Cancer misinformation on social media.

Social media is widely used globally by patients, families of patients, health professionals, scientists, and other stakeholders who seek and share information related to cancer. Despite many benefits of social media for cancer care and research, there is also a substantial risk of exposure to misinformation, or inaccurate information about cancer. Types of misinformation vary from inaccurate information about cancer risk factors or unproven treatment options to conspiracy theories and public relations articles or advertisements appearing as reliable medical content. Many characteristics of social media networks-such as their extensive use and the relative ease it allows to share information quickly-facilitate the spread of misinformation. Research shows that inaccurate and misleading health-related posts on social media often get more views and engagement (e.g., likes, shares) from users compared with accurate information. Exposure to misinformation can have downstream implications for health-related attitudes and behaviors. However, combatting misinformation is a complex process that requires engagement from media platforms, scientific and health experts, governmental organizations, and the general public. Cancer experts, for example, should actively combat misinformation in real time and should disseminate evidence-based content on social media. Health professionals should give information prescriptions to patients and families and support health literacy. Patients and families should vet the quality of cancer information before acting upon it (e.g., by using publicly available checklists) and seek recommended resources from health care providers and trusted organizations. Future multidisciplinary research is needed to identify optimal ways of building resilience and combating misinformation across social media.

HCI-modelling for improving the clinical usability of digital health technologies.

INTRODUCTION: Digital Health Technologies (DHTs) have been shown to have variable usability as measured by efficiency, effectiveness and user satisfaction despite large-scale government projects to regulate and standardise user interface (UI) design. We hypothesised that Human-Computer Interaction (HCI) modelling could improve the methodology for DHT design and regulation, and support the creation of future evidence-based UI standards and guidelines for DHTs. METHODOLOGY: Using a Design Science Research (DSR) framework, we developed novel UI components that adhered to existing standards and guidelines (combining the NHS Common User Interface (CUI) standard and the NHS Design System). We firstly evaluated the Patient Banner UI component for compliance with the two guidelines and then used HCI-modelling to evaluate the "Add New Patient" workflow to measure time to task completion and cognitive load. RESULTS: Combining the two guidelines to produce new UI elements is technically feasible for the Patient Banner and the Patient Name Input components. There are some inconsistencies between the NHS Design System and the NHS CUI when implementing the Patient Banner. HCI-modelling successfully quantified challenges adhering to the NHS CUI and the NHS Design system for the "Add New Patient" workflow. DISCUSSION: We successfully developed new design artefacts combing two major design guidelines for DHTs. By quantifying usability issues using HCI-modelling, we have demonstrated the feasibility of a methodology that combines HCI-modelling into a human-centred design (HCD) process could enable the development of standardised UI elements for DHTs that is more scientifically robust than HCD alone. CONCLUSION: Combining HCI-modelling and Human-Centred Design could improve scientific progress towards developing safer and more user-friendly DHTs.

Artificial intelligence-enhanced patient evaluation: bridging art and science.

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

MetaTron: advancing biomedical annotation empowering relation annotation and collaboration.

BACKGROUND: The constant growth of biomedical data is accompanied by the need for new methodologies to effectively and efficiently extract machine-readable knowledge for training and testing purposes. A crucial aspect in this regard is creating large, often manually or semi-manually, annotated corpora vital for developing effective and efficient methods for tasks like relation extraction, topic recognition, and entity linking. However, manual annotation is expensive and time-consuming especially if not assisted by interactive, intuitive, and collaborative computer-aided tools. To support healthcare experts in the annotation process and foster annotated corpora creation, we present MetaTron. MetaTron is an open-source and free-to-use web-based annotation tool to annotate biomedical data interactively and collaboratively; it supports both mention-level and document-level annotations also integrating automatic built-in predictions. Moreover, MetaTron enables relation annotation with the support of ontologies, functionalities often overlooked by off-the-shelf annotation tools. RESULTS: We conducted a qualitative analysis to compare MetaTron with a set of manual annotation tools including TeamTat, INCEpTION, LightTag, MedTAG, and brat, on three sets of criteria: technical, data, and functional. A quantitative evaluation allowed us to assess MetaTron performances in terms of time and number of clicks to annotate a set of documents. The results indicated that MetaTron fulfills almost all the selected criteria and achieves the best performances. CONCLUSIONS: MetaTron stands out as one of the few annotation tools targeting the biomedical domain supporting the annotation of relations, and fully customizable with documents in several formats-PDF included, as well as abstracts retrieved from PubMed, Semantic Scholar, and OpenAIRE. To meet any user need, we released MetaTron both as an online instance and as a Docker image locally deployable.

