Unleashing the full potential of digital outcome measures in clinical trials: eight questions that need attention.

The use of digital health technologies to measure outcomes in clinical trials opens new opportunities as well as methodological challenges. Digital outcome measures may provide more sensitive and higher-frequency measurements but pose vital statistical challenges around how such outcomes should be defined and validated and how trials incorporating digital outcome measures should be designed and analysed. This article presents eight methodological questions, exploring issues such as the length of measurement period, choice of summary statistic and definition and handling of missing data as well as the potential for new estimands and new analyses to leverage the time series data from digital devices. The impact of key issues highlighted by the eight questions on a primary analysis of a trial are illustrated through a simulation study based on the 2019 Bellerophon INOPulse trial which had time spent in MVPA as a digital outcome measure. These eight questions present broad areas where methodological guidance is needed to enable wider uptake of digital outcome measures in trials.

Konectom™ cognitive processing speed test enables reliable remote, unsupervised cognitive assessment in people with multiple sclerosis: Exploring the use of substitution time as a novel digital outcome measure.

BACKGROUND: The Konectom™ smartphone-based cognitive processing speed (CPS) test is designed to assess processing speed and account for impact of visuomotor function on performance. OBJECTIVE: Evaluate reliability and validity of Konectom CPS Test, performed in clinic and remotely. METHODS: Data were collected from people with multiple sclerosis (PwMS) aged 18-64 years and healthy control participants (HC) matched for age, sex, and education. Remote test-retest reliability (intraclass correlation coefficients, ICC); correlation with established clinical measures (Spearman correlation coefficients); group analyses between cognitively impaired/unimpaired PwMS; and influence of age, sex, education, and upper limb motor function on CPS Test measures were assessed. RESULTS: Eighty PwMS and 66 HC participated. CPS Test measures from remote tests had good test-retest reliability (ICC of 0.67-0.87) and correlated with symbol digit modalities test (highest |ρ| = 0.80, p < 0.0001). Remote measures were stable (change from baseline < 5%) and correlated with MS disability (highest |ρ| = 0.39, p = 0.0004) measured by Expanded Disability Status Scale. CPS Test measures displayed sensitivity to cognitive impairment (highest d = 1.47). Demographics and motor function had the lowest impact on CPS Test substitution time, a measure accounting for visuomotor function. CONCLUSION: Konectom CPS Test measures provide valid, reliable remote measurements of cognitive processing speed in PwMS.

Improving Quality of Care for Status Epilepticus: Putting Protocols into Practice.

PURPOSE OF REVIEW: Timely treatment of status epilepticus (SE) improves outcomes, however gaps between recommended and implemented care are common. This review analyzes obstacles and explores interventions to optimize effective, evidence-based treatment of SE. RECENT FINDINGS: Seizure action plans, rescue medications, and noninvasive wearables with seizure detection capabilities can facilitate early intervention for prolonged seizures in the home and school. In the field, standardized EMS protocols, EMS education, and screening tools can address variability in SE definitions and treatment, particularly benzodiazepine dosing. In the emergency room and hospital, provider education, SE order sets and alerts, and rapid EEG technologies, can shorten time to first-line therapy, second-line therapy, and EEG initiation. Widespread, sustained improvement in SE care remains challenging. A multipronged approach including emphasis on pre-hospital intervention, treatment protocols adapted to local contexts, and SE databases to systematically collect process and outcome metrics have the potential to transform SE treatment and outcomes.

Disability Ethics and Education in the Age of Artificial Intelligence: Identifying Ability Bias in ChatGPT and Gemini.

