Mobile Applications for Patient-centered Care Coordination: A Review of Human Factors Methods Applied to their Design, Development, and Evaluation

A GDPR-Compliant Dynamic Consent Mobile Application for the Australasian Type-1 Diabetes Data Network

Australia has a high prevalence of diabetes, with approximately 1.2 million Australians diagnosed with the disease. In 2012, the Australasian Diabetes Data Network (ADDN) was established with funding from the Juvenile Diabetes Research Foundation (JDRF). ADDN is a national diabetes registry which captures longitudinal information about patients with type-1 diabetes (T1D). Currently, the ADDN data are directly contributed from 42 paediatric and 17 adult diabetes centres across Australia and New Zealand, i.e., where the data are pre-existing in hospital systems and not manually entered into ADDN. The historical data in ADDN have been de-identified, and patients are initially afforded the opportunity to opt-out of being involved in the registry; however, moving forward, there is an increased demand from the clinical research community to utilise fully identifying data. This raises additional demands on the registry in terms of security, privacy, and the nature of patient consent. General Data Protection Regulation (GDPR) is an increasingly important mechanism allowing individuals to have the right to know about their health data and what those data are being used for. This paper presents a mobile application being designed to support the ADDN data collection and usage processes and aligning them with GDPR. The app utilises Dynamic Consent-an informed specific consent model, which allows participants to view and modify their research-driven consent decisions through an interactive interface. It focuses specifically on supporting dynamic opt-in consent to both the registry and to associated sub-projects requesting access to and use of the patient data for research purposes.

Health Information Technologies in a Resource-Limited Setting: Knowledge, Attitude, and Practice of Health Professionals

The use of health information technology significantly enhances patient outcomes. As a result, policymakers from developing countries have placed strong emphasis on formulating eHealth policies and initiatives. However, there have not been many successful deployments to show for. The role of individual factors in the successful implementation of these technologies is indispensable. Therefore, this study assesses healthcare professionals' knowledge, attitudes, and practice of health information technology. An institution-based cross-sectional study was conducted at the University of Gondar Comprehensive Specialized Hospital from November 15 to December 29, 2020. A structured, self-administered questionnaire was used to collect data. Student's t-test was used to learn if there were any significant differences in practice habits between participants with and without previous information technology-related training. In addition, first-order partial correlation was conducted to identify the relationship of knowledge and attitude with practice. A total of 347 health professionals responded to the questionnaire, yielding an 87.2% response rate. Most health professionals are not aware of how to use health information technologies. Notably, practice levels were low and needed prompt action from responsible authorities. Previous training did not work very well to improve the practice levels of health professionals. However, the positive attitude of these professionals encourages policymakers and implementers to engage closely.

"Hey Siri, Help Me Take Care of My Child": A Feasibility Study With Caregivers of Children With Special Healthcare Needs Using Voice Interaction and Automatic Speech Recognition in Remote Care Management

Background

About 23% of households in the United States have at least one child who has special healthcare needs. As most care activities occur at home, there is often a disconnect and lack of communication between families, home care nurses, and healthcare providers. Digital health technologies may help bridge this gap.

Objective

We conducted a pre-post study with a voice-enabled medical note taking (diary) app (SpeakHealth) in a real world setting with caregivers (parents, family members) of children with special healthcare needs (CSHCN) to understand feasibility of voice interaction and automatic speech recognition (ASR) for medical note taking at home.

Methods

In total, 41 parents of CSHCN were recruited. Participants completed a pre-study survey collecting demographic details, technology and care management preferences. Out of 41, 24 participants completed the study, using the app for 2 weeks and completing an exit survey. The app facilitated caregiver note-taking using voice interaction and ASR. An exit survey was conducted to collect feedback on technology adoption and changes in technology preferences in care management. We assessed the feasibility of the app by descriptively analyzing survey responses and user data following the key focus areas of acceptability, demand, implementation and integration, adaptation and expansion. In addition, perceived effectiveness of the app was assessed by comparing perceived changes in mobile app preferences among participants. In addition, the voice data, notes, and transcriptions were descriptively analyzed for understanding the feasibility of the app.

