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Objective: Social media is used in the context of healthcare, for example in interventions for promoting health. Since social media are easily accessible they have potential to promote health equity. This paper studies relevant factors impacting on health equity considered in social media interventions. Methods: We searched for literature to identify potential relevant factors impacting on health equity considered in social media interventions. We included studies that reported examples of health interventions using social media, focused on health equity, and analyzed health equity factors of social media. We identified Information about health equity factors and targeted groups. Results: We found 17 relevant articles. Factors impacting on health equity reported in the included papers were extracted and grouped into three categories: digital health literacy, digital ethics, and acceptability. Conclusions: Literature shows that it is likely that digital technologies will increase health inequities associated with increased age, lower level of educational attainment, and lower socio-economic status. To address this challenge development of social media interventions should consider participatory design principles, visualization, and theories of social sciences.

The lifespan of older people is growing together with their proportion in the population Growth in the older population. This leads to an increasing need for support services for older adults who will inevitably experience a significant decrease in their cognitive capabilities and mental health conditions. Cognitive function, including memory, attention, sleeping, problem-solving activities, or speed processing, are playing a crucial role in everyone’s independent daily life. Technologies can help maintain their independence and improve quality of life, reducing the care costs. In this regards, smart home applications (SMAs) offer a solution to the complex needs of older adults and their families, monitoring physiological and functional issues, as well as aiding in emergency detection and response. This chapter provides an overview of current applications reported in the scientific literature, identifies the frameworks proposed for designing these types of applications, and defines evidence based recommendations for designing SMAs for cognitive health of older adults.

The lifespan of older people is growing together with their proportion in the population growth in the older population. This leads to an increasing need for support services for older adults who will inevitably experience a significant decrease in their cognitive capabilities and mental health conditions. Cognitive functions, including memory, attention, sleeping, problem-solving activities, or speed processing, are playing a crucial role in everyone’s independent daily life. Technologies can help maintain their independence and improve quality of life, reducing the care costs. In this regard, Smart home applications (SMAs) offer a solution to the complex needs of older adults and their families, monitoring physiological and functional issues, as well as aiding in emergency detection and response. This chapter provides an overview of current applications reported in the scientific literature, identifies the frameworks proposed for designing these types of applications, and defines evidence-based recommendations for designing SMAs for cognitive health of older adults., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Applied Ergonomics and Design

Background: Physical activity (PA) is the most well-established lifestyle factor associated with breast cancer (BC) survival. Even women with advanced BC may benefit from moderate PA. However, most BC symptoms and treatment side effects are barriers to PA. Mobile health coaching systems can implement functionalities and features based on behavioral change theories to promote healthier behaviors. However, to increase its acceptability among women with BC, it is essential that these digital persuasive systems are designed considering their contextual characteristics, needs, and preferences. Objective: This study aimed to examine the potential acceptability and feasibility of a mobile-based intervention to promote PA in patients with BC; assess usability and other aspects of the user experience; and identify key considerations and aspects for future improvements, which may help increase and sustain acceptability and engagement. Methods: A mixed methods case series evaluation of usability and acceptability was conducted in this study. The study comprised 3 sessions: initial, home, and final sessions. Two standardized scales were used: the Satisfaction with Life Scale and the International Physical Activity Questionnaire–Short Form. Participants were asked to use the app at home for approximately 2 weeks. App use and PA data were collected from the app and stored on a secure server during this period. In the final session, the participants filled in 2 app evaluation scales and took part in a short individual interview. They also completed the System Usability Scale and the user version of the Mobile App Rating Scale. Participants were provided with a waist pocket, wired in-ear headphones, and a smartphone. They also received printed instructions. A content analysis of the qualitative data collected in the interviews was conducted iteratively, ensuring that no critical information was overlooked. Results: The International Physical Activity Questionnaire–Short Form found

Dental anxiety in children is a well-documented problem in the scienti c literature. Tools mediated by Information Technology have been shown to positively in uence children’s mood based on distraction as well as relaxing activities. We propose an empirical method of codesign with children to generate app content for reducing dental anxiety. e results are embedded in text through a thick description as an ethnographic technique. e method was applied to 163 children (6–8 years old) from a summer school and a primary school, obtaining multimedia products that were integrated into an app prototype. Finally, although this use case of the presented method is applied to the health eld, it can be transferred to any other eld of application of codesign to children by using material that is speci c to new scenarios.

