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IntroductionThe present study investigates the feasibility and usability of a sensor-based eHealth treatment in psychotherapy for pediatric obsessive-compulsive disorder (OCD), and explores the promises and pitfalls of this novel approach. With eHealth interventions, therapy can be delivered in a patient's home environment, leading to a more ecologically valid symptom assessment and access to experts even in rural areas. Furthermore, sensors can help indicate a patient's emotional and physical state during treatment. Finally, using sensors during exposure with response prevention (E/RP) can help individualize therapy and prevent avoidance behavior.MethodsIn this study, we developed and subsequently evaluated a multimodal sensor-based eHealth intervention during 14 video sessions of cognitive-behavioral therapy (CBT) in 20 patients with OCD aged 12-18. During E/RP, we recorded eye movements and gaze direction via eye trackers, and an ECG chest strap captured heart rate (HR) to identify stress responses. Additionally, motion sensors detected approach and avoidance behavior.ResultsThe results indicate a promising application of sensor-supported therapy for pediatric OCD, such that the technology was well-accepted by the participants, and the therapeutic relationship was successfully established in the context of internet-based treatment. Patients, their parents, and the therapists all showed high levels of satisfaction with this form of therapy and rated the wearable approach in the home environment as helpful, with fewer OCD symptoms perceived at the end of the treatment.DiscussionThe goal of this study was to gain a better understanding of the psychological and physiological processes that occur in pediatric patients during exposure-based online treatment. In addition, 10 key considerations in preparing and conducting sensor-supported CBT for children and adolescents with OCD are explored at the end of the article. This approach has the potential to overcome limitations in eHealth interventions by allowing the real-time transmission of objective data to therapists, once challenges regarding technical support and hardware and software usability are addressed. Clinical Trial Registrationwww.ClinicalTrials.gov, identifier (NCT05291611).

BackgroundThe use of mobile tools in nursing care is indispensable. Given the importance of nurses’ acceptance of these tools in delivering effective care, this issue requires greater attention. ObjectiveThis study aims to design the Mobile Health Tool Acceptance Scale for Nurses based on the Expectation-Confirmation Theory and to evaluate it psychometrically. MethodsUsing a Waltz-based approach grounded in existing tools and the constructs of the Expectation-Confirmation Theory, the initial version of the scale was designed and evaluated for face and content validity. Construct validity was examined through exploratory factor analysis, concurrent validity, and known-group comparison. Reliability was assessed using measures of internal consistency and stability. ResultsThe initial version of the scale consisted of 33 items. During the qualitative and quantitative content validity stage, 1 item was added and 1 item was removed. Exploratory factor analysis, retaining 33 items, identified 5 factors that explained 70.53% of the variance. A significant positive correlation was found between the scores of the designed tool and nurses’ attitudes toward using mobile-based apps in nursing care (r=0.655, P

BackgroundIn recent years, the fast-paced adoption of digital health (DH) technologies has transformed health care delivery. However, this rapid evolution has also led to challenges such as uncoordinated development and information silos, impeding effective health care integration. Recognizing these challenges, nations have developed digital health strategies (DHSs), aligning with their national health priorities and guidance from global frameworks. The World Health Organization (WHO)’s Global Strategy on Digital Health 2020-2025 (GSDH) guides national DHSs. ObjectiveThis study analyzes the DHSs of Tanzania and Germany as case studies and assesses their alignment with the GSDH and identifies strengths, shortcomings, and areas for improvement. MethodsA comparative policy analysis was conducted, focusing on the DHSs of Tanzania and Germany as case studies, selected for their contrasting health care systems and cooperative history. The analysis involved a three-step process: (1) assessing consistency with the GSDH, (2) comparing similarities and differences, and (3) evaluating the incorporation of emergent technologies. Primary data sources included national eHealth policy documents and related legislation. ResultsBoth Germany’s and Tanzania’s DHSs align significantly with the WHO’s GSDH, incorporating most of its 35 elements, but each missing 5 distinct elements. Specifically, Tanzania’s DHS lacks in areas such as knowledge management and capacity building for leaders, while Germany’s strategy falls short in engaging health care service providers and beneficiaries in development phases and promoting health equity. Both countries, however, excel in other aspects like collaboration, knowledge transfer, and advancing national DHSs, reflecting their commitment to enhancing DH infrastructures. The high ratings of both countries on the Global Digital Health Monitor underscore their substantial progress in DH, although challenges persist in adopting the rapidly advancing technologies and in the need for more inclusive and comprehensive strategies. ConclusionsThis study reveals that both Tanzania and Germany have made significant strides in aligning their DHSs with the WHO’s GSDH. However, the rapid evolution of technologies like artificial intelligence and machine learning presents challenges in keeping strategies up-to-date. This study recommends the development of more comprehensive, inclusive strategies and regular revisions to align with emerging technologies and needs. The research underscores the importance of context-specific adaptations in DHSs and highlights the need for broader, strategic guidelines to direct the future development of the DH ecosystem. The WHO’s GSDH serves as a crucial blueprint for national DHSs. This comparative analysis demonstrates the value and challenges of aligning national strategies with global guidelines. Both Tanzania and Germany offer valuable insights into developing and implementing effective DHSs, highlighting the importance of continuous adaptation and context-specific considerations. Future policy assessments require in-depth knowledge of the country’s health care needs and structure, supplemented by stakeholder input for a comprehensive evaluation.

