Challenges and opportunities in mobile e-coaching.

Background: Mobile e-health technologies have proven to provide tailored assessment, intervention, and coaching capabilities for various usage scenarios. Thanks to their spread and adoption, smartphones are one of the most important carriers for such applications. Problem: However, the process of design, realization, evaluation, and implementation of these e-health solutions is wicked and challenging, requiring multiple stakeholders and expertise. Method: Here, we present a tailorable intervention and interaction e-health solution that allows rapid prototyping, development, and evaluation of e-health interventions at scale. This platform allows researchers and clinicians to develop ecological momentary assessment, just-in-time adaptive interventions, ecological momentary intervention, cohort studies, and e-coaching and personalized interventions quickly, with no-code, and in a scalable way. Result: The Twente Intervention and Interaction Instrument (TIIM) has been used by over 320 researchers in the last decade. We present the ecosystem and synthesize the main scientific output from clinical and research studies in different fields. Discussion: The importance of mobile e-coaching for prediction, management, and prevention of adverse health outcomes is increasing. A profound e-health development strategyand strategic, technical, and operational investments are needed to prototype, develop, implement, and evaluate e-health solutions. TIIM ecosystem has proven to support these processes. This paper ends with the main research opportunities in mobile coaching, including intervention mechanisms, fine-grained monitoring, and inclusion of objective biomarker data.

Addendum: The Dutch COVID-19 Notification App: Lessons Learned From a Mixed Methods Evaluation Among End Users and Contact-Tracing Employees.

[This corrects the article DOI: 10.2196/38904.].

Appreciating the Good Things in Life During the Covid-19 Pandemic: A Randomized Controlled Trial and Evaluation of a Gratitude App.

The Covid-19 pandemic has had many negative consequences on the general public mental health. The aim of this study was to test the effectiveness of and satisfaction with an app with gratitude exercises to improve the mental health of people with reduced mental well-being due to the Covid-19 pandemic, as well as potential mechanisms of well-being change and dose-response relationships. A two-armed randomized controlled trial design was used, with two groups receiving the 6-week gratitude intervention app either immediately (intervention group, n = 424) or after 6 weeks (waiting list control group, n = 425). Assessments took place online at baseline (T0), six weeks later (T1) and at 12 weeks (T2), measuring outcomes (i.e., mental well-being, anxiety, depression, stress), and potential explanatory variables (i.e., gratitude, positive reframing, rumination). Linear mixed models analyses showed that when controlled for baseline measures, the intervention group scored better on all outcome measures compared to the control group at T1 (d = .24-.49). These effects were maintained at T2. The control group scored equally well on all outcome measures at T2 after following the intervention. Effects of the intervention on well-being were partially explained by gratitude, positive reframing, and rumination, and finishing a greater number of modules was weakly related to better outcomes. The intervention was generally appealing, with some room for improvement. The results suggest that a mobile gratitude intervention app is a satisfactory and effective way to improve the mental health of the general population during the difficult times of a pandemic.

The Dutch COVID-19 Notification App: Lessons Learned From a Mixed Methods Evaluation Among End Users and Contact-Tracing Employees.

