Designing Personalised mHealth solutions: An overview

Introduction Mobile health, or mHealth, is based on mobile information and communication technologies and provides solutions for empowering individuals to participate in healthcare. Personalisation techniques have been used to increase user engagement and adherence to interventions delivered as mHealth solutions. This study aims to explore the current state of personalisation in mHealth, including its current trends and implementation. Materials and Methods We conducted a review following PRISMA guidelines. Four databases (PubMed, ACM Digital Library, IEEE Xplore, and APA PsycInfo) were searched for studies on mHealth solutions that integrate personalisation. The retrieved papers were assessed for eligibility and useful information regarding integrated personalisation techniques. Results Out of the 1,139 retrieved studies, 62 were included in the narrative synthesis. Research interest in the personalisation of mHealth solutions has increased since 2020. mHealth solutions were mainly applied to endocrine, nutritional, and metabolic diseases; mental, behavioural, or neurodevelopmental diseases; or the promotion of healthy lifestyle behaviours. Its main purposes are to support disease self-management and promote healthy lifestyle behaviours. Mobile applications are the most prevalent technological solution. Although several design models, such as user-centred and patient-centred designs, were used, no specific frameworks or models for personalisation were followed. These solutions rely on behaviour change theories, use gamification or motivational messages, and personalise the content rather than functionality. A broad range of data is used for personalisation purposes. There is a lack of studies assessing the efficacy of these solutions; therefore, further evidence is needed. Discussion Personalisation in mHealth has not been well researched. Although several techniques have been integrated, the effects of using a combination of personalisation techniques remain unclear. Although personalisation is considered a persuasive strategy, many mHealth solutions do not employ it. Conclusions Open research questions concern guidelines for successful personalisation techniques in mHealth, design frameworks, and comprehensive studies on the effects and interactions among multiple personalisation techniques

Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework

Background: Conversational agents (CAs), also known as chatbots, are computer programs that simulate human conversations by using predetermined rule-based responses or artificial intelligence algorithms. They are increasingly used in health care, particularly via smartphones. There is, at present, no conceptual framework guiding the development of smartphone-based, rule-based CAs in health care. To fill this gap, we propose structured and tailored guidance for their design, development, evaluation, and implementation. Objective: The aim of this study was to develop a conceptual framework for the design, evaluation, and implementation of smartphone-delivered, rule-based, goal-oriented, and text-based CAs for health care. Methods: We followed the approach by Jabareen, which was based on the grounded theory method, to develop this conceptual framework. We performed 2 literature reviews focusing on health care CAs and conceptual frameworks for the development of mobile health interventions. We identified, named, categorized, integrated, and synthesized the information retrieved from the literature reviews to develop the conceptual framework. We then applied this framework by developing a CA and testing it in a feasibility study. Results: The Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER) conceptual framework includes 8 iterative steps grouped into 3 stages, as follows: design, comprising defining the goal, creating an identity, assembling the team, and selecting the delivery interface; development, including developing the content and building the conversation flow; and the evaluation and implementation of the CA. They were complemented by 2 cross-cutting considerations-user-centered design and privacy and security-that were relevant at all stages. This conceptual framework was successfully applied in the development of a CA to support lifestyle changes and prevent type 2 diabetes. Conclusions: Drawing on published evidence, the DISCOVER conceptual framework provides a step-by-step guide for developing rule-based, smartphone-delivered CAs. Further evaluation of this framework in diverse health care areas and settings and for a variety of users is needed to demonstrate its validity. Future research should aim to explore the use of CAs to deliver health care interventions, including behavior change and potential privacy and safety concerns. Keywords: chatbot; conceptual framework; conversational agent; digital health; mHealth; mobile health; mobile phone

