User Preferences Related to Multimedia Elements of a Mobile Application to Prevent Diabetes

Pinillos-Patiño, Yisel; Herazo-Beltrán, Yaneth; Rodríguez-Cordero, Orlando; Bermejo, Amada Esther Escorcia; Martelo, Enrique; Claros, José Armando Vidarte; García, José Hoover Vanegas; Moreno, Gabriela

doi:10.4258/hir.2020.26.4.295

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Apart from using location and time contexts, some context-aware reminder systems employ short messages (SMS), commonly termed as text messages, in prompting users. This mechanism has been widely used in the health and business sectors [56], [57], [58, [59], [60], [61]. The work of Eggerth et al [62] combined artificial neural network (ANN) and near field communications (NFC) in designing a reminder system to manage adherence to long-term medications.…”

Section: Developmental Trends Of the Applicationsmentioning

confidence: 99%

A Robust Mechanism for Categorizing Context-Aware Applications into Generations

Opoku

2021

EJECE

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The hunt to categorize context-aware applications has been a prevalent issue to developers of context-aware applications. The previous categorizations were based on the functions of the applications. These mechanisms yielded limited results since many applications could not be categorized. This paper categorizes applications into four generations based on developmental trends through a literature survey. The first generation applications focused on data acquisition and used hardware sensors. The second generation applications focused on knowledge acquisition and used software sensors, semantic language and ontology-based modelling languages. The third generation applications focused on intelligent reasoning and used mechanisms to handle information uncertainty. The fourth generation applications deprecate cumbersome ruleset implementations and focus on artificial intelligence whilst taking into consideration the effect of the dynamics of users’ background and preference on contextual information. The study demonstrated that when applications, methods or technologies can be categorized over some time, it is better to classify them into generations.

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Section: Developmental Trends Of the Applicationsmentioning

confidence: 99%

A Robust Mechanism for Categorizing Context-Aware Applications into Generations

Opoku

2021

EJECE

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Assessing Technology Innovation of Mobile Health Apps for Medical Care Providers

Galetsi

Katsaliaki

Kumar

2023

IEEE Trans. Eng. Manage.

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Towards telemedicine for antenatal care visits in a post-COVID era: a development study and feasibility of a software system (Preprint)

Thongploy¹,

Phonyiam²,

Chaovalit³

et al. 2022

Preprint

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BACKGROUND A preparation for telemedicine has become increasingly urgent in recent years as the world is facing new challenges in how to provide healthcare from a physical distance. Using a chatbot technology, healthcare providers can use a chatbot which is a computer program to automatically converse with patients through text. In antenatal care clinics, a chatbot can be used to receive patient-specific data and give advice to support care of pregnant women. OBJECTIVE To develop a mobile technology as a chatbot software system to support pregnant women for monitoring gestational weight gain and health lifestyle during pregnancy METHODS The present study demonstrated how the chatbot program, REMI, (Robot for Expecting Mother’s Information) was developed with the LINE platform. Thai singleton pregnant women with pre-pregnancy body mass index (BMI) above 18.5 kg/m2 were enrolled with REMI. An individual gestational weight gain was recorded in the program by the subject themselves. The healthcare provider can review patients’ data and give advice. All data were collected and analyzed. The pregnant women who came for routine antenatal care were enrolled as a control group. RESULTS Four hundred and fifty-two pregnant women were registered in REMI chatbot program. Two hundred forty- five pregnant women were registered and received antenatal care in Thammasat University Hospital. Normal and high pre-pregnancy BMI women were 93 (37.96%), 152 (62.04%) in REMI group and 2 (0.97%), 205 (99.03%) in the routine antenatal care group, respectively. The number of pregnant women with excessive weight gain who gain weight more than 0.5 kg/week in normal BMI, overweight group (BMI 18.5-24.9) and 0.25 kg/week in obesity and marked obesity group (BMI>=25) were not different between REMI chatbot group and usual antenatal care group. Most of pregnant women who registered to use the REMI chatbot program had used it frequently and they can understand the program quickly. CONCLUSIONS For pregnant women, a mobile app such as REMI chatbot program can provide support for pregnant women between consultation visits, thereby reducing the number of required outpatient clinic visits and help clinicians in terms of education and monitoring their patients. CLINICALTRIAL Not applicable

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User Preferences Related to Multimedia Elements of a Mobile Application to Prevent Diabetes

Cited by 4 publications

References 23 publications

A Robust Mechanism for Categorizing Context-Aware Applications into Generations

A Robust Mechanism for Categorizing Context-Aware Applications into Generations

Assessing Technology Innovation of Mobile Health Apps for Medical Care Providers

Towards telemedicine for antenatal care visits in a post-COVID era: a development study and feasibility of a software system (Preprint)

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