Dataset Information

Understanding Implementation of a Digital Self-Monitoring Intervention for Relapse Prevention in Psychosis: Protocol for a Mixed Method Process Evaluation.

ABSTRACT:

Background

Relapse is common in people who experience psychosis and is associated with many negative consequences, both societal and personal. People who relapse often exhibit changes (early warning signs [EWS]) in the period before relapse. Successful identification of EWS offers an opportunity for relapse prevention. However, several known barriers impede the use of EWS monitoring approaches. Early signs Monitoring to Prevent relapse in psychosis and prOmote Well-being, Engagement, and Recovery (EMPOWER) is a complex digital intervention that uses a mobile app to enhance the detection and management of self-reported changes in well-being. This is currently being tested in a pilot cluster randomized controlled trial. As digital interventions have not been widely used in relapse prevention, little is known about their implementation. Process evaluation studies run in parallel to clinical trials can provide valuable data on intervention feasibility.

Objective

This study aims to transparently describe the protocol for the process evaluation element of the EMPOWER trial. We will focus on the development of a process evaluation framework sensitive to the worldview of service users, mental health staff, and carers; the aims of the process evaluation itself; the proposed studies to address these aims; and a plan for integration of results from separate process evaluation studies into one overall report.

Methods

The overall process evaluation will utilize mixed methods across 6 substudies. Among them, 4 will use qualitative methodologies, 1 will use a mixed methods approach, and 1 will use quantitative methodologies.

Results

The results of all studies will be triangulated into an overall analysis and interpretation of key implementation lessons. EMPOWER was funded in 2016, recruitment finished in January 2018. Data analysis is currently under way and the first results are expected to be submitted for publication in December 2019.

Conclusions

The findings from this study will help identify implementation facilitators and barriers to EMPOWER. These insights will inform both upscaling decisions and optimization of a definitive trial.

Trial registration

ISRCTN Registry ISRCTN99559262; http://www.isrctn.com/ISRCTN99559262.

International registered report identifier (irrid)

DERR1-10.2196/15634.

SUBMITTER: Allan S

PROVIDER: S-EPMC6930509 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

Understanding Implementation of a Digital Self-Monitoring Intervention for Relapse Prevention in Psychosis: Protocol for a Mixed Method Process Evaluation.

Allan Stephanie S Mcleod Hamish H Bradstreet Simon S Beedie Sara S Moir Bethany B Gleeson John J Farhall John J Morton Emma E Gumley Andrew A

JMIR research protocols 20191210 12

<h4>Background</h4>Relapse is common in people who experience psychosis and is associated with many negative consequences, both societal and personal. People who relapse often exhibit changes (early warning signs [EWS]) in the period before relapse. Successful identification of EWS offers an opportunity for relapse prevention. However, several known barriers impede the use of EWS monitoring approaches. Early signs Monitoring to Prevent relapse in psychosis and prOmote Well-being, Engagement, and ...[more]

PMID: 31821154

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Project description:BackgroundRelapsed and refractory multiple myeloma (RRMM) is a bone marrow cancer that requires systemic treatment, which often results in severe symptom burden. Recent studies have found that electronic patient-reported outcome (ePRO) interventions implemented in the clinic setting have had positive outcomes for other oncology populations. Evidence of the efficacy of a similar approach is lacking for patients with RRMM.ObjectiveRecent recommendations for digital health interventions call for the publication of descriptions of iterative development processes in order to improve reproducibility and comparability. This study is an implementation pilot aiming to evaluate the acceptability and appropriateness of an ePRO intervention for patients with RRMM and to explore its impact on clinic workflow.MethodsA total of 11 patients with RRMM were recruited from the John Theurer Cancer Center in Hackensack, New Jersey. Patients used a mobile app to report on 17 symptoms at 4 sessions, each a week apart. Patients could also report symptoms ad hoc. When reports met predefined thresholds, the clinic was alerted and patients received automated guidance. Study end points were assessed using qualitative and quantitative methods.ResultsA total of 9 patients (mean age 69.7 years) completed the study. Overall, 83% (30/36) of weekly sessions were completed. Patients found the frequency and time required to complete reporting acceptable. All patients agreed that the app was easy to use and understand. Providers felt the alerts they received required refinement. Patients and providers agreed it would be beneficial for patients to report for longer than 4 weeks. Patients felt that the training they received was adequate but contained too much information for a single session. All patients found the symptoms tracked to be appropriate; providers suggested shortening the list. All patients understood how to use the app for weekly reporting but had confusion about using it ad hoc. Providers felt the ad hoc feature could be removed. Neither patients nor providers viewed the in-app data reports but agreed on their potential value. Patients reported benefitting from symptom reporting through increased awareness of their symptoms. Clinic staff reported that app alerts were too numerous and redundant. They had difficulty responding to alerts within their existing workflow, partially because the data were not integrated into the electronic medical record system.ConclusionsOverall, the intervention was found to be acceptable and appropriate for patients with RRMM. Points of friction integrating the intervention into the clinic workflow were identified. Clinic staff provided recommendations for addressing these issues. Once such modifications are implemented, ePRO data from patients with RRMM could be used to inform and improve clinical research and care. This study underlines the importance of an iterative approach to implementation that includes all stakeholders in order to ensure successful adoption.