Designing personalised mHealth solutions: An overview

Introduction: Mobile health, or mHealth, is based on mobile information and communication technologies and provides solutions for empowering individuals to participate in healthcare. Personalisation techniques have been used to increase user engagement and adherence to interventions delivered as mHealth solutions. This study aims to explore the current state of personalisation in mHealth, including its current trends and implementation. Materials and Methods: We conducted a review following PRISMA guidelines. Four databases (PubMed, ACM Digital Library, IEEE Xplore, and APA PsycInfo) were searched for studies on mHealth solutions that integrate personalisation. The retrieved papers were assessed for eligibility and useful information regarding integrated personalisation techniques. Results: Out of the 1,139 retrieved studies, 62 were included in the narrative synthesis. Research interest in the personalisation of mHealth solutions has increased since 2020. mHealth solutions were mainly applied to endocrine, nutritional, and metabolic diseases; mental, behavioural, or neurodevelopmental diseases; or the promotion of healthy lifestyle behaviours. Its main purposes are to support disease self- management and promote healthy lifestyle behaviours. Mobile applications are the most prevalent technological solution. Although several design models, such as user-centred and patient-centred designs, were used, no specific frameworks or models for personalisation were followed. These solutions rely on behaviour change theories, use gamification or motivational messages, and personalise the content rather than functionality. A broad range of data is used for personalisation purposes. There is a lack of studies assessing the efficacy of these solutions; therefore, further evidence is needed. Discussion: Personalisation in mHealth has not been well researched. Although several techniques have been integrated, the effects of using a combination of personalisation techniques remain unclear. Although personalisation is considered a persuasive strategy, many mHealth solutions do not employ it. Conclusions: Open research questions concern guidelines for successful personalisation techniques in mHealth, design frameworks, and comprehensive studies on the effects and interactions among multiple personalisation techniques

Psychiatr Q

ObjectiveEffective and scalable lifestyle interventions are needed to address high rates of obesity in people with serious mental illness (SMI). This pilot study evaluated the feasibility of a behavioral weight loss intervention enhanced with peer support and mobile health (mHealth) technology for obese individuals with SMI.MethodsThe Diabetes Prevention Program Group Lifestyle Balance intervention enhanced with peer support and mHealth technology was implemented in a public mental health setting. Thirteen obese individuals with SMI participated in a pre-post pilot study of the 24-week intervention. Feasibility was assessed by program attendance, and participant satisfaction and suggestions for improving the model. Descriptive changes in weight and fitness were also explored.ResultsOverall attendance amounted to approximately half (56%) of weekly sessions. At 6-month follow-up, 45% of participants had lost weight, and 45% showed improved fitness by increasing their walking distance. Participants suggested a number of modifications to increase the relevance of the intervention for people with SMI, including less didactic instruction and more active learning, a simplified dietary component, more in depth technology training, and greater attention to mental health.ConclusionsThe principles of standard behavioral weight loss treatment provide a useful starting point for promoting weight loss in people with SMI. However, adaptions to standard weight loss curricula are needed to enhance engagement, participation, and outcomes to respond to the unique challenges of individuals with SMI.K12 HS021695/HS/AHRQ HHS/United StatesR01 MH089811/MH/NIMH NIH HHS/United StatesU48 DP005018/DP/NCCDPHP CDC HHS/United StatesU48DP005018/ACL HHS/United States2017-09-01T00:00:00Z26462674PMC4929042vault:1799

J Ment Health

BackgroundSerious mental illness is one of the leading causes of disability worldwide. Emerging mobile health (mHealth) and eHealth interventions may afford opportunities for reaching this at-risk group.AimTo review the evidence on using emerging mHealth and eHealth technologies among people with serious mental illness.MethodsWe searched MEDLINE, PsychINFO, CINAHL, Scopus, Cochrane Central, and Web of Science through July 2014. Only studies reporting outcomes for mHealth or eHealth interventions, defined as remotely delivered using mobile, online, or other devices, targeting people with schizophrenia, schizoaffective disorder, or bipolar disorder, were included.ResultsForty-six studies spanning 12 countries were included. Interventions were grouped into four categories: 1) illness self-management and relapse prevention; 2) promoting adherence to medications and/or treatment; 3) psychoeducation, supporting recovery, and promoting health and wellness; and 4) symptom monitoring. The interventions were consistently found to be highly feasible and acceptable, though clinical outcomes were variable but offered insight regarding potential effectiveness.ConclusionsOur findings confirm the feasibility and acceptability of emerging mHealth and eHealth interventions among people with serious mental illness; however, it is not possible to draw conclusions regarding effectiveness. Further rigorous investigation is warranted to establish effectiveness and cost benefit in this population.R01 MH104555/MH/NIMH NIH HHS/United StatesR01 MH104555/MH/NIMH NIH HHS/United StatesU48DP005018/DP/NCCDPHP CDC HHS/United States2016-06-28T00:00:00Z26017625PMC492480

The usability, acceptability, and satisfaction of a digital mental health tool for patients with breast and prostate cancer

