Optimizing Pediatric Drug Therapy: Pediatric Pharmacokinetics and Administration Routes.

BackgroundDepression and anxiety disorders are common, and treatment often includes psychological interventions. Digital health interventions, delivered through technologies such as web-based programs and mobile apps, are increasingly used in mental health treatment. Acceptability, the extent to which an intervention is viewed positively, has been identified as contributing to patient adherence and engagement with digital health interventions. Acceptability, therefore, impacts the benefit derived from using digital health interventions in treatment. Understanding the acceptability of digital mental health interventions among patients with depression or anxiety disorders is essential to maximize the effectiveness of their treatment.ObjectiveThis review investigated the acceptability of technology-based interventions among patients with depression or anxiety disorders.MethodsA systematic review was performed based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and PROSPERO (International Prospective Register of Systematic Reviews) guidelines. We searched PubMed, Web of Science, and Ovid in May 2022. Studies were included if they evaluated digital interventions for the treatment of depression or anxiety disorders and investigated their acceptability among adult patients. Studies were excluded if they targeted only specific populations (eg, those with specific physical health conditions), investigated acceptability in healthy individuals or patients under the age of 18 years, involved no direct interaction between patients and technologies, used technology only as a platform for traditional care (eg, videoconferencing), had patients using technologies only in clinical or laboratory settings, or involved virtual reality technologies. Acceptability outcome data were narratively synthesized by the direction of acceptability using vote counting. Included studies were evaluated using levels of evidence from the Oxford Centre for Evidence-Based Medicine. The risk of bias was assessed using a tool designed for this review and GRADE (Grading of Recommendations, Assessment, Development, and Evaluation).ResultsA total of 143 articles met the inclusion criteria, comprising 67 (47%) articles on interventions for depression, 65 (45%) articles on interventions for anxiety disorders, and 11 (8%) articles on interventions for both. Overall, 90 (63%) were randomized controlled trials, 50 (35%) were other quantitative studies, and 3 (2%) were qualitative studies. Interventions used web-based programs, mobile apps, and computer programs. Cognitive behavioral therapy was the basis of 71% (102/143) of the interventions. Digital mental health interventions were generally acceptable among patients with depression or anxiety disorders, with 88% (126/143) indicating positive acceptability, 8% (11/143) mixed results, and 4% (6/143) insufficient information to categorize the direction of acceptability. The available research evidence was of moderate quality.ConclusionsDigital mental health interventions seem to be acceptable to patients with depression or anxiety disorders. Consistent use of validated measures for acceptability would enhance the quality of evidence. Careful design of acceptability as an evaluation outcome can further improve the quality of evidence and reduce the risk of bias.Trial RegistrationOpen Science Framework Y7MJ4; https://doi.org/10.17605/OSF.IO/SPR8M

BackgroundThere is a high prevalence of children and young people (CYP) experiencing mental health (MH) problems. Owing to accessibility, affordability, and scalability, an increasing number of digital health interventions (DHIs) have been developed and incorporated into MH treatment. Studies have shown the potential of DHIs to improve MH outcomes. However, the modes of delivery used to engage CYP in digital MH interventions may differ, with implications for the extent to which findings pertain to the level of engagement with the DHI. Knowledge of the various modalities could aid in the development of interventions that are acceptable and feasible.ObjectiveThis review aimed to (1) identify modes of delivery used in CYP digital MH interventions, (2) explore influencing factors to usage and implementation, and (3) investigate ways in which the interventions have been evaluated and whether CYP engage in DHIs.MethodsA literature search was performed in the Cochrane Library, Excerpta Medica dataBASE (EMBASE), Medical Literature Analysis and Retrieval System Online (MEDLINE), and PsycINFO databases using 3 key concepts “child and adolescent mental health,” “digital intervention,” and “engagement.” Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed using rigorous inclusion criteria and screening by at least two reviewers. The selected articles were assessed for quality using the mixed methods appraisal tool, and data were extracted to address the review aims. Data aggregation and synthesis were conducted and presented as descriptive numerical summaries and a narrative synthesis, respectively.ResultsThis study identified 6 modes of delivery from 83 articles and 71 interventions for engaging CYP: (1) websites, (2) games and computer-assisted programs, (3) apps, (4) robots and digital devices, (5) virtual reality, and (6) mobile text messaging. Overall, 2 themes emerged highlighting intervention-specific and person-specific barriers and facilitators to CYP’s engagement. These themes encompassed factors such as suitability, usability, and acceptability of the DHIs and motivation, capability, and opportunity for the CYP using DHIs. The literature highlighted that CYP prefer DHIs with features such as videos, limited text, ability to personalize, ability to connect with others, and options to receive text message reminders. The findings of this review suggest a high average retention rate of 79% in studies involving various DHIs.ConclusionsThe development of DHIs is increasing and may be of interest to CYP, particularly in the area of MH treatment. With continuous technological advancements, it is important to know which modalities may increase engagement and help CYP who are facing MH problems. This review identified the existing modalities and highlighted the influencing factors from the perspective of CYP. This knowledge provides information that can be used to design and evaluate new interventions and offers important theoretical insights into how and why CYP engage in DHIs.

