METHODS: This is a cross-sectional study with two-stage stratified random sampling. In total, 3977 older persons participated in this study. Face-to-face interviews were conducted using a mobile device to obtain information about socio-demographic background, food insecurity, non-communicable diseases, social support and living arrangements. Descriptive and multiple complex sample logistic regression analyses were performed for data analysis.
RESULTS: The overall prevalence of food insecurity among older persons was 10.4%. Older persons from rural areas with no or only primary and secondary education, income less than RM 2000 (USD 477.57), at risk of malnutrition and not receiving very high social support were more likely to be food-insecure.
CONCLUSION: Approximately, one-tenth of Malaysian older adults were classified as food-insecure; particularly those living in rural areas from lower socio-economic status, not receiving very high social support and malnourished were more likely to be at risk. A specific nutrition program, such as meals on wheels and food vouchers, should be targeted toward older persons who are at risk to improve their malnutrition status. Geriatr Gerontol Int 2020; 20: 73-78.
MATERIALS AND METHODS: An auricular prosthesis, a complete denture, and anterior and posterior crowns were constructed using conventional methods and laser scanned to create computerized 3D meshes. The meshes were optimized independently by four computer-aided design software (Meshmixer, Meshlab, Blender, and SculptGL) to 100%, 90%, 75%, 50%, and 25% levels of original file size. Upon optimization, the following parameters were virtually evaluated and compared; mesh vertices, file size, mesh surface area (SA), mesh volume (V), interpoint discrepancies (geometric similarity based on virtual point overlapping), and spatial similarity (volumetric similarity based on shape overlapping). The influence of software and optimization on surface area and volume of each prosthesis was evaluated independently using multiple linear regression.
RESULTS: There were clear observable differences in vertices, file size, surface area, and volume. The choice of software significantly influenced the overall virtual parameters of auricular prosthesis [SA: F(4,15) = 12.93, R2 = 0.67, p < 0.001. V: F(4,15) = 9.33, R2 = 0.64, p < 0.001] and complete denture [SA: F(4,15) = 10.81, R2 = 0.67, p < 0.001. V: F(4,15) = 3.50, R2 = 0.34, p = 0.030] across optimization levels. Interpoint discrepancies were however limited to <0.1mm and volumetric similarity was >97%.
CONCLUSION: Open-source mesh optimization of smaller dental prostheses in this study produced minimal loss of geometric and volumetric details. SculptGL models were most influenced by the amount of optimization performed.
Methods: The accuracy of Webgazer.js for software-based gaze tracking is tested under different lighting conditions. Predefined time delays of a prototype diagnosis task automation script are contrasted against with manual delays based on human time estimation to understand how automation influences diagnosis accuracy. SLI diagnosis binary classifier was built and tested based on randomised parameters. The obtained results were cross-compared to Singlims_ES.exe for equality.
Results: Webgazer.js achieved an average accuracy of 88.755% under global lighting conditions, 61.379% under low lighting conditions and 52.7% under face-focused lighting conditions. The diagnosis task automation script found to execute with actual time delays with a deviation percentage no more than 0.04%, while manually executing time delays based on human time estimation resulted in a deviation percentage of not more than 3.37%. One-tailed test probability value produced by both the newly built classifier and Singlims_ES were observed to be similar up to three decimal places.
Conclusion: The results obtained should serve as a foundation for further evaluation of computer tools to help speech language pathologists diagnose SLI.
DESIGN: In-depth and focus group interviews were conducted with participants who have engaged in telemedicine. Questions included were participants' perception on the programme being used, satisfaction as well as engagement with the telemedicine programme. All interviews and focus groups were audio-recorded and transcribed verbatim. Data were analysed using a thematic approach.
PARTICIPANTS AND SETTING: People with type 2 diabetes (n=48) who participated in a randomised controlled study which examined the use of telemedicine for diabetes management were recruited from 11 primary care clinics located within the Klang Valley.
RESULTS: Twelve focus groups and two in-depth interviews were conducted. Four themes emerged from the analysis: (1) generational difference; (2) independence and convenience, (3) sharing of health data and privacy and (4) concerns and challenges. The main obstacles found in patients using the telemedicine systems were related to internet connectivity and difficulties experienced with system interface. Cost was also another significant concern raised by participants. Participants in this study were primarily positive about the benefits of telemedicine, including its ability to provide real-time data and disease monitoring and the reduction in clinic visits.
CONCLUSION: Despite the potential benefits of telemedicine in the long-term care of diabetes, there are several perceived barriers that may limit the effectiveness of this technology. As such, collaboration between educators, healthcare providers, telecommunication service providers and patients are required to stimulate the adoption and the use of telemedicine.NCT0246680.