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Background Silent paroxysmal atrial fibrillation (AF) may be difficult to diagnose, and AF burden is hard to establish. In contrast to conventional diagnostic devices, photoplethysmography (PPG)–driven smartwatches or wristbands allow for long-term continuous heart rhythm assessment. However, most smartwatches lack an integrated PPG-AF algorithm. Adding a standalone PPG-AF algorithm to these wrist devices might open new possibilities for AF screening and burden assessment. Objective The aim of this study was to assess the accuracy of a well-known standalone PPG-AF detection algorithm added to a popular wristband and smartwatch, with regard to discriminating AF and sinus rhythm, in a group of patients with AF before and after cardioversion (CV). Methods Consecutive consenting patients with AF admitted for CV in a large academic hospital in Amsterdam, the Netherlands, were asked to wear a Biostrap wristband or Fitbit Ionic smartwatch with Fibricheck algorithm add-on surrounding the procedure. A set of 1-min PPG measurements and 12-lead reference electrocardiograms was obtained before and after CV. Rhythm assessment by the PPG device-software combination was compared with the 12-lead electrocardiogram. Results A total of 78 patients were included in the Biostrap-Fibricheck cohort (156 measurement sets) and 73 patients in the Fitbit-Fibricheck cohort (143 measurement sets). Of the measurement sets, 19/156 (12%) and 7/143 (5%), respectively, were not classifiable by the PPG algorithm due to bad quality. The diagnostic performance in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy was 98%, 96%, 96%, 99%, 97%, and 97%, 100%, 100%, 97%, and 99%, respectively, at an AF prevalence of ~50%. Conclusions This study demonstrates that the addition of a well-known standalone PPG-AF detection algorithm to a popular PPG smartwatch and wristband without integrated algorithm yields a high accuracy for the detection of AF, with an acceptable unclassifiable rate, in a semicontrolled environment.

UNSTRUCTURED The article addresses and discusses several issues and perspectives for women in medical leadership from the European perspective.

UNSTRUCTURED The Networking, Open Discussion, Engagement, and Self-Promotion (NODES) framework is a strategy that women in medicine can deploy at conferences to broaden their professional networks and engage with colleagues. The NODES Framework was designed and implemented for use at the Women in Medicine Summit, an annual conference that unites women to combat gender inequality in medicine. Intentional use of social media at conferences utilizing the Networking, Open Discussion, Engagement, and Self-Promotion (NODES) framework by women in medicine can increase the visibility of research projects and may lead to speaking engagements and awards.

BACKGROUND The management of chronic pelvic pain in women with endometriosis is complex and includes long-term use of opioids. Patients not fully responsive to drugs, or ineligible for surgical treatments need efficient alternatives to improve their quality of life and avoid long-term sequelae. OBJECTIVE This randomized controlled trial, software under test (Endocare) vs. sham, aimed to assess the effects of repeated at home administrations of a 20-minutes virtual reality (VR) solution on pain in women experiencing pelvic pain due to endometriosis. METHODS Patients were instructed to use VR headsets twice daily during at least 2 days and up to 5 days starting on their first day of painful periods. Pain perception was measured using a numerical scale (0 – 10) before and 60, 120, 180 minutes after each treatment administration. General pain, stress, fatigue, medication intake and quality of life were daily reported by patients. RESULTS 102 patients were included in the final analysis (Endocare: n=51/102, 50%; Sham: n= 51/102; 50%). The mean age was 32.88 years (SD 6.96) and the mean pain intensity before treatment was 6.53 (SD 1.74) and 6.22 (SD 1.69) for the Endocare group and the control group (P=.48). Pain intensity decreased in both groups from day 1 to day 5 as well as medication use. Maximum pain intensity reduction was 51.58% (SD 35.33) at D2.T120 and 27.37% (SD 27.23) at D3.T180 for the Endocare group and the control group respectively. The Endocare was significantly superior to the sham on day 1 (T120, P=.037; T180, P=.001), day 2 (T0, P=.022; T60, T120, T180, all P CONCLUSIONS This study confirmed the effectiveness and safety of self-repeated administrations of a virtual reality immersive treatment used at home while reducing overall pain medication intake in women diagnosed with endometriosis experiencing moderate-to-severe pelvic pain and supports. CLINICALTRIAL Registration: Clinicaltrials.gov NCT05172492, https://clinicaltrials.gov/ct2/show/NCT05172492

