Objective: To evaluate psychometrics of wearable devices measuring physical activity (PA) in ambulant children with gait abnormalities due to neuromuscular conditions. Data Sources: We searched PubMed, Embase, PsycINFO, CINAHL, and SPORTDiscus in March 2023. Study Selection: We included studies if (1) participants were ambulatory children (2-19y) with gait abnormalities, (2) reliability and validity were analyzed, and (3) peer-reviewed studies in the English language and full-text were available. We excluded studies of children with primarily visual conditions, behavioral diagnoses, or primarily cognitive disability. We performed independent screening and inclusion, data extraction, assessment of the data, and grading of results with 2 researchers. Data Extraction: Our report follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We assessed methodological quality with Consensus-based Standards for the selection of health measurement instruments. We extracted data on reported reliability, measurement error, and validity. We performed meta-analyses for reliability and validity coefficient values. Data Synthesis: Of 6911 studies, we included 26 with 1064 participants for meta-analysis. Results showed that wearables measuring PA in children with abnormal gait have high to very high reliability (intraclass correlation coefficient [ICC]+, test-retest reliability=0.81; 95% confidence interval [CI], 0.74-0.89; I2=88.57%; ICC+, interdevice reliability=0.99; 95% CI, 0.98-0.99; I2=71.01%) and moderate to high validity in a standardized setting (r+, construct validity=0.63; 95% CI, 0.36-0.89; I2=99.97%; r+, criterion validity=0.68; 95% CI, 0.57-0.79; I2=98.70%; r+, criterion validity cutoff point based=0.69; 95% CI, 0.58-0.80; I2=87.02%). The methodological quality of all studies included in the meta-analysis was moderate. Conclusions: There was high to very high reliability and moderate to high validity for wearables measuring PA in children with abnormal gait, primarily due to neurological conditions. Clinicians should be aware that several moderating factors can influence an assessment.
Europe’s aging population is leading to a growing number of people affected by chronic disease, which will continue over the coming decades. Healthcare systems are under pressure to deliver appropriate care, partly due to the burden imposed on their limited financial and human resources by the growing number of people with (multiple) chronic diseases. Therefore, there is a strong call for patient self-management to meet these patients’ healthcare needs. While many patients experience medication self-management as difficult, it poses additional challenges for people with limited health literacy. This thesis aims to explore the needs of patients with a chronic disease and limited health literacy regarding medication self-management and how support for medication self-management can be tailored to those needs.
A substantial proportion of chronic disease patients do not respond to self-management interventions, which suggests that one size interventions do not fit all, demanding more tailored interventions. To compose more individualized strategies, we aim to increase our understanding of characteristics associated with patient activation for self-management and to evaluate whether these are disease-transcending. A cross-sectional survey study was conducted in primary and secondary care in patients with type-2 Diabetes Mellitus (DM-II), Chronic Obstructive Pulmonary Disease (COPD), Chronic Heart Failure (CHF) and Chronic Renal Disease (CRD). Using multiple linear regression analysis, we analyzed associations between self-management activation (13-item Patient Activation Measure; PAM-13) and a wide range of socio-demographic, clinical, and psychosocial determinants. Furthermore, we assessed whether the associations between the determinants and the PAM were disease-transcending by testing whether disease was an effect modifier. In addition, we identified determinants associated with low activation for self-management using logistic regression analysis. We included 1154 patients (53% response rate); 422 DM-II patients, 290 COPD patients, 223 HF patients and 219 CRD patients. Mean age was 69.6±10.9. Multiple linear regression analysis revealed 9 explanatory determinants of activation for selfmanagement: age, BMI, educational level, financial distress, physical health status, depression, illness perception, social support and underlying disease, explaining a variance of 16.3%. All associations, except for social support, were disease transcending. This study explored factors associated with varying levels of activation for self-management. These results are a first step in supporting clinicians and researchers to identify subpopulations of chronic disease patients less likely to be engaged in self-management. Increased scientific efforts are needed to explain the greater part of the factors that contribute to the complex nature of patient activation for self-management.
