Parental quality of life and self-efficacy in pediatric asthma.

Objective: Self-efficacy is the personal belief that a behavior can produce a desired result; and in asthma, self-efficacy in asthma care has been related to improvements in asthma outcomes and children's quality of life. To appreciate the full burden of asthma on families, the relationship between parental self-efficacy and quality of life also needs further study. We aim to characterize this relationship.Methods: Secondary analysis of measurements of parents of children with persistent asthma (n = 252; ages 4-17 years) from a large urban area were identified from a randomized trial; the association between baseline assessments of parental quality of life, measured by the Pediatric Asthma Caregiver's Quality of Life Questionnaire (PACQLQ), and parental self-efficacy, measured through the Parental Asthma Management Self-Efficacy Scale (PAMSES), were examined through multivariable linear regression.Results: Parental self-efficacy in asthma was positively associated with quality of life among parents of racially and ethnically diverse children (p = 0.01). Confidence in using medications correctly (p = 0.03), having inhalers during a child's serious breathing problem (p = 0.02), and knowing which medications to use during a child's serious breathing problem (p = 0.04) were associated with a clinically meaningful difference in parental quality of life. Other significant factors associated with parental quality of life included Hispanic/Latino ethnicity (p < 0.01) of the child and Asthma Control Test scores (p < 0.01).Conclusion: The findings suggest that improving parental confidence on when and how to use their child's asthma medications, particularly during an asthma attack, might be clinically meaningful in enhancing parent's quality of life.

Hidden Markov model-based activity recognition for toddlers.

OBJECTIVE: Physical activity has been shown to impact future health outcomes in adults, but little is known about the long-term impact of physical activity in toddlers. Accurately measuring the specific types and amounts of physical activity in toddlers will help us to understand, predict, and better affect their future health outcomes. Although activity recognition has been extensively developed for adults as well as older children, toddlers move in ways that are significantly different from older children, indicating the need for a more tailored approach. APPROACH: In this study, 22 toddlers wore Actigraph waist-worn accelerometers which recorded their movements during guided play. The toddlers were videotaped and their activities were later annotated for the following eight distinct activity classes: lying down, being carried, riding in a stroller, sitting, standing, running/walking, crawling, and climbing up/down. Accelerometer data were extracted in 2 s signal windows and paired with the activities the toddlers were performing during that time interval. MAIN RESULTS: A variety of classifiers were tuned to a validation set. A random forest classifier was found to achieve the highest accuracy of 63.8% in a test set. To improve the accuracy, a hidden Markov model (HMM) was applied by providing the predictions of the static classifiers as observations. The HMM was able to improve the accuracy to 64.8% with all five classifiers increasing the accuracy an average of 1.3% points (95% confidence interval = 0.7-1.9, p  < 0.01). When the three most misclassified activities (sitting, standing, and riding in a stroller) were collapsed together, the accuracy increased to 79.3%. SIGNIFICANCE: Further refinement of the toddler activity recognition classifier will enable more accurate measurements of toddler activity and improve future health outcomes of toddlers.

Hip and Wrist-Worn Accelerometer Data Analysis for Toddler Activities.

Although accelerometry data are widely utilized to estimate physical activity and sedentary behavior among children age 3 years or older, for toddlers age 1 and 2 year(s), accelerometry data recorded during such behaviors have been far less examined. In particular, toddler's unique behaviors, such as riding in a stroller or being carried by an adult, have not yet been examined. The objective of this study was to describe accelerometry signal outputs recorded during participation in nine types of behaviors (i.e., running, walking, climbing up/down, crawling, riding a ride-on toy, standing, sitting, riding in a stroller/wagon, and being carried by an adult) among toddlers. Twenty-four toddlers aged 13 to 35 months (50% girls) performed various prescribed behaviors during free play in a commercial indoor playroom while wearing ActiGraph wGT3X-BT accelerometers on a hip and a wrist. Participants' performances were video-recorded. Based on the video data, accelerometer data were annotated with behavior labels to examine accelerometry signal outputs while performing the nine types of behaviors. Accelerometer data collected during 664 behavior assessments from the 21 participants were used for analysis. Hip vertical axis counts for walking were low (median = 49 counts/5 s). They were significantly lower than those recorded while a toddler was "carried" by an adult (median = 144 counts/5 s; p < 0.01). While standing, sitting, and riding in a stroller, very low hip vertical axis counts were registered (median ≤ 5 counts/5 s). Although wrist vertical axis and vector magnitude counts for "carried" were not higher than those for walking, they were higher than the cut-points for sedentary behaviors. Using various accelerometry signal features, machine learning techniques showed 89% accuracy to differentiate the "carried" behavior from ambulatory movements such as running, walking, crawling, and climbing. In conclusion, hip vertical axis counts alone may be unable to capture walking as physical activity and "carried" as sedentary behavior among toddlers. Machine learning techniques that utilize additional accelerometry signal features could help to recognize behavior types, especially to differentiate being "carried" from ambulatory movements.