Multiple sclerosis (MS) is one of the world’s most common neurologic disorders. Social media have been proposed as a way to maintain and even increase social interaction for people with MS. The objective of this work is to identify and compare the topics on Twitter during the first wave of COVID-19 pandemic. Methods: Data was collected using the Twitter API between 9/2/2019 and 13/5/2020. SentiStrength was used to analyze data with the day that the pandemic was declared used as a turning point. Frequency-inverse document frequency (tf-idf) was used for each unigram and calculated the gains in tf-idf value. A comparative analysis of the relevance of words and categories among the datasets was performed. Results: The original dataset contained over 610k tweets, our final dataset had 147,963 tweets. After the 10th of march some categories gained relevance in positive tweets (“Healthcare professional”, “Chronic conditions”, “Condition burden”), while in negative tweets “Emotional aspects” became more relevant and “COVID-19” emerged as a new topic. Conclusions: Our work provides insight on how COVID-19 has changed the online discourse of people with MS.

Introduction: Multiple sclerosis (MS) is one of the world's most common neurologic disorders. Social media have been proposed as a way to maintain and even increase social interaction for people with MS. The objective of this work is to identify and compare the topics on Twitter during the first wave of COVID-19 pandemic. Methods: Data was collected using the Twitter API between 9/2/2019 and 13/5/2020. SentiStrength was used to analyze data with the day that the pandemic was declared used as a turning point. Frequency-inverse document frequency (tf-idf) was used for each unigram and calculated the gains in tf-idf value. A comparative analysis of the relevance of words and categories among the datasets was performed. Results: The original dataset contained over 610k tweets, our final dataset had 147,963 tweets. After the 10th of march some categories gained relevance in positive tweets ("Healthcare professional", "Chronic conditions", "Condition burden"), while in negative tweets "Emotional aspects" became more relevant and "COVID-19" emerged as a new topic. Conclusions: Our work provides insight on how COVID-19 has changed the online discourse of people with MS., Applied Ergonomics and Design

Objectives: Using participatory health informatics (PHI) to detect disease outbreaks or learn about pandemics has gained interest in recent years. However, the role of PHI in understanding and managing pandemics, citizens’ role in this context, and which methods are relevant for collecting and processing data are still unclear, as is which types of data are relevant. This paper aims to clarify these issues and explore the role of PHI in managing and detecting pandemics. Methods: Through a literature review we identified studies that explore the role of PHI in detecting and managing pandemics. Studies from five databases were screened: PubMed, CINAHL (Cumulative Index to Nursing and Allied Health Literature), IEEE Xplore, ACM (Association for Computing Machinery) Digital Library, and Cochrane Library. Data from studies fulfilling the eligibility criteria were extracted and synthesized narratively. Results: Out of 417 citations retrieved, 53 studies were included in this review. Most research focused on influenza-like illnesses or COVID-19 with at least three papers on other epidemics (Ebola, Zika or measles). The geographic scope ranged from global to concentrating on specific countries. Multiple processing and analysis methods were reported, although often missing relevant information. The majority of outcomes are reported for two application areas: crisis communication and detection of disease outbreaks. Conclusions: For most diseases, the small number of studies prevented reaching firm conclusions about the utility of PHI in detecting and monitoring these disease outbreaks. For others, e.g., COVID-19, social media and online search patterns corresponded to disease patterns, and detected disease outbreak earlier than conventional public health methods, thereby suggesting that PHI can contribute to disease and pandemic monitoring.

Background and objective: Social media could be valuable tools to support people with multiple sclerosis (MS). There is little evidence on the MSrelated topics that are discussed on social media, and the sentiment linked to these topics. The objective of this work is to identify the MS-related main topics discussed on Twitter, and the sentiment linked to them. Methods: Tweets dealing with MS in the English language were extracted. Latent-Dirilecht Allocation (LDA) was used to identify the main topics discussed in these tweets. Iterative inductive process was used to group the tweets into recurrent topics. The sentiment analysis of these tweets was performed using SentiStrength. Results: LDA’ identified topics were grouped into 4 categories, tweets dealing with: related chronic conditions; condition burden; disease-modifying drugs; and awarenessraising. Tweets on condition burden and related chronic conditions were the most negative (p<0.001). A significant lower positive sentiment was found for both tweets dealing with disease-modifying drugs, condition burden, and related chronic conditions (p<0.001). Only tweets on awareness-raising were most positive than the average (p<0.001). Discussion: The use of both tools to identify the main discussed topics on social media and to analyse the sentiment of these topics, increases the knowledge of the themes that could represent the bigger burden for persons affected with MS. This knowledge can help to improve support and therapeutic approaches addressed to them.

