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2.
JMIR Mhealth Uhealth ; 9(1): e25018, 2021 01 22.
Artigo em Inglês | MEDLINE | ID: mdl-33480854

RESUMO

BACKGROUND: The classic Marshmallow Test, where children were offered a choice between one small but immediate reward (eg, one marshmallow) or a larger reward (eg, two marshmallows) if they waited for a period of time, instigated a wealth of research on the relationships among impulsive responding, self-regulation, and clinical and life outcomes. Impulsivity is a hallmark feature of self-regulation failures that lead to poor health decisions and outcomes, making understanding and treating impulsivity one of the most important constructs to tackle in building a culture of health. Despite a large literature base, impulsivity measurement remains difficult due to the multidimensional nature of the construct and limited methods of assessment in daily life. Mobile devices and the rise of mobile health (mHealth) have changed our ability to assess and intervene with individuals remotely, providing an avenue for ambulatory diagnostic testing and interventions. Longitudinal studies with mobile devices can further help to understand impulsive behaviors and variation in state impulsivity in daily life. OBJECTIVE: The aim of this study was to develop and validate an impulsivity mHealth diagnostics and monitoring app called Digital Marshmallow Test (DMT) using both the Apple and Android platforms for widespread dissemination to researchers, clinicians, and the general public. METHODS: The DMT app was developed using Apple's ResearchKit (iOS) and Android's ResearchStack open source frameworks for developing health research study apps. The DMT app consists of three main modules: self-report, ecological momentary assessment, and active behavioral and cognitive tasks. We conducted a study with a 21-day assessment period (N=116 participants) to validate the novel measures of the DMT app. RESULTS: We used a semantic differential scale to develop self-report trait and momentary state measures of impulsivity as part of the DMT app. We identified three state factors (inefficient, thrill seeking, and intentional) that correlated highly with established measures of impulsivity. We further leveraged momentary semantic differential questions to examine intraindividual variability, the effect of daily life, and the contextual effect of mood on state impulsivity and daily impulsive behaviors. Our results indicated validation of the self-report sematic differential and related results, and of the mobile behavioral tasks, including the Balloon Analogue Risk Task and Go-No-Go task, with relatively low validity of the mobile Delay Discounting task. We discuss the design implications of these results to mHealth research. CONCLUSIONS: This study demonstrates the potential for assessing different facets of trait and state impulsivity during everyday life and in clinical settings using the DMT mobile app. The DMT app can be further used to enhance our understanding of the individual facets that underlie impulsive behaviors, as well as providing a promising avenue for digital interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03006653; https://www.clinicaltrials.gov/ct2/show/NCT03006653.


Assuntos
Avaliação Momentânea Ecológica , Comportamento Impulsivo , Aplicativos Móveis/normas , Telemedicina , Adulto , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Autorrelato , Autocontrole
3.
JMIR Public Health Surveill ; 7(1): e25701, 2021 01 06.
Artigo em Inglês | MEDLINE | ID: mdl-33326411

RESUMO

BACKGROUND: Digital proximity tracing apps have been released to mitigate the transmission of SARS-CoV-2, the virus known to cause COVID-19. However, it remains unclear how the acceptance and uptake of these apps can be improved. OBJECTIVE: This study aimed to investigate the coverage of the SwissCovid app and the reasons for its nonuse in Switzerland during a period of increasing incidence of COVID-19 cases. METHODS: We collected data between September 28 and October 8, 2020, via a nationwide online panel survey (COVID-19 Social Monitor, N=1511). We examined sociodemographic and behavioral factors associated with app use by using multivariable logistic regression, whereas reasons for app nonuse were analyzed descriptively. RESULTS: Overall, 46.5% (703/1511) of the survey participants reported they used the SwissCovid app, which was an increase from 43.9% (662/1508) reported in the previous study wave conducted in July 2020. A higher monthly household income (ie, income >CHF 10,000 or >US $11,000 vs income ≤CHF 6000 or

Assuntos
/psicologia , Busca de Comunicante/instrumentação , Aplicativos Móveis/normas , Adulto , Idoso , /transmissão , Busca de Comunicante/tendências , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Aplicativos Móveis/estatística & dados numéricos , Inquéritos e Questionários , Suíça
4.
J Med Internet Res ; 22(12): e19452, 2020 12 15.
Artigo em Inglês | MEDLINE | ID: mdl-33320101