Digital health technology provides opportunities to leverage artificial intelligence and other digital applications to promote cardiovascular health. Digital health technologies include artificial intelligence (such as machine learning [ML], neural networks),1 analytic systems, mobile apps, wearables, email, text messaging, and telemedicine.2 In this article, we review the role of digital technology in cardiovascular health and a selection of recent studies to evaluate the evidence of its effectiveness. Artificial intelligence is broadly defined as the capability of computer systems to perform tasks similar to humans.3 Examples include vision, speech, pattern recognition, and decision making. Machine learning is the ability of the computer program to learn from experience. This typically occurs from analysis of large sets of data processed through human-derived algorithms to enhance, predict, and explain outcomes.4 An example of the use of ML in clinical care is cardiovascular disease (CVD) prediction and electrocardiographic interpretation. Neural networks, named after the human nervous system, are nonlinear statistic models that control where signals are sent. Neural networks can be used for decision making such as cardiovascular diagnosis confirmation. Digital Technology Use in Cardiovascular Risk Assessment Several studies have demonstrated improved CVD risk factor identification using ML compared with traditional risk assessment tools. Researchers developed an ML risk calculator and compared it with the American College of Cardiology/American Heart Association CVD risk calculator in 6459 participants from the Multi-Ethnic Study of Atherosclerosis.5 Study participants were free of CVD at baseline and followed for 13 years. Results revealed that the American College of Cardiology/American Heart Association risk calculator was less precise: statin therapy was recommended to 46% of the sample, with 23.8% of CVD events occurring in those not recommended a statin. In comparison, the ML risk calculator recommended a statin to 11% of the sample, with 14.4% of CVD events occurring in those not recommended a statin.5 Similarly in 3 cohorts from Australia, 4 ML models were developed and compared with the 2008 Framingham model. The ML models provided 2.7% to 5.2% better predictions across all 3 cohorts.6 Taken together, the authors of these studies suggest ML provides promise in providing more precise estimates of CVD risk. Digital Health Interventions for Cardiovascular Disease Prevention Digital health interventions have the potential to provide a personalized approach to promote cardiovascular health. Behavior change theory is a key component of digital interventions and includes theoretical frameworks such as supportive accountability,7 self-efficacy theory,8 social cognitive theory, and the health belief model.9 Precision healthcare has been promoted for decades. Many of the challenges in operationalizing precision healthcare are healthcare accessibility, scheduling, care continuity, and inadequate knowledge exchange between provides and patients.10 Thus, promotion of healthy lifestyles and lifestyle risk factor reduction remain inadequately addressed in patients with CVD.11 To achieve sustainable change, individual-level personalized strategies may be leveraged through digital health interventions. Evidence of the effectiveness of digital health interventions has varied but is promising overall. Text messaging has been successfully used to provide information regarding healthy diet and physical activity recommendations, monitoring, and individual feedback. Text messaging has resulted in improvements in diet and activity in many (TextMe,12 Mobile MyPlate,13 MyQuest,14 Text-To-Move15), but not all studies.16 Smartphone/mobile apps have been designed to improve dietary and physical activity behavior. Examples include apps that track dietary patterns and activity through user input of text or visual images.17,18 Users can set their own goals and receive feedback on progress toward goals. Reviews of smartphone apps have had variable results with many demonstrating short-term improvement. Villinger et al19 conducted a systematic review and meta-analysis of the effectiveness of mobile app interventions on nutrition behaviors (41 studies, 27 randomized controlled trials [RCTs]). Findings revealed significantly improved nutrition behaviors and nutrition-related outcomes (P = .004 and P = .043, respectively). A second systematic review of 27, primarily RCTs, found significant between-group improvements in 19 of the 27 studies.20 A meta-analysis of 6 RCTs in adults using a smartphone app as the primary component of the intervention revealed a trend for more steps per day in the intervention compared with the control groups, with programs lasting less than 3 months more effective than longer programs.21 Taken together, text messaging and smartphone/mobile apps have the potential to improve lifestyle behaviors associated with cardiovascular health. The addition of strategies to increase sustainability of the effects needs to be assessed. Digital Health Interventions: Primary and Secondary Prevention Widmer et al2 conducted a meta-analysis of 51 RCTs and cohort studies using digital health interventions for the prevention of CVD events and risk factor modification. Subgroup analyses of primary prevention studies (2 studies) did not provide evidence of a statistically significant reduction in CVD outcomes. However, evaluation of individual risk factors in primary prevention studies found a significant reduction in weight (11 studies; −3.35 lb), systolic blood pressure (23 studies; mean difference, −2.12 mm Hg), total cholesterol (13 studies; mean difference, −5.19 mg/dL), low-density lipoprotein cholesterol (8 studies; mean difference, −4.96 mg/dL), and glucose (6 studies; mean difference, −1.38 mg/dL).2 A subgroup analysis of secondary prevention studies demonstrated a significant impact of digital interventions on CVD outcomes (relative risk, 0.60; a 40% relative risk reduction), improvement in body mass index (6 studies; mean difference, −0.31 kg/m2) but no improvement in weight, systolic blood pressure, total cholesterol, low-density lipoprotein cholesterol, and glucose. Taken together, this meta-analysis suggested that digital interventions were beneficial not only in lowering CVD events in higher-risk patients but also in lowering risk factors in primary prevention approaches.2 In a second meta-analysis conducted by Akinosun et al,11 researchers analyzed 25 RCTs in patients with traditional CVD risk factors who received a digital intervention versus usual care.11 Findings revealed benefits in total cholesterol (mean difference, −0.29), high-density lipoprotein cholesterol (mean difference, −0.09), low-density lipoprotein (mean difference, 0.18), physical activity (mean difference 0.23), physical inactivity (relative risk, 0.54), and diet (relative risk, 0.79). There was no significant improvement in body mass index, systolic and diastolic blood pressure, hemoglobin A1C, alcohol intake, smoking, and medication adherence. Authors concluded that digital interventions were more effective at improving healthy behaviors than reducing unhealthy behaviors. In patients who experienced a myocardial infarction, a digital health intervention providing medication reminders, vital sign and activity tracking, education, and outpatient care coordination resulted in a 52% lower 30-day readmission rate compared with usual care.22 Sociodemographic characteristics (age, sex, and race) did not influence use of the digital intervention, highlighting a potential role for digital interventions in the promotion of equity in social determinants of health.23 Digital Health Interventions in Cardiac Rehabilitation Cardiac rehabilitation is an essential component of secondary prevention of CVD.24 Some patients face barriers in participation in cardiac rehabilitation due to physical accessibility, time, and travel.25 Digital health interventions have the potential to bridge these barriers and increase participation. Digital delivery of cardiac rehabilitation therapy with real-time personalized support has several advantages.26 In a systematic review of 31 studies in which authors examined digital health interventions for cardiac rehabilitation, the results revealed that cardiac rehabilitation program adherence was greater in patients using digital interventions than traditional methods alone. Secondary benefits were found in self-efficacy, weight management, diet, and quality of life. Taken together, digital cardiac rehabilitation was feasible and effective whether used alone or in combination with traditional cardiac rehabilitation.26 Conclusion Digital health technology is an evolving field with tremendous potential to improve cardiovascular health. Cardiovascular disease remains the major cause of death in the United States. The age-adjusted mortality rate has increased in the last decade. More people died from CVD causes in 2020 (nearly 900 000 deaths) than any year since 2003.27 Opportunities to reduce CVD and CVD risk have not been fully leveraged, and digital technology interventions have the potential to meet this need. Digital health technology also has the potential to provide equitable and personalized care. Device data, electronic medical record data, and social determinants of health data provide an opportunity to combine and identify longitudinal trends and risk factors before CVD begins. In the future, large data sets can be created that can be analyzed using ML to identify patterns and structures within and among the data to provide a more robust risk assessment to promote CVD prevention.

