Co-creative development of an eHealth nursing intervention: Self-management support for outpatients with cancer pain.

INTRODUCTION: Co-creative methods, having an iterative character and including different perspectives, allow for the development of complex nursing interventions. Information about the development process is essential in providing justification for the ultimate intervention and crucial in interpreting the outcomes of subsequent evaluations. This paper describes a co-creative method directed towards the development of an eHealth intervention delivered by registered nurses to support self-management in outpatients with cancer pain. METHODS: Intervention development was divided into three consecutive phases (exploration of context, specification of content, organisation of care). In each phase, researchers and technicians addressed five iterative steps: research, ideas, prototyping, evaluation, and documentation. Health professionals and patients were consulted during research and evaluation steps. RESULTS: Collaboration of researchers, health professionals, patients and technicians was positive and valuable in optimising outcomes. The intervention includes a mobile application for patients and a web application for nurses. Patients are requested to monitor pain, adverse effects and medication intake, while being provided with graphical feedback, education and contact possibilities. Nurses monitor data, advise patients, and collaborate with the treating physician. CONCLUSION: Integration of patient self-management and professional care by means of eHealth key into well-known barriers and seem promising in improving cancer pain follow-up. Nurses are able to make substantial contributions because of their expertise, focus on daily living, and their bridging function between patients and health professionals in different care settings. Insights from the intervention development as well as the intervention content give thought for applications in different patients and care settings.

Self-management support intervention to control cancer pain in the outpatient setting: a randomized controlled trial study protocol.

BACKGROUND: Pain is a prevalent and distressing symptom in patients with cancer, having an enormous impact on functioning and quality of life. Fragmentation of care, inadequate pain communication, and reluctance towards pain medication contribute to difficulties in optimizing outcomes. Integration of patient self-management and professional care by means of healthcare technology provides new opportunities in the outpatient setting. METHODS/DESIGN: This study protocol outlines a two-armed multicenter randomized controlled trial that compares a technology based multicomponent self-management support intervention with care as usual and includes an effect, economic and process evaluation. Patients will be recruited consecutively via the outpatient oncology clinics and inpatient oncology wards of one academic hospital and one regional hospital in the south of the Netherlands. Irrespective of the stage of disease, patients are eligible when they are diagnosed with cancer and have uncontrolled moderate to severe cancer (treatment) related pain defined as NRS≥4 for more than two weeks. Randomization (1:1) will assign patients to either the intervention or control group; patients in the intervention group receive self-management support and patients in the control group receive care as usual. The intervention will be delivered by registered nurses specialized in pain and palliative care. Important components include monitoring of pain, adverse effects and medication as well as graphical feedback, education, and nurse support. Effect measurements for both groups will be carried out with questionnaires at baseline (T0), after 4 weeks (T1) and after 12 weeks (T2). Pain intensity and quality of life are the primary outcomes. Secondary outcomes include self-efficacy, knowledge, anxiety, depression and pain medication use. The final questionnaire contains also questions for the economic evaluation that includes both cost-effectiveness and cost-utility analysis. Data for the process evaluation will be gathered continuously over the study period and focus on recruitment, reach, dose delivered and dose received. DISCUSSION: The proposed study will provide insight into the effectiveness of the self-management support intervention delivered by nurses to outpatients with uncontrolled cancer pain. Study findings will be used to empower patients and health professionals to improve cancer pain control. TRIAL REGISTRATION: NCT02333968 December 29, 2014.

Experiences of multidisciplinary development team members during user-centered design of telecare products and services: a qualitative study.

