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BackgroundThe 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. ObjectiveThis 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. MethodsTwo 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. ResultsTechnical 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. ConclusionsTechnology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations.

BackgroundDespite 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. ObjectiveThe 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. MethodsA 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. ResultsParticipants (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 program and schedule, and positive communication. Opinions varied in terms of social and game-like system possibilities. In addition, they expressed a desire for a highly personalized coaching experience based on as much information collected from them as possible (eg, disease stage, physical limitations, preferences) to provide individualized progress information, dynamic adjustment of the training plan, and context-aware activity suggestions (eg, based on weather and location). Participants also wanted the app to be validated or backed by professionals and were willing to share their data in exchange for a more personalized experience. ConclusionsThis work suggests the need to develop simple, guiding, encouraging, trustworthy, and personalized PA coaching apps. The findings are in line with behavioral and personalization theories and methods that can be used to inform intervention design decisions. This paper opens new possibilities for the design of personalized and motivating PA coaching app experiences for breast cancer survivors, which might ultimately facilitate the sustained adherence of these individuals to the recommended levels of activity.

BackgroundMultiple 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. ObjectiveThe 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. MethodsA 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). ResultsEthical 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. ConclusionsFindings from this study will be used to help understand the role that mHealth can play in fatigue management in MS. Trial RegistrationClinicalTrials.gov NCT04244214; https://clinicaltrials.gov/ct2/show/NCT04244214 International Registered Report Identifier (IRRID)PRR1-10.2196/18196

BackgroundPrivacy 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. ObjectiveThis 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. MethodsWe 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. ResultsThe 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. ConclusionsThe privacy assessment of mHealth apps is a complex task, as the criteria used by different authors for their evaluations are very heterogeneous. Although some studies about privacy assessment have been conducted, a very large set of items to evaluate privacy has been used up until now. In-app information and privacy policies are primarily utilized by the scientific community to extract privacy information from mHealth apps. The creation of a scale based on more objective criteria is a desirable step forward for privacy assessment in the future.

BackgroundCancer 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. ObjectiveThe 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. MethodsBased 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. ResultsThe 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. ConclusionsIn 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.

BackgroundHypertensive disorders are the most common complications during pregnancy, occurring in 5% to 11% of pregnancies; gestational hypertension and preeclampsia are the leading causes of perinatal and maternal morbidity and mortality, especially in low- and middle-income countries (LMIC) where maternal and perinatal mortality ratios are still high. Pregnant women with hypertensive disorders could greatly benefit from mobile health (mHealth) solutions as a novel way to identify and control early symptoms, as shown in an increasing number of publications in the field. Such digital health solutions may overcome access limiting factors and the lack of skilled medical professionals and finances commonly presented in resource-poor environments. ObjectiveThe aim of this study was to conduct a literature review of mHealth solutions used as support in hypertensive disorders during pregnancy, with the objective to identify the most relevant protocols and prototypes that could influence and improve current clinical practice. MethodsA methodological review following a scoping methodology was conducted. Manuscripts published in research journals reporting technical information of mHealth solutions for hypertensive disorders in pregnancy were included, categorizing articles in different groups: Diagnosis and Monitoring, mHealth Decision Support System, Education, and Health Promotion, and seven research questions were posed to study the manuscripts. ResultsThe search in electronic research databases yielded 327 articles. After removing duplicates, 230 articles were selected for screening. Finally, 11 articles met the inclusion criteria, and data were extracted from them. Very positive results in the improvement of maternal health and acceptability of solutions were found, although most of the studies involved a small number of participants, and none were complete clinical studies. Accordingly, none of the reported prototypes were integrated in the different health care systems. Only 4 studies used sensors for physiological measurements, and only 2 used blood pressure sensors despite the importance of this physiological parameter in the control of hypertension. The reported mHealth solutions have great potential to improve clinical practice in areas lacking skilled medical professionals or with a low health care budget, of special relevance in LMIC, although again, no extensive clinical validation has been carried out in these environments. ConclusionsmHealth solutions hold enormous potential to support hypertensive disorders during pregnancy and improve current clinical practice. Although very positive results have been reported in terms of usability and the improvement of maternal health, rigorous complete clinical trials are still necessary to support integration in health care systems. There is a clear need for simple mHealth solutions specifically developed for resource-poor environments that meet the United Nations Sustainable Development Goal (SDG); of enormous interest in LMIC.

