine differences in technology affinity across levels of daily digital tool usage while controlling for potential confounders, a multivariate analysis of covariance (MANCOVA) was conducted with age, gender, education, and profession as covariates. In addition, multiple regression analyses were performed to assess independent associations between digital tool usage and technology affinity dimensions. All analyses were conducted using IBM SPSS Statistics version 29. RESULTS:Data from 1,211 healthcare professionals were analyzed. Higher levels of daily digital tool usage were significantly associated with stronger technology affinity across all dimensions, independent of sociodemographic and professional factors. Younger and male participants demonstrated higher affinity scores. Differences between nurses and other healthcare professionals were small; however, given the aggregated nature of the occupational variable, these findings should be interpreted with caution and do not permit conclusions regarding differences between specific professional groups. CONCLUSIONS:Everyday digital engagement appears to be closely associated with positive technology attitudes among healthcare professionals, although causal relationships cannot be inferred. Educational and professional development programs should therefore integrate structured opportunities for practical digital tool use and address age- and gender-related differences in digital confidence. Strengthening digital competence through experiential learning may enhance readiness for digital innovation in healthcare settings.

Background Nurses in long-term care face high documentation-related job demands, including time pressure, informational strain and frequent interruptions. This study examined whether an AI speech assistant for nursing documentation is associated with reductions in these demands and whether technostress influences these changes. Methods A single-group longitudinal pre–post survey was conducted in 14 long-term care facilities in Germany. In total, 52 registered nurses completed an online questionnaire at baseline before implementation of the AI speech assistant, and 44 participated again at follow-up. Documentation-related job demands (information deficits, information overload, work intensity, interruptions) and voize-specific challenge and hindrance technostressors were analysed using linear mixed models accounting for repeated measures and clustering within facilities. Results Following implementation, the AI speech assistant was associated with significant reductions in all four demand subscales. Technostressors showed a selective moderating pattern: higher perceived usefulness coincided with stronger relief in work intensity, higher perceived unreliability was associated with a smaller reduction in information deficits, and higher perceived uncertainty was associated with weaker reductions in interruptions. Conclusions In this sample, early implementation of the AI speech assistant was associated with lower documentation-related job demands, with technostress showing only selective moderation. Reliable performance and transparent updates may help sustain perceived relief. Trial Registration: German Clinical Trials Register (DRKS00035512; drks.de/search/en/trial/DRKS00035512; Date of Registration: 2025-02-07; retrospectively registered).

Background:Nurses in long-term care spend up to one-third of their working time on documentation, contributing to administrative burden and limited time for direct care. Artificial intelligence (AI) speech assistants have shown potential to accelerate documentation, but longitudinal evidence from real-world long-term care settings remains scarce. Objective:This study aimed to evaluate whether implementing a domain-specific, mobile AI speech assistant is associated with reduced documentation time in German long-term care under routine conditions. Secondary objectives included examining usability, perceived documentation effort, interruptions, and workplace satisfaction. Methods:A pre-post time-motion study with full-shift observation was conducted. Continuous, event-based observations were performed before (t0) and after (t1) implementation of the mobile speech assistant voize. The primary outcome was total documentation time per morning shift based on direct observations. In addition to observations, questionnaires were administered to assess perceived documentation effort, interruptions, satisfaction with the documentation system, and workplace satisfaction. The primary end point was analyzed using a linear mixed-effects model. Secondary, self-reported outcomes were analyzed exploratorily via paired pre-post differences with pooling across multiple imputations and Holm-Bonferroni correction. Results:A total of 52 registered nurses from 14 long-term care facilities participated (mean age 42.37, SD 12.37 years; 42/52, 80.8% female). Across 770 observed hours, the observed total documentation time per morning shift decreased significantly by an adjusted mean of 15 (SE 3.36) minutes, t46.29=-4.46, P<.001, with a 95% CI of -21.75 to -8.23, corresponding to an approximately 28% reduction relative to the baseline mean. Holm-Bonferroni-corrected exploratory analyses indicated significant declines in self-reported documentation time and interruptions, and satisfaction with the documentation system improved, while workplace satisfaction showed no significant change. Usability was rated as acceptable. Conclusions:This study provides real-world evidence from a single-group pre-post design that an AI-based speech assistant is associated with reduced documentation workload in long-term care. In this sample, the integration of a mobile, domain-specific speech system into daily workflows coincided with substantially decreased documentation time and improved perceived efficiency. Beyond these observed time savings, such technology has the potential to alleviate workload, free time for resident care, and enhance working conditions. These findings are also relevant for policy discussions on addressing the nursing workforce shortage, showing that well-integrated, speech-enabled documentation systems can support more sustainable long-term care environments.