BACKGROUND Patient online access to medical records is assumed to facilitate patient empowerment and advance patient-centred healthcare. However, to date, the actual effect of online access on patient perceptions regarding these and other outcomes is insufficiently empirically tested. OBJECTIVE To investigate the effects that patients perceive from online access on patient empowerment, the patient-provider relationship, and informed decision-making. METHODS A nationwide two-wave longitudinal survey study among Dutch adults was conducted (N=2402). Linear regression analyses were conducted. In Model 1, perceived effect of online access (use measured at T1) on sixteen outcomes (measured at T2), associated with use of online access to general practice medical records in previous research, was investigated. Model 2 included socio-demographic factors and patient characteristics as confounders. RESULTS Users indicated more strongly than non-users that online access would increase their participation in healthcare, improve the relationship with their general practitioner, and support informed decision-making. These results were robust when adjusted for influence of confounders. Effect sizes were very small (B ranging between -0.39 and 0.28). Higher digital and health literacy were associated with higher ratings of almost all effects. CONCLUSIONS Online access has the potential to empower patients and foster patient informed decision-making. Effects in this study were small but might grow over time. Possibly other factors such as attitude of general practitioners towards online access moderate these effects. Results indicate that potential benefits of online access might be(come) unevenly distributed. We advise future exploration of the conditions under which online access can improve healthcare system functioning and efficiency without increasing health inequality. CLINICALTRIAL

UNSTRUCTURED Understanding the impact of caregiving responsibilities on women in medicine is crucial for ensuring a healthy and intact workforce, as caregiving responsibilities have the potential to affect the careers of women in healthcare along the entire pipeline, from students and trainees to physicians, physician-scientists, and biomedical researchers.

BACKGROUND The COVID-19 pandemic has brought to spotlight the critical role played by balanced and healthy diet in bolstering the human immune system. There is burgeoning interest in nutrition-related information on social media platforms like twitter. There is a critical need to assess and understand public opinion, attitudes and sentiments towards nutrition-related information shared on twitter. OBJECTIVE This study uses text mining to analyze nutrition-related messages on Twitter to identify and analyze how the general public perceives various food groups and diets for improving immunity to the SARS-CoV2 virus. METHODS We gathered 71,178 nutrition-related tweets that were posted between January, 01 2020 and September 30, 2020. Correlated Explanation (CorEx) text mining algorithm was used to identify frequently-discussed topics that users mentioned as contributing to immunity building against coronavirus. We assessed the relative importance of these topics and performed a sentiment analysis. We also qualitatively examined the tweets to gain a closer understanding of nutrition-related topics and food groups. RESULTS Topic modelling yielded ten topics that users discussed frequently on twitter viz. proteins, whole grains, fruits, vegetables, dairy-related, spices and herbs, fluids, supplements, avoidable foods and specialty diets. Supplements was the most-frequently discussed topic (33.6%) with a higher proportion (87.75%) exhibiting a positive sentiment with a score of 0.41. Consuming fluids (24.85%) and fruits (20.80%) were the second and third most frequent topics with favorable, positive sentiments. Spices and herbs (12.25%) and avoidable foods (12.11%) were also frequently discussed. Negative sentiments were observed for a higher proportion of avoidable foods (84.31%) with a sentiment score of -0.39. CONCLUSIONS This study identified ten important food groups and associated sentiments that users discussed as a means to improving immunity. Our findings can help dieticians and nutritionists to frame appropriate interventions and diet programs. CLINICALTRIAL

