The aim of this paper is to assess the usability of a chatbot for mental health care within a social enterprise. Chatbots are becoming more prevalent in our daily lives, as we can now use them to book flights, manage savings, and check the weather. Chatbots are increasingly being used in mental health care, with the emergence of "virtual therapists". In this study, the usability of a chatbot named iHelpr has been assessed. iHelpr has been developed to provide guided self-assessment, and tips for the following areas: stress, anxiety, depression, sleep, and self esteem. This study used a questionnaire developed by Chatbottest, and the System Usability Scale to assess the usability of iHelpr. The participants in this study enjoyed interacting with the chatbot, and found it easy to use. However, the study highlighted areas that need major improvements, such as Error Management and Intelligence. A list of recommendations has been developed to improve the usability of the iHelpr chatbot.

Overall, estimation of abdominal fat distribution parameters from CT scans performed on patients with acute pancreatitis indicates a strong association between visceral fat, severe acute pancreatitis, and the subsequent development of systemic complications. These data suggest that visceral fat volume should be incorporated into future predictive scoring systems.

The aim of this paper is to outline the design of a chatbot to be used within mental health counselling. One of the main causes of the burden of disease worldwide is mental health problems. Mental health contributes to 28% of the total burden of disease, compared to 16% each for cancer and heart disease in the UK. Stress, anxiety or depression accounted for 15.8 million days of sickness absence across the UK in 2016. By 2020, the gap between the demand for mental health care and the resources the National Health Service (NHS) can provide is likely to widen, therefore providers are increasingly needing to find more cost-effective ways to deliver mental health care. Digital Interventions have been created to help with these issues, for example anxiety, stress and depression. Chatbots can be incorporated into digital interventions, or used as standalone interventions. Chatbots can be a more interactive experience for the user to receive information, or complete diagnostic tools, or to even be used for counselling. A demo chatbot was created using interactive emoji's and GIFs to improve the user experience when searching for online self-help tips. This chatbot will be further developed and incorporated into a full web based programme for mental health in the workplace. It is envisaged that the chatbot will be able to provide initial counselling, and lead users into the correct services or self-help information.

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BackgroundThe size-specific dose estimate (SSDE) is a dose-related metrics that incorporates patient size into its calculation. It is usually derived from the volume computed tomography dose index (CTDIvol) by applying a conversion factor determined from manually measured anteroposterior and lateral skin-to-skin patient diameters at the midslice level on computed tomography (CT) localiser images, an awkward, time-consuming, and not highly reproducible technique. The objective of this study was to evaluate the potential for the use of body mass index (BMI) as a size-related metrics alternative to the midslice effective diameter (DE) to obtain a size-specific dose (SSDE) in abdominal CT.MethodsIn this retrospective study of patients who underwent abdominal CT for the investigation of inflammatory bowel disease, the DE was measured on the midslice level on CT-localiser images of each patient. This was correlated with patient BMI and the linear regression equation relating the quantities was calculated. The ratio between the internal and the external abdominal diameters (DRATIO) was also measured to assess correlation with radiation dose. Pearson correlation analysis and linear regression models were used.ResultsThere was good correlation between DE and patient BMI (r = 0.88). An equation allowing calculation of DE from BMI was calculated by linear regression analysis as follows: DE = 0.76 (BMI) + 9.4. A weak correlation between radiation dose and DRATIO was demonstrated (r = 0.45).ConclusionsPatient BMI can be used to accurately estimate DE, obviating the need to measure anteroposterior and lateral diameters in order to calculate a SSDE for abdominal CT.