Effects of mental health self-efficacy on outcomes of a mobile phone and web intervention for mild-to-moderate depression, anxiety and stress: secondary analysis of a randomised controlled trial.

Background: Online psychotherapy is clinically effective yet why, how, and for whom the effects are greatest remain largely unknown. In the present study, we examined whether mental health self-efficacy (MHSE), a construct derived from Bandura’s Social Learning Theory (SLT), influenced symptom and functional outcomes of a new mobile phone and web-based psychotherapy intervention for people with mild-to-moderate depression, anxiety and stress. Methods: STUDY I: Data from 49 people with symptoms of depression, anxiety and/or stress in the mild-to-moderate range were used to examine the reliability and construct validity of a new measure of MHSE, the Mental Health Self-efficacy Scale (MHSES). STUDY II: We conducted a secondary analysis of data from a recently completed randomised controlled trial (N = 720) to evaluate whether MHSE effected post-intervention outcomes, as measured by the Depression, Anxiety and Stress Scales (DASS) and Work and Social Adjustment Scale (WSAS), for people with symptoms in the mild-to-moderate range. Results: STUDY I: The data established that the MHSES comprised a unitary factor, with acceptable internal reliability (Cronbach’s alpha = .89) and construct validity. STUDY II: The intervention group showed significantly greater improvement in MHSE at post-intervention relative to the control conditions (p’s < = .000). MHSE mediated the effects of the intervention on anxiety and stress symptoms. Furthermore, people with low pre-treatment MHSE reported the greatest post-intervention gains in depression, anxiety and overall distress. No effects were found for MHSE on work and social functioning. Conclusion: Mental health self-efficacy influences symptom outcomes of a self-guided mobile phone and web-based psychotherapeutic intervention and may itself be a worthwhile target to increase the effectiveness and efficiency of online treatment programs

Translational approaches to the biology of Autism:false dawn or a new era?

Discovering novel treatments for Autism Spectrum Disorders (ASD) is a challenge. Its etiology and pathology remain largely unknown, the condition shows wide clinical diversity, and case identification is still solely based on symptomatology. Hence clinical trials typically include samples of biologically and clinically heterogeneous individuals. ‘Core deficits', that is, deficits common to all individuals with ASD, are thus inherently difficult to find. Nevertheless, recent reports suggest that new opportunities are emerging, which may help develop new treatments and biomarkers for the condition. Most important, several risk gene variants have now been identified that significantly contribute to ASD susceptibility, many linked to synaptic functioning, excitation–inhibition balance, and brain connectivity. Second, neuroimaging studies have advanced our understanding of the ‘wider' neural systems underlying ASD; and significantly contributed to our knowledge of the complex neurobiology associated with the condition. Last, the recent development of powerful multivariate analytical techniques now enable us to use multi-modal information in order to develop complex ‘biomarker systems', which may in the future be used to assist the behavioral diagnosis, aid patient stratification and predict response to treatment/intervention. The aim of this review is, therefore, to summarize some of these important new findings and highlight their potential significant translational value to the future of ASD research