OBJECTIVE: To identify and quantify ability bias in generative artificial intelligence large language model chatbots, specifically OpenAI's ChatGPT and Google's Gemini. DESIGN: Observational study of language usage in generative artificial intelligence models. SETTING: Investigation-only browser profile restricted to ChatGPT and Gemini. PARTICIPANTS: Each chatbot generated 60 descriptions of people prompted without specified functional status, 30 descriptions of people with a disability, 30 descriptions of patients with a disability, and 30 descriptions of athletes with a disability (N=300). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Generated descriptions produced by the models were parsed into words that were linguistically analyzed into favorable qualities or limiting qualities. RESULTS: Both large language models significantly underestimated disability in a population of people, and linguistic analysis showed that descriptions of people, patients, and athletes with a disability were generated as having significantly fewer favorable qualities and significantly more limitations than people without a disability in both ChatGPT and Gemini. CONCLUSIONS: Generative artificial intelligence chatbots demonstrate quantifiable ability bias and often exclude people with disabilities in their responses. Ethical use of these generative large language model chatbots in medical systems should recognize this limitation, and further consideration should be taken in developing equitable artificial intelligence technologies.

Developing a novel mobile application for cognitive behavioral therapy for insomnia for people with schizophrenia: integration of wearable and environmental sleep sensors.

BACKGROUND: People with serious mental illnesses (SMIs) have three-fold higher rates of comorbid insomnia than the general population, which has downstream effects on cognitive, mental, and physical health. Cognitive Behavioral Therapy for Insomnia (CBT-i) is a safe and effective first-line treatment for insomnia, though the therapy's effectiveness relies on completing nightly sleep diaries which can be challenging for some people with SMI and comorbid cognitive deficits. Supportive technologies such as mobile applications and sleep sensors may aid with completing sleep diaries. However, commercially available CBT-i apps are not designed for individuals with cognitive deficits. To aid with this challenge, we have developed an integrated mobile application, named "Sleep Catcher," that will automatically incorporate data from a wearable fitness tracker and a bed sensor to track nightly sleep duration, overnight awakenings, bed-times, and wake-times to generate nightly sleep diaries for CBT-i. METHODS: The application development process will be described-writing algorithms to generating useful data, creating a clinician web portal to oversee patients and the mobile application, and integrating sleep data from device platforms and user input. RESULTS: The mobile and web applications were developed using Flutter, IBM Code Engine, and IBM Cloudant database. The mobile application was developed with a user-centered approach and incremental changes informed by a series of beta tests. Special user-interface features were considered to address the challenges of developing a simple and effective mobile application targeting people with SMI. CONCLUSION: There is strong potential for synergy between engineering and mental health expertise to develop technologies for specific clinical populations. Digital health technologies allow for the development of multi-disciplinary solutions to existing health disparities in vulnerable populations, particularly in people with SMI.

Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study.

BACKGROUND: Depressive disorders have substantial global implications, leading to various social consequences, including decreased occupational productivity and a high disability burden. Early detection and intervention for clinically significant depression have gained attention; however, the existing depression screening tools, such as the Center for Epidemiologic Studies Depression Scale, have limitations in objectivity and accuracy. Therefore, researchers are identifying objective indicators of depression, including image analysis, blood biomarkers, and ecological momentary assessments (EMAs). Among EMAs, user-generated text data, particularly from diary writing, have emerged as a clinically significant and analyzable source for detecting or diagnosing depression, leveraging advancements in large language models such as ChatGPT. OBJECTIVE: We aimed to detect depression based on user-generated diary text through an emotional diary writing app using a large language model (LLM). We aimed to validate the value of the semistructured diary text data as an EMA data source. METHODS: Participants were assessed for depression using the Patient Health Questionnaire and suicide risk was evaluated using the Beck Scale for Suicide Ideation before starting and after completing the 2-week diary writing period. The text data from the daily diaries were also used in the analysis. The performance of leading LLMs, such as ChatGPT with GPT-3.5 and GPT-4, was assessed with and without GPT-3.5 fine-tuning on the training data set. The model performance comparison involved the use of chain-of-thought and zero-shot prompting to analyze the text structure and content. RESULTS: We used 428 diaries from 91 participants; GPT-3.5 fine-tuning demonstrated superior performance in depression detection, achieving an accuracy of 0.902 and a specificity of 0.955. However, the balanced accuracy was the highest (0.844) for GPT-3.5 without fine-tuning and prompt techniques; it displayed a recall of 0.929. CONCLUSIONS: Both GPT-3.5 and GPT-4.0 demonstrated relatively reasonable performance in recognizing the risk of depression based on diaries. Our findings highlight the potential clinical usefulness of user-generated text data for detecting depression. In addition to measurable indicators, such as step count and physical activity, future research should increasingly emphasize qualitative digital expression.