Results

The majority of the recruited parents were 35–44 years old (22, 53.7%), part of a two-parent household (30, 73.2%), white (37, 90.2%), had more than one child (31, 75.6%), lived in Ohio (37, 90.2%), used mobile health apps, mobile note taking apps or calendar apps (28, 68.3%) and patient portal apps (22, 53.7%) to track symptoms and health events at home. Caregivers had experience with voice technology as well (32, 78%). Among those completed the post-study survey (in Likert Scale 1–5), ~80% of the caregivers agreed or strongly agreed that using the app would enhance their performance in completing tasks (perceived usefulness; mean = 3.4, SD = 0.8), the app is free of effort (perceived ease of use; mean = 3.2, SD = 0.9), and they would use the app in the future (behavioral intention; mean = 3.1, SD = 0.9). In total, 88 voice interactive patient notes were generated with the majority of the voice recordings being less than 20 s in length (66%). Most noted symptoms and conditions, medications, treatment and therapies, and patient behaviors. More than half of the caregivers reported that voice interaction with the app and using transcribed notes positively changed their preference of technology to use and methods for tracking symptoms and health events at home.

Conclusions

Our findings suggested that voice interaction and ASR use in mobile apps are feasible and effective in keeping track of symptoms and health events at home. Future work is suggested toward using integrated and intelligent systems with voice interactions with broader populations.

A knowledge-based self-pre-diagnosis system to predict Covid-19 in smartphone users using personal data and observed symptoms

Covid-19 is an acute respiratory infection and presents various clinical features ranging from no symptoms to severe pneumonia and death. Medical expert systems, especially in diagnosis and monitoring stages, can give positive consequences in the struggle against Covid-19. In this study, a rule-based expert system is designed as a predictive tool in self-pre-diagnosis of Covid-19. The potential users are smartphone users, healthcare experts and government health authorities. The system does not only share the data gathered from the users with experts, but also analyzes the symptom data as a diagnostic assistant to predict possible Covid-19 risk. To do this, a user needs to fill out a patient examination card that conducts an online Covid-19 diagnostic test, to receive an unconfirmed online test prediction result and a set of precautionary and supportive action suggestions. The system was tested for 169 positive cases. The results produced by the system were compared with the real PCR test results for the same cases. For patients with certain symptomatic findings, there was no significant difference found between the results of the system and the confirmed test results with PCR test. Furthermore, a set of suitable suggestions produced by the system were compared with the written suggestions of a collaborated health expert. The suggestions deduced and the written suggestions of the health expert were similar and the system suggestions in line with suggestions of the expert. The system can be suitable for diagnosing and monitoring of positive cases in the areas other than clinics and hospitals during the Covid-19 pandemic. The results of the case studies are promising, and it demonstrates the applicability, effectiveness, and efficiency of the proposed approach in all communities.

A natural language processing pipeline to synthesize patient-generated notes toward improving remote care and chronic disease management: a cystic fibrosis case study

Objectives

Patient-generated health data (PGHD) are important for tracking and monitoring out of clinic health events and supporting shared clinical decisions. Unstructured text as PGHD (eg, medical diary notes and transcriptions) may encapsulate rich information through narratives which can be critical to better understand a patient’s condition. We propose a natural language processing (NLP) supported data synthesis pipeline for unstructured PGHD, focusing on children with special healthcare needs (CSHCN), and demonstrate it with a case study on cystic fibrosis (CF).

Materials and Methods

The proposed unstructured data synthesis and information extraction pipeline extract a broad range of health information by combining rule-based approaches with pretrained deep-learning models. Particularly, we build upon the scispaCy biomedical model suite, leveraging its named entity recognition capabilities to identify and link clinically relevant entities to established ontologies such as Systematized Nomenclature of Medicine (SNOMED) and RXNORM. We then use scispaCy’s syntax (grammar) parsing tools to retrieve phrases associated with the entities in medication, dose, therapies, symptoms, bowel movements, and nutrition ontological categories. The pipeline is illustrated and tested with simulated CF patient notes.