RESUMO

BACKGROUND: Chronic kidney disease (CKD) is a global health burden. Self-management plays a key role in improving modifiable risk factors. OBJECTIVE: The aim of this study was to evaluate the effectiveness of wearable devices, a health management platform, and social media at improving the self-management of CKD, with the goal of establishing a new self-management intervention model. METHODS: In a 90-day prospective experimental study, a total of 60 people with CKD at stages 1-4 were enrolled in the intervention group (n=30) and control group (n=30). All participants were provided with wearable devices that collected exercise-related data. All participants maintained dietary diaries using a smartphone app. All dietary and exercise information was then uploaded to a health management platform. Suggestions about diet and exercise were provided to the intervention group only, and a social media group was created to inspire the participants in the intervention group. Participants' self-efficacy and self-management questionnaire scores, Kidney Disease Quality of Life scores, body composition, and laboratory examinations before and after the intervention were compared between the intervention and control groups. RESULTS: A total of 49 participants completed the study (25 in the intervention group and 24 in the control group); 74% of the participants were men and the mean age was 51.22 years. There were no differences in measured baseline characteristics between the groups except for educational background. After the intervention, the intervention group showed significantly higher scores for self-efficacy (mean 171.28, SD 22.92 vs mean 142.21, SD 26.36; P<.001) and self-management (mean 54.16, SD 6.71 vs mean 47.58, SD 6.42; P=.001). Kidney Disease Quality of Life scores were also higher in the intervention group (mean 293.16, SD 34.21 vs mean 276.37, SD 32.21; P=.02). The number of steps per day increased in the intervention group (9768.56 in week 1 and 11,389.12 in week 12). The estimated glomerular filtration rate (eGFR) of the intervention group was higher than that of the control group (mean 72.47, SD 24.28 vs mean 59.69, SD 22.25 mL/min/1.73m2; P=.03) and the decline in eGFR was significantly slower in the intervention group (-0.56 vs -4.58 mL/min/1.73m2). There were no differences in body composition between groups postintervention. CONCLUSIONS: The use of wearable devices, a health management platform, and social media support not only strengthened self-efficacy and self-management but also improved quality of life and a slower eGFR decline in people with CKD at stages 1-4. These results outline a new self-management model to promote healthy lifestyle behaviors for patients with CKD. TRIAL REGISTRATION: ClinicalTrials.gov NCT04617431; https://www.clinicaltrials.gov/ct2/show/NCT04617431.


Assuntos
Aplicativos Móveis/normas , Qualidade de Vida/psicologia , Insuficiência Renal Crônica/terapia , Autogestão/métodos , Mídias Sociais/tendências , Telemedicina/métodos , Dispositivos Eletrônicos Vestíveis/normas , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Insuficiência Renal Crônica/psicologia
5.
J Med Internet Res ; 22(12): e19127, 2020 12 18.
Artigo em Inglês | MEDLINE | ID: mdl-33337337

RESUMO

BACKGROUND: Chatbots are applications that can conduct natural language conversations with users. In the medical field, chatbots have been developed and used to serve different purposes. They provide patients with timely information that can be critical in some scenarios, such as access to mental health resources. Since the development of the first chatbot, ELIZA, in the late 1960s, much effort has followed to produce chatbots for various health purposes developed in different ways. OBJECTIVE: This study aimed to explore the technical aspects and development methodologies associated with chatbots used in the medical field to explain the best methods of development and support chatbot development researchers on their future work. METHODS: We searched for relevant articles in 8 literature databases (IEEE, ACM, Springer, ScienceDirect, Embase, MEDLINE, PsycINFO, and Google Scholar). We also performed forward and backward reference checking of the selected articles. Study selection was performed by one reviewer, and 50% of the selected studies were randomly checked by a second reviewer. A narrative approach was used for result synthesis. Chatbots were classified based on the different technical aspects of their development. The main chatbot components were identified in addition to the different techniques for implementing each module. RESULTS: The original search returned 2481 publications, of which we identified 45 studies that matched our inclusion and exclusion criteria. The most common language of communication between users and chatbots was English (n=23). We identified 4 main modules: text understanding module, dialog management module, database layer, and text generation module. The most common technique for developing text understanding and dialogue management is the pattern matching method (n=18 and n=25, respectively). The most common text generation is fixed output (n=36). Very few studies relied on generating original output. Most studies kept a medical knowledge base to be used by the chatbot for different purposes throughout the conversations. A few studies kept conversation scripts and collected user data and previous conversations. CONCLUSIONS: Many chatbots have been developed for medical use, at an increasing rate. There is a recent, apparent shift in adopting machine learning-based approaches for developing chatbot systems. Further research can be conducted to link clinical outcomes to different chatbot development techniques and technical characteristics.


