| dc.description.abstract | Healthcare professionals (HCPs) in lower- and middle-income countries (LMICs) like
Bangladesh often face high levels of workplace stress, which can negatively impact their
mental well-being. However, despite its significant impact, this issue is frequently over-
looked, leaving many HCPs vulnerable to burnout and other mental health challenges.
Mobile health (mHealth) tools, integrated with wearable devices like smartwatches, have
the potential to play a significant role in managing workplace stress among HCPs by lever-
aging physiological signals. This study explored the design and usability evaluation of a
user-centered mHealth tool named ‘FreedHCP,’ aimed at managing stress among HCPs in
Bangladesh. The research objectives include conducting design requirements and needfind-
ing analysis, developing a high-fidelity prototype, evaluating the usability of FreedHCP,
and proposing a deep learning (DL) based method for automatic stress detection that
could be implemented in future applications. A survey involving 71 HCPs revealed that
high workload, patient and family pressure, and staffing shortages were major stressors,
while social support, taking short breaks, and time management were effective coping
strategies. Participants also indicated a strong preference for app features such as guided
meditation sessions, personalized stress management plans, real-time health tracking, and
willingness to use a smartwatch-based mHealth app for real-time stress monitoring. Based
on these insights, the FreedHCP was developed with five core feature categories: ‘Assigned
Tasks’, ‘Notification Settings’, ‘Get Help’, ‘Wellness Check’, and ‘Supervisor Dashboard’.
The usability evaluation revealed that the ‘Smart Monitoring’ feature from the ‘Wellness
Check’ category was the most liked, with 29.2% of votes. The app achieved a mean System
Usability Scale (SUS) score of 71.77. Moreover, an overwhelming 95.8% of participants
expressed willingness to use and recommend FreedHCP to colleagues. In parallel, medical
students are another vulnerable group significantly affected by mental health challenges
such as anxiety, depression, and burnout due to the intense pressures of their academic
and clinical environments. These mental health issues, further worsened by a demanding
workload and the emotional burden of patient care, can adversely impact both personal
wellness and professional development. To address these concerns, a dataset of 886 Swiss
medical students was analyzed to automate the screening process for anxiety, depression,
and burnout using Machine Learning (ML) and Deep Learning (DL) approaches. The
analysis compares the performance of two advanced computational models: an Ensem-
ble classifier, integrating Random Forest (RF), Naive Bayes (NB), and Light Gradient-
Boosting Machine (LightGBM), and a Deep Neural Network (DNN). The DNN model
emerges as the better performing method by demonstrating accuracy rates of 81.4% for
depression, 76.65% for anxiety, and 73.59% for burnout. Comparative analyses further
validate the DNN’s efficacy against the Ensemble classifier, thereby providing a promising
method for automated clinical diagnosis for mental health professionals. This research
not only demonstrates the utility of mHealth solutions in stress management but also
highlights the promise of Artificial Intelligence (AI) driven models for advancing mental
health care in professional healthcare environments. | en_US |
