Single case experimental designs in agricultural advisor training: A novel method for evaluating capacity building in farmer mental health interventions

Single-case experimental designs (SCEDs) have grown in popularity in the fields such as education, psychology, medicine, and rehabilitation. Although SCEDs are valid experimental designs for determining evidence-based practices, they encounter some challenges in analyses of data. One of these challenges, missing data, is likely to be occurred frequently in SCEDs research due to repeated measurements over time. Since missing data is a critical factor that can weaken the validity and generalizability of a study, it is important to determine the characteristics of missing data in SCEDs, which are especially conducted with a small number of participants. In this regard, this study aimed to describe missing data features in SCEDs studies in detail. To accomplish this goal, 465 published SCEDs studies within the recent 5 years in six journals were included in the investigation. The overall results showed that the prevalence of missing data among SCEDs articles in at least one phase, as at least one data point, was approximately 30%. In addition, the results indicated that the missing data rates were above 10% within most studies where missing data occurred. Although missing data is so common in SCEDs research, only a handful of studies (5%) have handled missing data; however, their methods are traditional. In analyzing SCEDs data, several methods are proposed considering missing data ratios in the literature. Therefore, missing data rates determined in this study results can shed light on the analyses of SCEDs data with proper methods by improving the validity and generalizability of study results.

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.

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

Missing data is inevitable in single-case experimental designs (SCEDs) studies due to repeated measures over a period of time. Despite this fact, SCEDs implementers such as researchers, teachers, clinicians, and school psychologists usually ignore missing data in their studies. Performing analyses without considering missing data in an intervention study using SCEDs or a meta-analysis study including SCEDs studies in a topic can lead to biased results and affect the validity of individual or overall results. In addition, missingness can undermine the generalizability of SCEDs studies. Considering these drawbacks, this study aims to give descriptive and advisory information to SCEDs practitioners and researchers about missing data in single-case data. To accomplish this task, the study presents information about missing data mechanisms, item level and unit level missing data, planned missing data designs, drawbacks of ignoring missing data in SCEDs, and missing data handling methods. Since single imputation methods among missing data handling methods do not require complicated statistical knowledge, are easy to use, and hence are more likely to be used by practitioners and researchers, the present study evaluates single imputation methods in terms of intervention effect sizes and missing data rates by using a real and hypothetical data sample. This study encourages SCEDs implementers, and also meta-analysts to use some of the single imputation methods to increase the generalizability and validity of the study results in case they encounter missing data in their studies.

BackgroundDigital mental health interventions stand to play a critical role in managing the mental health impact of the COVID-19 pandemic. Thus, enhancing their uptake is a key priority. General practitioners (GPs) are well positioned to facilitate access to digital interventions, but tools that assist GPs in identifying suitable patients are lacking.ObjectiveThis study aims to evaluate the suitability of a web-based mental health screening and treatment recommendation tool (StepCare) for improving the identification of anxiety and depression in general practice and, subsequently, uptake of digital mental health interventions.MethodsStepCare screens patients for symptoms of depression (9-item Patient Health Questionnaire) and anxiety (7-item Generalized Anxiety Disorder scale) in the GP waiting room. It provides GPs with stepped treatment recommendations that include digital mental health interventions for patients with mild to moderate symptoms. Patients (N=5138) from 85 general practices across Australia were invited to participate in screening.ResultsScreening identified depressive or anxious symptoms in 43.09% (1428/3314) of patients (one-quarter were previously unidentified or untreated). The majority (300/335, 89.6%) of previously unidentified or untreated patients had mild to moderate symptoms and were candidates for digital mental health interventions. Although less than half were prescribed a digital intervention by their GP, when a digital intervention was prescribed, more than two-thirds of patients reported using it.ConclusionsImplementing web-based mental health screening in general practices can provide important opportunities for GPs to improve the identification of symptoms of mental illness and increase patient access to digital mental health interventions. Although GPs prescribed digital interventions less frequently than in-person psychotherapy or medication, the promising rates of uptake by GP-referred patients suggest that GPs can play a critical role in championing digital interventions and maximizing the associated benefits.