Project description:BackgroundRelapse is a common experience for people diagnosed with psychosis, which is associated with increased service costs and profound personal and familial distress. EMPOWER (Early signs Monitoring to Prevent relapse in psychosis and prOmote Well-being, Engagement, and Recovery) is a peer worker-supported digital intervention that aims to enable service users to self-monitor their mental health with the aim of encouraging self-management and the shared use of personal data to promote relapse prevention. Digital interventions have not been widely used in relapse prevention and, therefore, little is currently known about their likely implementation-both within trials and beyond.ObjectiveSeeking the perspectives of all relevant stakeholder groups is recommended in developing theories about implementation because this can reveal important group differences in understandings and assumptions about whether and for whom the intervention is expected to work. However, the majority of intervention implementation research has been retrospective. This study aimed to discover and theoretically frame implementation expectations in advance of testing and synthesize these data into a framework.MethodsTo develop a hypothetical implementation framework, 149 mental health professionals, carers, and people diagnosed with psychosis participated in 25 focus groups in both Australia and the United Kingdom. An interview schedule informed by the normalization process theory was used to explore stakeholders' expectations about the implementation of the EMPOWER intervention. Data were analyzed using thematic analysis and then theoretically framed using the Medical Research Council guidelines for understanding the implementation of complex interventions.ResultsAll groups expected that EMPOWER could be successfully implemented if the intervention generated data that were meaningful to mental health staff, carers, and service users within their unique roles. However, there were key differences between staff, carers, and service users about what facilitators and barriers that stakeholders believe exist for intervention implementation in both the cluster randomized controlled trial stage and beyond. For example, service user expectations mostly clustered around subjective user experiences, whereas staff and carers spoke more about the impact upon staff interactions with service users.ConclusionsA hypothetical implementation framework synthesized from stakeholder implementation expectations provides an opportunity to compare actual implementation data gathered during an ongoing clinical trial, giving valuable insights into the accuracy of these stakeholders' previous expectations. This is among the first studies to assess and record implementation expectations for a newly developed digital intervention for psychosis in advance of testing in a clinical trial.Trial registrationISRCTN Registry ISRCTN99559262; http://www.isrctn.com/ISRCTN99559262.

Project description:BackgroundCommunity-based educational programs can complement clinical strategies to increase cancer screenings and encourage healthier lifestyles to reduce cancer burden. However, implementation quality can influence program outcomes and is rarely formally evaluated in community settings. This mixed-methods study aimed to characterize implementation of a community-based cancer prevention program using the Consolidated Framework for Implementation Research (CFIR), determine if implementation was related to participant outcomes, and identify barriers and facilitators to implementation that could be addressed.MethodsThis study utilized quantitative participant evaluation data (n = 115) and quantitative and qualitative data from semi-structured interviews with program instructors (N = 13). At the participant level, demographic data (age, sex, insurance status) and behavior change intention were captured. Instructor data included implementation of program components and program attendance to create a 7-point implementation score of fidelity and reach variables. Degree of program implementation (high and low) was operationalized based on these variables (low: 0-4, high: 5-7). Relationships among degree of implementation, participant demographics, and participant outcomes (e.g., intent to be physically active or limit alcohol) were assessed using linear or ordinal logistic mixed effects models as appropriate. Interview data were transcribed and coded deductively for CFIR constructs, and constructs were then rated for magnitude and valence. Patterns between ratings of high and low implementation programs were used to determine constructs that manifested as barriers or facilitators.ResultsProgram implementation varied with scores ranging from 4 to 7. High implementation was related to greater improvements in intention to be physically active (p < 0.05), achieve a healthy weight (p < 0.05), and limit alcohol (p < 0.01). Eight constructs distinguished between high and low implementation programs. Design quality and packaging, compatibility, external change agents, access to knowledge and information, and experience were facilitators of implementation and formally appointed internal implementation leaders was a barrier to implementation.ConclusionsAs higher implementation was related to improved participant outcomes, program administrators should emphasize the importance of fidelity in training for program instructors. The CFIR can be used to identify barriers and/or facilitators to implementation in community interventions, but results may be unique from clinical contexts.