Patient-reported symptom monitoring and adherence to therapy in patients with newly diagnosed chronic myeloid leukemia.

BACKGROUND: The authors assessed the clinical utility of patient-reported symptom monitoring in the setting of newly diagnosed chronic myeloid leukemia (CML). The primary objective was to evaluate adherence to therapy. METHODS: The authors conducted an international prospective study that included patients with newly diagnosed, chronic-phase CML. Before clinical consultation, patients were provided a tablet computer to self-rate their symptoms, and the results were available in real time to each physician during the patient's visit. Adherence was assessed by pill count and with a validated self-reported questionnaire. The proportions of optimal responders at 3 and 6 months were assessed according to the European LeukemiaNet criteria. RESULTS: Between July 2020 and August 2021, 94 patients with a median age of 57 years were enrolled. Pill count adherence analysis indicated that 86 of 93 evaluable patients (92.5%) took at least 90% of prescribed tyrosine kinase inhibitor therapy during the 6-month observation period. The online platform was well accepted by patients and physicians. An optimal response was achieved by 69 of 79 patients (87.3%) at 3 months and by 61 of 81 patients (75.3%) at 6 months. CONCLUSIONS: Patient-reported symptom monitoring from the beginning of therapy in patients with CML may be critical to improve adherence to therapy and early molecular response rates (ClinicalTrials.gov identifier NCT04384848).

The IBD Clinic of Tomorrow: Holistic, Patient-Centric, and Value-based Care.

There is increasing recognition of the associated bi-directional impact of inflammatory bowel disease (IBD) on patient well-being and the potential benefit of multidisciplinary teams to address these unique needs. At certain IBD centers, there has been an evolution towards patient-centric, holistic care to enhance well-being and improve health-related outcomes. Multiple models, incorporating various disciplines, care modalities, digital tools and care delivery, and resource support have arisen in IBD. Although most IBD centers of excellence are now incorporating such multidisciplinary care models, many practices still practice IBD-limited specialty care, limiting evaluations and interventions to the IBD itself and its direct consequences (eg, extraintestinal manifestations). In this piece, we seek to review the evolution of IBD care towards a patient-centric, holistic model (termed 360 IBD Care) including the role and impact of digital health tools, monitoring, and delivery in IBD, and a shift towards value-based care models with discussion of payor priorities in IBD. We also suggest potential opportunities for IBD practitioners to incorporate elements of holistic care on a local scale. Together, we hope such care models will enhance not only IBD-specific health outcomes, but also improve the general well-being of our patients with IBD today and tomorrow.

Acceptability of data linkage to identify women at risk of postnatal complication for the development of digital risk prediction tools and interventions to better optimise postnatal care, a qualitative descriptive study design.

BACKGROUND: Pregnancy acts as a cardiovascular stress test. Although many complications resolve following birth, women with hypertensive disorder of pregnancy have an increased risk of developing cardiovascular disease (CVD) long-term. Monitoring postnatal health can reduce this risk but requires better methods to identity high-risk women for timely interventions. METHODS: Employing a qualitative descriptive study design, focus groups and/or interviews were conducted, separately engaging public contributors and clinical professionals. Diverse participants were recruited through social media convenience sampling. Semi-structured, facilitator-led discussions explored perspectives of current postnatal assessment and attitudes towards linking patient electronic healthcare data to develop digital tools for identifying postpartum women at risk of CVD. Participant perspectives were gathered using post-it notes or a facilitator scribe and analysed thematically. RESULTS: From 27 public and seven clinical contributors, five themes regarding postnatal check expectations versus reality were developed, including 'limited resources', 'low maternal health priority', 'lack of knowledge', 'ineffective systems' and 'new mum syndrome'. Despite some concerns, all supported data linkage to identify women postnatally, targeting intervention to those at greater risk of CVD. Participants outlined potential benefits of digitalisation and risk prediction, highlighting design and communication needs for diverse communities. CONCLUSIONS: Current health system constraints in England contribute to suboptimal postnatal care. Integrating data linkage and improving education on data and digital tools for maternal healthcare shows promise for enhanced monitoring and improved future health. Recognised for streamlining processes and risk prediction, digital tools may enable more person-centred care plans, addressing the gaps in current postnatal care practice.