BackgroundDepression and anxiety frequently coexist with chronic musculoskeletal pain and can negatively impact patients’ responses to standard orthopedic treatments. Nevertheless, mental health is not routinely addressed in the orthopedic care setting. If effective, a digital mental health intervention may be a feasible and scalable method of addressing mental health in an orthopedic setting.ObjectiveWe aimed to compare 2-month changes in mental and physical health between orthopedic patients who received a digital mental health intervention in addition to usual orthopedic care, those who received usual orthopedic care only (without a specific mental health intervention), and those who received in-person care with a psychologist as part of their orthopedic treatment plan.MethodsIn this single-center retrospective cohort study involving ancillary analysis of a pilot feasibility study, 2-month self-reported health changes were compared between a cohort of orthopedic patients who received access to a digital mental health intervention (Wysa) and 2 convenience sample comparison cohorts (patients who received usual orthopedic care without a specific mental health intervention and patients who received in-person care with a psychologist as part of their orthopedic treatment plan). All patients were 18 years or older and reported elevated symptoms of depression or anxiety at an orthopedic clinic visit (Patient-Reported Outcomes Measurement Information System [PROMIS] Depression or Anxiety score ≥55). The digital intervention was a multi-component mobile app that used chatbot technology and text-based access to human counselors to provide cognitive behavioral therapy, mindfulness training, and sleep tools, among other features, with an emphasis on behavioral activation and pain acceptance. Outcomes of interest were between-cohort differences in the 2-month longitudinal changes in PROMIS Depression and Anxiety scores (primary outcomes) and PROMIS Pain Interference and Physical Function scores (secondary outcomes).ResultsAmong 153 patients (mean age 55, SD 15 years; 128 [83.7%] female; 51 patients per cohort), patients who received the digital mental health intervention showed clinically meaningful improvements at the 2-month follow-up for all PROMIS measures (mean longitudinal improvement 2.8-3.7 points; P≤.02). After controlling for age and BMI, the improvements in PROMIS Depression, Pain Interference, and Physical Function were meaningfully greater than longitudinal changes shown by patients who received usual orthopedic care (mean between-group difference 2.6-4.8 points; P≤.04). Improvements in PROMIS Physical Function were also meaningfully greater than longitudinal changes shown by patients who received in-person psychological counseling (mean between-group difference 2.4 points; P=.04).ConclusionsPatients who received a digital mental health intervention as part of orthopedic care reported greater 2-month mean improvements in depression, pain interference, and physical function than patients who received usual orthopedic care. They also reported a greater mean improvement in physical function and comparable improvements in depression, anxiety, and pain interference compared with orthopedic patients who received in-person psychological counseling.

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.

Difficulties in emotion regulation are a significant contributing factor to delinquent behavior in adolescence. These adolescents struggle with recognizing, comprehending, and controlling emotions, which impedes the effectiveness of current forensic treatments. In addition, forensic care often faces challenges regarding treatment engagement due to a lack of motivation and difficulties building an alliance between clients and caregivers. The use of Feelee, an app that collects and displays active and passive data, is promising to support adolescents in obtaining more insight into their emotion regulation abilities. Furthermore, the integration of smartphone apps, like Feelee, offers new perspectives to increase adolescents' engagement and adherence to treatment. This study presents the research protocol for evaluating the initial effects of the Feelee app on emotion regulation among adolescents in the forensic outpatient setting. The Feelee app integrates with treatment as usual, and the multiple single-case experimental design methodology is discussed in detail. A multiple single-case experimental ABA design was applied to examine the initial effectiveness of Feelee. A total of 24 participants from 2 forensic outpatient care centers completed a 2-week baseline (phase A1), 4-week intervention (phase B), and a 2-week follow-up (phase A2). The primary outcome, emotional regulation, is measured daily using self-reports via the smartphone. Secondary outcomes, including emotional differentiation, insight and self-reflection, emotional awareness, and treatment-related factors such as motivation and therapeutic alliance, are assessed through questionnaires administered at preintervention, postintervention, and follow-up points. Quantitative analyses follow single-case experimental design methods, including visual analysis of individual trajectories, standardized mean difference permutation distance tests, and Cohen d at the group level. A 95% CI is calculated per participant to assess change reliability. Secondary outcomes are analyzed using the Reliable Change Index. Qualitative follow-up interviews are analyzed using thematic analysis at both the individual and group levels. Data collection started in June 2023 and was completed in January 2025. By the time of final manuscript submission, 89 participants had been recruited and 24 had enrolled in the study. Study results will be published in peer-reviewed journals and presented at national and international conferences throughout 2025. This study aims to evaluate the effectiveness of the Feelee app in enhancing emotion regulation skills. By using a multiple single-case experimental ABA design, we will get a first insight into the addition of Feelee to treatment as usual in the forensic outpatient setting. Study strengths include the low-threshold addition, ecological validity, and the use of both quantitative and qualitative research methods. Further implications for clinical practice are discussed. Central Committee on Research Involving Human Subjects NL-OMON54390; https://onderzoekmetmensen.nl/en/trial/54390 and ClinicalTrials.gov NCT06509360; https://clinicaltrials.gov/study/NCT06509360. DERR1-10.2196/64756.