Objectives: To identify the different ethical issues that should be considered in participatory health through social media from different stakeholder perspectives (i.e., patients/service users, health professionals, health information technology (IT) professionals, and policy makers) in any healthcare context. Methods: We implemented a two-round survey composed of open ended questions in the first round, aggregated into a list of ethical issues rated for importance by participants in the second round, to generate a ranked list of possible ethical issues in participatory health based on healthcare professionals’ and policy makers’ opinions on both their own point of view and their beliefs for other stakeholders’ perspectives. Results: Twenty-six individuals responded in the first round of the survey. Multiple ethical issues were identified for each perspective. Data privacy, data security, and digital literacy were common themes in all perspectives. Thirty-three individuals completed the second round of the survey. Data privacy and data security were ranked among the three most important ethical issues in all perspectives. Quality assurance was the most important issue from the healthcare professionals’ perspective and the second most important issue from the patients’ perspective. Data privacy was the most important consideration for patients/service users. Digital literacy was ranked as the fourth most important issue, except for policy makers’ perspective. Conclusions: Different stakeholders’ opinions fairly agreed that there are common ethical issues that should be considered across the four groups (patients, healthcare professionals, health IT professionals, policy makers) such as data privacy, security, and quality assurance.

Background: Privacy has always been a concern, especially in the health domain. The proliferation of mobile health (mHealth) apps has led to a large amount of sensitive data being generated. Some authors have performed privacy assessments of mHealth apps. They have evaluated diverse privacy components; however, different authors have used different criteria for their assessments. Objective: This scoping review aims to understand how privacy is assessed for mHealth apps, focusing on the components, scales, criteria, and scoring methods used. A simple taxonomy to categorize the privacy assessments of mHealth apps based on component evaluation is also proposed. Methods: We followed the methodology defined by Arksey and O’Malley to conduct a scoping review. Included studies were categorized based on the privacy component, which was assessed using the proposed taxonomy. Results: The database searches retrieved a total of 710 citations—24 of them met the defined selection criteria, and data were extracted from them. Even though the inclusion criteria considered articles published since 2009, all the studies that were ultimately included were published from 2014 onward. Although 12 papers out of 24 (50%) analyzed only privacy, 8 (33%) analyzed both privacy and security. Moreover, 4 papers (17%) analyzed full apps, with privacy being just part of the assessment. The evaluation criteria used by authors were heterogeneous and were based on their experience, the literature, and/or existing legal frameworks. Regarding the set of items used for the assessments, each article defined a different one. Items included app permissions, analysis of the destination, analysis of the content of communications, study of the privacy policy, use of remote storage, and existence of a password to access the app, among many others. Most of the included studies provided a scoring method that enables the comparison of privacy among apps. Conclusions: The privacy assessment of mHealth apps is a co

Background: Despite growing evidence supporting the vital benefits of physical activity (PA) for breast cancer survivors, the majority do not meet the recommended levels of activity. Mobile app–based PA coaching interventions might be a feasible strategy to facilitate adherence of breast cancer survivors to the PA guidelines. To engage these individuals, PA apps need to be specifically designed based on their needs and preferences and to provide targeted support and motivation. However, more information is needed to understand how these technologies can provide individual and relevant experiences that have the ability to increase PA adherence and retain the individual’s interest in the long term. Objective: The aim of this study is to explore insights from breast cancer survivors on motivational and personalization strategies to be used in PA coaching apps and interventions. Methods: A qualitative study was conducted, using individual semistructured interviews, with 14 breast cancer survivors. The moderator asked open-ended questions and made use of a slideshow presentation to elicit the participants’ perspectives on potential mobile app–based intervention features. Transcribed interviews were evaluated by 3 reviewers using thematic content analysis. Results: Participants (mean age 53.3, SD 8.7 years) were White women. In total, 57% (8/14) of the participants did not adhere to the PA guidelines. In general, participants had access to and were interested in using technology. The identified themes included (1) barriers to PA, (2) psychological mediators of PA motivation, (3) needs and suggestions for reinforcing motivation support, (4) personalization aspects of the PA coaching experience, and (5) technology trustworthiness. Motivational determinants included perceived control, confidence and perceived growth, and connectedness. Participants were interested in having a straightforward app for monitoring and goal setting, which would include a prescribed activity progr

According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. Consequently, the objectives, data collection techniques, development process and the validation metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using the methodology proposed by Arksey and O’Malley et al. and the PRISMA protocol was adopted to select the relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75% of the analyzed studies implement machine learning-based models using Python.

