Prediction of Glycemia Based on Diabetes Self-Monitoring Data

2015 ◽  
Vol 8 (2) ◽  
pp. 113
Author(s):  
Marián Tárník ◽  
Vladimír Bátora ◽  
Tomáš Ludwig ◽  
Ivan Ottinger ◽  
Eva Miklovičová ◽  
...  
Keyword(s):  
Monitoring Data ◽  
Self Monitoring

scholarly journals Long-Term Evaluation of Counselor Efficiency at Providing Feedback in a Technology-Based Behavioral Weight Loss Intervention (Preprint)

2020 ◽  
Author(s):  
Margaret Fahey ◽  
Robert C. Klesges ◽  
Mehmet Kocak ◽  
Leslie Gladney ◽  
Gerald W. Talcott ◽  
...  
Keyword(s):  
Weight Loss ◽  
Electronic Mail ◽  
Detailed Examination ◽  
Monitoring Data ◽  
Weight Loss Intervention ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Future Technology ◽  
Study Implementation ◽  
Weight Loss Interventions

BACKGROUND Feedback for participants’ self-monitoring is a crucial, and costly, component of technology-based weight loss interventions. Detailed examination of interventionist time when reviewing and providing feedback for online self-monitoring data is unknown. OBJECTIVE Study purpose was to longitudinally examine time counselors spent providing feedback on participant self-monitoring data (i.e., diet, physical activity, weight) in a 12-month technology-based weight loss intervention. We hypothesized that counselors would deliver feedback to participants more quickly over time. METHODS Time counselors (N=10) spent reviewing and providing feedback to participants via electronic mail (e-email) was longitudinally examined for all counselors across the three years of study implementation. Descriptives were observed for counselor feedback duration across counselors by 12 annual quarters (i.e., three-month periods). Differences in overall duration times by each consecutive annual quarter were analyzed using Wilcoxon-Mann-Whitney tests. RESULTS There was a decrease in counselor feedback duration from first to second quarter [Mean (M) = 53 to 46 minutes], and from second to third (M= 46 to 30). A trend suggested a decrease from third to fourth quarters (M = 30 to 26), but no changes were found in subsequent quarters. Consistent with hypothesis, counselors increased their efficiency in providing feedback. Across 12-months, mean time counselors needed to review participant self-monitoring and provide feedback decreased from 53 to 26 minutes. CONCLUSIONS Counselors needed increasingly less time to review online self-monitoring data and provide feedback after the initial nine months of study implementation. Results inform counselor costs for future technology-based behavioral weight loss interventions. For example, regardless of increasing counselor efficiency, 25-30 minutes per feedback message is a high cost for interventions. One possibility for reducing costs would be generating computer-automated feedback. CLINICALTRIAL NCT02063178

scholarly journals Official control and self-monitoring: Data agreement report in the integrated food safety system of an Italian dairy chain

2019 ◽  
Vol 97 ◽  
pp. 185-190
Author(s):  
Francesco Martelli ◽  
Claudia Giacomozzi ◽  
Roberto Dragone ◽  
Carlo Boselli ◽  
Simonetta Amatiste ◽  
...  
Keyword(s):  
Food Safety ◽  
Safety System ◽  
Monitoring Data ◽  
Self Monitoring ◽  
Official Control

scholarly journals Prediction of Bipolar Disorder with Voice Analysis using Machine Learning Techniques

2019 ◽  
Vol 8 (4) ◽  
pp. 11437-11440
Keyword(s):  
Bipolar Disorder ◽  
Random Forest ◽  
Monitoring Data ◽  
Machine Learning Techniques ◽  
Phone Call ◽  
Support Vector ◽  
Self Monitoring ◽  
Phone Calls ◽  
Behavioral Activities ◽  
The Voice