Abstract Background Technology can support healthy aging and empower older adults to live independently. However, technology adoption by older adults, particularly assistive technology (AT), is limited and little is known about the types of AT used among older adults. This study explored the use of key information and communication technologies (ICT) and AT among community-dwelling adults aged ≥ 65. Methods A cross-sectional study was conducted among community-dwelling adults aged ≥ 65 in southern Germany using a paper-based questionnaire. The questionnaire included questions on the three domains sociodemographic aspects, health status, and technology use. Technology use was considered separately for key ICT (smartphone, computer/laptop, and tablet) and a range of 31 different AT. Data were analyzed using descriptive statistics, univariate analyses, and Bernoulli Naïve Bayes modelling. Results The questionnaire was answered by 616 participants (response rate: 24.64%). ICT were used by 497 (80.68%) participants and were associated with lower age, higher level of education, living together with someone, availability of internet connection, higher interest in technology, and better health status (p

Cardiovascular disease is one of the leading causes of death worldwide. Scarce resources and rising costs are pushing healthcare systems to their limits. There is an urgency to develop, optimize and evaluate technologies that provide more effective care for patients. Modern technologies, such as mobile health (mHealth) applications, can provide relief as a key strategy. To integrate digital interventions into care structures, a detailed impact assessment of all professional mHealth applications is needed. The aim of this study is to analyze the standardized tools used in the field of cardiovascular disease. The results show that questionnaires, usage logs, and key indicators are predominantly used. Although the identified mHealth interventions are specific to cardiovascular disease and thus require particular questions to evaluate apps, the user readiness, usability, or quality of life criteria are non-specific. Therefore, the results contribute to understanding how different mHealth interventions can be assessed, categorized, evaluated, and accepted.

IntroductionGeriatric co-management is known to improve treatment of older adults in various clinical settings, however, widespread application of the concept is limited due to restricted resources. Digitalization may offer options to overcome these shortages by providing structured, relevant information and decision support tools for medical professionals. We present the SURGE-Ahead project (Supporting SURgery with GEriatric co-management and Artificial Intelligence) addressing this challenge.MethodsA digital application with a dashboard-style user interface will be developed, displaying 1) evidence-based recommendations for geriatric co-management and 2) artificial intelligence-enhanced suggestions for continuity of care (COC) decisions. The development and implementation of the SURGE-Ahead application (SAA) will follow the Medical research council framework for complex medical interventions. In the development phase a minimum geriatric data set (MGDS) will be defined that combines parametrized information from the hospital information system with a concise assessment battery and sensor data. Two literature reviews will be conducted to create an evidence base for co-management and COC suggestions that will be used to display guideline-compliant recommendations. Principles of machine learning will be used for further data processing and COC proposals for the postoperative course. In an observational and AI-development study, data will be collected in three surgical departments of a University Hospital (trauma surgery, general and visceral surgery, urology) for AI-training, feasibility testing of the MGDS and identification of co-management needs. Usability will be tested in a workshop with potential users. During a subsequent project phase, the SAA will be tested and evaluated in clinical routine, allowing its further improvement through an iterative process.DiscussionThe outline offers insights into a novel and comprehensive project that combines geriatric co-management with digital support tools to improve inpatient surgical care and continuity of care of older adults.Trial registrationGerman clinical trials registry (Deutsches Register für klinische Studien, DRKS00030684), registered on 21st November 2022.