Assuntos
Aplicativos Móveis/normas , Comunicação , Humanos , Projetos de Pesquisa
6.
J Med Internet Res ; 22(12): e16322, 2020 12 18.
Artigo em Inglês | MEDLINE | ID: mdl-33337340

RESUMO

BACKGROUND: Mobile health apps have emerged as useful tools for patients and clinicians alike, sharing health information or assisting in clinical decision-making. Prostate cancer (PCa) risk calculator mobile apps have been introduced to assess risks of PCa and high-grade PCa (Gleason score ≥7). The Rotterdam Prostate Cancer Risk Calculator and Coral-Prostate Cancer Nomogram Calculator apps were developed from the 2 most-studied PCa risk calculators, the European Randomized Study of Screening for Prostate Cancer (ERSPC) and the North American Prostate Cancer Prevention Trial (PCPT) risk calculators, respectively. A systematic review has indicated that the Rotterdam and Coral apps perform best during the prebiopsy stage. However, the epidemiology of PCa varies among different populations, and therefore, the applicability of these apps in a Taiwanese population needs to be evaluated. This study is the first to validate the PCa risk calculator apps with both biopsy and prostatectomy cohorts in Taiwan. OBJECTIVE: The study's objective is to validate the PCa risk calculator apps using a Taiwanese cohort of patients. Additionally, we aim to utilize postprostatectomy pathology outcomes to assess the accuracy of both apps with regard to high-grade PCa. METHODS: All male patients who had undergone transrectal ultrasound prostate biopsies in a single Taiwanese tertiary medical center from 2012 to 2018 were identified retrospectively. The probabilities of PCa and high-grade PCa were calculated utilizing the Rotterdam and Coral apps, and compared with biopsy and prostatectomy results. Calibration was graphically evaluated with the Hosmer-Lemeshow goodness-of-fit test. Discrimination was analyzed utilizing the area under the receiver operating characteristic curve (AUC). Decision curve analysis was performed for clinical utility. RESULTS: Of 1134 patients, 246 (21.7%) were diagnosed with PCa; of these 246 patients, 155 (63%) had high-grade PCa, according to the biopsy results. After confirmation with prostatectomy pathological outcomes, 47.2% (25/53) of patients were upgraded to high-grade PCa, and 1.2% (1/84) of patients were downgraded to low-grade PCa. Only the Rotterdam app demonstrated good calibration for detecting high-grade PCa in the biopsy cohort. The discriminative ability for both PCa (AUC: 0.779 vs 0.687; DeLong's method: P<.001) and high-grade PCa (AUC: 0.862 vs 0.758; P<.001) was significantly better for the Rotterdam app. In the prostatectomy cohort, there was no significant difference between both apps (AUC: 0.857 vs 0.777; P=.128). CONCLUSIONS: The Rotterdam and Coral apps can be applied to the Taiwanese cohort with accuracy. The Rotterdam app outperformed the Coral app in the prediction of PCa and high-grade PCa. Despite the small size of the prostatectomy cohort, both apps, to some extent, demonstrated the predictive capacity for true high-grade PCa, confirmed by the whole prostate specimen. Following our external validation, the Rotterdam app might be a good alternative to help detect PCa and high-grade PCa for Taiwanese men.