To investigate the effects of digital health interventions for improving adherence to oral iron supplementation in pregnant women. Five databases were searched from their inception to October 2023 with no date restrictions. Randomized controlled trials (RCTs) that assessed the effects of digital health interventions on adherence to oral iron supplementation (e.g., tablets and capsules) compared to non-digital health interventions for pregnant women were eligible. We calculated standardized mean differences (SMDs) and mean differences (MDs) with 95% confidence intervals (CIs) for continuous variables using the inverse variance method. We calculated odds ratios (OR) with 95%CI for categorical variables using the Mantel-Haenszel model. The certainty of the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. The risk of bias of the included RCTs was assessed using the Cochrane risk of bias tool 2.0. Ten trials with 1,633 participants were included. Based on 7 trials, digital health interventions can improve objective adherence rate comparing with non-digital health interventions (1,289 participants, OR = 4.07 [2.19, 7.57], p < 0.001, I2 = 69%) in pregnant women. Digital health interventions can improve subjective adherence behavior comparing with non-digital health interventions (3 trials, 434 participants, SMD = 0.82 [0.62, 1.01], p < 0.001, I2 = 0%) in pregnant women. Based on 3 trials, digital health interventions can improve tablets consumption comparing with non-digital health interventions (333 participants, SMD = 1.00 [0.57, 1.42], p < 0.001, I2 = 66%) in pregnant women. Digital health interventions can improve hemoglobin level comparing with non-digital health interventions (7 trials, 1,216 participants, MD = 0.59 [0.31, 0.88], p < 0.001, I2 = 93%) in pregnant women. Digital health interventions were effective at improving adherence to oral iron supplementation and hemoglobin levels in pregnant women.

