scholarly journals Machine Learning Approaches to Understanding and Predicting Patterns of Adherence

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 553-554
Author(s):  
Aditya Bhattacharya ◽  
Shubo Tian ◽  
Nelson Roque ◽  
Zhe He ◽  
Walter Boot ◽  
...  
Keyword(s):  
Strong Predictor ◽  
Just In Time ◽  
Learning Approaches ◽  
Classification Models ◽  
Technology Proficiency ◽  
Aggregated Data ◽  
Reminder System ◽  
Adaptive Interventions ◽  
Home Based ◽  
Predictors Of Adherence

Abstract In cognitive training of older adults, adherence is a major challenge, but appropriate just-in-time adaptive interventions can improve adherence. To understand adherence patterns and predictors of adherence lapses, we aggregated data from two previous trials (N > 230) involving home-based cognitive interventions. This dataset, detailing 40,000 intervention interactions, contains information about intervention engagement and measures of objective and subjective cognitive performance, demographics, technology proficiency, and attitudes. Exploratory analyses were conducted to understand patterns and predictors of faltering adherence, using classification models, together with feature selection to remove redundant variables. Adherence behaviors in a week were predictive of quitting the following week. Game parameters such as the time of play were weak indicators of future playing patterns, whereas game success was a strong predictor of adherence. These and other useful observations will be incorporated in the design and development of the smart reminder system to be deployed in the APPT project.

scholarly journals Key facets to build up eHealth and mHealth interventions to enhance physical activity, sedentary behavior and nutrition in healthy subjects – An umbrella review

2020 ◽  
Author(s):  
Janis Fiedler ◽  
Tobias Eckert ◽  
Kathrin Wunsch ◽  
Alexander Woll
Keyword(s):  
Physical Activity ◽  
Behavior Change ◽  
Sedentary Behavior ◽  
Healthy Eating ◽  
Social Contexts ◽  
Just In Time ◽  
Umbrella Review ◽  
Behavior Change Techniques ◽  
Adaptive Interventions ◽  
The Impact

Abstract Background: Electronic (eHealth) and mobile (mHealth) health interventions can provide a large coverage, and are promising tools to change health behavior (i.e. physical activity, sedentary behavior and healthy eating). However, the determinants of intervention effectiveness in primary prevention has not been explored yet. Therefore, the objectives of this umbrella review were to evaluate intervention effectiveness, to explore the impact of pre-defined determinants of effectiveness (i.e. theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions), and to provide recommendations for future research and practice in the field of primary prevention delivered via e/mHealth technology.Methods: PubMed, Scopus, Web of Science and the Cochrane Library were searched for systematic reviews and meta-analyses (reviews) published between January 1990 and May 2020. Reviews reporting on e/mHealth behavior change interventions in physical activity, sedentary behavior and/or healthy eating for healthy subjects (i.e. subjects without physical or physiological morbidities which would influence the realization of behaviors targeted by the respective interventions) were included if they also investigated respective theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions. Included studies were ranked concerning their methodological quality and qualitatively synthesized.Results: The systematic search reveled eleven systematic reviews and meta-analyses of moderate quality. The majority of original research studies within the reviews found e/mHealth interventions to be effective, but the results showed a high heterogeneity concerning assessment methods and outcomes, making them difficult to compare. Whereas theoretical foundation and behavior change techniques were suggested to be potential positive determinants of effective interventions, the impact of social context remains unclear. None of the reviews included just-in-time adaptive interventions.Conclusion: Findings of this umbrella review support the use of e/mHealth to enhance physical activity and healthy eating and reduce sedentary behavior. The general lack of precise reporting and comparison of confounding variables in reviews and original research studies as well as the limited number of reviews for each health behavior constrains the generalization and interpretation of results. Further research is needed on study-level to investigate effects of versatile determinants of e/mHealth efficiency, using a theoretical foundation and additionally explore the impact of social contexts and more sophisticated approaches like just-in-time adaptive interventions.Trial registration: The protocol for this umbrella review was a priori registered with PROSPERO: CRD42020147902.