Assuntos
Aplicativos Móveis/normas , Neoplasias da Próstata/diagnóstico , Medição de Risco/métodos , Idoso , Estudos de Coortes , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Taiwan
7.
Artigo em Inglês | MEDLINE | ID: mdl-33317134

RESUMO

Digital health interventions may improve different behaviours. However, the rapid proliferation of technological solutions often does not allow for a correct assessment of the quality of the tools. This study aims to review and assess the quality of the available mobile applications (apps) related to interventions for low back pain. Two reviewers search the official stores of Android (Play Store) and iOS (App Store) for localisation in Spain and the United Kingdom, in September 2019, searching for apps related to interventions for low back pain. Seventeen apps finally are included. The quality of the apps is measured using the Mobile App Rating Scale (MARS). The scores of each section and the final score of the apps are retrieved and the mean and standard deviation obtained. The average quality ranges between 2.83 and 4.57 (mean 3.82) on a scale from 1 (inadequate) to 5 (excellent). The best scores are found in functionality (4.7), followed by aesthetic content (mean 4.1). Information (2.93) and engagement (3.58) are the worst rated items. Apps generally have good overall quality, especially in terms of functionality and aesthetics. Engagement and information should be improved in most of the apps. Moreover, scientific evidence is necessary to support the use of applied health tools.


Assuntos
Dor Lombar , Aplicativos Móveis , Humanos , Dor Lombar/terapia , Aplicativos Móveis/normas , Espanha , Reino Unido
9.
J Pediatr Psychol ; 45(10): 1106-1113, 2020 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-33068424

RESUMO

BACKGROUND: The COVID-19 pandemic has ignited wider clinical adoption of digital health tools, including mobile health apps (mHealth apps), to address mental and behavioral health concerns at a distance. While mHealth apps offer many compelling benefits, identifying effective apps in the crowded and largely unregulated marketplace is laborious. Consumer demand and industry productivity are increasing, although research is slower, making it challenging for providers to determine the most credible and safe apps for patients in need. OBJECTIVES/METHODS: This commentary offers a practical, empirically guided framework and associated resources for selecting appropriate mHealth apps for pediatric populations during the pandemic and beyond. RESULTS: In the first stage, Narrow the target problem, end user, and contender apps. Beginning the search with continuously updated websites that contain expert app ratings can help expedite this process (e.g., Psyberguide). Second, Explore each contender app's: (a) scientific and theoretical support (e.g., are app components consistent with health behavior change theories?), (b) privacy policies, and (c) user experience (e.g., through crowdsourcing feedback about app usability and appeal via social media). Third, use clinical expertise and stakeholder feedback to Contextualize whether the selected app is a good fit for a particular patient and/or caregiver (e.g., by considering age, race/ethnicity, ability, gender, sexual orientation, technology access), including conducting a brief self-pilot of the app. CONCLUSION: Youth are increasingly turning to technology for support, especially during the pandemic, and pediatric psychologists must be primed to recommend the most credible tools. We offer additional recommendations for rapidly disseminating evidence-based apps to the public.


Assuntos
Betacoronavirus , Infecções por Coronavirus/prevenção & controle , Transtornos Mentais/terapia , Aplicativos Móveis/normas , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Quarentena/psicologia , Telemedicina/métodos , Adolescente , Criança , Infecções por Coronavirus/complicações , Infecções por Coronavirus/psicologia , Humanos , Estudos Longitudinais , Masculino , Transtornos Mentais/complicações , Transtornos Mentais/psicologia , Pneumonia Viral/complicações , Pneumonia Viral/psicologia
10.
PLoS One ; 15(9): e0238694, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32915836