Behavior Change Approaches in Digital Technology-Based Physical Rehabilitation Interventions Following Stroke: Scoping Review.

BACKGROUND: Digital health technologies (DHTs) are increasingly used in physical stroke rehabilitation to support individuals in successfully engaging with the frequent, intensive, and lengthy activities required to optimize recovery. Despite this, little is known about behavior change within these interventions. OBJECTIVE: This scoping review aimed to identify if and how behavior change approaches (ie, theories, models, frameworks, and techniques to influence behavior) are incorporated within physical stroke rehabilitation interventions that include a DHT. METHODS: Databases (Embase, MEDLINE, PsycINFO, CINAHL, Cochrane Library, and AMED) were searched using keywords relating to behavior change, DHT, physical rehabilitation, and stroke. The results were independently screened by 2 reviewers. Sources were included if they reported a completed primary research study in which a behavior change approach could be identified within a physical stroke rehabilitation intervention that included a DHT. Data, including the study design, DHT used, and behavior change approaches, were charted. Specific behavior change techniques were coded to the behavior change technique taxonomy version 1 (BCTTv1). RESULTS: From a total of 1973 identified sources, 103 (5%) studies were included for data charting. The most common reason for exclusion at full-text screening was the absence of an explicit approach to behavior change (165/245, 67%). Almost half (45/103, 44%) of the included studies were described as pilot or feasibility studies. Virtual reality was the most frequently identified DHT type (58/103, 56%), and almost two-thirds (65/103, 63%) of studies focused on upper limb rehabilitation. Only a limited number of studies (18/103, 17%) included a theory, model, or framework for behavior change. The most frequently used BCTTv1 clusters were feedback and monitoring (88/103, 85%), reward and threat (56/103, 54%), goals and planning (33/103, 32%), and shaping knowledge (33/103, 32%). Relationships between feedback and monitoring and reward and threat were identified using a relationship map, with prominent use of both of these clusters in interventions that included virtual reality. CONCLUSIONS: Despite an assumption that DHTs can promote engagement in rehabilitation, this scoping review demonstrates that very few studies of physical stroke rehabilitation that include a DHT overtly used any form of behavior change approach. From those studies that did consider behavior change, most did not report a robust underpinning theory. Future development and research need to explicitly articulate how including DHTs within an intervention may support the behavior change required for optimal engagement in physical rehabilitation following stroke, as well as establish their effectiveness. This understanding is likely to support the realization of the transformative potential of DHTs in stroke rehabilitation.

Value of Engagement in Digital Health Technology Research: Evidence Across 6 Unique Cohort Studies.

BACKGROUND: Wearable digital health technologies and mobile apps (personal digital health technologies [DHTs]) hold great promise for transforming health research and care. However, engagement in personal DHT research is poor. OBJECTIVE: The objective of this paper is to describe how participant engagement techniques and different study designs affect participant adherence, retention, and overall engagement in research involving personal DHTs. METHODS: Quantitative and qualitative analysis of engagement factors are reported across 6 unique personal DHT research studies that adopted aspects of a participant-centric design. Study populations included (1) frontline health care workers; (2) a conception, pregnant, and postpartum population; (3) individuals with Crohn disease; (4) individuals with pancreatic cancer; (5) individuals with central nervous system tumors; and (6) families with a Li-Fraumeni syndrome affected member. All included studies involved the use of a study smartphone app that collected both daily and intermittent passive and active tasks, as well as using multiple wearable devices including smartwatches, smart rings, and smart scales. All studies included a variety of participant-centric engagement strategies centered on working with participants as co-designers and regular check-in phone calls to provide support over study participation. Overall retention, probability of staying in the study, and median adherence to study activities are reported. RESULTS: The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies. Median adherence to study activities varied by study population. Severely ill cancer populations and postpartum mothers showed the lowest adherence to personal DHT research tasks, largely the result of physical, mental, and situational barriers. Except for the cancer and postpartum populations, median adherences for the Oura smart ring, Garmin, and Apple smartwatches were over 80% and 90%, respectively. Median adherence to the scheduled check-in calls was high across all but one cohort (50%, IQR 20%-75%: low-engagement cohort). Median adherence to study-related activities in this low-engagement cohort was lower than in all other included studies. CONCLUSIONS: Participant-centric engagement strategies aid in participant retention and maintain good adherence in some populations. Primary barriers to engagement were participant burden (task fatigue and inconvenience), physical, mental, and situational barriers (unable to complete tasks), and low perceived benefit (lack of understanding of the value of personal DHTs). More population-specific tailoring of personal DHT designs is needed so that these new tools can be perceived as personally valuable to the end user.