BackgroundPoor mental health among university students remains a pressing public health issue. Over the past few years, digital health interventions have been developed and considered promising in increasing psychological wellbeing among university students. Therefore, this umbrella review aims to synthesize evidence on digital health interventions targeting university students and to evaluate their effectiveness.MethodsA systematic literature search was performed in April 2021 searching PubMed, Psychology and Behavioural Science Collection, Web of Science, ERIC, and Scopus for systematic reviews and meta-analyses on digital mental health interventions targeting university students. The review protocol was registered in the International Prospective Register of Systematic Reviews PROSPERO [CRD42021234773].ResultsThe initital literature search resulted in 806 records of which seven remained after duplicates were removed and evaluated against the inclusion criteria. Effectiveness was reported and categorized into the following six delivery types: (a) web-based, online/computer-delivered interventions (b) computer-based Cognitive Behavior Therapy (CBT), (c) mobile applications and short message service (d) virtual reality interventions (e) skills training (f) relaxation and exposure-based therapy. Results indicated web-based online/computer delivered-interventions were effective or at least partially effective at decressing depression, anxiety, stress and eating disorder symptoms. This was similar for skills-training interventions, CBT-based intervention and mobile applications. However, digital mental health interventions using virtual reality and relaxation, exposure-based therapy was inconclusive. Due to the variation in study settings and inconsistencies in reporting, effectiveness was greatly dependent on the delivery format, targeted mental health problem and targeted purpose group.ConclusionThe findings provide evidence for the beneficial effect of digital mental health interventions for university students. However, this review calls for a more systematic approach in testing and reporting the effectiveness of digital mental health interventions.

