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Why do healthcare workers adopt digital health technologies - A cross-sectional study integrating the TAM and UTAUT model in a developing economy
International Journal of Information Management Data Insights,
2023
DOI:10.1016/j.jjimei.2023.100186
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[2]
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A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study
JMIR Diabetes,
2023
DOI:10.2196/49113
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[3]
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Why do healthcare workers adopt digital health technologies - A cross-sectional study integrating the TAM and UTAUT model in a developing economy
International Journal of Information Management Data Insights,
2023
DOI:10.1016/j.jjimei.2023.100186
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[4]
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Exploring the Impact of IoT Adoption on Elderly Non-Communicable Disease Patients: Attitudes, Subjective Norms, and Perceived Behavioral Control in Malaysia
2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon),
2023
DOI:10.1109/SmartTechCon57526.2023.10391307
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[5]
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Game not over: Explaining older adults’ use and intention to continue using a gamified eHealth service
Health Informatics Journal,
2022
DOI:10.1177/14604582221106008
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Insight into online teaching behaviour of lecturers in Zimbabwean universities during the COVID-19 era and beyond: issues and challenges
The International Journal of Information and Learning Technology,
2021
DOI:10.1108/IJILT-07-2021-0104
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Impacts of Anthropometric, Biochemical, Socio-Demographic, and Dietary Habits Factors on the Health Status of Urban Corporate People in a Developing Country
Healthcare,
2020
DOI:10.3390/healthcare8030188
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Blood Uric Acid Prediction Model through Machine Learning Approaches (Preprint)
JMIR Medical Informatics,
2020
DOI:10.2196/18331
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