Towards a consensus around standards for smartphone apps and digital mental health

Mental disorders impact one in four people worldwide, yet access to care is challenging for those who suffer from them1. Mental health apps offer the potential to overcome access barriers for the nearly three billion people projected to own a smartphone by 2020. Although there are over 10,000 mental health apps commercially available, there are few resources available to help end users (patients, clinicians and health care organizations) to evaluate the quality and suitability of these products. Thus, there is an urgent need for an agreement about appropriate standards, principles and practices in research and evaluation of these tools.We represent leaders in mHealth research, industry and health care systems from around the globe, and we seek here to promote consensus on implementing these standards and principles for the evaluation of mental health apps. At a minimum, standards should include consideration of: a) data safety and privacy, b) effectiveness, c) user experience/adherence, d) data integration. Our consensus on the challenges and recommendations in each of these areas is presented below

Does mentoring matter: results from a survey of faculty mentees at a large health sciences university

Background: To determine the characteristics associated with having a mentor, the association of mentoring with self-efficacy, and the content of mentor–mentee interactions at the University of California, San Francisco (UCSF), we conducted a baseline assessment prior to implementing a comprehensive faculty mentoring program. Method: We surveyed all prospective junior faculty mentees at UCSF. Mentees completed a web-based, 38-item survey including an assessment of self-efficacy and a needs assessment. We used descriptive and inferential statistics to determine the association between having a mentor and gender, ethnicity, faculty series, and self-efficacy. Results: Our respondents (n=464, 56%) were 53% female, 62% white, and 7% from underrepresented minority groups. More than half of respondents (n=319) reported having a mentor. There were no differences in having a mentor based on gender or ethnicity (p≥0.05). Clinician educator faculty with more teaching and patient care responsibilities were statistically significantly less likely to have a mentor compared with faculty in research intensive series (p<0.001). Having a mentor was associated with greater satisfaction with time allocation at work (p<0.05) and with higher academic self-efficacy scores, 6.07 (sd = 1.36) compared with those without a mentor, 5.33 (sd = 1.35, p<0.001). Mentees reported that they most often discussed funding with the mentors, but rated highest requiring mentoring assistance with issues of promotion and tenure. Conclusion: Findings from the UCSF faculty mentoring program may assist other health science institutions plan similar programs. Mentoring needs for junior faculty with greater teaching and patient care responsibilities must be addressed

Does cognition predict treatment response and remission in psychotherapy for late-life depression?

Objectives To identify cognitive predictors of geriatric depression treatment outcome. Method Older participants completed baseline measures of memory and executive function, health, and baseline and post-treatment Hamilton Depression Scales (HAM-D) in a 12-week trial comparing psychotherapies (problem-solving vs. supportive; N = 46). We examined cognitive predictors to identify treatment responders (i.e., HAM-D scores reduced by ≥50%) and remitters (i.e., post-treatment HAM-D score ≤10). Results Empirically derived decision trees identified poorer performance on switching (i.e., Trails B), with a cut-score of ≥82 predicting psychotherapy responders. No other cognitive or health variables predicted psychotherapy outcomes in the decision trees. Conclusions Psychotherapies that support or improve the executive skill of switching may augment treatment response for older patients exhibiting executive dysfunction in depression. If replicated, Trails B has potential as a brief cognitive tool for clinical decision-making in geriatric depression

Does cognition predict treatment response and remission in psychotherapy for late-life depression? (vol 23, pg 215, 2015)

The authors regret that the above article did not examine pharmacotherapy— “and pharmacotherapy”, and “or nortriptyline versus paroxetine” should be deleted (p. 216). The corrected text should read: We derived decision trees to identify cognitive and therapeutic predictors of treatment response and remission for psychotherapy using signal detection software for receiver operator characteristics (ROCs). …Baseline variables examined as predictors of response and remission in the ROC analyses included: demographics (age, sex, education, ethnicity), treatment type (PST versus ST), depression (HAM-D), health (SF-36 mental and physical health functioning), and cognition (memory and executive functioning). The authors would like to apologise for any inconvenience caused

survey of faculty mentees at a large

mentoring matter: results from