RESUMO

Android is the most widely used mobile operating system (OS). A large number of third-party Android application (app) markets have emerged. The absence of third-party market regulation has prompted research institutions to propose different malware detection techniques. However, due to improvements of malware itself and Android system, it is difficult to design a detection method that can efficiently and effectively detect malicious apps for a long time. Meanwhile, adopting more features will increase the complexity of the model and the computational cost of the system. Permissions play a vital role in the security of the Android apps. Term Frequency-Inverse Document Frequency (TF-IDF) is used to assess the importance of a word for a file set in a corpus. The static analysis method does not need to run the app. It can efficiently and accurately extract the permissions from an app. Based on this cognition and perspective, in this paper, a new static detection method based on TF-IDF and Machine Learning is proposed. The system permissions are extracted in Android application package's (Apk's) manifest file. TF-IDF algorithm is used to calculate the permission value (PV) of each permission and the sensitivity value of apk (SVOA) of each app. The SVOA and the number of the used permissions are learned and tested by machine learning. 6070 benign apps and 9419 malware are used to evaluate the proposed approach. The experiment results show that only use dangerous permissions or the number of used permissions can't accurately distinguish whether an app is malicious or benign. For malware detection, the proposed approach achieve up to 99.5% accuracy and the learning and training time only needs 0.05s. For malware families detection, the accuracy is 99.6%. The results indicate that the method for unknown/new sample's detection accuracy is 92.71%. Compared against other state-of-the-art approaches, the proposed approach is more effective by detecting malware and malware families.


Assuntos
Telefone Celular , Segurança Computacional/normas , Aprendizado de Máquina , Aplicativos Móveis/normas , Algoritmos , Coleta de Dados , Humanos
11.
J Med Syst ; 44(9): 164, 2020 Aug 10.
Artigo em Inglês | MEDLINE | ID: mdl-32779002

RESUMO

The global impact of COVID-19 pandemic has led to a rapid development and utilization of mobile health applications. These are addressing the unmet needs of healthcare and public health system including contact tracing, health information dissemination, symptom checking and providing tools for training healthcare providers. Here we provide an overview of mobile applications being currently utilized for COVID-19 and their assessment using the Mobile Application Rating Scale. We performed a systematic review of the literature and mobile platforms to assess mobile applications currently utilized for COVID-19, and a quality assessment of these applications using the Mobile Application Rating Scale (MARS) for overall quality, Engagement, Functionality, Aesthetics, and Information. Finally, we provide an overview of the key salient features that should be included in mobile applications being developed for future use. Our search identified 63 apps that are currently being used for COVID-19. Of these, 25 were selected from the Google play store and Apple App store in India, and 19 each from the UK and US. 18 apps were developed for sharing up to date information on COVID-19, and 8 were used for contact tracing while 9 apps showed features of both. On MARS Scale, overall scores ranged from 2.4 to 4.8 with apps scoring high in areas of functionality and lower in Engagement. Future steps should involve developing and testing of mobile applications using assessment tools like the MARS scale and the study of their impact on health behaviours and outcomes.


Assuntos
Betacoronavirus , Infecções por Coronavirus/prevenção & controle , Aplicativos Móveis/normas , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Smartphone/normas , Telemedicina/normas , Humanos , Disseminação de Informação/métodos
12.
J Med Internet Res ; 22(8): e21613, 2020 08 27.
Artigo em Inglês | MEDLINE | ID: mdl-32759100

RESUMO

BACKGROUND: The current COVID-19 pandemic is showing negative effects on human health as well as on social and economic life. It is a critical and challenging task to revive public life while minimizing the risk of infection. Reducing interactions between people by social distancing is an effective and prevalent measure to reduce the risk of infection and spread of the virus within a community. Current developments in several countries show that this measure can be technologically accompanied by mobile apps; meanwhile, privacy concerns are being intensively discussed. OBJECTIVE: The aim of this study was to examine central cognitive variables that may constitute people's motivations for social distancing, using an app, and providing health-related data requested by two apps that differ in their direct utility for the individual user. The results may increase our understanding of people's concerns and convictions, which can then be specifically addressed by public-oriented communication strategies and appropriate political decisions. METHODS: This study refers to the protection motivation theory, which is adaptable to both health-related and technology-related motivations. The concept of social trust was added. The quantitative survey included answers from 406 German-speaking participants who provided assessments of data security issues, trust components, and the processes of threat and coping appraisal related to the prevention of SARS-CoV-2 infection by social distancing. With respect to apps, one central focus was on the difference between a contact tracing app and a data donation app. RESULTS: Multiple regression analyses showed that the present model could explain 55% of the interindividual variance in the participants' motivation for social distancing, 46% for using a contact tracing app, 42% for providing their own infection status to a contact tracing app, and 34% for using a data donation app. Several cognitive components of threat and coping appraisal were related to motivation measurements. Trust in other people's social distancing behavior and general trust in official app providers also played important roles; however, the participants' age and gender did not. Motivations for using and accepting a contact tracing app were higher than those for using and accepting a data donation app. CONCLUSIONS: This study revealed some important cognitive factors that constitute people's motivation for social distancing and using apps to combat the COVID-19 pandemic. Concrete implications for future research, public-oriented communication strategies, and appropriate political decisions were identified and are discussed.