BACKGROUND: User-centered design (UCD) methodologies can help take the needs and requirements of potential end-users into account during the development of innovative telecare products and services. Understanding how members of multidisciplinary development teams experience the UCD process might help to gain insight into factors that members with different backgrounds consider critical during the development of telecare products and services. OBJECTIVE: The primary objective of this study was to explore how members of multidisciplinary development teams experienced the UCD process of telecare products and services. The secondary objective was to identify differences and similarities in the barriers and facilitators they experienced. METHODS: Twenty-five members of multidisciplinary development teams of four Research and Development (R&D) projects participated in this study. The R&D projects aimed to develop telecare products and services that can support self-management in elderly people or patients with chronic conditions. Seven participants were representatives of end-users (elderly persons or patients with chronic conditions), three were professional end-users (geriatrician and nurses), five were engineers, four were managers (of R&D companies or engineering teams), and six were researchers. All participants were interviewed by a researcher who was not part of their own development team. The following topics were discussed during the interviews: (1) aim of the project, (2) role of the participant, (3) experiences during the development process, (4) points of improvement, and (5) what the project meant to the participant. RESULTS: Experiences of participants related to the following themes: (1) creating a development team, (2) expectations regarding responsibilities and roles, (3) translating user requirements into technical requirements, (4) technical challenges, (5) evaluation of developed products and services, and (6) valorization. Multidisciplinary team members from different backgrounds often reported similar experienced barriers (eg, different members of the development team speak a "different language") and facilitators (eg, team members should voice expectations at the start of the project to prevent miscommunication at a later stage). However, some experienced barriers and facilitators were reported only by certain groups of participants. For example, only managers reported the experience that having different ideas about what a good business case is within one development team was a barrier, whereas only end-users emphasized the facilitating role of project management in end-user participation and the importance of continuous feedback from researchers on input of end-users. CONCLUSIONS: Many similarities seem to exist between the experienced barriers and facilitators of members of multidisciplinary development teams during UCD of telecare products and services. However, differences in experiences between team members from various backgrounds exist as well. Insights into these similarities and differences can improve understanding between team members from different backgrounds, which can optimize collaboration during the development of telecare products and services.

The value of PROMs for predicting erectile dysfunction in prostate cancer patients with Bayesian network.

Purpose: This study aims to develop and externally validate a clinically plausible Bayesian network structure to predict one-year erectile dysfunction in prostate cancer patients by combining expert knowledge with evidence from data using clinical and Patient-reported outcome measures (PROMs) data. In addition, compare and contrast structures that stem from PROM information and routine clinical data. Summary of background: For men with localized prostate cancer, choosing the optimal treatment can be challenging since each option comes with different side effects, such as erectile dysfunction, which negatively impacts their quality of life. Our previous findings demonstrate that logistic regression models are able to identify patients at high risk of erectile dysfunction. However, methods such as Bayesian networks may be more successful, as they intricately represent the causal relations between the variables. Patients and methods: 946 prostate cancer patients from 65 Dutch hospitals were considered to develop the Bayesian network structure. Continuous variables were discretized before analysis based on expert opinions and literature. Patients with missing information and variables with more than 25% of missing information were excluded. Prostate cancer treating physicians first determined the relationships (arcs) between the available variables. The structures were then modified based on algorithmically derived structures using the hill-climbing algorithm. Structural Performance was evaluated based on the area under the curve (AUC) values and calibration plots on the training and test data. Results: BMI and prostate volume via MRI were excluded from this analysis due to their high percentage of missingness (>45 %). The final cohort was reduced to 505 and 216 after excluding 157 and 68 patients with missing information, respectively. The AUC of the PROM structure was better than the clinical structure in both the train and test data. The structure that combined both sources of information had an AUC value of 0.94 (0.92 - 0.96) and 0.84171 (0.77 91) in the train and test data, respectively. Conclusion: Bayesian network structures derived from PROM information by complimenting expert knowledge with evidence from the data produce a clinically plausible structure that is more performant than structures from clinical data. Our study supports the growing global recognition of incorporating the patient's perspective in outcomes research for better decision-making and optimal outcomes. However, a structure that combines both sources of information gives a more holistic view of the patient with actionable insights and improved discriminative power.

Cocreation to Facilitate Communication and Collaboration Between Multidisciplinary Stakeholders in eHealth Research and Development: Case Study of the CARRIER (Coronary Artery Disease: Risk Estimations and Interventions for Prevention and Early Detection) Consortium.