BackgroundDigital mental health interventions are increasingly prevalent in the current context of rapidly evolving technology, and research indicates that they yield effectiveness outcomes comparable to in-person treatment. Integrating professionals (ie, psychologists and physicians) into digital mental health interventions has become common, and the inclusion of guidance within programs can increase adherence to interventions. However, employing professionals to enhance mental health programs may undermine the scalability of digital interventions. Therefore, delegating guidance tasks to paraprofessionals (peer supporters, technicians, lay counsellors, or other nonclinicians) can help reduce costs and increase accessibility.ObjectiveThis systematic review and meta-analysis evaluates the effectiveness, adherence, and other process outcomes of nonclinician-guided digital mental health interventions.MethodsFour databases (MEDLINE, Embase, CINAHL, and PsycINFO) were searched for randomized controlled trials published between 2010 and 2020 examining digital mental health interventions. Three journals that focus on digital intervention were hand searched; gray literature was searched using ProQuest and the Cochrane Central Register of Control Trials (CENTRAL). Two researchers independently assessed risk of bias using the Cochrane risk-of-bias tool version 2. Data were collected on effectiveness, adherence, and other process outcomes, and meta-analyses were conducted for effectiveness and adherence outcomes. Nonclinician-guided interventions were compared with treatment as usual, clinician-guided interventions, and unguided interventions.ResultsThirteen studies qualified for inclusion. Nonclinician-guided interventions yielded higher posttreatment effectiveness outcomes when compared to conditions involving control programs (eg, online psychoeducation and monitored attention control) or wait-list controls (k=7, Hedges g=–0.73; 95% CI –1.08 to –0.38). There were also significant differences between nonclinician-guided interventions and unguided interventions (k=6, Hedges g=–0.17; 95% CI –0.23 to –0.11). In addition, nonclinician-guided interventions did not differ in effectiveness from clinician-guided interventions (k=3, Hedges g=0.08; 95% CI –0.01 to 0.17). These results suggest that guided digital mental health interventions are helpful to improve mental health outcomes regardless of the qualifications of the individual performing the intervention, and that the presence of a nonclinician guide improves effectiveness outcomes compared to having no guide. Nonclinician-guided interventions did not yield significantly different adherence outcomes when compared with unguided interventions (k=3, odds ratio 1.58; 95% CI 0.51 to 4.92), although a general trend of improved adherence was observed within nonclinician-guided interventions.ConclusionsIntegrating paraprofessionals and nonclinicians appears to improve the outcomes of digital mental health interventions, and may also enhance adherence outcomes (though this trend was nonsignificant). Further research should focus on the specific types of tasks these paraprofessionals can successfully provide (ie, psychosocial support, therapeutic alliance, and technical augmentation) and their associated outcomes.Trial RegistrationPROSPERO International Prospective Register of Systematic Reviews CRD42020191226; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=191226

The purpose of this report is to develop a theoretical model based on empirical evidence that can serve as a foundation for the science of peer-support factors that facilitate engagement in digital health interventions for people with serious mental illness (SMI). A review of the literature on how peer-support specialist interaction with consumers with SMI in digital health behavior change interventions enhances engagement. Unlike relationships with other health providers, peer-to-consumer relationships are based on reciprocal accountability -meaning that peer-support specialists and consumer mutually help and learn from each other. Under the recovery model of mental illness, reciprocal accountability suggests autonomy, flexible expectations, shared lived experience, and bonding influence engagement in digital interventions. Separate yet related components of reciprocal accountability in the context of digital health intervention engagement include (1) goal setting, (2) task agreement, and (3) bonding. Hope and sense of belonging are hypothesized moderators of peer-support factors in digital health interventions. Peer-support factors help people with SMI learn to live sucessfully both in the clinic and community. Peer-support specialists add value and complement traditional mental health treatment through their professional training and lived experience with a mental illness. The proposed model is a pioneering step towards understanding how peer-support factors impact engagement in digital health behavior change interventions among people with a lived experience of SMI. The model presents proposed factors underlying the reciprocal accountability processes in the context of digital health intervention engagement. This model and related support factors can be used to examine or identify research questions and hypotheses.

