Are nomothetic or ideographic approaches superior in predicting daily exercise behaviors? Analyzing N-of-1 mHealth data

Keyphrases

• Nomothetic Approach 100%
• Ideographic Approach 100%
• N-of-1 100%
• Daily Exercise 100%
• MHealth 100%
• Exercise Behavior 100%
• Nomothetic 50%
• Misclassification Error 50%
• Personalized Exercise 33%
• Ideographic 33%
• Stress Experience 33%
• Recent Advances 16%
• Health Behavior 16%
• Stress Behavior 16%
• Physical Activity 16%
• Actigraphy 16%
• Random Forest 16%
• Sensing Technology 16%
• Between-person 16%
• Exogenous Variables 16%
• Public Archives 16%
• Healthy Lifestyle Intervention 16%
• Emotional Factors 16%
• Daylight 16%
• Smartphone Technology 16%
• Random Tree 16%
• Classification Tree 16%
• Mobile Application 16%
• Stress Effect 16%
• Population Level 16%
• Temperature Determination 16%
• Differential Response 16%
• Individual Stress 16%
• One-size-fits-all 16%
• Behavior Prediction 16%
• Multiple Data Sources 16%
• Technology Learning 16%
• Decision Model 16%
• Mobile Health Technology 16%
• Ecological Momentary Assessment 16%
• Self-determination 16%
• Machine Learning Analytics 16%
• Environmental Variables 16%
• Idiographic Assessment 16%
• Individual-specific 16%
• Stress Exposure 16%
• Determination Factor 16%
• Prediction Algorithms 16%
• Person-specific 16%

Computer Science

• Classification Error 100%
• Random Decision Forest 66%
• Collected Data 33%
• Exogenous Variable 33%
• Previous Exercise 33%
• Stress Exposure 33%
• Multiple Data Source 33%
• Mobile App 33%
• Learning Analytics 33%
• Decision Model 33%
• Emotional Behavior 33%
• Environmental Variable 33%
• Healthy Lifestyle 33%
• Population Level 33%
• Classification Tree 33%
• Machine Learning 33%
• Learning System 33%

Psychology

• MHealth 100%
• Exercise Behavior 100%
• Physical Activity 16%
• Decision Model 16%
• Healthy Lifestyle 16%
• Self-Determination 16%
• Individual Differences 16%
• Learning Analytics 16%
• Ecological Momentary Assessment 16%