Results

The proposed hybrid deep-learning rule-based approach can operate over a variety of natural language note types and allow customization for a given patient or cohort. Viable information was successfully extracted from simulated CF notes. This hybrid pipeline is robust to misspellings and varied word representations and can be tailored to accommodate the needs of a specific patient, cohort, or clinician.

Discussion

The NLP pipeline can extract predefined or ontology-based entities from free-text PGHD, aiming to facilitate remote care and improve chronic disease management. Our implementation makes use of open source models, allowing for this solution to be easily replicated and integrated in different health systems. Outside of the clinic, the use of the NLP pipeline may increase the amount of clinical data recorded by families of CSHCN and ease the process to identify health events from the notes. Similarly, care coordinators, nurses and clinicians would be able to track adherence with medications, identify symptoms, and effectively intervene to improve clinical care. Furthermore, visualization tools can be applied to digest the structured data produced by the pipeline in support of the decision-making process for a patient, caregiver, or provider.

Conclusion

Our study demonstrated that an NLP pipeline can be used to create an automated analysis and reporting mechanism for unstructured PGHD. Further studies are suggested with real-world data to assess pipeline performance and further implications.

Feasibility of a Voice-Enabled Medical Diary App (SpeakHealth) for Caregivers of Children With Special Health Care Needs and Health Care Providers: Mixed Methods Study

Background

Children with special health care needs (CSHCN) require more than the usual care management and coordination efforts from caregivers and health care providers (HCPs). Health information and communication technologies can potentially facilitate these efforts to increase the quality of care received by CSHCN.

Objective

In this study, we aim to assess the feasibility of a voice-enabled medical diary app (SpeakHealth) by investigating its potential use among caregivers and HCPs.

Methods

Following a mixed methods approach, caregivers of CSHCN were interviewed (n=10) and surveyed (n=86) about their care management and communication technology use. Only interviewed participants were introduced to the SpeakHealth app prototype, and they tested the app during the interview session. In addition, we interviewed complex care HCPs (n=15) to understand their perception of the value of a home medical diary such as the SpeakHealth app. Quantitative data were analyzed using descriptive statistics and correlational analyses. Theoretical thematic analysis was used to analyze qualitative data.

Results

The survey results indicated a positive attitude toward voice-enabled technology and features; however, there was no strong correlation among the measured items. The caregivers identified communication, information sharing, tracking medication, and appointments as fairly and highly important features of the app. Qualitative analysis revealed the following two overarching themes: enablers and barriers in care communication and enablers and barriers in communication technologies. The subthemes included parent roles, care communication technologies, and challenges. HCPs found the SpeakHealth app to be a promising tool for timely information collection that could be available for sharing information with the health system. Overall, the findings demonstrated a variety of needs and challenges for caregivers of CSHCN and opportunities for voice-enabled, interactive medical diary apps in care management and coordination. Caregivers fundamentally look for better information sharing and communication with HCPs. Health care and communication technologies can potentially improve care communication and coordination in addressing the patient and caregiver needs.

Conclusions

The perspectives of caregivers and providers suggested both benefits and challenges in using the SpeakHealth app for medical note-taking and tracking health events at home. Our findings could inform researchers and developers about the potential development and use of a voice-enabled medical diary app.

Artificial Intelligence-Powered Smartphone App to Facilitate Medication Adherence: Protocol for a Human Factors Design Study

BACKGROUND

Medication Guides consisting of crucial interactions and side effects are extensive and complex. Due to the exhaustive information, patients do not retain the necessary medication information, which can result in hospitalizations and medication nonadherence. A gap exists in understanding patients' cognition of managing complex medication information. However, advancements in technology and artificial intelligence (AI) allow us to understand patient cognitive processes to design an app to better provide important medication information to patients.

OBJECTIVE

Our objective is to improve the design of an innovative AI- and human factor-based interface that supports patients' medication information comprehension that could potentially improve medication adherence.