Traumatic brain injury (TBI) leads to cognitive linguistic deficits that significantly impact on quality of life and well-being. Digital health offers timely access to specialized services; however, there are few synthesized reviews in this field. This review evaluates and synthesizes reports of digital health interventions in TBI rehabilitation and caregiver education. Systematic searches of nine databases (PsycINFO, MEDLINE, CINAHL, Embase, Cochrane Library, Scopus, Web of Science Core Collection, speechBITE, and PsycBITE) were conducted from database inception to February 2022. Studies were included of interventions where the primary treatment focus (> 50%) was on improving communication, social, psychological or cognitive skills of people with TBI and/or communication partners. Data on participants, characteristics of the interventions, outcome measures and findings were collected. Risk of bias was accounted for through methodological quality assessments (PEDro-P and PEDro+, Risk of Bias in N-of-1 Trials) and intervention description. Qualitative data was analyzed using thematic synthesis. Forty-four articles met eligibility criteria: 20 randomized controlled trials, three single-case experimental designs, six non-randomized controlled trials, nine case series studies, and two case studies. Studies comprised 3666 people with TBI and 213 carers. Methodological quality was varied and intervention description was poor. Most interventions were delivered via a single digital modality (e.g., telephone), with few using a combination of modalities. Five interventions used co-design with key stakeholders. Digital health interventions for people with TBI and their caregivers are feasible and all studies reported positive outcomes; however, few included blind assessors. Improved methodological rigor, clearly described intervention characteristics and consistent outcome measurement is recommended. Further research is needed regarding multi-modal digital health interventions.

This paper presents an argument for more extensive use of single-case experimental research designs in medical education research. Single-case experimental designs consist of a group of experimental techniques that are widely used in the social sciences but are just beginning to be utilized by medical researchers. The method emphasizes reliable observations of behavior, repeated measurements of outcome, and individualized tailoring of objectives for each subject; all of these occur within a system that allows an experimental analysis to be conducted. Single-case designs are particularly useful when only small numbers of participants are available for a relatively long period of time. Trends in medical education toward individualized instruction, adult-centered learning, and fine-grained analyses of medical skills and knowledge make this field especially amenable to single-case experimental designs. Issues of internal and external validity, generality, practicality, and ethics are discussed, and several typical designs are illustrated. While the emergence of qualitative research methods in medical education may prove useful, single-case designs can maintain experimental science's emphasis on methodologic rigor, while allowing the flexibility often needed to conduct research in applied settings.

Limited research has directly addressed the challenges of higher education for students with autism, who face additional difficulties in navigating social, personal and academic obstacles. With more students experiencing mental health difficulties whilst at university, therapeutic interventions on offer need to be suitable for those accessing support. Cognitive behavioural therapy (CBT) is widely used to support university students, as it is firmly established as an effective treatment for a range of issues, including social and generalised anxiety in typically developing populations (NICE, 2013; NICE, 2019). However, the efficacy of CBT for individuals with autistic spectrum condition (ASC) is less well known, despite the high prevalence rates of anxiety in this population. This paper seeks to address a gap in the literature and uses a single-case (A-B) experimental design over 16 sessions to reduce co-morbid social and generalised anxiety in a university student with high-functioning ASC. Clark’s (2001) cognitive model of social anxiety and Wells’ (1997) cognitive model of generalised anxiety were employed to formulate anxiety experienced in this case. Standardised outcome measures were used for social anxiety, i.e. the Social Phobia Inventory (SPIN), and generalised anxiety, i.e. the Generalised Anxiety Disorder-7 (GAD-7), in conjunction with idiographic ratings to assess the impact of therapy. Findings indicate that CBT was an acceptable and useful intervention with mixed results; discrepancies were found between clinical change recorded on standardised measures compared with idiographic ratings. This paper discusses the use of standardised measures of anxiety for individuals with ASC and identifies directions for further research. Key learning aims (1) To appreciate the unique mental health challenges of university students with ASC. (2) To identify psychological interventions that are suitable for individuals with ASC. (3) To consider the value in employing more than one evidence-based cognitive model of anxiety when clients present with co-morbid mental health issues. (4) To question the utility of using standardised outcome measures compared with idiographic measures in therapy.

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.

This paper introduces the Special Issue of Neuropsychological Rehabilitation on Single Case Experimental Design (SCED) methodology. SCED studies have a long history of use in evaluating behavioural and psychological interventions, but in recent years there has been a resurgence of interest in SCED methodology, driven in part by the development of standards for conducting and reporting SCED studies. Although there is consensus on some aspects of SCED methodology, the question of how SCED data should be analysed remains unresolved. This Special Issues includes two papers discussing aspects of conducting SCED studies, five papers illustrating use of SCED methodology in clinical practice, and nine papers that present different methods of SCED data analysis. A final Discussion paper summarises points of agreement, highlights areas where further clarity is needed, and ends with a set of resources that will assist researchers conduct and analyse SCED studies.