The impact and use of digital health tools vary considerably among individuals dealing with somatic illnesses, such as cancer. This variability can be attributed to several factors, such as sociodemographic characteristics, baseline mental health, perception of the intervention’s usefulness, ease of use, and early engagement with the system. In this thesis, we aimed to examine the influence and interaction among these indicators on the usability, acceptability, satisfaction, and clinical effectiveness of a digital health tool in individuals with breast and prostate cancer. All studies were based on data from the NEVERMIND trial, a clinical randomized controlled trial that included patients with five different somatic illnesses. Our study included 255 participants (at baseline) who were diagnosed with breast or prostate cancer. Half of the participants (n=129) were allocated to the NEVERMIND system, whereas the other half (n=125) were allocated to the treatment as usual (control) group. Those in the NEVERMIND system group were involved in the use of the NEVERMIND digital health tool, comprising a mobile app and sensorized shirt (shirt), over a 12-week period. Data from baseline assessments and follow-ups at four and 12 weeks were used. The aim was to assess the usability, acceptability, and satisfaction of the NEVERMIND system, as well as the factors associated with these dimensions. This Ph.D. project also examined how usability and acceptability impacted the clinical effectiveness of the NEVERMIND system on depressive and stress symptoms. Study I. We investigated the association between baseline sociodemographic characteristics and usability assessed at four and 12 weeks of using the NEVERMIND system among 108 patients with breast and prostate cancer who received and used the system. The NEVERMIND system had good usability according to the usability questionnaires. Higher favourability of the mobile app was observed among women (breast cancer patients) compared to men (prostate cancer patients); however, men had significantly higher use of the overall system. Study II. The relationships between sex, education, baseline depressive and stress symptoms, perceived ease of use, perceived usefulness, and system usage at various stages were examined using Bayesian Structural Equation Modelling in a path analysis of 129 patients with breast and prostate cancer. Higher perceived usefulness and initial usage were associated with a higher level of usage at 12 weeks. The results indicated that a better understanding of the system’s benefits and early engagement were key drivers of its sustained use and clinical effectiveness in improving mental health outcomes. Study III. In a sample of 255 patients with breast and prostate cancer, we examined the relationship between the clinical effectiveness, usability, and acceptability of the NEVERMIND system when treating depressive and stress symptoms in patients with breast and prostate cancer. The results showed that patients in the NEVERMIND group had a greater reduction in depressive symptoms than those in the control group at the 12-week follow-up. The findings also showed that users who utilized the system for more than six weeks experienced a statistically significant decrease in both depressive symptoms and stress symptoms compared to those who used it for less than two weeks. Study IV. This study looked at the overall satisfaction of users (68 with breast cancer and 39 with prostate cancer) with the NEVERMIND system. Satisfaction was measured at four and 12 weeks using a one-item questionnaire with two open-ended follow-up questions about user experiences. An inductive and deductive thematic analysis was conducted by using the NEVERMIND system’s components as a sensitizing concept which was then refined and interpreted through the lens of Information Systems (IS) success model. The findings show that 68.24% of users rated the system as good or excellent at four weeks, with a slight decrease to 65.42% at 12 weeks. Three themes emerged from the thematic analysis: (1) Fostering Personal Agency and Motivation, (2) Engagement and Interaction Experiences, and (3) Content Quality and Relevance. Gender differences emerged in the prioritization of emotional support among female users and self-awareness among male users. The satisfaction and challenges faced by users underscore the importance of a user-centric approach that focuses on holistic well-being, user engagement, personalized content, and technical stability. This study also contributes to the broader literature by utilizing IS success model as a framework for interpreting user satisfaction. Conclusions. Higher levels of usability, acceptability, and satisfaction in the NEVERMIND system may contribute to improving the mental health outcomes of patients with breast and prostate cancer, both independently from each other, and even more so when high levels of engagement, acceptance, use, and satisfaction coexist. They emphasize the importance of perceived usefulness, initial engagement, and user-centric design in different components of the NEVERMIND system and confirms the multidimensionality of successful digital health tools implementation. Moreover, the notable differences in usability and preference between genders indicate that tailored and personalized strategies might serve as effective means to address diverse user needs. Taken together, these insights strengthen scientific evidence for healthcare experts and digital health innovators and developers, guiding them towards creating and designing digital health tools through user-centric and multi-domain approaches

Translating knowledge for action against stroke – using 5-minute videos for stroke survivors and caregivers to improve post-stroke outcomes: study protocol for a randomized controlled trial (Movies4Stroke)

