Evaluation of a smartphone application for self-help for patients with social anxiety disorder: a randomized controlled study-SMASH.

BACKGROUND: There is growing evidence that Internet-based cognitive behavioral therapy (ICBT) is as effective as a stand-alone treatment and helps facilitating access to treatment. Given the complexity of the treatment, we argue that the effect of ICBT could be even greater if guided by a therapist, as this could increase treatment adherence. We modified an established and well-evaluated treatment approach and developed a mobile application for treating social anxiety disorder (SAD). In the present study, we compare the efficacy of app use alone (APP) with video-based, therapist-guided app use (TG-APP) and with a wait-list control group (WLC) in terms of symptom reduction, and various secondary outcomes such as increase in quality of life or decrease of general psychological distress. METHODS/DESIGN: A within-between interaction design with randomization to one of three conditions will be used. In the APP condition, patients receive only the app without any additional contact with therapists, while in the TG-APP condition, therapists provide 8 sessions of video-based treatment in addition to using the app. The study will be conducted in two university outpatient treatment centers with reliably diagnosed SAD patients. The primary outcome will be defined as change in SAD symptoms, as measured by the Liebowitz Social Anxiety Scale (expert rating). Furthermore, a wide range of self-reports and clinician ratings for other symptoms (depression, general psychopathology) or quality of life will be used. A simulation-based power analysis for a 3 × 2 interaction effect (group × time) on the primary outcome in a linear mixed model resulted in a total sample size of N = 165. DISCUSSION: The present study will be one of the first to examine the additional benefit of therapist-guided video sessions regarding the use of an app treating SAD. Study results are pivotal to future treatment application in SAD.

Validation of adult height prediction based on automated bone age determination in the Paris Longitudinal Study of healthy children.

BACKGROUND: An adult height prediction model based on automated determination of bone age was developed and validated in two studies from Zurich, Switzerland. Varied living conditions and genetic backgrounds might make the model less accurate. OBJECTIVE: To validate the adult height prediction model on children from another geographical location. MATERIALS AND METHODS: We included 51 boys and 58 girls from the Paris Longitudinal Study of children born 1953 to 1958. Radiographs were obtained once or twice a year in these children from birth to age 18. Bone age was determined using the BoneXpert method. Radiographs in children with bone age greater than 6 years were considered, in total 1,124 images. RESULTS: The root mean square deviation between the predicted and the observed adult height was 2.8 cm for boys in the bone age range 6-15 years and 3.1 cm for girls in the bone age range 6-13 years. The bias (the average signed difference) was zero, except for girls below bone age 12, where the predictions were 0.8 cm too low. CONCLUSION: The accuracy of the BoneXpert method in terms of root mean square error was as predicted by the model, i.e. in line with what was observed in the Zurich studies.