BACKGROUND: Collaboration with diverse stakeholders in eHealth research is fundamental yet complex. Stakeholders from various disciplines do not "speak the same language" and have different levels of power and interest, resulting in contrasting objectives, priorities, and expectations. An approach to constructive communication and collaboration is necessary to overcome this complex dynamic. Cocreation, known in the field of eHealth most often to involve end users, may also be suitable for facilitating stakeholder engagement and alignment. OBJECTIVE: This paper provides insights into the application of cocreation, specifically in the early phases of research that focus on involving and aligning relevant stakeholders from different academic and professional backgrounds. METHODS: The case for this study was a group discussion with members of a multidisciplinary consortium that works on developing a personalized eHealth intervention for atherosclerotic cardiovascular disease. Using stakeholder mapping, health and medicine experts, big data scientists, software developers, and an innovation manager (N=8) were invited to participate. The discussion was based on a user scenario and structured according to the Six Thinking Hats of de Bono, representing 6 different types of thinking. The discussion was recorded, transcribed verbatim, and analyzed thematically with the use of ATLAS.ti software. RESULTS: First, informative and intuitive thinking served the preparatory purpose of familiarization with the project details and other participants. Second, positive and critical thinking constituted the body of the discussion and resulted in an in-depth conversation. Third, creative and organizational thinking were action oriented and focused on solutions and planning to safeguard future progress. The participants repeatedly reflected on various intervention-related themes, ranging from intervention content to technical functionalities and from legal requirements to implementation in practice. Moreover, project-related matters were discussed, including stakeholder management and time and budget constraints. CONCLUSIONS: This paper demonstrates how cocreation can be of value for multidisciplinary stakeholder engagement and alignment. Based on stakeholder mapping (with whom to discuss), a dream user scenario (what to discuss), and the Six Thinking Hats of de Bono (how to discuss), the participants shared information, discussed differences, searched for solutions, and moved toward a collective approach regarding intervention development. The lessons learned may further improve the understanding of how cocreation can contribute to multidisciplinary collaboration.

Taking shared decision making for prostate cancer to the next level: Requirements for a Dutch treatment decision aid with personalized risks on side effects.

Background: Different curative treatment modalities need to be considered in case of localized prostate cancer, all comparable in terms of survival and recurrence though different in side effects. To better inform patients and support shared decision making, the development of a web-based patient decision aid including personalized risk information was proposed. This paper reports on requirements in terms of content of information, visualization of risk profiles, and use in practice. Methods: Based on a Dutch 10-step guide about the setup of a decision aid next to a practice guideline, an iterative and co-creative design process was followed. In collaboration with various groups of experts (health professionals, usability and linguistic experts, patients and the general public), research and development activities were continuously alternated. Results: Content requirements focused on presenting information only about conventional treatments and main side effects; based on risk group; and including clear explanations about personalized risks. Visual requirements involved presenting general and personalized risks separately; through bar charts or icon arrays; and along with numbers or words, and legends. Organizational requirements included integration into local clinical pathways; agreement about information input and output; and focus on patients' numeracy and graph literacy skills. Conclusions: The iterative and co-creative development process was challenging, though extremely valuable. The translation of requirements resulted in a decision aid about four conventional treatment options, including general or personalized risks for erection, urinary and intestinal problems that are communicated with icon arrays and numbers. Future implementation and validation studies need to inform about use and value in practice.

A comparison of machine learning models for predicting urinary incontinence in men with localized prostate cancer.

Introduction: Urinary incontinence (UI) is a common side effect of prostate cancer treatment, but in clinical practice, it is difficult to predict. Machine learning (ML) models have shown promising results in predicting outcomes, yet the lack of transparency in complex models known as "black-box" has made clinicians wary of relying on them in sensitive decisions. Therefore, finding a balance between accuracy and explainability is crucial for the implementation of ML models. The aim of this study was to employ three different ML classifiers to predict the probability of experiencing UI in men with localized prostate cancer 1-year and 2-year after treatment and compare their accuracy and explainability. Methods: We used the ProZIB dataset from the Netherlands Comprehensive Cancer Organization (Integraal Kankercentrum Nederland; IKNL) which contained clinical, demographic, and PROM data of 964 patients from 65 Dutch hospitals. Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms were applied to predict (in)continence after prostate cancer treatment. Results: All models have been externally validated according to the TRIPOD Type 3 guidelines and their performance was assessed by accuracy, sensitivity, specificity, and AUC. While all three models demonstrated similar performance, LR showed slightly better accuracy than RF and SVM in predicting the risk of UI one year after prostate cancer treatment, achieving an accuracy of 0.75, a sensitivity of 0.82, and an AUC of 0.79. All models for the 2-year outcome performed poorly in the validation set, with an accuracy of 0.6 for LR, 0.65 for RF, and 0.54 for SVM. Conclusion: The outcomes of our study demonstrate the promise of using non-black box models, such as LR, to assist clinicians in recognizing high-risk patients and making informed treatment choices. The coefficients of the LR model show the importance of each feature in predicting results, and the generated nomogram provides an accessible illustration of how each feature impacts the predicted outcome. Additionally, the model's simplicity and interpretability make it a more appropriate option in scenarios where comprehending the model's predictions is essential.