The change in the speech is the responsive and well-founded measure of the depression and obsession of the bipolar disorder. This analysis mainly focuses on perceiving the voice attributes and phone calls data is collected as it acts as a main search-space maker for bipolar clutters. By combining the voice features with the phone call data based on their behavioral activities, self- monitoring data control and illness activities the accuracy would increase to effective states. The voice attributes and smartphones collect the activities of sample phone data and self-monitoring data. These activities are the root cause of the expansion of two symptoms: depression and obsession. These symptoms were introduced by a researcher who was rendered with smartphones. The phone call data were examined through a statistical random forest algorithm. The states were extracted from daily phone calls and are classified using voice attributes. These attributes are more determined and accurate to classify the maniac states. The main subject in comparing the voice attributes and self-observed data with the behavioral activities of phone call data is that these attributes increase the efficiency, vulnerability, and definiteness of classifying the affective states. The techniques used to detect the voice features are support vector machine (SVM) random forest. the proposed system will enhance the performance of the prediction of all the techniques. By comparing all these techniques by finding the accuracy of each technique we can know which technique predicts more accurately.

scholarly journals A visual analytics approach for pattern-recognition in patient-generated data

2018 ◽  
Vol 25 (10) ◽  
pp. 1366-1374 ◽  
Author(s):  
Daniel J Feller ◽  
Marissa Burgermaster ◽  
Matthew E Levine ◽  
Arlene Smaldone ◽  
Patricia G Davidson ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Visual Analytics ◽  
Behavioral Interventions ◽  
Participatory Design ◽  
Information Overload ◽  
Monitoring Data ◽  
Self Monitoring ◽  
Glucose Levels ◽  
Within Subjects ◽  
Study Participants

Abstract Objective To develop and test a visual analytics tool to help clinicians identify systematic and clinically meaningful patterns in patient-generated data (PGD) while decreasing perceived information overload. Methods Participatory design was used to develop Glucolyzer, an interactive tool featuring hierarchical clustering and a heatmap visualization to help registered dietitians (RDs) identify associative patterns between blood glucose levels and per-meal macronutrient composition for individuals with type 2 diabetes (T2DM). Ten RDs participated in a within-subjects experiment to compare Glucolyzer to a static logbook format. For each representation, participants had 25 minutes to examine 1 month of diabetes self-monitoring data captured by an individual with T2DM and identify clinically meaningful patterns. We compared the quality and accuracy of the observations generated using each representation. Results Participants generated 50% more observations when using Glucolyzer (98) than when using the logbook format (64) without any loss in accuracy (69% accuracy vs 62%, respectively, p = .17). Participants identified more observations that included ingredients other than carbohydrates using Glucolyzer (36% vs 16%, p = .027). Fewer RDs reported feelings of information overload using Glucolyzer compared to the logbook format. Study participants displayed variable acceptance of hierarchical clustering. Conclusions Visual analytics have the potential to mitigate provider concerns about the volume of self-monitoring data. Glucolyzer helped dietitians identify meaningful patterns in self-monitoring data without incurring perceived information overload. Future studies should assess whether similar tools can support clinicians in personalizing behavioral interventions that improve patient outcomes.

scholarly journals Counselor Surveillance of Digital Self‐Monitoring Data: A Pilot Randomized Controlled Trial

Obesity ◽  
2020 ◽  
Vol 28 (12) ◽  
pp. 2339-2346
Author(s):  
Meghan L. Butryn ◽  
Mary K. Martinelli ◽  
Nicole T. Crane ◽  
Kathryn Godfrey ◽  
Savannah R. Roberts ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Controlled Trial ◽  
Monitoring Data ◽  
Self Monitoring ◽  
Randomized Controlled ◽  
Pilot Randomized Controlled Trial

scholarly journals Encounters with Self-Monitoring Data on ICT Use

2019 ◽  
Vol 40 (s1) ◽  
pp. 125-140 ◽  
Author(s):  
Minna Saariketo
Keyword(s):  
Qualitative Research ◽  
Research Data ◽  
The Self ◽  
Monitoring Data ◽  
Tracking Data ◽  
Daily Lives ◽  
Self Monitoring ◽  
Ict Use ◽  
Research Interviews ◽  
Access To Data

Abstract This article elaborates on the prospects for research interventions that repurpose the means of datafication to create possibilities for people to reflect on what it means in their daily lives. The research data consist of qualitative research interviews (n=13) in which media diaries and tracking data from the participants’ smartphones and computers served as prompts for reflection. The experiences from the self-monitoring and the encounters with tracked data by self-identified avid ICT users are analysed to gain a better understanding of the kinds of possibilities for reflexivity that are enabled when people have access to data that are rarely available to them.

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