Digital Health Technology Use Among Older Adults: Exploring the Impact of Frailty on Utilization, Purpose, and Satisfaction in Korea.

BACKGROUND: The importance of digital technology is increasing among older adults. In this study, the digital health technology utilization status, purpose, and satisfaction of older adults were investigated according to frailty. METHODS: A face-to-face survey was conducted among adults aged 65 years or older. Frailty was defined using the Korean version of the fatigue, resistance, ambulation, illnesses, and loss of weight scale. RESULTS: A total of 505 participants completed the survey, with 153 (30.3%) identified as pre-frail or frail and 352 (69.7%) as healthy. All respondents used smartphones; 440 (87.1%) were application users, and 290 (57.4%) were healthcare application users. Wearable devices were used by only 36 patients (7.1%). Pre-frail or frail respondents used social media more frequently than healthy respondents (19.4% vs. 7.4%, P < 0.001). Among the respondents, 319 (63.2%) were not able to install or delete the application themselves, and 277 (54.9%) stated that the application was recommended by their children (or partner). Pre-frail and frail respondents used more healthcare applications to obtain health information (P = 0.002) and were less satisfied with wearable devices (P = 0.02). CONCLUSION: The usage rate of digital devices, including mobile phones among older adults in Korea is high, whereas that of wearable devices is low. There was a notable difference in the services used by pre-frail and frail respondents compared to healthy respondents. Therefore, when developing digital devices for pre-frail and frail older adults, it is crucial to incorporate customized services that meet their unique needs, particularly those services that they frequently use.

Digital Health in Diabetes and Cardiovascular Disease.

BACKGROUND: Digital health technologies are rapidly evolving and transforming the care of diabetes and cardiovascular disease (CVD). PURPOSE OF THE REVIEW: In this review, we discuss emerging approaches incorporating digital health technologies to improve patient outcomes through a more continuous, accessible, proactive, and patient-centered approach. We discuss various mechanisms of potential benefit ranging from early detection to enhanced physiologic monitoring over time to helping shape important management decisions and engaging patients in their care. Furthermore, we discuss the potential for better individualization of management, which is particularly important in diseases with heterogeneous and complex manifestations, such as diabetes and cardiovascular disease. This narrative review explores ways to leverage digital health technology to better extend the reach of clinicians beyond the physical hospital and clinic spaces to address disparities in the diagnosis, treatment, and prevention of diabetes and cardiovascular disease. CONCLUSION: We are at the early stages of the shift to digital medicine, which holds substantial promise not only to improve patient outcomes but also to lower the costs of care. The review concludes by recognizing the challenges and limitations that need to be addressed for optimal implementation and impact. We present recommendations on how to navigate these challenges as well as goals and opportunities in utilizing digital health technology in the management of diabetes and prevention of adverse cardiovascular outcomes.

A landscape analysis of digital health technology in medical schools: preparing students for the future of health care.