METHODS

This study has three aims. Aim 1 has three phases: (1) an observational study to understand patient perception of fear and biases regarding medication information, (2) an eye-tracking study to understand the attention locus for medication information, and (3) a psychological refractory period (PRP) paradigm study to understand functionalities. Observational data will be collected, such as audio and video recordings, gaze mapping, and time from PRP. A total of 50 patients, aged 18-65 years, who started at least one new medication, for which we developed visualization information, and who have a cognitive status of 34 during cognitive screening using the TICS-M test and health literacy level will be included in this aim of the study. In Aim 2, we will iteratively design and evaluate an AI-powered medication information visualization interface as a smartphone app with the knowledge gained from each component of Aim 1. The interface will be assessed through two usability surveys. A total of 300 patients, aged 18-65 years, with diabetes, cardiovascular diseases, or mental health disorders, will be recruited for the surveys. Data from the surveys will be analyzed through exploratory factor analysis. In Aim 3, in order to test the prototype, there will be a two-arm study design. This aim will include 900 patients, aged 18-65 years, with internet access, without any cognitive impairment, and with at least two medications. Patients will be sequentially randomized. Three surveys will be used to assess the primary outcome of medication information comprehension and the secondary outcome of medication adherence at 12 weeks.

RESULTS

Preliminary data collection will be conducted in 2021, and results are expected to be published in 2022.

CONCLUSIONS

This study will lead the future of AI-based, innovative, digital interface design and aid in improving medication comprehension, which may improve medication adherence. The results from this study will also open up future research opportunities in understanding how patients manage complex medication information and will inform the format and design for innovative, AI-powered digital interfaces for Medication Guides.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/21659.

Codesign and Usability Testing of a Mobile Application to Support Family-Delivered Enteral Tube Care

BACKGROUND

Enteral tubes are prevalent among children with medical complexity (CMC), and complications can lead to costly health care use. Our objective was to design and test the usability of a mobile application (app) to support family-delivered enteral tube care.

METHODS

Human-centered design methods (affinity diagramming, persona development, and software development) were applied with family caregivers of CMC to develop a prototype. During 3 waves of usability testing with design refinement between waves, screen capture software collected user-app interactions and inductive content analysis of narrative feedback identified areas for design improvement. The National Aeronautics and Space Administration Task Load Index and the System Usability Scale quantified mental workload and ease of use.

RESULTS

Design participants identified core app functions, including displaying care routines, reminders, tracking inventory and health data, caregiver communication, and troubleshooting. Usability testing participants were 80% non-Hispanic white, 28% lived in rural settings, and 20% had not completed high school. Median years providing enteral care was 2 (range 1-14). Design iterations improved app function, simplification, and user experience. The mean System Usability Scale score was 76, indicating above-average usability. National Aeronautics and Space Administration Task Load Index revealed low mental demand, frustration, and effort. All 14 participants reported that they would recommend the app, and that the app would help with organization, communication, and caregiver transitions.

CONCLUSIONS

Using a human-centered codesign process, we created a highly usable mobile application to support enteral tube caregiving at home. Future work involves evaluating the feasibility of longitudinal use and effectiveness in improving self-efficacy and reduce device complications.

Efficacy of Osteoporosis Prevention Smartphone App

BACKGROUND

The Striving to be Strong study tested the efficacy of a multifaceted, theory-based, complex osteoporosis prevention smartphone application (app). We hypothesized use of the app would improve bone mineral density and trabecular bone scores.

METHODS

The study was a three-group, prospective, repeated-measure, longitudinal randomized trial. Baseline sample consisted of 290 healthy women between 40 and 60 years of age. Participants were randomly assigned to one of three groups: "Striving," a dynamically tailored, person-centered app; "Boning Up," a standardized osteoporosis-education e-book; and "Wait List," a participant's choice of intervention in the final 3 months of the 12-month study. Participants had or were provided a smart phone. Bone mineral density and trabecular bone scores were measured using dual-energy X-ray absorptiometry at baseline and 12 months. To assess engagement in health behavior change processes, ecological momentary assessments were administered via text messaging during the 12 months participants actively used the app.

RESULTS

The final sample reflects an 89.6% retention rate. There were decreases in bone mineral density over time but not among the three groups. The percentage of bone density lost over 12 months was lower than expected. Trabecular bone scores were not different over time or by group but improved across all three groups.