Impact of mHealth interventions on maternal, newborn, and child health from conception to 24 months postpartum in low- and middle-income countries: a systematic review.

BACKGROUND: Mobile health (mHealth) technologies have been harnessed in low- and middle-income countries (LMICs) to address the intricate challenges confronting maternal, newborn, and child health (MNCH). This review aspires to scrutinize the effectiveness of mHealth interventions on MNCH outcomes during the pivotal first 1000 days of life, encompassing the period from conception through pregnancy, childbirth, and post-delivery, up to the age of 2 years. METHODS: A comprehensive search was systematically conducted in May 2022 across databases, including PubMed, Cochrane Library, Embase, Cumulative Index to Nursing & Allied Health (CINAHL), Web of Science, Scopus, PsycINFO, and Trip Pro, to unearth peer-reviewed articles published between 2000 and 2022. The inclusion criteria consisted of (i) mHealth interventions directed at MNCH; (ii) study designs, including randomized controlled trials (RCTs), RCT variations, quasi-experimental designs, controlled before-and-after studies, or interrupted time series studies); (iii) reports of outcomes pertinent to the first 1000 days concept; and (iv) inclusion of participants from LMICs. Each study was screened for quality in alignment with the Cochrane Handbook for Systematic Reviews of Interventions and the Joanne Briggs Institute Critical Appraisal tools. The included articles were then analyzed and categorized into 12 mHealth functions and outcome domain categories (antenatal, delivery, and postnatal care), followed by forest plot comparisons of effect measures. RESULTS: From the initial pool of 7119 articles, we included 131 in this review, comprising 56 RCTs, 38 cluster-RCTs, and 37 quasi-experimental studies. Notably, 62% of these articles exhibited a moderate or high risk of bias. Promisingly, mHealth strategies, such as dispatching text message reminders to women and equipping healthcare providers with digital planning and scheduling tools, exhibited the capacity to augment antenatal clinic attendance and enhance the punctuality of child immunization. However, findings regarding facility-based delivery, child immunization attendance, and infant feeding practices were inconclusive. CONCLUSIONS: This review suggests that mHealth interventions can improve antenatal care attendance and child immunization timeliness in LMICs. However, their impact on facility-based delivery and infant feeding practices varies. Nevertheless, the potential of mHealth to enhance MNCH services in resource-limited settings is promising. More context-specific implementation studies with rigorous evaluations are essential.

Genetics providers' perspectives on the use of digital tools in clinical practice.

PURPOSE: Digital tools are increasingly incorporated into genetics practice to address challenges with the current model of care. Yet, genetics providers' perspectives on digital tool use are not well characterized. METHODS: Genetics providers across Canada were recruited. Semistructured interviews were conducted to ascertain their perspectives on digital tool use and the clinical practice factors that might inform digital tool integration. A qualitative interpretive description approach was used for analysis. RESULTS: Thirty-three genetics providers across 5 provinces were interviewed. Participants had favorable attitudes toward digital tool use. They were open to using digital tools in the pretest phase of the genetic testing pathway and for some posttest tasks or in a hybrid model of care. Participants expressed that digital tools could enhance efficiency and allow providers to spend more time practicing at the top of scope. Providers also described the need for careful consideration of the potential impact of digitalization on the clinician-patient dynamic, access to and equity of care, and unintended digital burden on providers. CONCLUSION: Genetics providers considered digital tools to represent a viable solution for improving access, efficiency, and quality of care in genetics practice. Successful use of digital tools in practice will require careful consideration of their potential unintended impacts.

Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine.