BackgroundMobile phone health apps are increasingly gaining attention in oncological care as potential tools for supporting cancer patients. Although the number of publications and health apps focusing on cancer is increasing, there are still few specifically designed for the most prevalent cancers diagnosed: breast and prostate cancers. There is a need to review the effect of these apps on breast and prostate cancer patients’ quality of life (QoL) and well-being. ObjectiveThe purposes of this study were to review the scientific literature on mobile phone apps targeting breast or prostate cancer patients and involving QoL and well-being (anxiety and depression symptoms) and analyze the clinical and technological characteristics, strengths, and weaknesses of these apps, as well as patients’ user experience with them. MethodsWe conducted a systematic review of peer-reviewed literature from The Cochrane Library, Excerpta Medica Database, PsycINFO, PubMed, Scopus, and MEDLINE to identify studies involving apps focused on breast and/or prostate cancer patients and QoL and/or well-being published between January 1, 2000, and July 12, 2017. Only trial studies which met the inclusion criteria were selected. The systematic review was completed with a critical analysis of the apps previously identified in the health literature research that were available from the official app stores. ResultsThe systematic review of the literature yielded 3862 articles. After removal of duplicates, 3229 remained and were evaluated on the basis of title and abstract. Of these, 3211 were discarded as not meeting the inclusion criteria, and 18 records were selected for full text screening. Finally, 5 citations were included in this review, with a total of 644 patients, mean age 52.16 years. Four studies targeted breast cancer patients and 1 focused on prostate cancer patients. Four studies referred to apps that assessed QoL. Only 1 among the 5 analyzed apps was available from the official app store. In 3 studies, an app-related intervention was carried out, and 2 of them reported an improvement on QoL. The lengths of the app-related interventions varied from 4 to 12 weeks. Because 2 of the studies only tracked use of the app, no effect on QoL or well-being was found. ConclusionsDespite the existence of hundreds of studies involving cancer-focused mobile phone apps, there is a lack of rigorous trials regarding the QoL and/or well-being assessment in breast and/or prostate cancer patients. A strong and collective effort should be made by all health care providers to determine those cancer-focused apps that effectively represent useful, accurate, and reliable tools for cancer patients’ disease management. Trial RegistrationPROSPERO CRD42017073069; https://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID= CRD42017073069 (Archived by WebCite at http://www.webcitation.org/6v38Clb9T)

Searches for autism on social media have soared, making it a top topic. Social media posts convey not only plain text, but also sentiments and emotions that provide insight into the experiences of the autism community. While sentiment analysis categorizes overall sentiment, emotion analysis provides nuanced insights into specific emotional states. The objective of this study is to identify emotions in posts related to autism and compare the emotions specifically contained in posts that include the hashtag #ActuallyAutistic with those that do not. Methods: We extracted a sample of X’ posts related to autism and used DistilBERT to assign one out of six emotions (sadness, joy, love, anger, fear, surprise) to each post. Results: We have analyzed a total of 414,287 posts, 98,602 (23.8%) of those included the hashtag #ActuallyAutistic. The most common expressed emotion was joy, which was expressed in 52.5% of the posts, followed by sadness, identified in 28.6% of the posts. 12% of the posts expressed fear, 4.9% reflected anger, 1.1% showed love, and 0.9% expressed surprise. Posts tagged as #ActuallyAutistic showed less joy (27.1% vs. 60.4% in posts without this hashtag, p<0.001) and more sadness (52.7% vs. 21.1% in those without the hashtag, p<0.001). Conclusions: The use of the hashtag #ActuallyAutistic is associated with a different emotional tone, characterized by less joy and more sadness. These results suggest the need for greater support and acceptance towards the autistic community, both online and in society in general. Insights from our study can be valuable for policy makers, health, educational or other programmes aiming at enhancing well-being, inclusiveness, improve services, and create a more compassionate and understanding atmosphere for autistic people. [ABSTRACT FROM AUTHOR]

Background: The design and development of patient-centered digital health solutions requires user involvement, for example through usability testing. Although there are guidelines for conducting usability tests, there is a lack of knowledge about the technical, human, and organizational factors that influence the success of the tests. Objective: To summarize the success factors of usability testing in the context of patient-centered digital health solutions. Method: We considered three case studies and collected experiences related to time management, relevance of results and challenges encountered. Results: Success factors relate to participant privacy and data protection, test environment setup, device and application readiness, user comfort and accessibility, test tools and procedures, and adaptability to user limitations. Conclusions: Small organizational and technical details can have a big impact on the outcome of a usability test. Considering the aspects mentioned in this paper will not only save resources but also the trust of the participating patients. [ABSTRACT FROM AUTHOR]