DISCUSSION

Small but positive results were observed across all groups, suggesting one or more aspects of participation might have affected outcomes, including dissemination of the intervention across groups, retention without participation, ecological momentary assessments functioning as both an intervention and measure, and selective engagement in research-based recommendations.

Capturing At-Home Health and Care Information for Children With Medical Complexity Using Voice Interactive Technologies: Multi-Stakeholder Viewpoint

Digital health tools and technologies are transforming health care and making significant impacts on how health and care information are collected, used, and shared to achieve best outcomes. As most of the efforts are still focused on clinical settings, the wealth of health information generated outside of clinical settings is not being fully tapped. This is especially true for children with medical complexity (CMC) and their families, as they frequently spend significant hours providing hands-on medical care within the home setting and coordinating activities among multiple providers and other caregivers. In this paper, a multidisciplinary team of stakeholders discusses the value of health information generated at home, how technology can enhance care coordination, and challenges of technology adoption from a patient-centered perspective. Voice interactive technology has been identified to have the potential to transform care coordination for CMC. This paper shares opinions on the promises, limitations, recommended approaches, and challenges of adopting voice technology in health care, especially for the targeted patient population of CMC.

A Technology-Assisted, Brief Mind-Body Intervention to Improve the Waiting Room Experience for Chemotherapy Patients: Randomized Quality Improvement Study

Background

Patients waiting for chemotherapy can experience stress, anxiety, nausea, and pain. Acupressure and meditation have been shown to control such symptoms.

Objective

This study aimed to evaluate the feasibility and effectiveness of an integrative medicine app to educate patients about these self-care tools in chemotherapy waiting rooms.

Methods

We screened and enrolled cancer patients in chemotherapy waiting rooms at two Memorial Sloan Kettering Cancer Center locations. Patients were randomly assigned into an intervention arm in which subjects watched acupressure and meditation instructional videos or a control arm in which they watched a time- and attention-matched integrative oncology lecture video. Before and after watching the videos, we asked the patients to rate four key symptoms: stress, anxiety, nausea, and pain. We performed the analysis of covariance to detect differences between the two arms postintervention while controlling for baseline symptoms.

Results

A total of 223 patients were enrolled in the study: 113 patients were enrolled in the intervention arm and 110 patients were enrolled in the control arm. In both groups, patients showed significant reductions in stress and anxiety from baseline (all P<.05), with the treatment arm reporting greater stress and anxiety reduction than the control arm (1.64 vs 1.15 in stress reduction; P=.01 and 1.39 vs 0.78 in anxiety reduction; P=.002). The majority of patients reported that the videos helped them pass time and that they would watch the videos again.

Conclusions

An integrative medicine self-care app in the waiting room improved patients’ experiences and reduced anxiety and stress. Future research could focus on expanding this platform to other settings to improve patients’ overall treatment experiences.

Methods for Evaluating the Content, Usability, and Efficacy of Commercial Mobile Health Apps

Commercial mobile apps for health behavior change are flourishing in the marketplace, but little evidence exists to support their use. This paper summarizes methods for evaluating the content, usability, and efficacy of commercially available health apps. Content analyses can be used to compare app features with clinical guidelines, evidence-based protocols, and behavior change techniques. Usability testing can establish how well an app functions and serves its intended purpose for a target population. Observational studies can explore the association between use and clinical and behavioral outcomes. Finally, efficacy testing can establish whether a commercial app impacts an outcome of interest via a variety of study designs, including randomized trials, multiphase optimization studies, and N-of-1 studies. Evidence in all these forms would increase adoption of commercial apps in clinical practice, inform the development of the next generation of apps, and ultimately increase the impact of commercial apps.

Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems

Objectives

Smartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients’ needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems.

Methods

We systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed.

Results

Data mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients’ self-management.

Conclusions

Embedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.

Posttraumatic Stress Disorder and Mobile Health: App Investigation and Scoping Literature Review

BACKGROUND

Posttraumatic stress disorder (PTSD) is a prevalent mental health issue among veterans. Access to PTSD treatment is influenced by geographic (ie, travel distance to facilities), temporal (ie, time delay between services), financial (ie, eligibility and cost of services), and cultural (ie, social stigma) barriers.