Artificial intelligence (AI) and machine learning (ML) methods are currently widely employed in medicine and healthcare. A PubMed search returns more than 100,000 articles on these topics published between 2018 and 2022 alone. Notwithstanding several recent reviews in various subfields of AI and ML in medicine, we have yet to see a comprehensive review around the methods' use in longitudinal analysis and prediction of an individual patient's health status within a personalized disease pathway. This review seeks to fill that gap. After an overview of the AI and ML methods employed in this field and of specific medical applications of models of this type, the review discusses the strengths and limitations of current studies and looks ahead to future strands of research in this field. We aim to enable interested readers to gain a detailed impression of the research currently available and accordingly plan future work around predictive models for deterioration in health status.

Telemedicine in HIV health care during the COVID-19 pandemic: An implementation research study in Buenos Aires, Argentina.

BACKGROUND: From October 2020 to October 2022, we conducted an implementation study to offer telemedicine (TM) across four HIV units of general public hospitals in Buenos Aires. The intervention used TM to provide a continuum of care to patients with HIV. METHODS AND SETTING: We used the RE-AIM framework to evaluate the strategy. The study started during a COVID-19 outbreak with strict lockdown policies and continued until return to normal practices. Implementation facilitation served as the core implementation strategy. RESULTS: We reached 4118 patients (58% of eligible individuals), and the main perceived benefits were the ability to avoid exposure to infectious diseases and reduced travel time and cost. After a median of 515 days of follow-up, 95.7% of participants with HIV were receiving antiretroviral therapy, and 87.8% were virally suppressed, with a median CD4+ count of 648 cells/µL. In total, 36.6% reported clinical events, and 20.4% presented with COVID-19 infection. The proportion of physicians adopting TM was 69.37%. After enrolment, 2406 of 5640 (43%) follow-up visits were conducted via TM. By the end of the study, 26.29% of appointments offered in the four centres were through TM, whereas 73.71% were in-person appointments. CONCLUSION: It was feasible to implement TM in the four centres in the public health sector in Buenos Aires, Argentina. It was acceptable for both patients and healthcare workers, and effectively reached a large proportion of the population served in these clinics. Both healthcare workers and patients consider it a model of care that will continue to be offered in the future.

Osteoarthritis Year In Review 2024: Rehabilitation and outcomes.

This Year in Review presents key highlights from recent research relating to osteoarthritis rehabilitation and its outcomes, defined as any non-pharmacological and non-surgical treatment that aims to improve osteoarthritis symptoms at any joint. Three databases (Medline, Embase, and CINAHL Plus) were searched between 1 March 2023 to 12 March 2024. Relevant studies were chosen based on the predefined inclusion/exclusion criteria, perceived clinical importance, quality, controversy in the field, or personal interest, and organised into four overarching themes (with 1-5 sub-themes each). The first theme related to uncertainties regarding exercise benefits. New work has challenged the clinical effectiveness of exercise on symptoms, as well as highlighted uncertainty around our understanding of both mechanisms of effects, how to enhance effectiveness and adherence, and which subgroups of people are more or less likely to improve with exercise. However, we also highlight new work confirming the role of exercise as a first-line management strategy. The second theme related to digital modes of service delivery. There was new evidence to support its effectiveness in improving symptoms and clear potential for creating and evaluating new mobile apps. New work also highlighted the potential future role artificial intelligence can have in providing treatment information and recommendations. The third theme related to patient education, and the call for change to the impairment-based narrative that prevails in osteoarthritis information. The fourth theme is related to weight loss. New work compared the effectiveness of different weight loss diets and explored alternative models of weight loss delivery.

A novel fracture liaison service using digital health: impact on mortality in hospitalized elderly osteoporotic fracture patients.