Development and external validation of multivariate prediction models for erectile dysfunction in men with localized prostate cancer.

While the 10-year survival rate for localized prostate cancer patients is very good (>98%), side effects of treatment may limit quality of life significantly. Erectile dysfunction (ED) is a common burden associated with increasing age as well as prostate cancer treatment. Although many studies have investigated the factors affecting erectile dysfunction (ED) after prostate cancer treatment, only limited studies have investigated whether ED can be predicted before the start of treatment. The advent of machine learning (ML) based prediction tools in oncology offers a promising approach to improve the accuracy of prediction and quality of care. Predicting ED may help aid shared decision-making by making the advantages and disadvantages of certain treatments clear, so that a tailored treatment for an individual patient can be chosen. This study aimed to predict ED at 1-year and 2-year post-diagnosis based on patient demographics, clinical data and patient-reported outcomes (PROMs) measured at diagnosis. We used a subset of the ProZIB dataset collected by the Netherlands Comprehensive Cancer Organization (Integraal Kankercentrum Nederland; IKNL) that contained information on 964 localized prostate cancer cases from 69 Dutch hospitals for model training and external validation. Two models were generated using a logistic regression algorithm coupled with Recursive Feature Elimination (RFE). The first predicted ED 1 year post-diagnosis and required 10 pre-treatment variables; the second predicted ED 2 years post-diagnosis with 9 pre-treatment variables. The validation AUCs were 0.84 and 0.81 for 1 year and 2 years post-diagnosis respectively. To immediately allow patients and clinicians to use these models in the clinical decision-making process, nomograms were generated. In conclusion, we successfully developed and validated two models that predicted ED in patients with localized prostate cancer. These models will allow physicians and patients alike to make informed evidence-based decisions about the most suitable treatment with quality of life in mind.

Digital Health Solutions to Reduce the Burden of Atherosclerotic Cardiovascular Disease Proposed by the CARRIER Consortium.

Digital health is a promising tool to support people with an elevated risk for atherosclerotic cardiovascular disease (ASCVD) and patients with an established disease to improve cardiovascular outcomes. Many digital health initiatives have been developed and employed. However, barriers to their large-scale implementation have remained. This paper focuses on these barriers and presents solutions as proposed by the Dutch CARRIER (ie, Coronary ARtery disease: Risk estimations and Interventions for prevention and EaRly detection) consortium. We will focus in 4 sections on the following: (1) the development process of an eHealth solution that will include design thinking and cocreation with relevant stakeholders; (2) the modeling approach for two clinical prediction models (CPMs) to identify people at risk of developing ASCVD and to guide interventions; (3) description of a federated data infrastructure to train the CPMs and to provide the eHealth solution with relevant data; and (4) discussion of an ethical and legal framework for responsible data handling in health care. The Dutch CARRIER consortium consists of a collaboration between experts in the fields of eHealth development, ASCVD, public health, big data, as well as ethics and law. The consortium focuses on reducing the burden of ASCVD. We believe the future of health care is data driven and supported by digital health. Therefore, we hope that our research will not only facilitate CARRIER consortium but may also facilitate other future health care initiatives.

Telemonitoring via Questionnaires Reduces Outpatient Healthcare Consumption in Parkinson's Disease.