OBJECTIVE

The emergence of mobile health (mHealth) apps has the potential to bridge many of these access gaps by providing remote resources and monitoring that can offer discrete assistance to trauma survivors with PTSD and enhance patient-clinician relationships. In this study, we investigate the current mHealth capabilities relevant to PTSD.

METHODS

This study consists of two parts: (1) a review of publicly available PTSD apps designed to determine the availability of PTSD apps, which includes more detailed information about three dominant apps and (2) a scoping literature review performed using a systematic method to determine app usage and efforts toward validation of such mHealth apps. App usage relates to how the end users (eg, clinicians and patients) are interacting with the app, whereas validation is testing performed to ensure the app's purpose and specifications are met.

RESULTS

The results suggest that though numerous apps have been developed to aid in the diagnosis and treatment of PTSD symptoms, few apps were designed to be integrated with clinical PTSD treatment, and minimal efforts have been made toward enhancing the usability and validation of PTSD apps.

CONCLUSIONS

These findings expose the need for studies relating to the human factors evaluation of such tools, with the ultimate goal of increasing access to treatment and widening the app adoption rate for patients with PTSD.

Are We There Yet? Human Factors Knowledge and Health Information Technology - the Challenges of Implementation and Impact

Objective: To review the developments in human factors (HF) research on the challenges of health information technology (HIT) implementation and impact given the continuing incidence of usability problems and unintended consequences from HIT development and use. Methods: A search of PubMed/Medline and Web of Science® identified HF research published in 2015 and 2016. Electronic health records (EHRs) and patient-centred HIT emerged as significant foci of recent HF research. The authors selected prominent papers highlighting ongoing HF and usability challenges in these areas. This selective rather than systematic review of recent HF research highlights these key challenges and reflects on their implications on the future impact of HF research on HIT. Results: Research provides evidence of continued poor design, implementation, and usability of HIT, as well as technology-induced errors and unintended consequences. The paper highlights support for: (i) strengthening the evidence base on the benefits of HF approaches; (ii) improving knowledge translation in the implementation of HF approaches during HIT design, implementation, and evaluation; (iii) increasing transparency, governance, and enforcement of HF best practices at all stages of the HIT system development life cycle. Discussion and Conclusion: HF and usability approaches are yet to become embedded as integral components of HIT development, implementation, and impact assessment. As HIT becomes ever-more pervasive including with patients as end-users, there is a need to expand our conceptualisation of the problems to be addressed and the suite of tactics and strategies to be used to calibrate our pro-active involvement in its improvement.

Impact of Patient-centered eHealth Applications on Patient Outcomes: A Review on the Mediating Influence of Human Factor Issues

OBJECTIVES

To examine the evidence of the impact of patient- centered eHealth applications on patient care and to analyze if and how reported human factor issues mediated the outcomes.

METHODS

We searched PubMed (2014-2015) for studies evaluating the impact of patient-centered eHealth applications on patient care (behavior change, self-efficacy, and patient health-related outcomes). The Systems Engineering Initiative for Patient Safety (SEIPS 2.0) model was used as a guidance framework to identify the reported human factors possibly impacting the effectiveness of an eHealth intervention.

RESULTS

Of the 348 potentially relevant papers, 10 papers were included for data analysis. None of the 10 papers reported a negative impact of the eHealth intervention. Seven papers involved a randomized controlled trial (RCT) study. Six of these RCTs reported a positive impact of the eHealth intervention on patient care. All 10 papers reported on human factor issues possibly mediating effects of patient-centered eHealth. Human factors involved patient characteristics, perceived social support, and (type of) interaction between patient and provider.

CONCLUSION

While the amount of patient-centered eHealth interventions increases, many questions remain as to whether and to what extent human factors mediate their use and impact. Future research should adopt a formal theory-driven approach towards human factors when investigating those factors' influence on the effectiveness of these interventions. Insights could then be used to better tailor the content and design of eHealth solutions according to patient user profiles, so as to enhance eHealth interventions impact on patient behavior, self-efficacy, and health-related outcomes.