We examined the performance of an intelligent fracture liaison service (FLS) assisted by digital health (DH) to reduce all-cause mortality (ACM) risk. According to our findings, the new FLS reduced ACM by 36%. INTRODUCTION: A well-designed secondary prevention program known as FLS enhances the bone densitometry-based assessment rate as well as osteoporosis (OP) medication usage following a fracture. However, there are only a few reports on FLS incorporating DH, and it remains unclear whether this integration has influenced patient ACM, which refers to the overall death rate from any cause during the study period. METHODS: This retrospective observational study was conducted on data from the Fragility Fracture Registration System database linked to the Regional Health Registration Platform of Kunshan City and the Population Death Registration System of Jiangsu Province for one tertiary-level A hospital in China. Patients aged ≥ 50 years, who experienced an OP fracture between January 1, 2017, and July 27, 2022, requiring hospitalization, were selected for analysis. We compared the outcomes of patients who received routine fragility fracture management (the no-FLS group) or FLS (the FLS group). We employed multivariable Cox regression with inverse probability weighting based on the propensity score (PS). RESULTS: Of 2317 patients, 756 (32.6%) received FLS and 1561 (67.4%) did not. Using PS matching, we minimized the baseline characteristic differences between the two groups in the propensity score-matched samples, relative to the unmatched samples. Based on our analysis, the new FLS reduced ACM by 36% (hazard ratio [HR], 0.64; 95% confidence interval [CI], 0.47 to 0.87; P-value = 0.004). Moreover, FLS patients experienced further reductions in fall-related mortality, refracture rate, and total refracture-related hospital costs, and had increased dual-energy X-ray absorptiometry (DXA) testing and treatment initiation rates, relative to the no-FLS patients. CONCLUSIONS: A new FLS model implementation assisted by DH can effectively reduce ACM among elderly patients with OP fractures requiring surgery. In future investigations, we recommend examining the scalability of this model.

Offloading and adherence through technological advancements: Modern approaches for better foot care in diabetes.

OBJECTIVE: This manuscript aims to provide a review and synthesis of contemporary advancements in footwear, sensor technology for remote monitoring, and digital health, with a focus on improving offloading and measuring and enhancing adherence to offloading in diabetic foot care. METHODS: A narrative literature review was conducted by sourcing peer-reviewed articles, clinical studies, and technological innovations. This paper includes a review of various strategies, from specifically designed footwear, smart insoles and boots to using digital health interventions, which aim to offload plantar pressure and help prevent and manage wounds more effectively by improving the adherence to such offloading. RESULTS: In-house specially made footwear, sensor technologies remotely measuring pressure and weight-bearing activity, exemplified for example, through applications like smart insoles and SmartBoot, and other digital health technologies, show promise in improving offloading and changing patient behaviour towards improving adherence to offloading and facilitating personalised care. This paper introduces the concept of gamification and emotive visual indicators as novel methods to enhance patient engagement. It further discusses the transformative role of digital health technologies in the modern era. CONCLUSIONS: The integration of technology with footwear and offloading devices offers unparallelled opportunities for improving diabetic foot disease management not only through better offloading but also through improved adherence to offloading. These advancements allow healthcare providers to personalise treatment plans more effectively, thereby promising a major improvement in patient outcomes in diabetic foot ulcer healing and prevention.

The Role of Human-Centered Design in Healthcare Innovation: a Digital Health Equity Case Study.

Healthcare delivery has become more complicated, particularly with the addition of digital tools and advanced technologies that can further exacerbate existing disparities. New approaches to solve complex, multi-faceted problems are needed. Human-centered design (HCD), also known as design thinking, is an innovative set of methods to develop solutions to these types of issues using collaborative, team-based, and empathetic approaches focused on end user experiences. Originally advanced in technology sectors, HCD has garnered growing attention in quality improvement, healthcare redesign, and public health and medical education. During the COVID-19 pandemic, our healthcare organization recognized notable differences in utilization of virtual (video-based) services among specific patient populations. In response, we mobilized, and using HCD, we collectively brainstormed ideas, rapidly developed prototypes, and iteratively adapted solutions to work toward addressing this digital divide and clinic and systems-level struggles with improving and maintaining digital health access. HCD approaches create a cohesive team-based structure that permits the dismantling of organizational hierarchies and departmental silos. Here we share lessons learned on implementing HCD into clinical care settings and how HCD can result in the development of site-specific, patient-centered innovations to address access disparities and to improve digital health equity.

Factors influencing participation in an online national diabetes prevention programme: A qualitative study with attenders and educators.