Assuntos
Betacoronavirus/patogenicidade , Infecções por Coronavirus/epidemiologia , Aplicativos Móveis/normas , Pneumonia Viral/epidemiologia , Estudos de Avaliação como Assunto , Humanos , Motivação , Pandemias , Inquéritos e Questionários
14.
Adv Exp Med Biol ; 1262: 59-94, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32613580

RESUMO

Smoking is a harmful habit, causing a range of severe consequences which could lead to premature death. This habit is still prevalent amongst young people. In order to protect children, effective early interventions supported by public instances need to be set in place. Raising awareness and educating the youth is crucial to change their mindset about the severity of smoking. Emerging technologies, such as augmented reality (AR) on mobile devices, have been shown to be useful in providing engaging experiences and educating children about a range of issues, including health and anatomy. This chapter presents a research which explores the use of AR as an exciting and engaging medium to effectively help educating children from 5 to 13 years about the effects of smoking. A mobile application, called SmokAR, was developed. This app includes AR visualization amongst other functionalities, whereby children are presented a realistic model of the human lungs of a healthy person and of a smoker. The aim of this research is to propose a transformative experience in order to put children off this dangerous habit whilst they gain knowledge about the effect of smoking on their organs. The anatomical accuracy of the 3D models and animations proposed by the app has been verified by an expert anatomist. A group of children (n = 17) also took part in usability and knowledge acquisition testing at the Glasgow Science Centre. Findings showed a significant high usability suggesting a user-friendly app design. Moreover, results also suggested that participants gained knowledge to a certain extent and felt discouraged from smoking after seeing the model of the smoker's lungs. Although there were several limitations to the study, the potential of the app to support learning and raising awareness is encouragingly positive. In addition, user testing in a more controlled environment, such as a classroom, can help gain further insights into the effectiveness and usability of the app. In the future, this simple but engaging approach to raise public awareness and support education could be used to further communicate with children about negative health effects of other harmful habits such as alcohol or drug consumption.


Assuntos
Realidade Aumentada , Vestuário , Aplicativos Móveis , Prevenção do Hábito de Fumar , Adolescente , Criança , Pré-Escolar , Humanos , Aprendizagem , Aplicativos Móveis/normas , Prevenção do Hábito de Fumar/métodos , Prevenção do Hábito de Fumar/normas
15.
Adv Exp Med Biol ; 1262: 115-147, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32613582

RESUMO

Arthritis is one of the most common disease states worldwide but is still publicly misunderstood and lacks engaging public awareness materials. Within the UK, the most prevalent types of arthritis are osteoarthritis (OA) and rheumatoid arthritis (RA). The two are commonly mistaken as the same disease but, in fact, have very different pathogenesis, symptoms and treatments. This chapter describes a study which aimed to assess whether an augmented reality (AR) application could be used to raise awareness about the difference between OA and RA.An application was created for Android tablets that included labelled 3D models, animations and AR scenes triggered from a poster. In total 11 adult participants tested the application taking part in a pretest and posttest which aim to measure the usability of the application and the acquisition of knowledge on OA and RA. A T-test was performed to assess the effectiveness of the application from the pretest and posttest questionnaire outcomes. Overall results were encouraging reporting a very significant acquisition of knowledge and a highly satisfactory user experience.