scholarly journals Key facets to build up eHealth and mHealth interventions to enhance physical activity, sedentary behavior and nutrition in healthy subjects – an umbrella review

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Janis Fiedler ◽  
Tobias Eckert ◽  
Kathrin Wunsch ◽  
Alexander Woll
Keyword(s):  
Physical Activity ◽  
Behavior Change ◽  
Sedentary Behavior ◽  
Healthy Eating ◽  
Social Contexts ◽  
Just In Time ◽  
Umbrella Review ◽  
Behavior Change Techniques ◽  
Adaptive Interventions ◽  
The Impact

Abstract Background Electronic (eHealth) and mobile (mHealth) health interventions can provide a large coverage, and are promising tools to change health behavior (i.e. physical activity, sedentary behavior and healthy eating). However, the determinants of intervention effectiveness in primary prevention has not been explored yet. Therefore, the objectives of this umbrella review were to evaluate intervention effectiveness, to explore the impact of pre-defined determinants of effectiveness (i.e. theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions), and to provide recommendations for future research and practice in the field of primary prevention delivered via e/mHealth technology. Methods PubMed, Scopus, Web of Science and the Cochrane Library were searched for systematic reviews and meta-analyses (reviews) published between January 1990 and May 2020. Reviews reporting on e/mHealth behavior change interventions in physical activity, sedentary behavior and/or healthy eating for healthy subjects (i.e. subjects without physical or physiological morbidities which would influence the realization of behaviors targeted by the respective interventions) were included if they also investigated respective theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions. Included studies were ranked concerning their methodological quality and qualitatively synthesized. Results The systematic search revealed 11 systematic reviews and meta-analyses of moderate quality. The majority of original research studies within the reviews found e/mHealth interventions to be effective, but the results showed a high heterogeneity concerning assessment methods and outcomes, making them difficult to compare. Whereas theoretical foundation and behavior change techniques were suggested to be potential positive determinants of effective interventions, the impact of social context remains unclear. None of the reviews included just-in-time adaptive interventions. Conclusion Findings of this umbrella review support the use of e/mHealth to enhance physical activity and healthy eating and reduce sedentary behavior. The general lack of precise reporting and comparison of confounding variables in reviews and original research studies as well as the limited number of reviews for each health behavior constrains the generalization and interpretation of results. Further research is needed on study-level to investigate effects of versatile determinants of e/mHealth efficiency, using a theoretical foundation and additionally explore the impact of social contexts and more sophisticated approaches like just-in-time adaptive interventions. Trial registration The protocol for this umbrella review was a priori registered with PROSPERO: CRD42020147902.

PREDICTORS OF ADHERENCE TO A HOME-BASED EXERCISE PROGRAM IN LIVER TRANSPLANT RECIPIENTS

2003 ◽  
Vol 35 (Supplement 1) ◽  
pp. S318
Author(s):  
J B. Krasnoff ◽  
J Raglin ◽  
D W. Zakutansky ◽  
S Park ◽  
J P. Wallace ◽  
...  
Keyword(s):  
Liver Transplant ◽  
Exercise Program ◽  
Transplant Recipients ◽  
Home Based ◽  
Liver Transplant Recipients ◽  
Predictors Of Adherence

scholarly journals The Development of a Smart Reminder System to Promote Adherence to Technology-Based Intervention and Assessment

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 553-553
Author(s):  
Walter Boot ◽  
Neil Charness
Keyword(s):  
Clinical Trials ◽  
Support System ◽  
Cognitive Intervention ◽  
Motivating Factors ◽  
Focus Group Study ◽  
Motivational Messages ◽  
Reminder System ◽  
Integrated Technology ◽  
Home Based ◽  
Adherence Support