Background The Collaboration for Research, Implementation, and Training in Critical Care in Asia (CCA) is implementing a critical care registry to capture real-time data to facilitate service evaluation, quality improvement, and clinical studies. Objective The purpose of this study is to examine stakeholder perspectives on the determinants of implementation of the registry by examining the processes of diffusion, dissemination, and sustainability. Methods This study is a qualitative phenomenological inquiry using semistructured interviews with stakeholders involved in registry design, implementation, and use in 4 South Asian countries. The conceptual model of diffusion, dissemination, and sustainability of innovations in health service delivery guided interviews and analysis. Interviews were coded using the Rapid Identification of Themes from Audio recordings procedure and were analyzed based on the constant comparison approach. Results A total of 32 stakeholders were interviewed. Analysis of stakeholder accounts identified 3 key themes: innovation-system fit; influence of champions; and access to resources and expertise. Determinants of implementation included data sharing, research experience, system resilience, communication and networks, and relative advantage and adaptability. Conclusions The implementation of the registry has been possible due to efforts to increase the innovation-system fit, influence of motivated champions, and the support offered by access to resources and expertise. The reliance on individuals and the priorities of other health care actors pose a risk to sustainability.

Background The full potential of eHealth technologies to support self-management and disease management for patients with chronic diseases is not being reached. A possible explanation for these lacking results is that during the development process, insufficient attention is paid to the needs, wishes, and context of the prospective end users. To overcome such issues, the user-centered design practice of creating personas is widely accepted to ensure the fit between a technology and the target group or end users throughout all phases of development. Objective In this study, we integrate several approaches to persona development into the Persona Approach Twente to attain a more holistic and structured approach that aligns with the iterative process of eHealth development. Methods In 3 steps, a secondary analysis was carried out on different parts of the data set using the Partitioning Around Medoids clustering method. First, we used health-related electronic patient record data only. Second, we added person-related data that were gathered through interviews and questionnaires. Third, we added log data. Results In the first step, 2 clusters were found, with average silhouette widths of 0.12 and 0.27. In the second step, again 2 clusters were found, with average silhouette widths of 0.08 and 0.12. In the third step, 3 clusters were identified, with average silhouette widths of 0.09, 0.12, and 0.04. Conclusions The Persona Approach Twente is applicable for mixed types of data and allows alignment of this user-centered design method to the iterative approach of eHealth development. A variety of characteristics can be used that stretches beyond (standardized) medical and demographic measurements. Challenges lie in data quality and fitness for (quantitative) clustering.

Background Scientific researchers who wish to reuse health data pertaining to individuals can obtain consent through an opt-in procedure or opt-out procedure. The choice of procedure may have consequences for the consent rate and representativeness of the study sample and the quality of the research, but these consequences are not well known. Objective This review aimed to provide insight into the consequences for the consent rate and consent bias of the study sample of opt-in procedures versus opt-out procedures for the reuse of routinely recorded health data for scientific research purposes. Methods A systematic review was performed based on searches in PubMed, Embase, CINAHL, PsycINFO, Web of Science Core Collection, and the Cochrane Library. Two reviewers independently included studies based on predefined eligibility criteria and assessed whether the statistical methods used in the reviewed literature were appropriate for describing the differences between consenters and nonconsenters. Statistical pooling was conducted, and a description of the results was provided. Results A total of 15 studies were included in this meta-analysis. Of the 15 studies, 13 (87%) implemented an opt-in procedure, 1 (7%) implemented an opt-out procedure, and 1 (7%) implemented both the procedures. The average weighted consent rate was 84% (60,800/72,418 among the studies that used an opt-in procedure and 96.8% (2384/2463) in the single study that used an opt-out procedure. In the single study that described both procedures, the consent rate was 21% in the opt-in group and 95.6% in the opt-out group. Opt-in procedures resulted in more consent bias compared with opt-out procedures. In studies with an opt-in procedure, consenting individuals were more likely to be males, had a higher level of education, higher income, and higher socioeconomic status. Conclusions Consent rates are generally lower when using an opt-in procedure compared with using an opt-out procedure. Furthermore, in studies with an opt-in procedure, participants are less representative of the study population. However, both the study populations and the way in which opt-in or opt-out procedures were organized varied widely between the studies, which makes it difficult to draw general conclusions regarding the desired balance between patient control over data and learning from health data. The reuse of routinely recorded health data for scientific research purposes may be hampered by administrative burdens and the risk of bias.