AIM: To explore factors affecting participation in the pilot of the synchronous online national diabetes prevention programme (NDPP) in Ireland from the perspectives of those who attended and the educators who recruited for and delivered the programme. METHODS: A qualitative study involving semi-structured interviews and focus groups with NDPP attenders (attended the assessment and at least one session) and educators (dietitians) on the programme. The Framework Method using the Theoretical Domains Framework (TDF) guided the analysis. RESULTS: Thirteen attenders took part in two online focus groups and five online or phone interviews. Eight educators took part. Four themes which cut across the TDF domains were identified as factors influencing participation; (i) lack of awareness of prediabetes and fear of diabetes, relating to attenders' fear of diabetes and lack of knowledge of prediabetes and diabetes prevention; (ii) perceived need for programme support to change health behaviour, concerning attenders' and educators' recognition of the need for the NDPP; (iii) trust in healthcare professionals (HCPs), relating to trust in HCPs to convey the seriousness of prediabetes and the value of diabetes prevention programmes (DPPs) and (iv) practical and personal ease of joining online, relating to the flexibility and accessibility of the synchronous online group format, the IT skills of attenders and educators and apprehension about group education. CONCLUSIONS: Raising awareness of prediabetes and the need for prevention programmes should be a priority for health services and HCPs. The synchronous online group format was seen as less daunting to join than a face-to-face programme and may be a useful option to encourage participation.

Clinical Validation of Digitally Acquired Clinical Data and Machine Learning Models for Remote Measurement of Psoriasis and Psoriatic Arthritis: A Proof-of-Concept Study.

OBJECTIVE: Psoriatic disease remains underdiagnosed and undertreated. We developed and validated a suite of novel, sensor-based smartphone assessments (Psorcast app) that can be self-administered to measure cutaneous and musculoskeletal signs and symptoms of psoriatic disease. METHODS: Participants with psoriasis (PsO) or psoriatic arthritis (PsA) and healthy controls were recruited between June 5, 2019, and November 10, 2021, at 2 academic medical centers. Concordance and accuracy of digital measures and image-based machine learning models were compared to their analogous clinical measures from trained rheumatologists and dermatologists. RESULTS: Of 104 study participants, 51 (49%) were female and 53 (51%) were male, with a mean age of 42.3 years (SD 12.6). Seventy-nine (76%) participants had PsA, 16 (15.4%) had PsO, and 9 (8.7%) were healthy controls. Digital patient assessment of percent body surface area (BSA) affected with PsO demonstrated very strong concordance (Lin concordance correlation coefficient [CCC] 0.94 [95% CI 0.91-0.96]) with physician-assessed BSA. The in-clinic and remote target plaque physician global assessments showed fair-to-moderate concordance (CCCerythema 0.72 [0.59-0.85]; CCCinduration 0.72 [0.62-0.82]; CCCscaling 0.60 [0.48-0.72]). Machine learning models of hand photos taken by patients accurately identified clinically diagnosed nail PsO with an accuracy of 0.76. The Digital Jar Open assessment categorized physician-assessed upper extremity involvement, considering joint tenderness or enthesitis (AUROC 0.68 [0.47-0.85]). CONCLUSION: The Psorcast digital assessments achieved significant clinical validity, although they require further validation in larger cohorts before use in evidence-based medicine or clinical trial settings. The smartphone software and analysis pipelines from the Psorcast suite are open source and freely available.

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.