Project description:BackgroundA high proportion of hypertensive patients remain above the target threshold for blood pressure, increasing the risk of adverse health outcomes. A digital intervention to facilitate healthcare practitioners (hereafter practitioners) to initiate planned medication escalations when patients' home readings were raised was found to be effective in lowering blood pressure over 12 months. This mixed-methods process evaluation aimed to develop a detailed understanding of how the intervention was implemented in Primary Care, possible mechanisms of action and contextual factors influencing implementation.MethodsOne hundred twenty-five practitioners took part in a randomised controlled trial, including GPs, practice nurses, nurse-prescribers, and healthcare assistants. Usage data were collected automatically by the digital intervention and antihypertensive medication changes were recorded from the patients' medical notes. A sub-sample of 27 practitioners took part in semi-structured qualitative process interviews. The qualitative data were analysed using thematic analysis and the quantitative data using descriptive statistics and correlations to explore factors related to adherence. The two sets of findings were integrated using a triangulation protocol.ResultsMean practitioner adherence to escalating medication was moderate (53%), and the qualitative analysis suggested that low trust in home readings and the decision to wait for more evidence influenced implementation for some practitioners. The logic model was partially supported in that self-efficacy was related to adherence to medication escalation, but qualitative findings provided further insight into additional potential mechanisms, including perceived necessity and concerns. Contextual factors influencing implementation included proximity of average readings to the target threshold. Meanwhile, adherence to delivering remote support was mixed, and practitioners described some uncertainty when they received no response from patients.ConclusionsThis mixed-methods process evaluation provided novel insights into practitioners' decision-making around escalating medication using a digital algorithm. Implementation strategies were proposed which could benefit digital interventions in addressing clinical inertia, including facilitating tracking of patients' readings over time to provide stronger evidence for medication escalation, and allowing more flexibility in decision-making whilst discouraging clinical inertia due to borderline readings. Implementation of one-way notification systems could be facilitated by enabling patients to send a brief acknowledgement response.Trial registration( ISRCTN13790648 ). Registered 14 May 2015.

Project description:BackgroundMobile health (mHealth) technologies have been used extensively in psychosis research. In contrast, their integration into real-world clinical care has been limited despite the broad availability of smartphone-based apps targeting mental health care. Most apps developed for treatment of individuals with psychosis have focused primarily on encouraging self-management skills of patients via practicing cognitive behavioral techniques learned during face-to-face clinical sessions (eg, challenging dysfunctional thoughts and relaxation exercises), reminders to engage in health-promoting activities (eg, exercising, sleeping, and socializing), or symptom monitoring. In contrast, few apps have sought to enhance the clinical encounter itself to improve shared decision-making (SDM) and therapeutic relationships with clinicians, which have been linked to positive clinical outcomes.ObjectiveThis qualitative study sought clinicians' input to develop First Episode Digital Monitoring (FREEDoM), an app-based mHealth intervention. FREEDoM was designed to improve the quality, quantity, and timeliness of clinical and functional data available to clinicians treating patients experiencing first-episode psychosis (FEP) to enhance their therapeutic relationship and increase SDM.MethodsFollowing the app's initial development, semistructured qualitative interviews were conducted with 11 FEP treatment providers at 3 coordinated specialty care clinics to elicit input on the app's design, the data report for clinicians, and planned usage procedures. We then generated a summary template and conducted matrix analysis to systematically categorize suggested adaptations to the evidence-based intervention using dimensions of the Framework for Reporting Adaptations and Modifications-Enhanced (FRAME) and documented the rationale for adopting or rejecting suggestions.ResultsThe clinicians provided 31 suggestions (18 adopted and 13 rejected). Suggestions to add or refine the content were most common (eg, adding questions in the app). Adaptations to context were most often related to plans for implementing the intervention, how the reported data were displayed to clinicians, and with whom the reports were shared. Reasons for suggestions primarily included factors related to health narratives and priorities of the patients (eg, focus on the functional impact of symptoms vs their severity), providers' clinical judgment (eg, need for clinically relevant information), and organizations' mission and culture. Reasons for rejecting suggestions included requests for data and procedures beyond the intervention's scope, concerns regarding dilution of the intervention's core components, and concerns about increasing patient burden while using the app.ConclusionsFREEDoM focuses on a novel target for the deployment of mHealth technologies in the treatment of FEP patients-the enhancement of SDM and improvement of therapeutic relationships. This study illustrates the use of the FRAME, along with methods and tools for rapid qualitative analysis, to systematically track adaptations to the app as part of its development process. Such adaptations may contribute to enhanced acceptance of the intervention by clinicians and a higher likelihood of integration into clinical care.Trial registrationClinicalTrials.gov NCT04248517; https://tinyurl.com/tjuyxvv6.