Digital health interventions (DHIs) are widely used to manage users' health in everyday life through digital devices. The use of DHIs generates various data, such as records of intervention use and the status of target symptoms, providing researchers with data-driven insights for improving these interventions even after deployment. Although DHI researchers have investigated these data, existing analysis practices have been fragmented, limiting a comprehensive understanding of the data. We proposed an analysis task model to help DHI researchers analyze observational data from a holistic perspective. This model was then used to prototype an interactive visual analytics tool. We aimed to evaluate the suitability of the model for DHI data analysis and explore task support using a visual analytics tool. We constructed a data analysis task model using 3 key components (ie, user grouping criteria) for DHI data analysis: user characteristics, user engagement with DHIs, and the effectiveness of DHIs on target symptoms based on comparisons before and after the intervention. On the basis of this model, we designed Maum Health Analytics, a medium-fidelity prototype of an interactive visual analytics tool. Each feature of the prototype was mapped one-to-one to the analysis task described in the model. To investigate whether the proposed model adequately reflects real-world DHI analysis needs, we conducted a preliminary user study with 5 groups of researchers (N=15). Participants explored the tool through scenario-based analysis tasks using in-the-wild data collected from a mobile DHI service targeting depressive symptoms. Following the session, we conducted interviews to assess the appropriateness of the defined tasks and the usability and practical utility of the visual analytics tool. DHI researchers responded positively to both the analysis task model and the visual analytics tool. In the interviews, participants noted that the tool supported the identification of users who needed additional care, informed content recommendations, and helped analyze intervention effectiveness in relation to user characteristics and engagement levels. They also appreciated the tool's role in simplifying analytic tasks and supporting communication across multidisciplinary teams. Additional suggestions included improvements for continuity across tasks and more detailed engagement metrics. We proposed an analysis task model and designed an interactive visual analytics tool to support DHI researchers. Our user study showed that the model allows a holistic investigation of DHI data by integrating key analysis components and that the prototype tool simplifies analytic tasks and enhances communication among researchers. As DHIs grow, the proposed model and tool can effectively meet the data analysis requirements of researchers and improve efficiency.

BACKGROUND Digital Health Interventions (DHIs) are widely used to manage users' health in everyday life through digital devices. The use of DHIs generates various data, such as records of intervention usage and the status of target symptoms, providing researchers with data-driven insights for improving these interventions even after deployment. Although DHI researchers have investigated this data, existing analysis practices have been carried out in a fragmented manner, limiting the comprehensive understanding of the data. OBJECTIVE We proposed an analysis task model to help DHI researchers analyze observational data from a holistic perspective. This model was then used to prototype an interactive visual analytics tool. Our objective is to evaluate the model’s suitability for DHI data analysis and explore task support through a visual analytics tool. METHODS We constructed an analysis task model based on data analysis practices from existing DHI research. Moreover, we designed 'Maum Health Analytics,' an initial prototype of an interactive visual analytics tool that supports the tasks included in the proposed model. To investigate whether our model adequately covers the DHI data analysis process, we conducted a preliminary user study with five groups of DHI researchers (n=15). During this process, we had them use Maum Health Analytics within given data analysis scenarios, providing analyzed results from in-the-wild data collected in a non-experimental setting through a mobile DHI service targeting depressive symptoms. After using the analytics tool, we interviewed the DHI researchers to determine whether the analysis tasks were appropriate and how the information provided by the tool could be utilized in practice. RESULTS Our analysis task model was created using three key components (i.e., user grouping criteria) for DHI data analysis: user characteristics, user engagement with DHIs, and the effectiveness of DHIs on the target symptom via pre-post comparisons. Furthermore, the prototype of interactive visual analytics was designed, with each feature mapped one-to-one to an analysis task described in the model. From the interview sessions, DHI researchers valued group-level analysis that enabled identifying users who need care, improving intervention content and recommendations, and understanding the effectiveness of DHIs in connection with user characteristics and engagement levels. They also noted several benefits of the model and tool, such as simplifying analysis tasks and supporting communication among diverse experts. CONCLUSIONS We proposed an analysis task model and designed an interactive visual analytics tool to support DHI researchers. Our user study showed that the model allows a holistic investigation of DHI data by integrating key analysis components, and the prototype tool simplifies analytic tasks and enhances communication among researchers. As DHIs grow, our model and tool could effectively meet the data analysis needs of researchers and improve efficiency.