Background: Existing evidence supports the many benefits of physical activity (PA) in breast cancer survival. However, few breast cancer survivors adhere to the recommended levels of activity. A PA coaching app that provides personalized feedback, guidance, and motivation to the user might have the potential to engage these individuals in a more active lifestyle, in line with the general recommendations. To develop a successful tool, it is important to involve the end users in the design process and to make theoretically grounded design decisions. Objective: This study aimed to execute the design process and early prototype evaluation of a personalized PA coaching app for posttreatment breast cancer survivors. In particular, the study explored a design combining behavioral theory and tailored coaching strategies. Methods: The design process was led by a multidisciplinary team, including technical and health professionals, and involved input from a total of 22 survivors. The process comprised 3 stages. In stage 1, the literature was reviewed and 14 patients were interviewed to understand the needs and considerations of the target population toward PA apps. In stage 2, the global use case for the tool was defined, the features were ideated and refined based on theory, and a digital interactive prototype was created. In stage 3, the prototype went through usability testing with 8 patients and was subjected to quality and behavior change potential evaluations by 2 human-computer interaction experts. Results: The design process has led to the conceptualization of a personalized coaching app for walking activities that addresses the needs of breast cancer survivors. The main features of the tool include a training plan and schedule, adaptive goal setting, real-time feedback and motivation during walking sessions, activity status through the day, activity history, weekly summary reports, and activity challenges. The system was designed to measure users’ cadence during walk

Background: Multiple sclerosis (MS) is one of the world’s most common neurologic disorders leading to severe disability in young adults. MS-related fatigue directly impacts on the quality of life and activity levels of people with MS. Self-management strategies are used to support them in the care of their health. Mobile health (mHealth) solutions can offer tools to help symptom management. Following a user-centered design and evidence-based process, an mHealth solution called More Stamina was created to help persons with MS manage their fatigue. Objective: The overall study aims are to explore the feasibility, acceptability, and usability of More Stamina, a mobile app for fatigue self-management for persons with MS. Methods: A mixed-methods, multicenter study will be used to assess the feasibility, acceptability, and usability of More Stamina. The study will take place during the third and fourth quarters of 2020 (Q3-Q4 2020) in 3 locations: Argentina, Spain, and Switzerland. A longitudinal cohort study will take place, and think-aloud protocols, open-ended interviews, and short answer questionnaires will be used. Persons with MS will be recruited from the different locations. This study seeks to enroll at least 20 patients that meet the criteria from each site for the longitudinal cohort study (total n=60). Results: Ethical approval has been granted in Argentina and is pending in Spain and Switzerland. Outcomes will be published in peer-reviewed medical journals and presented at international conferences. Conclusions: Findings from this study will be used to help understand the role that mHealth can play in fatigue management in MS. Trial Registration: ClinicalTrials.gov NCT04244214; https://clinicaltrials.gov/ct2/show/NCT04244214 International Registered Report Identifier (IRRID): PRR1-10.2196/18196

Background: Cancer patients are increasingly using mobile health (mHealth) apps to take control of their health. Many studies have explored their efficiency, content, usability, and adherence; however, these apps have created a new set of privacy challenges, as they store personal and sensitive data. Objective: The purpose of this study was to refine and evaluate a scale based on the General Data Protection Regulation and assess the fairness of privacy policies of mHealth apps. Methods: Based on the experience gained from our previous work, we redefined some of the items and scores of our privacy scale. Using the new version of our scale, we conducted a case study in which we analyzed the privacy policies of cancer Android apps. A systematic search of cancer mobile apps was performed in the Spanish version of the Google Play website. Results: The redefinition of certain items reduced discrepancies between reviewers. Thus, use of the scale was made easier, not only for the reviewers but also for any other potential users of our scale. Assessment of the privacy policies revealed that 29% (9/31) of the apps included in the study did not have a privacy policy, 32% (10/31) had a score over 50 out of a maximum of 100 points, and 39% (12/31) scored fewer than 50 points. Conclusions: In this paper, we present a scale for the assessment of mHealth apps that is an improved version of our previous scale with adjusted scores. The results showed a lack of fairness in the mHealth app privacy policies that we examined, and the scale provides developers with a tool to evaluate their privacy policies.

