Authoring of interventions with on-demand generation of screen for mobile devices

Cunha, Bruna C.R.; Rodrigues, Kamila Rios da Hora; Scalco, Leonardo Fernandes; Zaine, Isabela; Viel, Caio César; Pimentel, Maria da Graça Campos

doi:10.5753/webmedia.2018.4588

Cited by 5 publications

(9 citation statements)

References 8 publications

(11 reference statements)

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“…The ESPIM method evolved supporting real case studies conducted by specialists ( Figure 1 ) who used the method via the associated ESPIM system to manage interventions with their populations of interest [ 52 , 53 ]. These case studies are part of the empirical evaluations of the ESPIM system ( Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports

Cunha¹,

Rodrigues²,

Zaine³

et al. 2021

J Med Internet Res

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Background Health professionals initiating mobile health (mHealth) interventions may choose to adapt apps designed for other activities (eg, peer-to-peer communication) or to employ purpose-built apps specialized in the required intervention, or to exploit apps based on methods such as the experience sampling method (ESM). An alternative approach for professionals would be to create their own apps. While ESM-based methods offer important guidance, current systems do not expose their design at a level that promotes replicating, specializing, or extending their contributions. Thus, a twofold solution is required: a method that directs specialists in planning intervention programs themselves, and a model that guides specialists in adopting existing solutions and advises software developers on building new ones. Objective The main objectives of this study are to design the Experience Sampling and Programmed Intervention Method (ESPIM), formulated toward supporting specialists in deploying mHealth interventions, and the ESPIM model, which guides health specialists in adopting existing solutions and advises software developers on how to build new ones. Another goal is to conceive and implement a software platform allowing specialists to be users who actually plan, create, and deploy interventions (ESPIM system). Methods We conducted the design and evaluation of the ESPIM method and model alongside a software system comprising integrated web and mobile apps. A participatory design approach with stakeholders included early software prototype, predesign interviews with 12 health specialists, iterative design sustained by the software as an instance of the method’s conceptual model, support to 8 real case studies, and postdesign interviews. Results The ESPIM comprises (1) a list of requirements for mHealth experience sampling and intervention-based methods and systems, (2) a 4-dimension planning framework, (3) a 7-step-based process, and (4) an ontology-based conceptual model. The ESPIM system encompasses web and mobile apps. Eight long-term case studies, involving professionals in psychology, gerontology, computer science, speech therapy, and occupational therapy, show that the method allowed specialists to be actual users who plan, create, and deploy interventions via the associated system. Specialists’ target users were parents of children diagnosed with autism spectrum disorder, older persons, graduate and undergraduate students, children (age 8-12), and caregivers of older persons. The specialists reported being able to create and conduct their own studies without modifying their original design. A qualitative evaluation of the ontology-based conceptual model showed its compliance to the functional requirements elicited. Conclusions The ESPIM method succeeds in supporting specialists in planning, authoring, and deploying mobile-based intervention programs when employed via a software system designed and implemented according to its conceptual model. The ESPIM ontology–based conceptual model exposes the design of systems involving active or passive sampling interventions. Such exposure supports the evaluation, implementation, adaptation, or extension of new or existing systems.

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Section: Methodsmentioning

confidence: 99%

Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports

Cunha¹,

Rodrigues²,

Zaine³

et al. 2021

J Med Internet Res

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“…O ESPIM 8 disponibiliza duas interfaces de usuário, uma Web para especialistas e outra para dispositivos móveis Android, usada pelas populações de interesse desses especialistas (CUNHA et al, 2018;VIEL et al, 2017). O sistema permite o planejamento e a autoria de intervenções baseadas em dados coletados de maneira explícita e pervasiva, por meio dos sensores físicos do dispositivo.…”

Section: S I S T E M a E S P I Munclassified

“…Os componentes de infraestrutura computacional do ESPIM incluem: um aplicativo móvel para a plataforma Android, utilizado pela população de interesse estudada pelos especialistas; um sistema Web, para o planejamento das coletas e intervenções, utilizado por pesquisadores e profissionais e, um Web Service, responsável pelo armazenamento das informações (CUNHA et al, 2018;VIEL et al, 2017).…”

Section: S I S T E M a E S P I Munclassified

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Um Estudo Sobre Letramento Digital Para Idosos Com O Apoio De Um Aplicativo Móvel Personalizável

Zaine

Rodrigues

Viel

et al. 2021

Estud. interdiscip. envelhec.