Innovative health promotion strategies are crucial for enhancing global quality of life and curbing premature deaths. Digital health promotion is particularly impactful for young individuals often using internet-connected devices. Collaborative methodologies in digital intervention research offer insights into supporting youth during key life stages, such as adolescence. This review sought to examine the literature on digital health interventions for youth co-designed via participatory frameworks. Following the Joanna Briggs Institute Manual and an adapted Arksey & O'Malley's 6-stage framework, this review utilised the PRISMA-ScR checklist for structured reporting. Peer-reviewed research where young individuals (15-35 years) contributed to digital health intervention design was analysed. Systematic synthesis adhered to Braun & Clarke's Thematic Analysis Guidelines, mapping data to research queries and thematic framework. Eighteen articles were systematically synthesised, revealing seven main themes: digital tool, inquiry field, report aim, participatory activities, intervention attributes and behavioural change support. Seventeen distinctive digital health interventions were assessed, mostly within risk mitigation and mental health domains. Predominantly, interventions were web-based, with mental wellness websites emerging as the prevalent tool. User experience testing stood out as the primary reported outcome. Several innovative digital health interventions targeting youth have been identified. Platforms including social media, specialised apps, websites and video games are instrumental for health advice and clinical support dissemination, overcoming access and cost barriers. Participatory techniques are integral for the efficacy of digital health resources, encompassing youth aspirations and anticipations. Continued efforts will enrich comprehension of optimal practices in digital health promotion and intervention formulation.

This study identifies facilitators and challenges in the co-creation of digital health interventions for public health. An exploratory multiple case study design was used to analyse three co-created digital health interventions. Data were collected for three digital health interventions via expert interviews (n=11), open-ended survey responses (n=10), and project descriptions. Qualitative content analysis was conducted to identify common themes across cases. Key facilitators included leveraging co-creator expertise, maintaining regular co-creator exchanges, ensuring an adaptive development process, carrying out a needs analysis and adopting a feasible design. Key challenges included resource constraints, difficult co-creator relationships, poor planning of co-creation, difficulties in recruiting and acknowledging co-creators, distributing workload fairly, and unclear expectations and skills/knowledge gap amongst co-creators. Findings suggest that while the facilitators and challenges of digital health interventions reported here reflect previous literature relating to non-digital health interventions, digital settings present unique challenges, including effective integration of co-creators across iteration cycles and enabling collaboration across a large array of disciplines. Understanding facilitators and challenges in digital health intervention co-creation may help teams plan how to effectively include all relevant co-creators in the process. Future research should further explore how co-creation impacts digital health intervention adoption, user engagement, and long-term effectiveness.

BackgroundAn estimated 1 in 5 adolescents experience a mental health disorder each year; yet because of barriers to accessing and seeking care, most remain undiagnosed and untreated. Furthermore, the early emergence of psychopathology contributes to a lifelong course of challenges across a broad set of functional domains, so addressing this early in the life course is essential. With increasing digital connectivity, including in low- and middle-income countries, digital health technologies are considered promising for addressing mental health among adolescents and young people. In recent years, a growing number of digital health interventions, including more than 2 million web-based mental health apps, have been developed to address a range of mental health issues.ObjectiveThis review aims to synthesize the current evidence on digital health interventions targeting adolescents and young people with mental health conditions, aged between 10-24 years, with a focus on effectiveness, cost-effectiveness, and generalizability to low-resource settings (eg, low- and middle-income countries).MethodsWe searched MEDLINE, PubMed, PsycINFO, and Cochrane databases between January 2010 and June 2020 for systematic reviews and meta-analyses on digital mental health interventions targeting adolescents and young people aged between 10-24 years. Two authors independently screened the studies, extracted data, and assessed the quality of the reviews.ResultsIn this systematic overview, we included 18 systematic reviews and meta-analyses. We found evidence on the effectiveness of computerized cognitive behavioral therapy on anxiety and depression, whereas the effectiveness of other digital mental health interventions remains inconclusive. Interventions with an in-person element with a professional, peer, or parent were associated with greater effectiveness, adherence, and lower dropout than fully automatized or self-administered interventions. Despite the proposed utility of digital interventions for increasing accessibility of treatment across settings, no study has reported sample-specific metrics of social context (eg, socioeconomic background) or focused on low-resource settings.ConclusionsAlthough digital interventions for mental health can be effective for both supplementing and supplanting traditional mental health treatment, only a small proportion of existing digital platforms are evidence based. Furthermore, their cost-effectiveness and effectiveness, including in low- and middle-income countries, have been understudied. Widespread adoption and scale-up of digital mental health interventions, especially in settings with limited resources for health, will require more rigorous and consistent demonstrations of effectiveness and cost-effectiveness vis-à-vis the type of service provided, target population, and the current standard of care.