The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)–funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020–funded projects. Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges nee

Mobile monitoring for health and wellness is becoming more sophisticated and accurate, with an increased use of realtime personalization technologies that may improve the effectiveness of physical activity coaching systems. This study aimed to review real-time physical activity coaching applications that make use of personalization mechanisms. A scoping review, using the PRISMA-ScR checklist, was conducted on the literature published from July 2007 to July 2018. A data extraction tool was developed to analyze the systems on general characteristics, personalization, design foundations (behavior change and gamification) and evaluation methods. 28 papers describing 17 different mobile applications were included. The most used personalization concepts were Feedback (17/17), Goal Setting (15/17), User Targeting (9/17) and Inter-human Interaction (8/17), while the less commonly covered were Self-Learning (4/17), Context Awareness (3/17) and Adaptation (2/17). Few systems considered behavior change theories for design (6/17). A total of 42 instances of gamification-related elements were found across 15 systems, but only 6 explicitly mention its use. Most systems (15/17) were submitted to some type of evaluation. However, few assessed the effects of particular strategies or overall system effectiveness using randomized experimental designs (5/17). Although personalization is thought to improve user adherence in physical activity coaching applications, it is still far from reaching its full potential. We believe that future work should consider the theory and suggestions reported in prior work; leverage the needs of the target users for personalization; include behavior change foundations and explore gamification theory; and properly evaluate these systems.

Objective: Artificial intelligence (AI) provides people and professionals working in the field of participatory health informatics an opportunity to derive robust insights from a variety of online sources. The objective of this paper is to identify current state of the art and application areas of AI in the context of participatory health. Methods: A search was conducted across seven databases (PubMed, Embase, CINAHL, PsychInfo, ACM Digital Library, IEEExplore, and SCOPUS), covering articles published since 2013. Additionally, clinical trials involving AI in participatory health contexts registered at clinicaltrials.gov were collected and analyzed. Results: Twenty-two articles and 12 trials were selected for review. The most common application of AI in participatory health was the secondary analysis of social media data: self-reported data including patient experiences with healthcare facilities, reports of adverse drug reactions, safety and efficacy concerns about over-the-counter medications, and other perspectives on medications. Other application areas included determining which online forum threads required moderator assistance, identifying users who were likely to drop out from a forum, extracting terms used in an online forum to learn its vocabulary, highlighting contextual information that is missing from online questions and answers, and paraphrasing technical medical terms for consumers. Conclusions: While AI for supporting participatory health is still in its infancy, there are a number of important research priorities that should be considered for the advancement of the field. Further research evaluating the impact of AI in participatory health informatics on the psychosocial wellbeing of individuals would help in facilitating the wider acceptance of AI into the healthcare ecosystem.

Privacy is a major concern for breast cancer patients. When patients use mobile health applications (mHealth apps), many sensitive data are handled by the application developers. General Data Protection Regulation (GDPR) arises as a solution to privacy issues. In this paper, we analyze the privacy policy of a sample of mHealth apps for breast cancer patients, developing a scale to check if GDPR is complied. Despite privacy is a key factor in the adoption of the use of mHealth apps, the low level of compliance with the GDPR of the analyzed applications was quite surprising. Thus, application developers must be concerned about this matter.

Background: Contents published on social media have an impact on individuals and on their decision making. Knowing the sentiment toward diabetes is fundamental to understanding the impact that such information could have on people affected with this health condition and their family members. The objective of this study is to analyze the sentiment expressed in messages on diabetes posted on Twitter. Method: Tweets including one of the terms “diabetes,” “t1d,” and/or “t2d” were extracted for one week using the Twitter standard API. Only the text message and the number of followers of the users were extracted. The sentiment analysis was performed by using SentiStrength. Results: A total of 67 421 tweets were automatically extracted, of those 3.7% specifically referred to T1D; and 6.8% specifically mentioned T2D. One or more emojis were included in 7.0% of the posts. Tweets specifically mentioning T2D and that did not include emojis were significantly more negative than the tweets that included emojis (–2.22 vs −1.48, P < .001). Tweets on T1D and that included emojis were both significantly more positive and also less negative than tweets without emojis (1.71 vs 1.49 and −1.31 vs −1.50, respectively; P < .005). The number of followers had a negative association with positive sentiment strength (r = –.023, P < .001) and a positive association with negative sentiment (r = .016, P < .001). Conclusion: The use of sentiment analysis techniques on social media could increase our knowledge of how social media impact people with diabetes and their families and could help to improve public health strategies.