Additional file 6: Figure S2. of Estimating the accuracy of muscle response testing: two randomised-order blinded studies

Participant Flow Diagram - Experiment 2. (JPG 59 kb

Kotkan luotsipiirin toimintakertomus vuodelta 1986

Demographics of Practitioners - Experiments 1 & 2. Table S2. 2x2 Table for MRT for each Pair (n=48) in Experiment 1. Table S3. Correlations (r) with p-values among MRT. Table S4. 2x2 Tables for MRT for each Pair in Experiment 2 (n=20). Table S5. Correlations (r) among MRT Accuracy and Practitioner haracteristics for Experiments 1 & 2. p(2-tailed)<0.05. (XLS 75 kb

Additional file 4: Table S7. of Estimating the accuracy of muscle response testing: two randomised-order blinded studies

STARD checklist for reporting of studies of diagnostic accuracy: Experiment 2. (DOCX 19 kb

Additional file 8: Figure S3. of Estimating the accuracy of muscle response testing: two randomised-order blinded studies

kMMT Accuracy by Block with 95% Confidence Intervals. (DOCX 18 kb

Mobile health for pediatric weight management: systematic scoping review

Background: The prevalence and consequences of obesity among children and adolescents remain a leading global public health concern, and evidence-based, multidisciplinary lifestyle interventions are the cornerstone of treatment. Mobile electronic devices are widely used across socioeconomic categories and may provide a means of extending the reach and efficiency of health care interventions. Objective: We aimed to synthesize the evidence regarding mobile health (mHealth) for the treatment of childhood overweight and obesity to map the breadth and nature of the literature in this field and describe the characteristics of published studies. Methods: We conducted a systematic scoping review in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews, by searching nine academic databases in addition to gray literature for studies describing acceptability, usability, feasibility, effectiveness, adherence, or cost-effectiveness of interventions assessing mHealth for childhood obesity treatment. We also hand searched the reference lists of relevant articles. Studies aimed at the prevention of overweight or obesity were excluded, as were studies in which mHealth was not the primary mode of treatment delivery for at least one study arm or was not independently assessed. A random portion of all abstracts and full texts was double screened by a second reviewer to ensure consistency. Data were charted according to study characteristics, including design, participants, intervention content, behavior change theory (BCT) underpinning the study, mode of delivery, and outcomes measured. Results: We identified 42 eligible studies assessing acceptability (n=7), usability (n=2), feasibility or pilot studies (n=15), treatment effect (n=17), and fidelity (n=1). Change in BMI z-scores or percentiles was most commonly measured, among a variety of dietary, physical activity, psychological, and usability or acceptability measures. SMS, mobile apps, and wearable devices made up the majority of mobile interventions, and 69% (29/42) of the studies specified a BCT used. Conclusions: Pediatric weight management using mHealth is an emerging field, with most work to date aimed at developing and piloting such interventions. Few large trials are published, and these are heterogeneous in nature and rarely reported according to the Consolidated Standards of Reporting Trials for eHealth guidelines. There is an evidence gap in the cost-effectiveness analyses of such studies.</div

Quality of reporting.

<p>Proportion of validation studies with adequate and inadequate description of reporting characteristics.</p

Evolution of methodological quality over time.

<p>Proportion of validation studies satisfying (a) reporting characteristics and (b) design characteristics recommended in methodological standards.</p

Influence of design characteristics on the performance of clinical prediction rule in multivariable analysis.

<p>Influence of design characteristics on the performance of clinical prediction rule in multivariable analysis.</p

Fagan nomogram.

<p>Applying the sensitivity and specificity of (a) 90% as presented in the validation study [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145779#pone.0145779.ref058" target="_blank">58</a>] and (b) 81% from an unbiased study to a patient with 10% probability of rheumatoid arthritis.</p

Timeline demonstrating the stages that lead to the development of CARL.

<p>(A) Inventory by L Kendall Krause created. (B) The James Lind Initiative begins adding resources to Testing Treatments interactive. (C) European Communication on Research Awareness Needs (ECRAN) inventory created. (D) The Informed Healthcare Choices Project (IHC) formed, Members begin to identify useful resources. (E) Discussions between IHC and JLI lead to expressions of support for a new library, JLI agrees to coordinate development until 2019.</p