Project description:BackgroundTimely and comprehensive treatment in the form of early psychosis intervention (EPI) has become the standard of care for youth with psychosis. While EPI services were designed to be delivered in person, the COVID-19 pandemic required many EPI programs to rapidly transition to virtual delivery, with little evidence to guide intervention adaptations or to support the effectiveness and satisfaction with virtual EPI services.ObjectiveThis study aims to explore the adaptations required to deliver NAVIGATE, a model of coordinated specialty care used in EPI, in a virtual format. This study will evaluate implementation of the NAVIGATE model delivered virtually by describing the nature of the adaptations to the intervention, assessing fidelity to the EPI model and the satisfaction of clients, family members, and care providers. We will investigate barriers and facilitators to virtual NAVIGATE implementation, service engagement, and health equity impacts of this work.MethodsThe Centre for Addiction and Mental Health (Toronto, Ontario, Canada) transitioned to delivering NAVIGATE virtually early in the COVID-19 pandemic. The Framework for Reporting Adaptations and Modifications for Evidence-Based Interventions will be used to describe the adaptations required to deliver NAVIGATE virtually. Fidelity to the EPI model will be measured using the First Episode Psychosis Services Fidelity Scale and fidelity to NAVIGATE will be assessed by investigating adherence to its core components. Implementation facilitators and barriers will be explored using semistructured interviews with providers informed by the Consolidated Framework for Implementation Research. Satisfaction with virtually delivered NAVIGATE will be assessed with virtual client and provider experience surveys and qualitative interviews with clients, family members, and providers. Service engagement data will be collected through review of medical records, and potential impacts of virtually delivered NAVIGATE on different population groups will be assessed with the Health Equity Impact Assessment.ResultsVirtual clinical delivery of NAVIGATE started in March 2020 with additional adaptations and data collection is ongoing. Data will be analyzed using descriptive statistics and survival analysis for quantitative data. Qualitative data will be analyzed using thematic content analysis. Integration of qualitative and quantitative data will occur at the data collection, interpretation, and reporting levels following a convergent design.ConclusionsThis study will provide information regarding the type of intervention adaptations required for virtual delivery of NAVIGATE for youth with early psychosis, ensuring access to high-quality care for this population during the pandemic and beyond by guiding future implementation in similar contexts.International registered report identifier (irrid)DERR1-10.2196/34591.

Project description:BackgroundFalls are the most common hospitalized injury mechanism in children aged ≤1 years, and currently, there are no targeted prevention interventions. The prevention of falls in children of this age requires changes in the behavior of their caregivers, and theoretically informed digital behavior change interventions (DBCIs) may provide a unique mechanism for achieving effective intervention. However, user acceptance and the ability of DBCIs to effect the required changes in behavior are critical to their likelihood of success.ObjectiveThis study aims to evaluate a behavior theory-informed digital intervention developed following a user-centered approach for user experience, the potential for this intervention to prevent infant falls, and its impact on behavioral drivers underpinning fall risk in young children.MethodsParents of infants aged <1 year were recruited and asked to use the intervention for 3 months. A pre-post longitudinal design was used to examine the change in the potential to reduce the risk of falls after a 3-month exposure to the intervention. Postintervention data on behavioral drivers for fall prevention, user acceptability, and engagement with the app were also collected. Interviews were conducted to explore user experiences and identify areas for further improvement of the intervention.ResultsA total of 62 parents participated in the study. A statistically significant effect on the potential to reduce falls was observed after the intervention. This effect was higher for new parents. Parents agreed that the intervention targeted most of the target behavior drivers. The impact of behavior drivers and intervention on the potential for fall prevention had a positive correlation. The intervention demonstrated good levels of acceptability. Feedback from participants was mostly positive, and the primary area identified for further improvement was widening the scope of the intervention.ConclusionsThis study demonstrated the promise of a newly developed digital intervention to reduce the risk of infant falls, particularly among new parents. It also showed a positive influence of the DBCI on the drivers of parental behaviors that are important for fall reduction among infants. The acceptability of the app was high, and important insights were gained from users about how to further improve the app.

Dataset Information

Understanding Implementation of a Digital Self-Monitoring Intervention for Relapse Prevention in Psychosis: Protocol for a Mixed Method Process Evaluation.

Background

Objective

Methods

Results

Conclusions

Trial registration

International registered report identifier (irrid)

Publications

Understanding Implementation of a Digital Self-Monitoring Intervention for Relapse Prevention in Psychosis: Protocol for a Mixed Method Process Evaluation.

Similar Datasets

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