Abstract The overarching aim of the National Institute on Aging funded Adherence Promotion with Person-centered Technology (APPT) Project is to promote adherence to technology-based solutions designed to enhance the early detection and treatment of cognitive decline. The goal is to build and evaluate adaptive, tailored, and integrated technology-based adherence support systems for mobile software platforms that assess and train cognitive skill. The symposium describes the various steps of the development process of the APPT smart adherence support system. N. Charness will present an overview of the APPT project, its aims, and the clinical trials designed to assess the effectiveness of the APPT smart reminder system compared to typical reminder systems. S. Chakraborty will present detailed analyses of past cognitive intervention data to inform understanding of who is likely at risk for poor adherence and how adherence lapses might be predicted in advance to provide just-in-time adherence support. D. Carr will present an exploration of motivating factors for participants to engage in research, and these motivations will be tapped to help develop motivational messages for the APPT adherence support system to be used in the two planned clinical trials. M. Dieciuc will provide additional insights into motivations for engaging in home-based cognitive assessment and training derived from a focus group study. Finally, S. Zhang will describe the results of an initial pilot study examining the effectiveness of motivational reminder messages that match vs. mismatch participants’ own motivations. All results inform the design of the APPT system to maximize adherence.

scholarly journals Systematic evaluation of supervised machine learning for sample origin prediction using metagenomic sequencing data

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Julie Chih-yu Chen ◽  
Andrea D. Tyler
Keyword(s):  
Machine Learning ◽  
Prediction Error ◽  
Supervised Machine Learning ◽  
Detection Sensitivity ◽  
Metagenomic Sequencing ◽  
Learning Approaches ◽  
Microbial Abundance ◽  
Classification Models ◽  
The Impact ◽  
Analytical Approaches

Abstract Background The advent of metagenomic sequencing provides microbial abundance patterns that can be leveraged for sample origin prediction. Supervised machine learning classification approaches have been reported to predict sample origin accurately when the origin has been previously sampled. Using metagenomic datasets provided by the 2019 CAMDA challenge, we evaluated the influence of variable technical, analytical and machine learning approaches for result interpretation and novel source prediction. Results Comparison between 16S rRNA amplicon and shotgun sequencing approaches as well as metagenomic analytical tools showed differences in normalized microbial abundance, especially for organisms present at low abundance. Shotgun sequence data analyzed using Kraken2 and Bracken, for taxonomic annotation, had higher detection sensitivity. As classification models are limited to labeling pre-trained origins, we took an alternative approach using Lasso-regularized multivariate regression to predict geographic coordinates for comparison. In both models, the prediction errors were much higher in Leave-1-city-out than in 10-fold cross validation, of which the former realistically forecasted the increased difficulty in accurately predicting samples from new origins. This challenge was further confirmed when applying the model to a set of samples obtained from new origins. Overall, the prediction performance of the regression and classification models, as measured by mean squared error, were comparable on mystery samples. Due to higher prediction error rates for samples from new origins, we provided an additional strategy based on prediction ambiguity to infer whether a sample is from a new origin. Lastly, we report increased prediction error when data from different sequencing protocols were included as training data. Conclusions Herein, we highlight the capacity of predicting sample origin accurately with pre-trained origins and the challenge of predicting new origins through both regression and classification models. Overall, this work provides a summary of the impact of sequencing technique, protocol, taxonomic analytical approaches, and machine learning approaches on the use of metagenomics for prediction of sample origin.

Comparing Statistical and Machine Learning Classifiers: Alternatives for Predictive Modeling in Human Factors Research

2003 ◽  
Vol 45 (3) ◽  
pp. 408-423 ◽  
Author(s):  
Brian Carnahan ◽  
Gérard Meyer ◽  
Lois-Ann Kuntz
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Human Factors ◽  
Predictive Accuracy ◽  
Performance Outcomes ◽  
Learning Approaches ◽  
Classification Models ◽  
Machine Learning Classification ◽  
Human Factors Research

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches - genetic programming and decision tree induction - were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Towards Multi-level Modeling of Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health

2021 ◽  
Author(s):  
Sebastian Gruber ◽  
Bernd Neumayr ◽  
Michael Schrefl ◽  
Josef Niebauer
Keyword(s):  
Mobile Health ◽  
Just In Time ◽  
Adaptive Interventions ◽  
Multi Level
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