Adherence to recombinant human growth hormone (r-hGH; somatropin, [Saizen®], Merck Healthcare KGaA, Darmstadt, Germany) treatment is important to achieve positive growth and other outcomes in children with growth disorders. Automated injection devices can facilitate the delivery of r-hGH, injections of which are required daily for a number of years. The ability to adjust injection device settings may improve patient comfort and needle anxiety, influencing adoption and acceptance of such devices, thereby improving treatment adherence. Here, we present the results of a retrospective observational study which investigated the association between injection device settings and adherence in the first 3 months of treatment in patients with growth disorders. Patients aged ≥2 and <18.75 years of age at treatment start, with ≥3 months of adherence data from start of treatment with the third generation of the easypod® device (EP3; Merck Healthcare KGaA, Darmstadt, Germany) were selected (N=832). The two most chosen combinations of device settings at treatment start were the default settings for injection speed, depth and time, or a slow injection speed and default depth and time. These combinations also demonstrated the highest adherence rates (94% and 95%, respectively) compared to other device settings (89%). A higher proportion of patients with intermediate/low adherence in the first month of treatment (31%, n=18/59) changed the device settings during treatment compared with those with high adherence (16%, n=128/803) (p=0.005). The ability to adjust injection device settings offers a valuable opportunity for personalizing treatment, improving patient comfort and treatment adherence. [ABSTRACT FROM AUTHOR]

Background: A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications. Objective: The aim of this adapted Delphi study was to collect researchers' opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care. Methods: We invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items. Results: The first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data. Conclusions: Future research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice. [ABSTRACT FROM AUTHOR]

BackgroundMultiple sclerosis (MS) is a non-curable chronic inflammatory disease of the central nervous system that affects more than 2 million people worldwide. MS-related symptoms impact negatively on the quality of life of persons with MS, who need to be active in the management of their health. mHealth apps could support these patient groups by offering useful tools, providing reliable information, and monitoring symptoms. A previous study from this group identified needs, barriers, and facilitators for the use of mHealth solutions among persons with MS. It is unknown how commercially available health apps meet these needs. ObjectiveThe main objective of this review was to assess how the features present in MS apps meet the reported needs of persons with MS. MethodsWe followed a combination of scoping review methodology and systematic assessment of features and content of mHealth apps. A search strategy was defined for the two most popular app stores (Google Play and Apple App Store) to identify relevant apps. Reviewers independently conducted a screening process to filter apps according to the selection criteria. Interrater reliability was assessed through the Fleiss-Cohen coefficient (k=.885). Data from the included MS apps were extracted and explored according to classification criteria. ResultsAn initial total of 581 potentially relevant apps was found. After removing duplicates and applying inclusion and exclusion criteria, 30 unique apps were included in the study. A similar number of apps was found in both stores. The majority of the apps dealt with disease management and disease and treatment information. Most apps were developed by small and medium-sized enterprises, followed by pharmaceutical companies. Patient education and personal data management were among the most frequently included features in these apps. Energy management and remote monitoring were often not present in MS apps. Very few contained gamification elements. ConclusionsCurrently available MS apps fail to meet the needs and demands of persons with MS. There is a need for health professionals, researchers, and industry partners to collaborate in the design of mHealth solutions for persons with MS to increase adoption and engagement.

BackgroundMultiple sclerosis (MS) is one of the world’s most common neurologic disorders. Fatigue is one of most common symptoms that persons with MS experience, having significant impact on their quality of life and limiting their activity levels. Self-management strategies are used to support them in the care of their health. Mobile health (mHealth) solutions are a way to offer persons with chronic conditions tools to successfully manage their symptoms and problems. Gamification is a current trend among mHealth apps used to create engaging user experiences and is suggested to be effective for behavioral change. To be effective, mHealth solutions need to be designed to specifically meet the intended audience needs. User-centered design (UCD) is a design philosophy that proposes placing end users’ needs and characteristics in the center of design and development, involving users early in the different phases of the software life cycle. There is a current gap in mHealth apps for persons with MS, which presents an interesting area to explore. ObjectiveThe purpose of this study was to describe the design and evaluation process of a gamified mHealth solution for behavioral change in persons with MS using UCD. MethodsBuilding on previous work of our team where we identified needs, barriers, and facilitators for mHealth apps for persons with MS, we followed UCD to design and evaluate a mobile app prototype aimed to help persons with MS self-manage their fatigue. Design decisions were evidence-driven and guided by behavioral change models (BCM). Usability was assessed through inspection methods using Nielsen’s heuristic evaluation. ResultsThe mHealth solution More Stamina was designed. It is a task organization tool designed to help persons with MS manage their energy to minimize the impact of fatigue in their day-to-day life. The tool acts as a to-do list where users can input tasks in a simple manner and assign Stamina Credits, a representation of perceived effort, to the task to help energy management and energy profiling. The app also features personalization and positive feedback. The design process gave way to relevant lessons to the design of a gamified behavioral change mHealth app such as the importance of metaphors in concept design, negotiate requirements with the BCM constructs, and tailoring of gamified experiences among others. Several usability problems were discovered during heuristic evaluation and guided the iterative design of our solution. ConclusionsIn this paper, we designed an app targeted for helping persons with MS in their fatigue management needs. We illustrate how UCD can help in designing mHealth apps and the benefits and challenges that designers might face when using this approach. This paper provides insight into the design process of gamified behavioral change mHealth apps and the negotiation process implied in it.