Assuntos
Artrite Reumatoide , Realidade Aumentada , Educação em Saúde , Osteoartrite , Adulto , Artrite Reumatoide/patologia , Educação em Saúde/métodos , Educação em Saúde/normas , Humanos , Aplicativos Móveis/normas , Osteoartrite/patologia , Inquéritos e Questionários , Reino Unido
16.
Adv Exp Med Biol ; 1262: 183-202, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32613584

RESUMO

This chapter presents a methodological framework which could be used to produce accurate anatomical 3D models and animations of the developing skull, with a focus on the temporal bone. Initial modelling is based on information from core texts and visual references, before optimising these models for use in interactive real-time applications. A series of 3D modelling and animation workflows typically used in computer games and animation industry were tested and compared. Workflows most suitable for the production of a 3D visualisation of the developing temporal bone were documented in detail and used to produce the final 3D models. 3D models of the developing temporal bone were then implemented in an interactive mobile application, which allowed users to explore the 3D models on their Android mobile device and use augmented reality to enhance real-world information. Results of tests conducted in this research suggest that 3D modelling workflows which mimic the processes occurring during development of the temporal bone are most suitable for producing realistic 3D models. Animation workflows tested in this research have all shown potential to produce morphing animations of the developing temporal bone. The significant time required to create deformation setups and animations themselves however suggests that using scripting to automate these workflows would increase their usability in projects with a limited timeframe.


Assuntos
Desenvolvimento Ósseo , Modelos Anatômicos , Osteologia , Osso Temporal , Humanos , Imageamento Tridimensional , Aplicativos Móveis/normas , Osteologia/educação , Materiais de Ensino/normas , Osso Temporal/crescimento & desenvolvimento
17.
J Med Internet Res ; 22(6): e15449, 2020 06 15.
Artigo em Inglês | MEDLINE | ID: mdl-32538793

RESUMO

BACKGROUND: Adequate self-management skills are of great importance for patients with chronic obstructive pulmonary disease (COPD) to reduce the impact of COPD exacerbations. Using mobile health (mHealth) to support exacerbation-related self-management could be promising in engaging patients in their own health and changing health behaviors. However, there is limited knowledge on how to design mHealth interventions that are effective, meet the needs of end users, and are perceived as useful. By following an iterative user-centered design (UCD) process, an evidence-driven and usable mHealth intervention was developed to enhance exacerbation-related self-management in patients with COPD. OBJECTIVE: This study aimed to describe in detail the full UCD and development process of an evidence-driven and usable mHealth intervention to enhance exacerbation-related self-management in patients with COPD. METHODS: The UCD process consisted of four iterative phases: (1) background analysis and design conceptualization, (2) alpha usability testing, (3) iterative software development, and (4) field usability testing. Patients with COPD, health care providers, COPD experts, designers, software developers, and a behavioral scientist were involved throughout the design and development process. The intervention was developed using the behavior change wheel (BCW), a theoretically based approach for designing behavior change interventions, and logic modeling was used to map out the potential working mechanism of the intervention. Furthermore, the principles of design thinking were used for the creative design of the intervention. Qualitative and quantitative research methods were used throughout the design and development process. RESULTS: The background analysis and design conceptualization phase resulted in final guiding principles for the intervention, a logic model to underpin the working mechanism of the intervention, and design requirements. Usability requirements were obtained from the usability testing phases. The iterative software development resulted in an evidence-driven and usable mHealth intervention-Copilot, a mobile app consisting of a symptom-monitoring module, and a personalized COPD action plan. CONCLUSIONS: By following a UCD process, an mHealth intervention was developed that meets the needs and preferences of patients with COPD, is likely to be used by patients with COPD, and has a high potential to be effective in reducing exacerbation impact. This extensive report of the intervention development process contributes to more transparency in the development of complex interventions in health care and can be used by researchers and designers as guidance for the development of future mHealth interventions.


Assuntos
Aplicativos Móveis/normas , Doença Pulmonar Obstrutiva Crônica/terapia , Autogestão/métodos , Telemedicina/métodos , Humanos
19.
J Med Internet Res ; 22(5): e17101, 2020 05 22.
Artigo em Inglês | MEDLINE | ID: mdl-32441655

