In this case report we describe the development and early trialling of an iPad application replicating the Kessler 10+ (K10 +), a widely used brief measure of psychological distress. The application was the result of a collaboration between a youth mental health outreach service (Youthlink), a private application developer (Enabled) and local health service IT support. Therapists reported greater engagement with the iPad version of the K10+ compared to the pen/paper version and described how the application assisted them to collaboratively reflect with consumers on treatment progress.

Background: Google Glass is a wearable, head-mounted computer with display, photographic and videographic imaging capability, and connectivity to other devices through Wi-Fi and Bluetooth signaling.

Aims: To describe for the first time the use of Google Glass for use in indirect ophthalmoscopy and modification techniques to assist with its use.

Methods: A lightweight, portable light source was installed above the Glass aperture, a small tissue paper used to diffuse the light, and the arm of the headset was taped to the examiner’s glasses in order to bring the display into the right eye’s central visual field.

Results: Using a slightly modified Glass headset, the examiner documented the central and peripheral retina in a young male with ease.

Conclusion: We demonstrate for the first time that Glass, with minor modifications, can be used as a simple and effective method to perform and record a fundus examination.

Background: With the recent advent of smart phones, usage of medical apps is on rise. Smart phones are powerful devices that combine the conventional functions of a mobile phone with advanced computing capabilities enabling users to access software applications commonly termed as “apps”. Health care applications (apps) that are downloadable on to smart phones are increasingly becoming popular among clinicians.

Aim: The aim of the present study was to assess the usage of health care apps among Medical and Dental doctors.

Methodology: A descriptive cross sectional questionnaire based study was conducted on medical and dental doctors of Mamata hospitals, Khammam, Andhra Pradesh. A pretested, self administered questionnaire was used and it consists of questions regarding demographic data followed by usage of health care apps. Descriptive statistics were computed to demonstrate the frequency of responses and the comparisons were made using chi-square test. A p-value less than or equal to 0.05 was considered to be significant.

Results: A total of eighty doctors (48 Medical and 32 dental) completed the questionnaire. More males (n = 63) than females (n = 17) participated in the study. Participants had a mean age of 32.5 years. It was found that 68% of dental doctors and 70.45% of medical doctors are using health care apps on their smart phone. Most of the participants (58.8% of dental and 77.4% medical doctors) use the health care apps for knowledge purposes, while no dental doctors used the apps for diagnosis or treatment purposes. The majority of the dental doctors (41.17%) are using these apps for patient education purpose when compared to the medical doctors (3.22%).

Conclusion: There is a high usage rate of health care apps among both medical and dental doctors, with medical doctors using the apps for informational purposes, whereas dental doctors used the apps for patient education.

Background: Assessment of gait activity by accelerometry requires data analysis. Currently several methods are used to estimate step frequency. At present the relation between step frequency estimation, gait speed and minimal required time window length remains unknown.

Aims: The purpose of the study was to assess the accuracy of estimates of step frequency (SF) from trunk acceleration data analyzed with commonly used algorithms and time window lengths, at a wide range of gait speeds.

Method: Twenty healthy young subjects performed an incremental treadmill protocol from 1 km/h up to 6 km/h, with steps of 1 km/h. Each speed condition was maintained for two minutes. A waist worn accelerometer recorded trunk accelerations, while video analysis provided the correct number of steps taken during each gait speed condition. Accuracy of two commonly used signal analysis methods (autocorrelation, fast Fourier transformation) was examined with time windows of two, four and eight seconds.

Results: Our main finding was that accuracy of SF estimates with fast Fourier transformation and autocorrelation improved with increasing time window size, only at the lower gait speeds. Accuracy of SF estimation was lower at low gait speeds independent of the algorithm and time window used.

Conclusion: We recommend a minimum TW length of 4 seconds when using AC and PSD algorithms and when using the PSD algorithm to use spectral averaging, as this leads to better results at short TW and low gait speeds.