BACKGROUND: Parkinson's disease (PD) is best managed by neurologists, traditionally including frequent doctor-patient contact. Because of a rise in PD prevalence and associated healthcare costs, this personnel-intensive care may not be future proof. Telemedicine tools for home monitoring have shown to reduce healthcare consumption in several chronic diseases and also seem promising for PD. OBJECTIVE: To explore whether telemonitoring can reduce outpatient healthcare consumption in PD. METHODS: We conducted a cohort study with 116 outpatients with PD who used the telemedicine tool "myParkinsoncoach." The tool involved periodic monitoring, feedback, knowledge modules, and text message functionality. Retrospective data about PD-related healthcare consumption in the year before and after introduction of the tool were retrieved from the hospital information system. Additional data about tool-related activities performed by nursing staff were logged prospectively for 3 months. RESULTS: There was a 29% reduction in the number of outpatient visits (P < 0.001) in the year after introduction of the tool compared with the year before. A 39% reduction was seen in overall PD-related healthcare costs (P = 0.001). Similar results were found for patients ≥70 years old. Nursing staff spent on average 15.5 minutes per patient a month on monitoring the tool and follow-up activities. CONCLUSIONS: Study results demonstrate a significant reduction in PD-related healthcare consumption using telemonitoring. Notably, these results were also found in elderly patients. Further research is needed to confirm these findings, preferably taking a broader perspective on healthcare consumption and within a larger, multicenter and prospective setup.

Factors That Influence the Use of Electronic Diaries in Health Care: Scoping Review.

BACKGROUND: A large number of people suffer from psychosocial or physical problems. Adequate strategies to alleviate needs are scarce or lacking. Symptom variation can offer insights into personal profiles of coping and resilience (detailed functional analyses). Hence, diaries are used to report mood and behavior occurring in daily life. To reduce inaccuracies, biases, and noncompliance with paper diaries, a shift to electronic diaries has occurred. Although these diaries are increasingly used in health care, information is lacking about what determines their use. OBJECTIVE: The aim of this study was to map the existing empirical knowledge and gaps concerning factors that influence the use of electronic diaries, defined as repeated recording of psychosocial or physical data lasting at least one week using a smartphone or a computer, in health care. METHODS: A scoping review of the literature published between January 2000 and December 2018 was conducted using queries in PubMed and PsycInfo databases. English or Dutch publications based on empirical data about factors that influence the use of electronic diaries for psychosocial or physical purposes in health care were included. Both databases were screened, and findings were summarized using a directed content analysis organized by the Consolidated Framework for Implementation Research (CFIR). RESULTS: Out of 3170 articles, 22 studies were selected for qualitative synthesis. Eleven themes were determined in the CFIR categories of intervention, user characteristics, and process. No information was found for the CFIR categories inner (eg, organizational resources, innovation climate) and outer (eg, external policies and incentives, pressure from competitors) settings. Reminders, attractive designs, tailored and clear data visualizations (intervention), smartphone experience, and intrinsic motivation to change behavior (user characteristics) could influence the use of electronic diaries. During the implementation process, attention should be paid to both theoretical and practical training. CONCLUSIONS: Design aspects, user characteristics, and training and instructions determine the use of electronic diaries in health care. It is remarkable that there were no empirical data about factors related to embedding electronic diaries in daily clinical practice. More research is needed to better understand influencing factors for optimal electronic diary use.

Implementing Experience Sampling Technology for Functional Analysis in Family Medicine - A Design Thinking Approach.