BACKGROUND: The Dutch CoronaMelder (CM) app is the official Dutch contact-tracing app (CTA). It has been used to contain the spread of the SARS-CoV-2 in the Netherlands. It allows its users and those of connected apps to anonymously exchange warnings about potentially high-risk contacts with individuals infected with the SARS-CoV-2. OBJECTIVE: The goal of this mixed methods study is to understand the use of CTA in the pandemic and its integration into the Municipal Health Services (MHS) efforts of containment through contact tracing. Moreover, the study aims to investigate both the motivations and user experience-related factors concerning adherence to quarantine and isolation measures. METHODS: A topic analysis of 56 emails and a web-based survey of 1937 adults from the Netherlands, combined with a series of 48 in-depth interviews with end users of the app and 14 employees of the Dutch MHS involved in contact tracing, were conducted. Mirroring sessions were held (n=2) with representatives from the development (n=2) and communication teams (n=2) responsible for the creation and implementation of the CM app. RESULTS: Topic analysis and interviews identified procedural and technical issues in the use of the CTA. Procedural issues included the lack of training of MHS employees in the use of CTAs. Technical issues identified for the end users included the inability to send notifications without phone contact with the MHS, unwarranted notifications, and nightly notifications. Together, these issues undermined confidence in and satisfaction with the app's use. The interviews offered a deeper understanding of the various factors at play and their effects on users; for example, the mixed experiences of the app's users, the end user's own fears, and uncertainties concerning the SARS-CoV-2; problematic infrastructure at the time of the app's implementation on the side of the health services; the effects of the society-wide efforts in containment of the SARS-CoV-2 on the CM app's perception, resulting in further doubts concerning the app's effectiveness among MHS workers and citizens; and problems with adherence to behavioral measures propagated by the app because of the lack of confidence in the app and uncertainty concerning the execution of the behavioral measures. All findings were evaluated with the app's creators and have since contributed to improvements. CONCLUSIONS: Although most participants perceived the app positively, procedural and technical issues identified in this study limited satisfaction and confidence in the CM app and affected its adoption and long-term use. Moreover, these same issues negatively affected the CM app's effectiveness in improving compliance with behavioral measures aimed at reducing the spread of the SARS-CoV-2. This study offers lessons learned for future eHealth interventions in pandemics. Lessons that can aid in more effective design, implementation, and communication for more effective and readily adoptable eHealth applications.

Integrating Top-down and Bottom-up Requirements in eHealth Development: The Case of a Mobile Self-compassion Intervention for People With Newly Diagnosed Cancer.

BACKGROUND: Psychosocial eHealth interventions for people with cancer are promising in reducing distress; however, their results in terms of effects and adherence rates are quite mixed. Developing interventions with a solid evidence base while still ensuring adaptation to user wishes and needs is recommended to overcome this. As most models of eHealth development are based primarily on examining user experiences (so-called bottom-up requirements), it is not clear how theory and evidence (so-called top-down requirements) may best be integrated into the development process. OBJECTIVE: This study aims to investigate the integration of top-down and bottom-up requirements in the co-design of eHealth applications by building on the development of a mobile self-compassion intervention for people with newly diagnosed cancer. METHODS: Four co-design tasks were formulated at the start of the project and adjusted and evaluated throughout: explore bottom-up experiences, reassess top-down content, incorporate bottom-up and top-down input into concrete features and design, and synergize bottom-up and top-down input into the intervention context. These tasks were executed iteratively during a series of co-design sessions over the course of 2 years, in which 15 people with cancer and 7 nurses (recruited from 2 hospitals) participated. On the basis of the sessions, a list of requirements, a final intervention design, and an evaluation of the co-design process and tasks were yielded. RESULTS: The final list of requirements included intervention content (eg, major topics of compassionate mind training such as psychoeducation about 3 emotion systems and main issues that people with cancer encounter after diagnosis such as regulating information consumption), navigation, visual design, implementation strategies, and persuasive elements. The final intervention, Compas-Y, is a mobile self-compassion training comprising 6 training modules and several supportive functionalities such as a mood tracker and persuasive elements such as push notifications. The 4 co-design tasks helped overcome challenges in the development process such as dealing with conflicting top-down and bottom-up requirements and enabled the integration of all main requirements into the design. CONCLUSIONS: This study addressed the necessary integration of top-down and bottom-up requirements into eHealth development by examining a preliminary model of 4 co-design tasks. Broader considerations regarding the design of a mobile intervention based on traditional intervention formats and merging the scientific disciplines of psychology and design research are discussed.

The Dutch COVID-19 Contact Tracing App (the CoronaMelder): Usability Study.