Although Digital Health Technology is increasingly implemented in hospitals and clinics, physicians are not sufficiently equipped with the competencies needed to optimize technology utilization. Medical schools seem to be the most appropriate channel to better prepare future physicians for this development. The purpose of this research study is to investigate the extent to which top-ranked medical schools equip future physicians with the competencies necessary for them to leverage Digital Health Technology in the provision of care. This research work relied on a descriptive landscape analysis, and was composed of two phases: Phase I aimed at investigating the articulation of the direction of the selected universities and medical schools to identify any expressed inclination towards teaching innovation or Digital Health Technology. In phase II, medical schools' websites were analyzed to discover how innovation and Digital Health Technology are integrated in their curricula. Among the 60 medical schools that were analyzed, none mentioned any type of Digital Health Technology in their mission statements (that of the universities, in general, and medical schools, specifically). When investigating the medical schools' curricula to determine how universities nurture their learners in relation to Digital Health Technology, four universities covering different Digital Health Technology areas were identified. The results of the current study shed light on the untapped potential of working towards better equipping medical students with competencies that will enable them to leverage Digital Health Technology in their future practice and in turn enhance the quality of care.

Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy.

Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity. Digital health technologies (DHTs), such as wearable sensors, have been widely used to capture behaviors in clinical and real-world settings. In this work, we develop and validate a machine learning algorithm using wrist-wearing actigraphy that could objectively quantify nocturnal scratching events, therefore facilitating accurate assessment of disease progression, treatment effectiveness, and overall quality of life in AD patients. A total of seven subjects were enrolled in a study to generate data overnight in an inpatient setting. Several machine learning models were developed, and their performance was compared. Results demonstrated that the best-performing model achieved the F1 score of 0.45 on the test set, accompanied by a precision of 0.44 and a recall of 0.46. Upon satisfactory performance with an expanded subject pool, our automatic scratch detection algorithm holds the potential for objectively assessing sleep quality and disease state in AD patients. This advancement promises to inform and refine therapeutic strategies for individuals with AD.

Leveraging digital health technologies in heart failure self-care interventions to improve health equity.

For patients with heart failure, self-care is crucial, but recommendations for self-care are often not adhered to. Digital health interventions have the potential to help patients improve their self-care and, ultimately, their health outcomes, but without careful examination, digital interventions may generate further inequality. In this study, we use a digital health equity framework to examine challenges and opportunities related to digital health interventions across many domains of influence that may affect health equity. We draw from our own experience in conducting a decentralized clinical trial with multiple sensors and apps to help patients with heart failure improve adherence to key self-care behaviors. Finally, we provide recommendations for leveraging digital health interventions in research and practice to address health equity.

[Telemedicine for patients with facial palsy : Current developments and options in otorhinolaryngologic treatment]. / Telemedizin bei Fazialisparese : Aktuelle Entwicklungen und Möglichkeiten bei der Behandlung in der HNO-Heilkunde.

Digitalization is also becoming increasingly important in medicine. The COVID-19 pandemic has further accelerated this process and politicians are trying to create a framework for successful knowledge transfer and better digital medical care. This article describes the role of telemedicine in the treatment of patients suffering from facial nerve palsy. Facial nerve palsy has a wide range of effects, from limitations in facial mobility to psychological sequelae. While many of the acute, idiopathic facial nerve palsies improve after a few weeks, around a third of those affected develop synkinesis, involuntary movements that have lifelong functional and psychological consequences. Treatment includes various modalities, from medication and surgery to movement training. Telemedicine offers innovative solutions in cases of regional underuse, but also in the treatment of chronic facial nerve palsies. The article defines the term "telemedicine" in the current context and presents different types of application. A detailed analysis of the application scenarios of telemedicine in facial nerve palsy patients shows that despite a lack of evidence, many potentially useful concepts exist.

A Value Framework to Assess Patient-Facing Digital Health Technologies That Aim to Improve Chronic Disease Management: A Delphi Approach.