This study applied latent class analysis (LCA) to identify distinct technology acceptance groups among nursing staff using an AI speech assistant for nursing documentation (voize) in German long-term care facilities. Using a cross-sectional survey design ( N = 134) and three complementary indicator specifications derived from Unified Theory of Acceptance and Use of Technology (UTAUT2) constructs, we identified a consistent three-class solution with excellent entropy (.982): Champions (27.3%), Pragmatic Adopters (52.6%), and Reluctant Adopters (20.1%). Monte Carlo parameter-recovery simulations confirmed the robustness of this structure across simulated sample sizes from 100 to 1,000, with most recovery rates above 90%. Class profiles were validated through a Bayesian multivariate modelling approach with stacked posterior inference across 15 multiply imputed datasets, totaling 120,000 posterior draws. Compared with Pragmatic Adopters, Champions were more satisfied, perceived greater time savings, and were more willing to recommend the system, whereas Reluctant Adopters showed the opposite pattern across all practice-related outcomes. The clearest practical contrast was in likelihood to recommend: Champions rated the system 9.5 out of 10 on average, compared with 8.1 among Pragmatic Adopters and 4.7 among Reluctant Adopters. Champions endorsed nearly all three positive implementation outcomes on average—intention to continue use, better documentation, and better handovers—whereas Reluctant Adopters endorsed only about half. Age and duration of use did not meaningfully differentiate classes. These findings reveal distinct acceptance groups in AI-assisted nursing documentation and suggest that class-specific, targeted implementation strategies, rather than a uniform rollout, should be considered to support skeptical users while sustaining enthusiastic adoption.

The “Stay@Home – Treat@Home” project addresses the growing need for integrated care for geriatric patients, especially during times when family physicians are unavailable. The development involved stakeholder collaboration and the creation of a digital health diary. The project will evaluate the impact on emergency department admissions, quality of life, economic implications, and stakeholder satisfaction.

Introduction:Online communities provide valuable, peer-led spaces for discussing mental health issues, offering support that can complement traditional therapy. In this study, we adopt an interpretive approach by applying Yalom's group therapeutic factors to explore how mental health-focused Reddit discussions may reflect group therapy processes. Methods:We propose a practical methodological framework for large-scale qualitative research. Using a mixed-methods approach, we integrate advanced Natural Language Processing (NLP) techniques-including Large Language Models (GPT-3.5 Turbo 16k), cosine similarity, and BERTopic-with human validation to analyze 6,745 comments from mental health-focused Subreddits. Results:The results show that a large portion of the data can be interpreted through Yalom's therapeutic factors, such as Instillation of Hope, Group Cohesion, and Altruism, suggesting a generally supportive and empathetic online environment. However, unfiltered negative dynamics, including shared suffering and maladaptive coping strategies, also appeared in the discussions. Discussion:By grounding NLP-based analyses in a well-established therapeutic framework and incorporating human expertise, we demonstrate a transparent, scalable approach to examining large-scale online mental health data. These findings underscore the potential of online communities for enhancing peer-led mental health support, while emphasizing the importance of theoretical grounding in interpreting such digital spaces.

In the context of healthcare for chronic wound patients, especially those living in remote areas, alternative solutions for care at home are needed to avoid transportation and care for patients in hospitals. Based on the requirements of the clinical team, this paper proposes a solution that employs image processing to assist the caring personnel in objectively evaluating the wound as well as bio-printing of necessary gel-based patches for treating it. For reliable and secure connectivity, the solution leverages nomadic micro-networks based on technologies like 5G and beyond. The paper also dives into the key design aspects, considering aspects such as access control and interoperability. Regarding the image processing service, an evaluation of three promising wound surface detection algorithms using publicly available datasets is provided. The paper also includes lessons learned from the do-it-yourself bio-printer.

The "Stay@Home - Treat@Home" project addresses the growing need for integrated care for geriatric patients, especially during times when family physicians are unavailable. The development involved stakeholder collaboration and the creation of a digital health diary. The project will evaluate the impact on emergency department admissions, quality of life, economic implications, and stakeholder satisfaction.

INTRODUCTION. Mild cognitive impairment (MCI) involves small but noticeable declines in cognitive abilities. This study explores the effectiveness of mobile computerized cognitive training (cCT) to improve cognitive function in individuals with MCI.METHODS. A 12-week mobile cCT program (NeuroNation MED) was tested in a single-blinded, multicenter randomized controlled trial. The study included 288 MCI participants, with global cognition measured using scores from five cognitive domains.RESULTS. Analysis of Covariance showed improvements in global cognition (F(1, 286) = 8.06, p = .005, partial η² = .034), attention (F(1, 286) = 7.33, p = .007, partial η² = .034) and executive functions (F(1, 286) = 8.14, p = .004, partial η² = .031) in the intervention group compared to the control group. No changes between the experimental groups were found in memory, language, or visuospatial abilities. No correlation was found between cognitive changes and training adherence.DISCUSSION. Mobile cCT can effectively improve cognitive function in individuals with MCI, supporting its therapeutic potential.German Clinical Trials Register: DRKS00025133; Universal Trial Number: U1111-1277-8721. Ethical approval was granted by the Ethics Committee of Charité—Universitätsmedizin Berlin (reference number EA4/105/21) on June 10, 2021

