Browsing by Author "Eskofier, Bjoern"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
- ItemA Novel Mobile Phone App (OncoFood) to Record and Optimize the Dietary Behavior of Oncologic Patients: Pilot Study.(0000-00-00) Orlemann, Till; Reljic, Dejan; Zenker, Björn; Meyer, Julia; Eskofier, Bjoern; Thiemt, Jana; Herrmann, Hans Joachim; Neurath, Markus Friedrich; Zopf, YurdagülBACKGROUND Catabolism and tumor specific therapy lead to reduced nutrient intake and weight loss in cancer patients Maintaining a specific individualized diet can be challenging for the patient as the nutritional counseling options are limited Monitoring of nutrient intake and frequent feedback are however vital for successful nutritional therapy because they support the patient s compliance and realization of dietary therapeutic goals OBJECTIVE This study aimed at investigating the feasibility and applicability of a novel mobile phone app to assess and evaluate dietary behaviors in oncologic patients METHODS To determine dietary habits and food preferences in oncologic patients initially 1400 nutritional records were evaluated and analyzed The results provided the basis for creating a nutritional mobile phone app Key requirements for the app included simple handling recording the daily intake and a comparison of nutrient targets and current status In total 39 cancer patients were recruited for the study 15 patients dropped out prior to the study All patients received a nutritional anamnesis nutritional analysis and nutritional counseling Individual energy and nutrient aims were defined The intervention group n 12 additionally used the app Weight and body composition of each group were evaluated after 4 weeks RESULTS The app group gained significantly more weight P 045 mean weight 1 03 kg vs 1 46 kg Also skeletal muscle mass showed a significant increase in the app group P 009 mean skeletal muscle mass 0 58 kg vs 0 61 kg compared with the control group There was no significant difference between groups relating to the daily protein intake P 06 Additionally there was a decrease in macronutrient intake during the study period in the control group CONCLUSIONS Our study indicates that patients who track their daily dietary habits using a mobile phone app are more likely to reach their nutritional goals than the control patients Further large scale studies are needed to confirm these initial findings and test the applicability on a broader basis
- ItemReal-time ECG monitoring and arrhythmia detection using Android-based mobile devices.(2013-01-31) Gradl, Stefan; Kugler, Patrick; Lohmuller, Clemens; Eskofier, BjoernWe developed an application for Android based mobile devices that allows real time electrocardiogram ECG monitoring and automated arrhythmia detection by analyzing ECG parameters ECG data provided by pre recorded files or acquired live by accessing a Shimmer sensor node via Bluetooth can be processed and evaluated The application is based on the Pan Tompkins algorithm for QRS detection and contains further algorithm blocks to detect abnormal heartbeats The algorithm was validated using the MIT BIH Arrhythmia and MIT BIH Supraventricular Arrhythmia databases More than 99 of all QRS complexes were detected correctly by the algorithm Overall sensitivity for abnormal beat detection was 89 5 with a specificity of 80 6 The application is available for download and may be used for real time ECG monitoring on mobile devices
- ItemSomnography using unobtrusive motion sensors and Android-based mobile phones.(2013-10-10) Gradl, Stefan; Leutheuser, Heike; Kugler, Patrick; Biermann, Teresa; Kreil, Sebastian; Kornhuber, Johannes; Bergner, Matthias; Eskofier, BjoernSleep plays a fundamental role in the life of every human The prevalence of sleep disorders has increased significantly now affecting up to 50 of the general population Sleep is usually analyzed by extracting a hypnogram containing sleep stages The gold standard method polysomnography PSG requires subjects to stay overnight in a sleep laboratory and to wear a series of obtrusive devices This work presents an easy to use method to perform somnography at home using unobtrusive motion sensors Ten healthy male subjects were recorded during two consecutive nights Sensors from the Shimmer platform were placed in the bed to record accelerometer data while reference hypnograms were collected using a SOMNOwatch system A series of filters were used to extract a motion feature in 30 second epochs from the accelerometer signals The feature was used together with the ground truth information to train a Naive Bayes classifiers that distinguished wakefulness REM and non REM sleep Additionally the algorithm was implemented on an Android mobile phone Averaged over all subjects the classifier had a mean accuracy of 79 0 SD 9 2 for the three classes The mobile phone implementation was able to run in realtime during all experiments In future this will lead to a method for simple and unobtrusive somnography using mobile phones