Speak4Diet: A Mobile App for Monitoring Dietary Composition
Abstract
Dietary composition plays a crucial role in maintaining overall health and preventing various diseases. Mobile apps have emerged as a popular tool for tracking and monitoring dietary intake, but their effectiveness and limitations are not fully understood. The Speak4Diet app uses artificial intelligence to analyse and track the composition of users' diets. The present study aimed to evaluate the effectiveness of Speak4Diet in monitoring dietary intake and its potential as a tool for improving dietary habits and overall health. A convenience sample of 200 adults aged 18-65 yearswas recruited and data were collected over a period of 12 weeks. The results showed that the app was well-received by users and had a high level of engagement. Analysis of dietary intake data revealed that the app was able to identify deficiencies in nutrients and provide recommendations for improvement. Correlations were also found between certain dietary factors and health markers such as body mass index and blood pressure. These findings suggest that the Speak4Diet app is a useful tool for monitoring dietary intake and has the potential to be a valuable addition to traditional methods of dietary tracking and nutrition counselling. Further research is needed to fully understand the long-term effectiveness of the app and its impact on health outcomes.
References
Whig, P., & Ahmad, S. N. (2011a). On the performance of ISFET-based device for water quality monitoring. Int’l J. of Communications, Network and System Sciences, 4(11), 709.
Whig, P., & Ahmad, S. N. (2012a). A CMOS integrated CC-ISFET device for water quality monitoring. International Journal of Computer Science Issues, 9(4), 1694–1814.
Whig, P., & Ahmad, S. N. (2012f). Performance analysis of various readout circuits for monitoring quality of water using analog integrated circuits. International Journal of Intelligent Systems and Applications, 4(11), 103.
Whig, P., & Ahmad, S. N. (2013a). A novel pseudo-PMOS integrated ISFET device for water quality monitoring. Active and Passive Electronic Components, 2013.
Whig, P., & Ahmad, S. N. (2014a). Development of economical ASIC for PCS for water quality monitoring. Journal of Circuits, Systems and Computers, 23(06), 1450079.
Whig, P., & Ahmad, S. N. (2014c). Simulation of linear dynamic macro model of photo catalytic sensor in SPICE. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering.
kolla, V. ravi kiran. (2012). Heart Disease Prediction using Python Machine Learning. International Journal of Statistical Computation and Simulation, 4(1). Retrieved from https://journals.threws.com/index.php/IJSCS/article/view/149
meeeniga, N. reddy. (2013). Heart Disease Prediction using Deep Learning and Artificial intelligence. International Journal of Statistical Computation and Simulation, 5(1). Retrieved from https://journals.threws.com/index.php/IJSCS/article/view/150
meeeniga, N. reddy. (2014). Type 2 Diabetes mellitus treatment intensification and deintensification. Transaction on Recent Devlopment in Industrial IoT, 6(6). Retrieved from https://journals.threws.com/index.php/TRDAIoT/article/view/153
kolla, V. ravi kiran. (2011). WEATHER PREDICTION USING MACHINE LEARNING. Transaction on Recent Devlopment in Industrial IoT, 3(3). Retrieved from https://journals.threws.com/index.php/TRDAIoT/article/view/152