OBJECTIVES: Digital health technologies (DHTs) can optimise healthcare costs and improve quality and efficiency of care. However, the fast-paced rate of innovation and varying evidence standards can make it difficult for decision-makers to assess these technologies in an efficient and evidence-based manner. We sought to develop a comprehensive framework to assess the value of novel patient-facing DHTs used to manage chronic diseases by eliciting stakeholder value preferences. METHODS: Literature review and primary data collection from a three-round web-Delphi exercise was utilized. 79 participants from 5 stakeholder groups (patients, physicians, industry, decision makers, and influencers) and 3 countries (United States of America, United Kingdom, and Germany) took part. Likert scale data were statistically analyzed to determine intergroup differences in both country and stakeholder groups, stability of results, and overall consensus. RESULTS: The resulting co-created framework comprised 33 stable indicators with consensus from quantitative value judgments across domains: health inequalities, data rights and governance, technical and security, economic characteristics, clinical characteristics, and user preferences. Lack of stakeholder consensus was observed on the importance of value-based care models, optimizing resources for sustainable systems, and stakeholder involvement in DHT design, development, and implementation; however, this was because of high rates of neutrality and not negative judgments. Supply-side actors and academic experts were the most unstable stakeholder groups. CONCLUSION: Stakeholder value judgments revealed a need for a coordinated regulatory and health technology assessment policy response that updates laws to meet technological innovations, offers a pragmatic approach to evidence standards to assess DHTs, and involves stakeholders to understand and meet their needs.

Budget impact analysis of a digital monitoring platform for COPD.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive debilitating condition with frequent exacerbations that have a high burden for patients and society. Digital tools may help to reduce the economic burden for patients and payers by improving outcomes. The Propeller platform is a digital self-management tool that facilitates passive monitoring of inhaler medication utilization, potentially assisting the healthcare team to identify patients at risk of a COPD exacerbation who may require further intervention. This study estimated the budget impact of Propeller from commercial payer and Medicare fee-for-service payer perspectives. METHODS: An Excel-based model was used to estimate the budget impact of Propeller for COPD patients in commercial and Medicare population sizes of 5 million members. Data on prevalence, baseline healthcare resource utilization (HCRU), and baseline use of rescue and controller inhaler medications with unit costs (adjusted to 2020 US dollars) were obtained from peer-reviewed literature. Data on reductions in HCRU during Propeller usage were based on direct evidence. Estimates for costs of remote monitoring were obtained from publicly available information. All patients were assumed to have insurance claims related to ongoing remote monitoring. RESULTS: The estimated number of annual eligible COPD patients for commercial and Medicare was 212,200 and 606,600, respectively. Propeller decreased costs by an estimated $2,475 (commercial) and $915 (Medicare) per enrolled patient. The greatest increase in expenditure was for remote monitoring related expenses. After accounting for estimated reductions in hospitalizations, emergency department visits and short-acting beta-agonist use, total net savings were approximately $1.60 and $1.70 per-member per-month for commercial and Medicare payers, respectively. CONCLUSION: Propeller is projected to be cost saving from both the commercial and Medicare payer perspectives.

Impact of a digital web-based asthma platform, a real-life study.

BACKGROUND: Digital health technology (DHT) is a growing area in the treatment of chronic diseases. Study results on DHT's effect on asthma control have been mixed, but benefits have been seen for adherence, self-management, symptoms, and quality of life. The aim was to evaluate the impact of an interactive web-based asthma treatment platform on asthma exacerbations and health care visits. METHODS: In this real-life study, we retrospectively collected data on adult patients registered on a web-based interactive asthma treatment platform between December 2018 and May 2021. Patients who activated their accounts were active users, and patients who did not were inactive users and considered as controls. We compared the number of exacerbations, total number of exacerbation events defined as the sum of oral corticosteroid (OCS) and antimicrobial courses, emergency room visits, hospitalizations, and asthma-related health care visits before and one year after the registration on the platform. Statistical tests used included the t-test, Pearson's chi-square test and Poisson regression models. RESULTS: Of 147 patients registered on the platform, 106 activated their accounts and 41 did not. The active users had significantly fewer total number of exacerbation events (2.56 per person years, relative decline 0.78, 95% CI 0.6 to 1.0) and asthma-related health care visits (2.38 per person years, relative decline 0.84, 95% CI 0.74 to 0.96) than before registration to the platform, whereas the reductions in health care visits and the total number of exacerbation events were not significant in the inactive users. CONCLUSIONS: An interactive web-based asthma platform can reduce asthma-related health care visits and exacerbations when used actively.