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O uso de sistemas computacionais para cuidados com a saúde deidosos é cada vez mais comum. No campo da educação, no entanto,as investigações enfocam mais em questões técnicas de desenvolvimento ou em adaptação de tecnologias, e não no processo deensino-aprendizagem e aplicabilidade do uso de dispositivos móveis.Este trabalho buscou estimular, monitorar e avaliar o uso dessesdispositivos no ambiente natural por idosos participantes de cursosde alfabetização e letramento digital. Também buscou promover novasmetodologias para práticas de alfabetização e letramento digital paraidosos. Foi realizado um estudo de viabilidade explorando o uso deum sistema denominado ESPIM, como um novo recurso pedagógicoem cursos como este. O ESPIM permite a profissionais de diferentesáreas construírem programas de intervenção para realizar coletas dedados de forma remota com suas populações de interesse. Essapopulação responde a perguntas programadas pelos profissionaisusando um aplicativo móvel. As perguntas planejadas podem serquestões abertas, de múltipla escolha, de escolha única, solicitaçãodo envio de mídias como áudio, vídeo ou imagens. Neste estudoqualitativo, por duas semanas consecutivas, os participantes receberam alertas e instruções, por meio desse aplicativo, para realizaratividades práticas em casa sobre o conteúdo aprendido em salade aula. Os resultados obtidos descrevem um comportamentopositivo dos participantes em termos de engajamento na execuçãodas atividades de casa, e também descrevem feedbacks sobre oaplicativo, tais como facilidades e dificuldades, autonomia no usoe adesão ao recurso tecnológico como forma de apoio a cursoscomo o supracitado.

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“…Estudos na Psicologia e também na Computação mostram que utilizar soluções computacionais trazem resultados positivos no combate à evasão, essas soluções são desenvolvidas para disponibilizar e monitorar intervenções que oferecem apoio acadêmico aos estudantes (ZHENG; MOTTI, 2017;HARRER et al, 2018;HEKTNER;SCHMIDT;CSIKSZENTMIHALYI, 2006;EXLER et al, 2015;BAXTER;AVREKH;EVANS, 2015;CUNHA et al, 2018;ZAINE et al, 2019a). Algumas delas estão especificadas na Subseção 2.4.1.…”

Section: Intervenções Para Retençãounclassified

Evasão Escolar Universitária e Estratégias de Intervenções para Retenção do Estudante: Um Estudo de Caso na Universidade Federal de São Carlos

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School dropout is a concern for educational institutions, especially higher education, since it directly impacts management and academic results of institutions, as well as being directly related to social problems. The literature points out that analyzing this phenomenon is a positive factor for developing programs to combat dropout, in addition to planning interventional actions and academic monitoring. Studies shows positive results in the use of Machine Learning techniques for the early identification of students before they dropout, based on the exploration of data from academic systems. Although there are studies with Machine Learning, there were no records of works focused on developing intervention strategies supported by the visualization of academic data. This work aimed to understand the overview of a Brazilian public university, UFSCar, using the exploration and classification of academic data through Machine Learning techniques. The analysis of the data allowed us to obtain an overview of the university's dropout data and made it possible to prepare digital reports with information and statistics to assist university managers, heads of centers and departments, course coordinators and teachers in decision making. In addition, the main stakeholders were interviewed to report their difficulties in knowing the statistics on dropout and to validate the premises initially raised in this work. The reports were evaluated by these stakeholders and resulted in positive perceptions of use. A second intervention was conducted with students in partnership with ProEstudo, a program composed of Psychology professionals from the university. The partnership allowed the improvement of a computational solution, ESPIM, capable of carrying out remote interventions, thus enabling the development of intervention models to support the monitoring of students to combat academic difficulties and dropout. The remote interventions are being used by ProEstudo professionals during the university's academic period. The results of this research point out that data exploration is fundamental to obtain reliable information and visualize the dropout scenario in the institution; in addition, it was possible to confirm that the interventions carried out provide sufficient means to assist managers in decision making and support professionals in monitoring the students, which may result in reduced dropout rates.

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Authoring of interventions with on-demand generation of screen for mobile devices

Cited by 5 publications

References 8 publications

Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports

Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports

Um Estudo Sobre Letramento Digital Para Idosos Com O Apoio De Um Aplicativo Móvel Personalizável

Evasão Escolar Universitária e Estratégias de Intervenções para Retenção do Estudante: Um Estudo de Caso na Universidade Federal de São Carlos

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