The potential of digital health interventions to optimize healthcare is promising also in the context of spinal surgery. However, a systematic review assessing the quality of digital health interventions for spinal surgery patients and the potential effects on these patients is lacking. The objective of the current scoping review was to provide a systematic overview of digital health interventions for spinal surgery patients described in scientific literature. The focus was on describing the current digital health interventions, assessing the quality of these descriptions, reviewing the reported effects and assessing the methodological quality of the included studies. A total of 14 full-text articles, describing 11 digital health interventions were included in the final analysis. These digital health interventions ranged from a website and app to a mobile phone messaging system and mobile phone interface. Most digital health interventions aim to improve adherence to rehabilitation guidelines and physical health. The included studies were generally of moderate to high quality and showed significant effects on physical health. Vital aspects of digital interventions such as "working mechanism theory" and "prompts and reminders" were often absent in the description of interventions. The study of digital interventions for spinal surgery patient is emerging and promising. However, there is a scarcity of studies using a rigorous design. A more systematic and comprehensive framework for developing and describing digital interventions for spinal surgery patients is highly recommended.

IntroductionMental disorders such as depression are common, and an estimated 264 million people are affected by them throughout the world. In recent years, studies on digital health interventions to treat...

Digital health interventions (DHIs) have frequently been highlighted as one way to respond to increasing levels of mental health problems in children and young people. Whilst many are developed to address existing mental health problems, there is also potential for DHIs to address prevention and early intervention. However, there are currently limitations in the design and reporting of the development, evaluation and implementation of preventive DHIs that can limit their adoption into real-world practice. This scoping review aimed to examine existing evidence-based DHI interventions and review how well the research literature described factors that researchers need to include in their study designs and reports to support real-world implementation. A search was conducted for relevant publications published from 2013 onwards. Twenty-one different interventions were identified from 30 publications, which took a universal (n = 12), selective (n = 3) and indicative (n = 15) approach to preventing poor mental health. Most interventions targeted adolescents, with only two studies including children aged ≤10 years. There was limited reporting of user co-design involvement in intervention development. Barriers and facilitators to implementation varied across the delivery settings, and only a minority reported financial costs involved in delivering the intervention. This review found that while there are continued attempts to design and evaluate DHIs for children and young people, there are several points of concern. More research is needed with younger children and those from poorer and underserved backgrounds. Co-design processes with children and young people should be recognised and reported as a necessary component within DHI research as they are an important factor in the design and development of interventions, and underpin successful adoption and implementation. Reporting the type and level of human support provided as part of the intervention is also important in enabling the sustained use and implementation of DHIs.