Evidence requirements of permanently listed digital health applications (DiGA) and their implementation in the German DiGA directory: an analysis.

BACKGROUND: With its digital health application (DiGA)-system, Germany is considered one of Europe's pioneers in the field of evidence-based digital health. Incorporating DiGA into standard medical care must be based on evidence-based success factors; however, a comprehensive overview of the evidence required of scientific studies for their approval is lacking. OBJECTIVE: The study aims to, (1) identify specific requirements defined by the Federal Institute for Drugs and Medical Devices (German: Bundesinstitut für Arzneimittel- und Medizinprodukte; BfArM) to design adequate studies, proving a positive healthcare effect, and (2) to assess the evidence given for applications permanently listed in the DiGA directory. METHODS: A multi-step approach was used: (1) identification of the evidence requirements for applications permanently listed in the DiGA directory, (2) identification of the evidence available supporting them. RESULTS: All DiGA permanently listed in the DiGA directory (13 applications) are included in the formal analysis. Most DiGA addressed mental health (n = 7), and can be prescribed for one or two indications (n = 10). All permanently listed DiGA have demonstrated their positive healthcare effect through a medical benefit, and most of them provide evidence for one defined primary endpoint. All DiGA manufacturers conducted a randomized controlled trial. DISCUSSION: It is striking that- although patient-relevant structural and procedural improvements show high potential for improving care, especially in terms of processes - all DiGA have provided a positive care effect via a medical benefit. Although BfArM accepts study designs with a lower level of evidence for the proof of a positive healthcare effect, all manufacturers conducted a study with a high level of evidence. CONCLUSION: The results of this analysis indicate that permanently listed DiGA meet higher standards than required by the guideline.

Ethical perspectives on femtech: Moving from concerns to capability-sensitive designs.

Femtech is the collective name for technologies that address female health needs. Femtech applications can help women digitally track their period, manage their fertility, and support their pregnancy. Although femtech has beneficial potential, there are various ethical concerns to be raised with current femtech apps. In this article, we discuss three of the main ethical concerns with femtech apps regarding (1) medical reliability, (2) privacy, and (3) gender stereotyping and epistemic injustice, and we explore how Capability Sensitive Design, a novel design framework for health and well-being technologies, is able to mitigate these concerns and help create morally sensitive femtech.

Stakeholder preferences for attributes of digital health technologies to consider in health service funding.

OBJECTIVES: Health service providers are currently making decisions on the public funding of digital health technologies (DHTs) for managing chronic diseases with limited understanding of stakeholder preferences for DHT attributes. This study aims to understand the community, patient/carer, and health professionals' preferences to help inform a prioritized list of evaluation criteria. METHODS: An online best-worst scaling survey was conducted in Australia, New Zealand, Canada, and the United Kingdom to ascertain the relative importance of twenty-four DHT attributes among stakeholder groups using an efficient incomplete block design. The attributes were identified from a systematic review of DHT evaluation frameworks for consideration in a health technology assessment. Results were analyzed with multinomial models by stakeholder group and latent class. RESULTS: A total of 1,251 participants completed the survey (576 general community members, 543 patients/carers, and 132 health professionals). Twelve attributes achieved a preference score above 50 percent in the stakeholder group model, predominantly related to safety but also covering technical features, effectiveness, ethics, and economics. Results from the latent class model supported this prioritization. Overall, connectedness with the patient's healthcare team seemed the most important; with "Helps health professionals respond quickly when changes in patient care are needed" as the most highly prioritized of all attributes. CONCLUSIONS: It is proposed that these prioritized twelve attributes be considered in all evaluations of DHTs that manage chronic disease, supplemented with a limited number of attributes that reflect the specific perspective of funders, such as equity of access, cost, and system-level implementation considerations.