BACKGROUND Digital technologies may improve sexual and reproductive health (SRH) across diverse settings. Chatbots are computer programs designed to simulate human conversation and there is a growing interest in the potential for chatbots to provide responsive and accurate information, counselling, linkages to products and services, or a companion on a SRH journey. We conducted a realist review to identify assumptions about the value of chatbots for SRH and collate the evidence to support them. OBJECTIVE This review aimed to identify assumptions about the value of chatbots for SRH and collate the evidence to support them. METHODS We used a realist approach that starts with an initial program theory and generates causal explanations in the form of context-mechanism-outcome configurations (CMOCs) to test and develop that theory. We generated our program theory, drawing on the expertise within the research team, and then searched the literature to add depth and develop this theory with evidence. RESULTS The evidence supports our program theory, which suggests that chatbots are a promising intervention for SRH information and service delivery. This is because chatbots offer: anonymous and non-judgmental interactions that encourages disclosure of personal information, complex information in a responsive and conversational tone that increases understanding, links to SRH conversations within online and offline social networks, immediate support or service provision 24/7 by automating some tasks, and the potential to develop longer term relationships with users who return over time. However, chatbots may be less valuable where people find any conversation about SRH (even with a chatbot) stigmatizing, for those who lack confidential access to digital devices, where conversations do not feel natural and where chatbots are developed as standalone interventions without reference to service contexts. CONCLUSIONS Chatbots in SRH could be developed further to automate simple tasks and support service delivery. They should prioritize achieving an authentic conversational tone, could be developed to facilitate content sharing in social networks, should support longer term relationship building with their users and should be integrated into wider service networks CLINICALTRIAL

BACKGROUND Digital health interventions to manage chronic conditions have large potential. However, the benefits and harms are still questioned. OBJECTIVE This systematic review and meta-analysis investigated the benefits and harms of digital health interventions in promoting physical activity in people with chronic conditions. METHODS We searched MEDLINE, Embase, CINAHL, and CENTRAL from the inception to Oct. 2022. Eligible randomized controlled trials (RCTs) were included if they used a digital component in physical activity promotion in adults with one or more conditions: depression or anxiety, ischemic heart disease or heart failure, chronic obstructive pulmonary disease, knee or hip osteoarthritis, hypertension, or type 2 diabetes. The primary outcomes were objectively measured physical activity and physical function (e.g., walk or step tests). We used a random-effects model (restricted maximum likelihood) for the meta-analyses and meta-regression analyses to assess the impact of study-level covariates. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool, and the certainty of the evidence was assessed using GRADE. RESULTS Of 14 078 hits, 130 RCTs were included. Compared to usual care/minimal intervention, digital health interventions increased objectively measured physical activity (end-of-intervention: SMD 0.29, 95% confidence interval (CI) 0.21 to 0.37; follow-up: SMD 0.17, 95% CI 0.04 to 0.31), and physical function (end-of-intervention: SMD 0.36, 95% CI 0.12 to 0.59; follow-up: SMD 0.29; 95% CI 0.01 to 0.57). Secondary outcomes also favored the digital health interventions for subjectively measured physical activity and physical function, depression, anxiety, and health-related quality of life (HRQOL) at end-of-intervention, but only subjectively measured physical activity at follow-up. The risk of non-serious adverse events, but not serious, was higher in the digital health interventions in the end-of-intervention, but no difference was seen at follow-up. CONCLUSIONS Digital health interventions improved physical activity and physical function across various chronic conditions, while only end-of-intervention effects on depression, anxiety, and HRQOL were observed. The risk of non-serious adverse events is present during the intervention and should be addressed. Future studies should focus on better reporting, comparing different digital solutions' effects, and investigate how interventions effects are sustained beyond end-of-intervention. CLINICALTRIAL Prospero CRD42020189028