BACKGROUND: A paradigm shift in health care from illness to wellbeing requires new assessment technologies and intervention strategies. Self-monitoring tools based on the Experience Sampling Method (ESM) might provide a solution. They enable patients to monitor both vulnerability and resilience in daily life. Although ESM solutions are extensively used in research, a translation from science into daily clinical practice is needed. OBJECTIVE: To investigate the redesign process of an existing platform for ESM data collection for detailed functional analysis and disease management used by psychological assistants to the general practitioner (PAGPs) in family medicine. METHODS: The experience-sampling platform was reconceptualized according to the design thinking framework in three phases. PAGPs were closely involved in co-creation sessions. In the 'understand' phase, knowledge about end-users' characteristics and current eHealth use was collected (nominal group technique - 2 sessions with N = 15). In the 'explore' phase, the key needs concerning the platform content and functionalities were evaluated and prioritized (empathy mapping - 1 session with N = 5, moderated user testing - 1 session with N = 4). In the 'materialize' phase, the adjusted version of the platform was tested in daily clinical practice (4 months with N = 4). The whole process was extensively logged, analyzed using content analysis, and discussed with an interprofessional project group. RESULTS: In the 'understand' phase, PAGPs emphasized the variability in symptoms reported by patients. Therefore, moment-to-moment assessment of mood and behavior in a daily life context could be valuable. In the 'explore' phase, (motivational) functionalities, technological performance and instructions turned out to be important user requirements and could be improved. In the 'materialize' phase, PAGPs encountered barriers to implement the experience-sampling platform. They were insufficiently facilitated by the regional primary care group and general practitioners. CONCLUSION: The redesign process in co-creation yielded meaningful insights into the needs, desires and daily routines in family medicine. Severe barriers were encountered related to the use and uptake of the experience-sampling platform in settings where health care professionals lack the time, knowledge and skills. Future research should focus on the applicability of this platform in family medicine and incorporate patient experiences.

Feasibility of a mobile and web-based intervention to support self-management in outpatients with cancer pain.

PURPOSE: Cancer pain is a prevalent and distressing symptom. To enhance self-management in outpatients, a multi-component intervention was developed, integrating patient self-management and professional care through healthcare technology. This article describes feasibility of the intervention in everyday practice. METHOD: Patients with moderate to severe cancer pain (n = 11) and registered nurses specialized in pain and palliative care (n = 3) participated in a four-week study. The intervention involved daily monitoring, graphical feedback, education, and advice by means of a mobile application for patients and a web application for nurses. Learnability, usability and desirability were measured in patients with a 20-item questionnaire (1-5 scale), higher scores indicating better feasibility. Patients' adherence was based on completion rates from server logs. Single semi-structured interviews with patients and a focus group interview with nurses provided insight into experiences. RESULTS: Questionnaire findings confirmed learnability (4.8), usability (4.8) and desirability (4.6) of the application for patients. Average completion rates were 76.8% for pain monitoring, 50.4% for medication monitoring and 100% for education sessions. Interviews revealed that patients were pleased with the simplicity of the mobile application and appreciated different components. Nurses agreed upon the added value and were mostly positive about the possibilities of the web application. Patients and nurses provided ideas for improvements relating to the content and technical performance of the intervention. CONCLUSIONS: Study results demonstrate feasibility of the intervention in everyday practice. Provided that content-related and technical adjustments are made, the intervention enables patients with cancer pain to practice self-management and nurses to remotely support these patients.

Update on Prevalence of Pain in Patients With Cancer: Systematic Review and Meta-Analysis.

CONTEXT: Cancer pain has a severe impact on quality of life and is associated with numerous psychosocial responses. Recent studies suggest that treatment of cancer pain has improved during the last decade. OBJECTIVES: The aim of this review was to examine the present status of pain prevalence and pain severity in patients with cancer. METHODS: A systematic search of the literature published between September 2005 and January 2014 was performed using the databases PubMed, Medline, Embase, CINAHL, and Cochrane. Articles in English or Dutch that reported on the prevalence of cancer pain in an adult population were included. Titles and abstracts were screened by two authors independently, after which full texts were evaluated and assessed on methodological quality. Study details and pain characteristics were extracted from the articles with adequate study quality. Prevalence rates were pooled with meta-analysis; meta-regression was performed to explore determinants of pain prevalence. RESULTS: Of 4117 titles, 122 studies were selected for the meta-analyses on pain (117 studies, n = 63,533) and pain severity (52 studies, n = 32,261). Pain prevalence rates were 39.3% after curative treatment; 55.0% during anticancer treatment; and 66.4% in advanced, metastatic, or terminal disease. Moderate to severe pain (numerical rating scale score ≥5) was reported by 38.0% of all patients. CONCLUSION: Despite increased attention on assessment and management, pain continues to be a prevalent symptom in patients with cancer. In the upcoming decade, we need to overcome barriers toward effective pain treatment and develop and implement interventions to optimally manage pain in patients with cancer.