Smart mobile devices such as smartphones or tablets have become an important factor for collecting data in complex health scenarios (e.g., psychological studies, medical trials), and are more and more replacing traditional pen-and-paper instruments. However, simply digitizing such instruments does not yet realize the full potential of mobile devices: most modern smartphones have a variety of different sensor technologies (e.g., microphone, GPS data, camera, ...) that can also provide valuable data and potentially valuable insights for the medical purpose or the researcher. In this context, a significant development effort is required to integrate sensing capabilities into (existing) data collection applications. Developers may have to deal with platform-specific peculiarities (e.g., Android vs. iOS) or proprietary sensor data formats, resulting in unnecessary development effort to support researchers with such digital solutions. Therefore, a cross-platform mobile data collection framework has been developed to extend existing data collection applications with sensor capabilities and address the aforementioned challenges in the process. This framework will enable researchers to collect additional information from participants and environment, increasing the amount of data collected and drawing new insights from existing data.

BackgroundThe use of assistive technologies (ATs) to support older people has been fueled by the demographic change and technological progress in many countries. These devices are designed to assist seniors, enable independent living at home or in residential facilities, and improve quality of life by addressing age-related difficulties. ObjectiveWe aimed to evaluate the effectiveness of ATs on relevant outcomes with a focus on frail older adults. MethodsA systematic literature review of randomized controlled trials evaluating ATs was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The Ovid Medline, PsycINFO, SocIndex, CINAHL (Cumulative Index to Nursing and Allied Health Literature), CENTRAL (Cochrane Central Register of Controlled Trials), and IEEEXplore databases were searched from January 1, 2009, to March 15, 2019. ATs were included when aiming to support the domains autonomy, communication, or safety of older people with a mean age ≥65 years. Trials performed within a laboratory setting were excluded. Studies were retrospectively categorized according to the physical frailty status of participants. ResultsA total of 19 trials with a high level of heterogeneity were included in the analysis. Six device categories were identified: mobility, personal disease management, medication, mental support, hearing, and vision. Eight trials showed significant effectiveness in all or some of the primary outcome measures. Personal disease management devices seem to be the most effective, with four out of five studies showing significant improvement of disease-related outcomes. Frailty could only be assessed for seven trials. Studies including participants with significant or severe impairment showed no effectiveness. ConclusionsDifferent ATs show some promising results in well-functioning but not in frail older adults, suggesting that the evaluated ATs might not (yet) be suitable for this subgroup. The uncertainty of the effectiveness of ATs and the lack of high-quality research for many promising supportive devices were confirmed in this systematic review. Large studies, also including frail older adults, and clear standards are needed in the future to guide professionals, older users, and their relatives. Trial RegistrationPROSPERO CRD42019130249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=130249

During brain tumor resection surgery, it is essential to determine the tumor borders as the extent of resection is important for post-operative patient survival. The current process of removing a tissue sample for frozen section analysis has several shortcomings that might be overcome by confocal laser endomicroscopy (CLE). CLE is a promising new technology enabling the digital in vivo visualization of tissue structures in near real-time. Research on the socio-organizational impact of introducing this new methodology to routine care in neurosurgery and neuropathology is scarce. We analyzed a potential clinical workflow employing CLE by comparing it to the current process. Additionally, a small expert survey was conducted to collect data on the opinion of clinical staff working with CLE. While CLE can contribute to a workload reduction for neuropathologists and enable a shorter process and a more efficient use of resources, the effort for neurosurgeons and surgery assistants might increase. Experts agree that CLE offers huge potential for better diagnosis and therapy but also see challenges, especially due to the current state of experimental use, including a risk for misinterpretations and the need for special training. Future studies will show whether CLE can become part of routine care.