UNSTRUCTURED Digital mental health services are becoming increasingly valuable for addressing the global public health burden of mental ill-health. There is significant demand for scalable and effective online mental health services. Artificial intelligence (AI) has the potential to improve mental health through the deployment of chatbots. These chatbots can provide round-the-clock support and triage individuals who are reluctant to access traditional healthcare due to stigma. The aim of this viewpoint paper is to consider the feasibility of AI-powered platforms to support mental wellbeing. The Leora model is considered as a model with potential to provide mental health support. Leora is a conversational agent that uses AI to engage in conversations with users about their mental health and provide support for minimal-to-mild symptoms of anxiety and depression. The tool is designed to be accessible, personalised, and discreet, offering strategies for promoting wellbeing and acting as a virtual self-care coach. Across all AI-powered mental health services there are several challenges in the ethical development and deployment of AI in mental health treatment, including trust and transparency, bias and health inequity, and the potential for negative consequences. To ensure the effective and ethical use of AI in mental health care, researchers must carefully consider these challenges and engage with key stakeholders to provide high-quality mental health support. Validation of the Leora platform through rigorous user testing will be the next step in ensuring the model is effective.

Background Knee pain is highly prevalent worldwide, and this number is expected to rise in the future. The COVID-19 outbreak, in combination with the aging population, rising health care costs, and the need to make health care more accessible worldwide, has led to an increasing demand for digital health care applications to deliver care for patients with musculoskeletal conditions. Digital health and other forms of telemedicine can add value in optimizing health care for patients and health care providers. This might reduce health care costs and make health care more accessible while maintaining a high level of quality. Although expectations are high, there is currently no overview comparing digital health applications with face-to-face contact in clinical trials to establish a primary knee diagnosis in orthopedic surgery. Objective This study aimed to investigate the currently available digital health and telemedicine applications to establish a primary knee diagnosis in orthopedic surgery in the general population in comparison with imaging or face-to-face contact between patients and physicians. Methods A scoping review was conducted using the PubMed and Embase databases according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement. The inclusion criteria were studies reporting methods to determine a primary knee diagnosis in orthopedic surgery using digital health or telemedicine. On April 28 and 29, 2021, searches were conducted in PubMed (MEDLINE) and Embase. Data charting was conducted using a predefined form and included details on general study information, study population, type of application, comparator, analyses, and key findings. A risk-of-bias analysis was not deemed relevant considering the scoping review design of the study. Results After screening 5639 articles, 7 (0.12%) were included. In total, 2 categories to determine a primary diagnosis were identified: screening studies (4/7, 57%) and decision support studies (3/7, 43%). There was great heterogeneity in the included studies in algorithms used, disorders, input parameters, and outcome measurements. No more than 25 knee disorders were included in the studies. The included studies showed a relatively high sensitivity (67%-91%). The accuracy of the different studies was generally lower, with a specificity of 27% to 48% for decision support studies and 73% to 96% for screening studies. Conclusions This scoping review shows that there are a limited number of available applications to establish a remote diagnosis of knee disorders in orthopedic surgery. To date, there is limited evidence that digital health applications can assist patients or orthopedic surgeons in establishing the primary diagnosis of knee disorders. Future research should aim to integrate multiple sources of information and a standardized study design with close collaboration among clinicians, data scientists, data managers, lawyers, and service users to create reliable and secure databases.

Background Internet-based cognitive behavioral therapy (iCBT) services for common mental health disorders have been found to be effective. There is a need for strategies that improve implementation in routine practice. One-size-fits-all strategies are likely to be ineffective. Tailored implementation is considered as a promising approach. The self-guided integrated theory-based Framework for intervention tailoring strategies toolkit (ItFits-toolkit) supports local implementers in developing tailored implementation strategies. Tailoring involves identifying local barriers; matching selected barriers to implementation strategies; developing an actionable work plan; and applying, monitoring, and adapting where necessary. Objective This study aimed to compare the effectiveness of the ItFits-toolkit with implementation-as-usual (IAU) in implementing iCBT services in 12 routine mental health care organizations in 9 countries in Europe and Australia. Methods A stepped-wedge cluster randomized trial design with repeated measures was applied. The trial period lasted 30 months. The primary outcome was the normalization of iCBT delivery by service providers (therapists, referrers, IT developers, and administrators), which was measured with the Normalization Measure Development as a proxy for implementation success. A 3-level linear mixed-effects modeling was applied to estimate the effects. iCBT service uptake (referral and treatment completion rates) and implementation effort (hours) were used as secondary outcomes. The perceived satisfaction (Client Satisfaction Questionnaire), usability (System Usability Scale), and impact of the ItFits-toolkit by implementers were used to assess the acceptability of the ItFits-toolkit. Results In total, 456 mental health service providers were included in this study. Compared with IAU, the ItFits-toolkit had a small positive statistically significant effect on normalization levels in service providers (mean 0.09, SD 0.04; P=.02; Cohen d=0.12). The uptake of iCBT by patients was similar to that of IAU. Implementers did not spend more time on implementation work when using the ItFits-toolkit and generally regarded the ItFits-toolkit as usable and were satisfied with it. Conclusions The ItFits-toolkit performed better than the usual implementation activities in implementing iCBT services in routine practice. There is practical utility in the ItFits-toolkit for supporting implementers in developing and applying effective tailored implementation strategies. However, the effect on normalization levels among mental health service providers was small. These findings warrant modesty regarding the effectiveness of self-guided tailored implementation of iCBT services in routine practice. Trial Registration ClinicalTrials.gov NCT03652883; https://clinicaltrials.gov/ct2/show/NCT03652883 International Registered Report Identifier (IRRID) RR2-10.1186/s13063-020-04686-4