Participant timeline. (DOCX 48 kb

ProHealth eCoach: user-centered design and development of an eCoach app to promote healthy lifestyle with personalized activity recommendations

Background: Regular physical activity (PA), healthy habits, and an appropriate diet are recommended guidelines to maintain a healthy lifestyle. A healthy lifestyle can help to avoid chronic diseases and long-term illnesses. A monitoring and automatic personalized lifestyle recommendation system (i.e., automatic electronic coach or eCoach) with considering clinical and ethical guidelines, individual health status, condition, and preferences may successfully help participants to follow recommendations to maintain a healthy lifestyle. As a prerequisite for the prototype design of such a helpful eCoach system, it is essential to involve the end-users and subject-matter experts throughout the iterative design process. Methods: We used an iterative user-centered design (UCD) approach to understend context of use and to collect qualitative data to develop a roadmap for self-management with eCoaching. We involved researchers, non-technical and technical, health professionals, subject-matter experts, and potential end-users in design process. We designed and developed the eCoach prototype in two stages, adopting diferent phases of the iterative design process. In design workshop 1, we focused on identifying end-users, understanding the user’s context, specifying user requirements, designing and developing an initial low-fdelity eCoach prototype. In design workshop 2, we focused on maturing the low-fdelity solution design and development for the visualization of continuous and discrete data, artifcial intelligence (AI)-based interval forecasting, personalized recommendations, and activity goals. Results: The iterative design process helped to develop a working prototype of eCoach system that meets end-user’s requirements and expectations towards an efective recommendation visualization, considering diversity in culture, quality of life, and human values. The design provides an early version of the solution, consisting of wearable technology, a mobile app following the “Google Material Design” guidelines, and web content for self-monitoring, goal setting, and lifestyle recommendations in an engaging manner between the eCoach app and end-users. Conclusions: The adopted iterative design process brings in a design focus on the user and their needs at each phase. Throughout the design process, users have been involved at the heart of the design to create a working.publishedVersio

Methods and measures used to evaluate patient-operated mobile health interventions:Scoping literature review

Background: Despite the prevalence of mobile health (mHealth) technologies and observations of their impacts on patients’ health, there is still no consensus on how best to evaluate these tools for patient self-management of chronic conditions. Researchers currently do not have guidelines on which qualitative or quantitative factors to measure or how to gather these reliable data. Objective: This study aimed to document the methods and both qualitative and quantitative measures used to assess mHealth apps and systems intended for use by patients for the self-management of chronic noncommunicable diseases. Methods: A scoping review was performed, and PubMed, MEDLINE, Google Scholar, and ProQuest Research Library were searched for literature published in English between January 1, 2015, and January 18, 2019. Search terms included combinations of the description of the intention of the intervention (eg, self-efficacy and self-management) and description of the intervention platform (eg, mobile app and sensor). Article selection was based on whether the intervention described a patient with a chronic noncommunicable disease as the primary user of a tool or system that would always be available for self-management. The extracted data included study design, health conditions, participants, intervention type (app or system), methods used, and measured qualitative and quantitative data. Results: A total of 31 studies met the eligibility criteria. Studies were classified as either those that evaluated mHealth apps (ie, single devices; n=15) or mHealth systems (ie, more than one tool; n=17), and one study evaluated both apps and systems. App interventions mainly targeted mental health conditions (including Post-Traumatic Stress Disorder), followed by diabetes and cardiovascular and heart diseases; among the 17 studies that described mHealth systems, most involved patients diagnosed with cardiovascular and heart disease, followed by diabetes, respiratory disease, mental health conditions, cancer, and multiple illnesses. The most common evaluation method was collection of usage logs (n=21), followed by standardized questionnaires (n=18) and ad-hoc questionnaires (n=13). The most common measure was app interaction (n=19), followed by usability/feasibility (n=17) and patient-reported health data via the app (n=15). Conclusions: This review demonstrates that health intervention studies are taking advantage of the additional resources that mHealth technologies provide. As mHealth technologies become more prevalent, the call for evidence includes the impacts on patients’ self-efficacy and engagement, in addition to traditional measures. However, considering the unstructured data forms, diverse use, and various platforms of mHealth, it can be challenging to select the right methods and measures to evaluate mHealth technologies. The inclusion of app usage logs, patient-involved methods, and other approaches to determine the impact of mHealth is an important step forward in health intervention research. We hope that this overview will become a catalogue of the possible ways in which mHealth has been and can be integrated into research practice