RESUMO

BACKGROUND: Smartphone-based learning, or mobile learning (m-learning), has become a popular learning-and-teaching strategy in educational environments. Blended learning combines strategies such as m-learning with conventional learning to offer continuous training, anytime and anywhere, via innovative learning activities. OBJECTIVE: The main aim of this work was to examine the short-term (ie, 2-week) effects of a blended learning method using traditional materials plus a mobile app-the iPOT mobile learning app-on knowledge, motivation, mood state, and satisfaction among undergraduate students enrolled in a health science first-degree program. METHODS: The study was designed as a two-armed, prospective, single-blind, randomized controlled trial. Subjects who met the inclusion criteria were randomly assigned to either the intervention group (ie, blended learning involving traditional lectures plus m-learning via the use of the iPOT app) or the control group (ie, traditional on-site learning). For both groups, the educational program involved 13 lessons on basic health science. The iPOT app is a hybrid, multiplatform (ie, iOS and Android) smartphone app with an interactive teacher-student interface. Outcomes were measured via multiple-choice questions (ie, knowledge), the Instructional Materials Motivation Survey (ie, motivation), the Profile of Mood States scale (ie, mood state), and Likert-type questionnaires (ie, satisfaction and linguistic competence). RESULTS: A total of 99 students were enrolled, with 49 (49%) in the intervention group and 50 (51%) in the control group. No difference was seen between the two groups in terms of theoretical knowledge gain (P=.92). However, the intervention group subjects returned significantly higher scores than the control group subjects for all postintervention assessed items via the motivation questionnaire (all P<.001). Analysis of covariance (ANCOVA) revealed a significant difference in the confusion and bewilderment component in favor of the intervention group (P=.01), but only a trend toward significance in anger and hostility as well as total score. The intervention group subjects were more satisfied than the members of the control group with respect to five out of the six items evaluated: general satisfaction (P<.001), clarity of the instructions (P<.01), clarity with the use of the learning method (P<.001), enough time to complete the proposed exercises (P<.01), and improvement in the capacity to learn content (P<.001). Finally, the intervention group subjects who were frequent users of the app showed stronger motivation, as well as increased perception of greater gains in their English-language competence, than did infrequent users. CONCLUSIONS: The blended learning method led to significant improvements in motivation, mood state, and satisfaction compared to traditional teaching, and elicited statements of subjective improvement in terms of competence in English. TRIAL REGISTRATION: ClinicalTrials.gov NCT03335397; https://clinicaltrials.gov/ct2/show/NCT03335397.


Assuntos
Afeto/fisiologia , Aplicativos Móveis/normas , Motivação/fisiologia , Satisfação Pessoal , Adulto , Feminino , Humanos , Aprendizagem , Masculino , Estudos Prospectivos , Estudantes , Adulto Jovem
20.
Comput Inform Nurs ; 38(7): 358-366, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32427611

RESUMO

Cerebrovascular accident is a serious public health problem and requires the attention of professionals who can detect, diagnose, and provide care in a timely fashion. A quantitative quasi-experimental study was conducted using a mobile app called mSmartAVC for clinical evaluation of nursing care at the bedside. The study aimed at measuring the knowledge of nurses and nursing students in the detection and care of cerebrovascular accident. In this study, a total of 115 nurses from health services in the South of Brazil and 35 nursing students of a community university participated. The stages focused on development, modeling of clinical cases, problem-based learning, pretest (before) app use, and posttest (after) use of the app. The results of the pretest and posttest corrections showed a substantial statistical difference (P < .001), indicating a significant knowledge gain after the use of the app, particularly in terms of the detection scales and interpretation of the imaging tests. The mSmartAVC app used at the bedside supported decision-making for detection and nursing care. It was possible to confirm that the use of mobile apps plays an essential role as a learning tool for nurses and nursing students.


Assuntos
Educação Continuada em Enfermagem/métodos , Aprendizagem , Aplicativos Móveis/normas , Acidente Vascular Cerebral/enfermagem , Adulto , Brasil , Competência Clínica/normas , Competência Clínica/estatística & dados numéricos , Educação Continuada em Enfermagem/estatística & dados numéricos , Feminino , Humanos , Masculino , Aplicativos Móveis/estatística & dados numéricos , Enfermeiras e Enfermeiros/psicologia , Enfermeiras e Enfermeiros/normas , Enfermeiras e Enfermeiros/estatística & dados numéricos , Estudantes de Enfermagem/psicologia , Estudantes de Enfermagem/estatística & dados numéricos
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