UNSTRUCTURED This is a test MS 1 Feb 2023

BACKGROUND Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging Machine Learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. OBJECTIVE This study aims to summarize a comprehensive view of research on voice-affecting disorders that employs machine learning techniques for diagnosis and monitoring through voice samples where Systematic conditions, Non-Laryngeal aerodigestive disorders, and Neurological disorders are specifically of interest. METHODS This Systematic Literature Review (SLR) investigates the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and two authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only one article was included from the same author group. RESULTS In the analysis of the 145 included studies, Support Vector Machines were the most utilized ML technique (35%), and 60% of the studies investigated Parkinson’s disease (most studied disease). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the three investigated previously. Additionally, an upsurge in use of artificial neural network based architectures were observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, 75, nearly half of the studies relied on ten distinct datasets, and 7,5% used demographic data as input for ML models. CONCLUSIONS This SLR reviled considerable interest across multiple countries in employing ML techniques for diagnosing and monitoring voice-affecting disorders, with Parkinson’s Disease being the most studied disorder. However, the review identified several gaps, including limited and unbalanced dataset usage in studies, and focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights in to the current state of research on ML- based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research. CLINICALTRIAL

BACKGROUND Use of software to monitor Patient-Reported Outcome Measures (PROMs) can improve outcomes for cancer patients receiving anticancer therapy; however, evidence from applications used in routine clinical practice is lacking. OBJECTIVE We aimed to investigate adherence to, and patient perceptions of a weekly, web-based PROM symptom monitoring in routine clinical practice for patients with Multiple Myeloma. Moreover, we aimed to capture how clinical alerts prompted by the system influenced clinical care. METHODS We conducted a single-center longitudinal observational study to evaluate patient adherence to and perceptions of the PROM monitoring software in routine practice. Patients with Multiple Myeloma remotely completed weekly treatment-specific PROMs to monitor key symptoms via a dedicated web-based platform. Alarming symptoms triggered clinical alerts in the application for the treatment team who could initiate clinical interventions. The primary outcomes were the online assessment completion rate and patients’ perceptions of the monitoring program assessed by an evaluation questionnaire. Moreover, clinical alerts prompted by the system and consequential clinical interventions were analyzed. RESULTS Between July 2021 and June 2022, 55 patients were approached for participation; 39 patients participated (24 male [61%], mean age: 63.2 years, SD 9.2). The median assessment completion rate out of all weekly scheduled assessments was 70.3% (IQR 41.2-89.6). Most patients (77%) felt that the healthcare team was better informed on their health status due to the online assessments. Clinical alerts were triggered for 1758/14639 (12.0%) of reported symptoms. For 548/1758 (31.2%) of alerts, the symptom had been registered before and no further action was required; for 348/1758 (19.9%) of alerts, telephone consultation and self-management advice sufficed. Higher-level interventions were seldom needed in response to alerts: referral to a doctor or specialist (88/1758 of alerts, 5.0%), medication changes (22/1758, 1.3%), scheduling additional diagnostics (9/1758, 0.5%), or unplanned emergency visits (7/1758, 0.4%). Most patients (55%) reported the calls in response to alerts gave them “quite a bit” or “very much” of an added feeling of security during therapy. CONCLUSIONS Our study shows that high adherence to regular and tailored PROM monitoring can be achieved in routine clinical care. The findings provide valuable insight into how the PROM monitoring program and the clinical alerts and resulting interventions shaped clinical practice. CLINICALTRIAL NCT05036863 (https://clinicaltrials.gov/)