Objective- Hospital-acquired pressure ulcers are an important indicator of the quality of care. Most pressure ulcers are avoidable with a robust protocol for prevention, but prevention activities often have a low priority for senior management because the true costs to the hospital are not visible. Our aim was to raise awareness of the value of pressure ulcer prevention by estimating the excess length of inpatient stay associated with hospital-acquired pressure ulcers, and by assessing whether additional costs are covered by increased reimbursement. Methods- National activity data for hospitals in Germany are available through the InEK Data Browser. Data were extracted covering discharges from German hospitals between January 1 and December 31, 2021. Cases were selected according to the presence of a pressure ulcer diagnosis using ICD-10-GM codes L89.0-L89.3. Information was extracted for the ten most common German Diagnosis-Related Group (G-DRG) codes in patients with a secondary pressure ulcer diagnosis on mean length of stay and average reimbursement. Ulcer-associated excess length of stay was estimated by comparing cases within the same G-DRG with and without a pressure ulcer diagnosis. Results- Mean length of stay was higher in patients with a pressure ulcer than in patients with no ulcer by between 1.9 (all ages) and 2.4 days (patients aged >= 65) per case. In patients aged >= 65 years, 22.1% of cases with a pressure ulcer had a length of stay above the norm for the DRG. In the German system length of stay above the norm is not normally reimbursed. Excess length of stay between 1.9 and 2.4 days leads to a potential cost to a hospital of between 1,633 and 2,074 per case. Conclusion- Hospital-acquired pressure ulcers represent an important source of cost for a hospital which highlights the potential value of effective prevention.

Die COVID-19-Pandemie prägte seit mehr als drei Jahren das Leben von Menschen weltweit und stellte insbesondere pflegebedürftige Menschen, ihre Zu- und Angehörigen sowie professionelle Leistungserbringer_innen vor vielfältige Herausforderungen. Im Rahmen von leitfadengestützten Interviews wurden Pflegeberatende aus Berliner Pflegestützpunkten zu veränderten Beratungsbedarfen ihrer Klient_innen in den ersten zwei Jahren der Pandemie befragt. Die Auswertung der Ergebnisse erfolgte mithilfe einer Qualitativen Inhaltsanalyse nach Mayring. Es zeigt sich, dass besonders häufig Beschwerden zu Kontaktbeschränkungen geäußert wurden. Pflegende Angehörige schienen besonders belastet, da unterstützende Angebote wie Tages- oder Kurzzeitpflege wegfielen und es an geeigneten Notfallstrukturen fehlte.

BackgroundPain management depends on continuous pain assessment and a pain concept. In particular, pain assessment and treatment are major challenges for nursing home residents (NHR) with cognitive impairment (CI). Many caregivers often lack the knowledge to recognize and appropriately treat pain in this vulnerable group. Little is known about the proportion of NHR who are fundamentally dependent on external assessment for pain due to CI.ObjectiveThe aim of the study was to determine pain prevalence and management among NHR with and without CI. A second objective was to determine the proportion of NHR who are dependent on external assessment for pain.MethodsInformation on pain was collected from 3,437 NHR in multicenter cross-sectional surveys in 51 German nursing homes between 2014 and 2018. The presence of current pain in one-to-one interviews was determined as well as dependencies on third-party information, number of daily pain recordings, and administration of medication for pain. The analysis included a contingency table and log regression analyses.ResultsPain prevalence was 24.9% among NHR with severe CI and 40.4% among NHR without CI. Overall, 19.8% of all NHRs relied on a third-party assessment of pain. Significantly, NHR with severe CI were less likely to be classified as having pain (OR 0.51), to be assessed for pain several times a day (OR 0.53) or to receive pain medication (OR 0.55) compared with NHR without CI. No influence on pain management was shown for the type of pain assessment.ConclusionsThe study provides evidence of significant deficits in pain management among NHR with moderate and severe CI in nursing homes in Germany. NHR with moderate and severe CI are significantly less likely to be observed for pain or classified as pain sufferers and receive significantly less pain medication than NHR without CI. Intensive training of staff on pain management of NHR with severe CI is recommended.

Background In 2009, statutory regulations on information and counselling regarding nursing care needs, performed by so-called care advisors have been implemented for persons in need of long-term care and their relatives. In order to adequately prepare these care advisors, contemporary needs and requirements must be determined. The aim of the study was to determine the different needs of persons in need of long-term care and their relatives. Method Care advisors were interviewed via an online survey tool using a standardized questionnaire. A 5-point Likert scale was used to determine the needs regarding information and advice on 16 specific topics. In general, overall needs regarding information and advice of care recipients and relatives were recorded using a 10-point scale (1 low and 10 high). Using classification and regression trees (CRT) and random forest, the correlation between the individual main topics and the general need for advice was analyzed. Results The participating care advisors (n = 276) rated the general demand for information of people in need of care and their relatives with a mean of 7.8 and 9.2, respectively. For those in need of care, the strongest association of general information needs was the topic of housing advice For the relatives, the topic social law aspects and benefits was the most relevant association. Conclusion The general demand for information was rated very high. Since differences became obvious between those in need of care and their relatives, it is necessary to adjust care advice for these two groups.