The effect of digital health interventions on postpartum depression or anxiety: a systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: This study aimed to examine the effect of digital health interventions compared with treatment as usual on preventing and treating postpartum depression and postpartum anxiety. DATA SOURCES: Searches were conducted in Ovid MEDLINE, Embase, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov. STUDY ELIGIBILITY REQUIREMENTS: The systematic review included full-text randomized controlled trials comparing digital health interventions with treatment as usual for preventing or treating postpartum depression and postpartum anxiety. STUDY APPRAISAL AND SYNTHESIS METHODS: Two authors independently screened all abstracts for eligibility and independently reviewed all potentially eligible full-text articles for inclusion. A third author screened abstracts and full-text articles as needed to determine eligibility in cases of discrepancy. The primary outcome was the score on the first ascertainment of postpartum depression or postpartum anxiety symptoms after the intervention. Secondary outcomes included screening positive for postpartum depression or postpartum anxiety --as defined in the primary study --and loss to follow-up, defined as the proportion of participants who completed the final study assessment compared with the number of initially randomized participants. For continuous outcomes, the Hedges method was used to obtain standardized mean differences when the studies used different psychometric scales, and weighted mean differences were calculated when studies used the same psychometric scales. For categorical outcomes, pooled relative risks were estimated. RESULTS: Of 921 studies originally identified, 31 randomized controlled trials-corresponding to 5532 participants randomized to digital health intervention and 5492 participants randomized to treatment as usual-were included. Compared with treatment as usual, digital health interventions significantly reduced mean scores ascertaining postpartum depression symptoms (29 studies: standardized mean difference, -0.64 [95% confidence interval, -0.88 to -0.40]; I2=94.4%) and postpartum anxiety symptoms (17 studies: standardized mean difference, -0.49 [95% confidence interval, -0.72 to -0.25]; I2=84.6%). In the few studies that assessed screen-positive rates for postpartum depression (n=4) or postpartum anxiety (n=1), there were no significant differences between those randomized to digital health intervention and treatment as usual. Overall, those randomized to digital health intervention had 38% increased risk of not completing the final study assessment compared with those randomized to treatment as usual (pooled relative risk, 1.38 [95% confidence interval, 1.18-1.62]), but those randomized to app-based digital health intervention had similar loss-to-follow-up rates as those randomized to treatment as usual (relative risk, 1.04 [95% confidence interval, 0.91-1.19]). CONCLUSION: Digital health interventions modestly, but significantly, reduced scores assessing postpartum depression and postpartum anxiety symptoms. More research is needed to identify digital health interventions that effectively prevent or treat postpartum depression and postpartum anxiety but encourage ongoing engagement throughout the study period.

Periodic mobile application (eMOM) with self-tracking of glucose and lifestyle improves treatment of diet-controlled gestational diabetes without human guidance: a randomized controlled trial.

BACKGROUND: Digitalization with minimal human resources could support self-management among women with gestational diabetes and improve maternal and neonatal outcomes. OBJECTIVE: This study aimed to investigate if a periodic mobile application (eMOM) with wearable sensors improves maternal and neonatal outcomes among women with diet-controlled gestational diabetes without additional guidance from healthcare personnel. STUDY DESIGN: Women with gestational diabetes were randomly assigned in a 1:1 ratio at 24 to 28 weeks' gestation to the intervention or the control arm. The intervention arm received standard care in combination with use of the periodic eMOM, whereas the control arm received only standard care. The intervention arm used eMOM with a continuous glucose monitor, an activity tracker, and a food diary 1 week/month until delivery. The primary outcome was the change in fasting plasma glucose from baseline to 35 to 37 weeks' gestation. Secondary outcomes included capillary glucose, weight gain, nutrition, physical activity, pregnancy complications, and neonatal outcomes, such as macrosomia. RESULTS: In total, 148 women (76 in the intervention arm, 72 in the control arm; average age, 34.1±4.0 years; body mass index, 27.1±5.0 kg/m2) were randomized. The intervention arm showed a lower mean change in fasting plasma glucose than the control arm (difference, -0.15 mmol/L vs -2.7 mg/mL; P=.022) and lower capillary fasting glucose levels (difference, -0.04 mmol/L vs -0.7 mg/mL; P=.002). The intervention arm also increased their intake of vegetables (difference, 11.8 g/MJ; P=.043), decreased their sedentary behavior (difference, -27.3 min/d; P=.043), and increased light physical activity (difference, 22.8 min/d; P=.009) when compared with the control arm. In addition, gestational weight gain was lower (difference, -1.3 kg; P=.015), and there were less newborns with macrosomia in the intervention arm (difference, -13.1 %; P=.036). Adherence to eMOM was high (daily use >90%), and the usage correlated with lower maternal fasting (P=.0006) and postprandial glucose levels (P=.017), weight gain (P=.028), intake of energy (P=.021) and carbohydrates (P=.003), and longer duration of the daily physical activity (P=.0006). There were no significant between-arm differences in terms of pregnancy complications. CONCLUSION: Self-tracking of lifestyle factors and glucose levels without additional guidance improves self-management and the treatment of gestational diabetes, which also benefits newborns. The results of this study support the use of digital self-management and education tools in maternity care.