BACKGROUND Cardiovascular disease accounts for 17.9 million deaths globally each year. Many research study datasets have been collected to answer questions about the relationship between cardiometabolic health and accelerometer-measured physical activity. A further benefit of these datasets is that they can be used to answer additional health research questions beyond the original purpose of the data collected. This scoping review aimed to map the available datasets which have collected accelerometer measured physical activity and markers of cardiometabolic health. This data was then used to inform the development of a publicly available resource, named the Global Physical Activity Dataset catalogue (GPAD). This resource provides an online catalogue of datasets that have assessed cardiometabolic health markers and accelerometer-measured physical activity. The ambition of such a catalogue is to enable easier identification of potentially harmonisable datasets, to answer important questions in this area with greater statistical power and generalisability. OBJECTIVE This review aims to systematically identify datasets that have measured physical activity using accelerometers and cardiometabolic health markers using either an observational or interventional study design. METHODS Databases, trial registries and grey literature (inception until 02/2021-updated search from 02/2021 through 09/2022) were systematically searched to identify studies that have analysed datasets of physical activity and cardiometabolic health outcomes. To be eligible for inclusion, datasets must have measured physical activity using an accelerometric device in adults aged ≥18 years, a sample size greater than 400 participants (unless recruited participants in a lower middle income country where a sample size threshold was reduced to 100), utilised an observational, longitudinal or trial based study design and collected at least one cardiometabolic health marker (unless only body mass was measured). Two reviewers screened the search results to identify eligible studies and from these, the unique names of each dataset were recorded and characteristics about each dataset were extracted from several sources. RESULTS A total of 16,720 study reports were identified and after screening, 319 were eligible, with 122 unique datasets in these study reports meeting the review inclusion criteria. Datasets were found in 49 countries across five continents, with most developed in Europe (n=53) and the least in Africa and Oceania (n=4 and n=3 respectively). The most common accelerometric brand and device wear location was Actigraph and the waist respectively. Height and body mass were the most frequently measured cardiometabolic health markers in datasets (119/122 datasets), followed by blood pressure (82/122 datasets). The number of participants in the included datasets ranged from 103,712 to 120. Once the review processes had been completed the GPAD catalogue was developed to house all the identified datasets. CONCLUSIONS This review identified, and mapped the contents of, datasets from around the world that have collected potentially harmonisable accelerometer-measured physical activity and cardiometabolic health markers. The GPAD catalogue is an online open-source resource developed from the results of this review which aims to facilitate the harmonisation of datasets to produce evidence that will reduce the burden of disease from physical inactivity. CLINICALTRIAL

Background Assisted reproductive technologies (ARTs) are considered to be physically and mentally stressful. During their treatment trajectory, couples express high information and communication needs. They appreciate using the internet to obtain fertility-related information. In a previous study, we developed myFertiCare, an eHealth tool providing personalized information and interactive functionalities for infertile couples in order to improve patient-centered care. The app has already been successful in qualitative evaluations of usability. Objective The aim of the current study is to quantitatively evaluate the implementation of myFertiCare by using the human, organizational, and technology–fit (HOT-fit) framework and to study the effects of using myFertiCare on couples’ knowledge about infertility, their experience of the burden of infertility, and their experience of patient-centered care. With these results, implementation can be further improved, and patient-centered care can be enhanced. Methods A quantitative study was performed based on the HOT-fit framework using validated questionnaires focusing on the human, organizational, and technology domains. Questions were added on the effect of using myFertiCare on couples’ knowledge about infertility and treatment. Questions regarding the burden of infertility, the burden of infertility treatment, and the experience of patient-centeredness were based on the main items of the validated fertility quality of life (FertiQoL) and Patient-Centredness Questionnaire–Infertility questionnaires, respectively. Also, nonusers of the app were included to explore motivations for not using the app and identify opportunities for improvement. Finally, user data were analyzed to provide insight into multiple variables concerning app use. Results In the human and technology domains, myFertiCare showed good system usability, high user satisfaction, and high information and interface quality. In the organizational domain, implementation was considered to be sufficient by both patients and staff. Use of the app increased knowledge about the treatment, improved coping with the treatment, and enhanced the experience of patient-centeredness. User data showed that women were the main app users and that use of the app gradually declined during the treatment trajectory. Conclusions A multi-faceted online app, myFertiCare, has been successfully evaluated quantitatively for implementation by using the HOT-fit framework. Use of the app increased knowledge about the treatment, improved coping with the treatment, and enhanced the experience of patient-centeredness. App use could be improved by creating more publicity. By providing myFertiCare, professionals in fertility care are supported in guiding patients through their treatment trajectory and in delivering patient-centered care.