BACKGROUND: Adoption and evaluation of contact tracing tools based on information and communications technology may expand the reach and efficacy of traditional contact tracing methods in fighting COVID-19. The Dutch Ministry of Health, Welfare and Sports initiated and developed CoronaMelder, a COVID-19 contact tracing app. This app is based on a Google/Apple Exposure Notification approach and aims to combat the spread of the coronavirus among individuals by notifying those who are at increased risk of infection due to proximity to someone who later tests positive for COVID-19. The app should support traditional contact tracing by faster tracing and greater reach compared to regular contact tracing procedures. OBJECTIVE: The main goal of this study is to investigate whether the CoronaMelder is able to support traditional contact tracing employed by public health authorities. To achieve this, usability tests were conducted to answer the following question: is the CoronaMelder user-friendly, understandable, reliable and credible, and inclusive? METHODS: Participants (N=44) of different backgrounds were recruited: youth with varying educational levels, youth with an intellectual disability, migrants, adults (aged 40-64 years), and older adults (aged >65 years) via convenience sampling in the region of Twente in the Netherlands. The app was evaluated with scenario-based, think-aloud usability tests and additional interviews. Findings were recorded via voice recordings, observation notes, and the Dutch User Experience Questionnaire, and some participants wore eye trackers to measure gaze behavior. RESULTS: Our results showed that the app is easy to use, although problems occurred with understandability and accessibility. Older adults and youth with a lower education level did not understand why or under what circumstances they would receive notifications, why they must share their key (ie, their assigned identifier), and what happens after sharing. In particular, youth in the lower-education category did not trust or understand Bluetooth signals, or comprehend timing and follow-up activities after a risk exposure notification. Older adults had difficulties multitasking (speaking with a public health worker and simultaneously sharing the key in the app). Public health authorities appeared to be unprepared to receive support from the app during traditional contact tracing because their telephone conversation protocol lacks guidance, explanation, and empathy. CONCLUSIONS: The study indicated that the CoronaMelder app is easy to use, but participants experienced misunderstandings about its functioning. The perceived lack of clarity led to misconceptions about the app, mostly regarding its usefulness and privacy-preserving mechanisms. Tailored and targeted communication through, for example, public campaigns or social media, is necessary to provide correct information about the app to residents in the Netherlands. Additionally, the app should be presented as part of the national coronavirus measures instead of as a stand-alone app offered to the public. Public health workers should be trained to effectively and empathetically instruct users on how to use the CoronaMelder app.

Perceived Mental Workload Classification Using Intermediate Fusion Multimodal Deep Learning.

A lot of research has been done on the detection of mental workload (MWL) using various bio-signals. Recently, deep learning has allowed for novel methods and results. A plethora of measurement modalities have proven to be valuable in this task, yet studies currently often only use a single modality to classify MWL. The goal of this research was to classify perceived mental workload (PMWL) using a deep neural network (DNN) that flexibly makes use of multiple modalities, in order to allow for feature sharing between modalities. To achieve this goal, an experiment was conducted in which MWL was simulated with the help of verbal logic puzzles. The puzzles came in five levels of difficulty and were presented in a random order. Participants had 1 h to solve as many puzzles as they could. Between puzzles, they gave a difficulty rating between 1 and 7, seven being the highest difficulty. Galvanic skin response, photoplethysmograms, functional near-infrared spectrograms and eye movements were collected simultaneously using LabStreamingLayer (LSL). Marker information from the puzzles was also streamed on LSL. We designed and evaluated a novel intermediate fusion multimodal DNN for the classification of PMWL using the aforementioned four modalities. Two main criteria that guided the design and implementation of our DNN are modularity and generalisability. We were able to classify PMWL within-level accurate (0.985 levels) on a seven-level workload scale using the aforementioned modalities. The model architecture allows for easy addition and removal of modalities without major structural implications because of the modular nature of the design. Furthermore, we showed that our neural network performed better when using multiple modalities, as opposed to a single modality. The dataset and code used in this paper are openly available.

The use of technology in the context of frailty screening and management interventions: a study of stakeholders' perspectives.

BACKGROUND: Health and social care interventions show promise as a way of managing the progression of frailty in older adults. Information technology could improve the availability of interventions and services for older adults. The views of stakeholders on the acceptability of technological solutions for frailty screening and management have not been explored. METHODS: Focus groups were used to gather data from healthy and frail/pre-frail older adults, health and social care providers, and caregivers in three European countries - Italy, Poland and UK. Data were analysed using framework analysis in terms of facilitators or determinants of older adults' adoption of technology. RESULTS: Our findings clustered around the perceived value; usability, affordability and accessibility; and emotional benefits of frailty screening and management technology to stakeholders. We also noted issues relating to social support, previous experience of technology and confidence of stakeholders. CONCLUSIONS: Professionals and caregivers understand the benefits of technology to facilitate frailty care pathways but these views are tempered by concerns around social isolation. Frail older adults raised legitimate concerns about the accessibility and usability of technology, specifically around the potential for their personal information to be compromised. Solutions must be developed within a framework that addresses social contexts and avoids stigma around frailty and ageing.