Active video games to promote physical activity in children with cancer: a randomized clinical trial with follow-up.

BACKGROUND: Low levels of physical activity, musculoskeletal morbidity and weight gain are commonly reported problems in children with cancer. Intensive medical treatment and a decline in physical activity may also result in reduced motor performance. Therefore, simple and inexpensive ways to promote physical activity and exercise are becoming an increasingly important part of children's cancer treatment. METHODS: The aim of this study is to evaluate the effect of active video games in promotion of physical activity in children with cancer. The research is conducted as a parallel randomized clinical trial with follow-up. Patients between 3 and 16 years old, diagnosed with cancer and treated with vincristine in two specialized medical centers are asked to participate. Based on statistical estimates, the target enrollment is 40 patients. The intervention includes playing elective active video games and, in addition, education and consultations for the family. The control group will receive a general recommendation for physical activity for 30 minutes per day. The main outcomes are the amount of physical activity and sedentary behavior. Other outcomes include motor performance, fatigue and metabolic risk factors. The outcomes are examined with questionnaires, diaries, physical examinations and blood tests at baseline and at 2, 6, 12 and 30 months after the baseline. Additionally, the children's perceptions of the most enjoyable activation methods are explored through an interview at 2 months. DISCUSSION: This trial will help to answer the question of whether playing active video games is beneficial for children with cancer. It will also provide further reasoning for physical activity promotion and training of motor skills during treatment. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01748058 (October 15, 2012).

Handling real-world context awareness, uncertainty and vagueness in real-time human activity tracking and recognition with a fuzzy ontology-based hybrid method.

Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and Sensors 2014, 14 18132 high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches.

Physical activity among children: objective measurements using Fitbit One® and ActiGraph.

BACKGROUND: Self-quantification of health parameters is becoming more popular; thus, the validity of the devices requires assessments. The aim of this study was to evaluate the validity of Fitbit One step counts (Fitbit Inc., San Francisco, CA, USA) against Actigraph wActisleep-BT step counts (ActiGraph, LLC, Pensacola, FL, USA) for measuring habitual physical activity among children. DESIGN: The study was implemented as a cross-sectional experimental design in which participants carried two waist-worn activity monitors for five consecutive days. METHODS: The participants were chosen with a purposive sampling from three fourth grade classes (9-10 year olds) in two comprehensive schools. Altogether, there were 34 participants in the study. From these, eight participants were excluded from the analysis due to erroneous data. Primary outcome measures for step counts were Fitbit One and Actigraph wActisleep-BT. The supporting outcome measures were based on activity diaries and initial information sheets. Classical Bland-Altman plots were used for reporting the results. RESULTS: The average per-participant daily difference between the step counts from the two devices was 1937. The range was [116, 5052]. Fitbit One gave higher step counts for all but the least active participant. According to a Bland-Altman plot, the hourly step counts had a relative large mean bias across participants (161 step counts). The differences were partially explained by activity intensity: higher intensity denoted higher differences, and light intensity denoted lower differences. CONCLUSIONS: Fitbit One step counts are comparable to Actigraph step counts in a sample of 9-10-year-old children engaged in habitual physical activity in sedentary and light physical activity intensities. However, in moderate-to-vigorous physical activity, Fitbit One gives higher step counts when compared to Actigraph.