Background Most research on web-based help seeking for mental health problems has focused on the antecedents of this behavior. Therefore, little is known about the outcomes of web-based help seeking in general or in specific mental health issues. Objective This study was a systematic review and meta-analysis of the literature on the antecedents and consequences of web-based help-seeking behaviors for depressive symptoms. Methods A systematic literature search was carried out in 6 scientific databases, leading to 48 studies (for a total of 314,921 participants) included in the qualitative synthesis and 19 included in the meta-analysis. Results The results indicated a positive relationship between depressive symptoms and web-based help-seeking behaviors through online support groups (r=0.089; P=.009), and Generation Z (r=0.102; P=.008) tended to participate in support groups more than previous generations. In addition, web-based help seeking was positively related to empowerment (r=0.245; P=.004). Other forms of support reported included the internet and specific self-help tools, but no significant relationships were found with depressive symptoms. Conclusions More studies examining the outcomes are needed, together with a more rigorous assessment of web-based help-seeking behaviors. Ultimately, we propose a summary framework for the literature on this topic, including the antecedents, patterns of use, and outcomes of web-based help seeking in the context of depressive symptoms.

Background Lifestyle behaviors during the periconception period contribute to achievement of a successful pregnancy. Assessment of attitudes and practices toward these modifiable behaviors can aid in identifying gaps in unhealthy lifestyle behaviors with impact on intervention effectiveness. Objective This study investigates the effectiveness of coaching by the eHealth program Smarter Pregnancy during the periconception period on improvement of attitudes and practices toward fruit and vegetable intake and smoking in women attempting pregnancy through assisted reproductive technology (ART) or natural conception. Methods Women attempting pregnancy through ART (n=1060) or natural conception (n=631) were selected during the periconception period. The intervention groups, conceived through ART or naturally, received Smarter Pregnancy coaching for 24 weeks, whereas the control group conceived through ART and did not receive coaching. Attitudes and practices at baseline and follow-up periods were obtained from self-administered online questionnaire provided by the program. Attitudes were assessed in women with unhealthy behaviors as their intention to increase their fruit and vegetable intake and to quit smoking using a yes/no question. Outcomes on practices, suggesting effectiveness, included daily fruit (pieces) and vegetable (grams) intake, and if women smoked (yes/no). Changes in attitudes and practices were compared at 12 and 24 weeks with baseline between the ART intervention and ART control groups, and within the intervention groups between ART and natural conception. Changes in practices at 12 and 24 weeks were also compared with baseline between women with negative attitude and positive attitude within the intervention groups: ART and natural conception. Analysis was performed using linear and logistic regression models adjusted for maternal confounders and baseline attitudes and practices. Results The ART intervention group showed higher vegetable intake and lower odds for negative attitudes toward vegetable intake after 12 weeks (βadj=25.72 g, P Conclusions The eHealth Smarter Pregnancy program may improve vegetable intake–related attitudes and practices in women undergoing ART treatment. Women with no intention to increase fruit and vegetable intake had less improvement in their intakes. Despite small changes, this study demonstrates again that Smarter Pregnancy can be used to improve vegetable intake, which can complemented by blended care that combines face-to-face and online care to also improve fruit intake and smoking behavior.