By July 2020, large numbers of post-COVID patients were experiencing symptoms for weeks or months, but traditional National Health Service models of rehabilitation service delivery could not meet demand. Design and deploy a digital health intervention to provide digitally delivered, remotely supported rehabilitation to long COVID patients on complicated and evolving pathways. The multidisciplinary team combined established research methods based on engineering and computer science (considering safety, stability and user requirements) with those based on biomedical and health service research (considering effectiveness and population impact). Qualitative data comprised recordings of meetings between study team members and clinicians and semistructured interviews with clinician and patient users. Quantitative data comprised referral, registration and usage rates; demographic and clinical characteristics of patients; and patient-reported outcome measures. We created a modifiable digital health intervention, 'Living With COVID RecoveryTM developed by Living With Ltd', London, UK, that continues to be used by National Health Service trusts. The digital health intervention included integration into a clinical pathway, a clinician-facing dashboard, two-way messaging and a patient-facing app with information and evidence-based treatments. We aimed to register 1000 users. By study completion on 20 December 2022, there were 9781 patients invited, of whom 7679 (78.5%) had registered, at 33 National Health Service clinics. Data came from patients at long COVID clinics, however data were unlikely to be representative of people with long COVID. We could not observe clinics under lockdown and had limited access to patient digital health intervention users or to people not engaging with the digital health intervention. Patient user data were incomplete, with inconsistent patient-reported outcome measure and other questionnaire data completion and no data on initial severity of disease, vaccination status, comorbidities or other individual circumstances. Long COVID can be extremely debilitating, comparable to stage IV lung cancer in relation to fatigue and health-related quality of life. Care and rehabilitation should address the management of fatigue and reflect the impact of social disadvantage on symptom severity. With sufficient resources, a digital health intervention can be developed quickly and effectively using agile methodology and bringing together a genuinely multidisciplinary team, including, importantly, an industry partner. Digital health intervention product design and deployment are both important in getting National Health Service trusts, healthcare professionals and patients to engage with a digital health intervention. Projects should work closely with all user groups. Lockdown and the unmet need of a new patient group encouraged those who might otherwise have been reluctant to try a digital health intervention. Many patients and clinics accepted this digital remote support, which helped patients feel cared for while reducing strain on health services. This may encourage acceptance of other digital health intervention, although medical record integration remains a deterrent to clinics. This research focused on the development, deployment and evaluation of a digitally enabled rehabilitation programme for long COVID. Clinical effectiveness will be assessed within the Symptoms, Trajectory, Inequalities and Management: Understanding Long-COVID to Address and Transform Existing Integrated Care Pathways (UCL, London, UK) study. This synopsis presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR132243.

Digital health interventions can support health-related knowledge transfer, for example through websites or mobile applications, and may reduce health inequalities by making health care available, where access is difficult, and by translating content to overcome language barriers. However, digital health intervention can also increase health inequalities due to the digital divide. To reach digitally excluded populations, design and delivery mechanisms need to specifically address this issue. This review was conducted during the evolving COVID-19 pandemic and informed the rapid design, deployment and evaluation of a post-COVID-19 rehabilitation digital health intervention: 'Living with COVID Recovery' (LWCR). LWCR needed to be engaging and usable for patients and to avoid exacerbating health inequalities. LWCR was introduced as a service into 33 NHS clinics, was used by 7679 patients, and evaluation ran from August 2020 to December 2022. To identify evidence-based digital health intervention design and deployment features conducive to mitigating the digital divide. Cochrane Library, Epistemonikos, National Institute for Health and Care Excellence Evidence, PROSPERO, PubMed (with MEDLINE and Europe PMC) and Turning Research into Practice; OpenGrey and Google Scholar were searched for primary research studies published in English from 1 October 2011 to 1 October 2021. Adults who were likely to be affected by the digital divide, including older age, minority ethnic groups, lower income/education level and in any healthcare setting. Any digital health intervention with features of design and/or deployment intended to enable access and engagement by the population of focus. Any or none. Any related to participants' access and/or use of digital health intervention and/or change in digital skills and confidence. Data from studies that met the inclusion criteria were extracted, narratively synthesised and thematically analysed. Twenty-two papers met the inclusion criteria. Digital health interventions evaluated included telehealth, text message interventions, virtual assistants, self-management programmes and decision aids. Co-development with end-users, user testing through iterative design cycles, digital health interventions that also helped improve digital skills and digital health literacy, tailoring for low literacy through animations, pictures, videos and writing for low reading ages; virtual assistants to collect information from patients and guide the use of a digital health intervention. Free devices and data, or signposting to sources of cheap/free devices and Wi-Fi, text message interventions, providing 'human support', providing tailored digital skills education as part of the intervention and enabling peer/family support. Our search extended to late 2021, and there has been a massive increase in the literature following the pandemic. However, as our review was undertaken to inform the LWCR digital health intervention design and deployment, we have reported the results that informed this work. The studies included in the review were heterogeneous, so generalisability may be limited. Few randomised controlled trials assessed the digital health intervention's impact on digital health skills by using validated measures. Using the design and deployment findings described above when developing digital health interventions may help overcome the digital divide. Beyond informing the LWCR digital health intervention development, the review findings have wider implications for the equitable design, delivery and evaluation of digital health interventions. This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR132243.