David C. Mohr

Depression and cortisol responses to psychological stress: A meta-analysis

The purpose of this meta-analysis is to examine the association between depression and cortisol responses to psychological stressors. A total of seven studies comparing plasma or cortisol responses to psychological stressors in clinically depressed (MDD) and non-depressed (ND) individuals (N = 196: 98 MDD, 98 ND; 83 men, 113 women; mean age = 40 years) were included. Sample size-adjusted effect sizes (Cohens d statistic) were calculated and averaged across baseline (before stressor onset), stress (stressor onset up to 25 min after stressor offset), and recovery (more than 25 min after stressor offset) periods. Overall, MDD and ND individuals exhibited similar baseline and stress cortisol levels, but MDD patients had much higher cortisol levels during the recovery period than their ND counterparts. There was also a significant time of day effect in which afternoon studies were more likely to reveal higher baseline cortisol levels, blunted stress reactivity, and impaired recovery in MDD patients. This blunted reactivity-impaired recovery pattern observed among the afternoon studies was most pronounced in studies with older and more severely depressed patients.

Mobile health technology evaluation: the mHealth evidence workshop.

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.

Clinical Depression Versus Distress Among Patients With Type 2 Diabetes: Not Just a Question of Semantics

OBJECTIVE—We sought to determine differences between structured interviews, symptom questionnaires, and distress measures for assessment of depression in patients with diabetes. RESEARCH DESIGN AND METHODS—We assessed 506 diabetic patients for major depressive disorder (MDD) by a structured interview (Composite International Diagnostic Interview [CIDI]), a questionnaire for depressive symptoms (Center for Epidemiological Studies Depression Scale [CESD]), and on the Diabetes Distress Scale. Demographic characteristics, two biological variables (A1C and non-HDL cholesterol), and four behavioral management measures (kilocalories, calories of saturated fat, number of fruit and vegetable servings, and minutes of physical activity) were assessed. Comparisons were made between those with and without depression on the CIDI and the CESD. RESULTS—Findings showed that 22% of patients reached CESD ≥16, and 9.9% met a CIDI diagnosis of MDD. Of those above CESD cut points, 70% were not clinically depressed, and 34% of those who were clinically depressed did not reach CESD scores ≥16. Those scoring ≥16, compared with those <16 on the CESD, had higher A1C, kilocalories, and calories of saturated fat and lower physical activity. No differences were found using the CIDI. Diabetes distress was minimally related to MDD but substantively linked to CESD scores and to outcomes. CONCLUSIONS—Most patients with diabetes and high levels of depressive symptoms are not clinically depressed. The CESD may be more reflective of general emotional and diabetes-specific distress than clinical depression. Most treatment of distress, however, is based on the depression literature, which suggests the need to consider different interventions for distressed but not clinically depressed diabetic patients.

Harnessing Context Sensing to Develop a Mobile Intervention for Depression

Background Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder. Objective The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing. Methods We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients’ mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients’ self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks. Results Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (betaweek = –.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (betaweek = –.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (bweek = –.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (betaweek = –.71, P < .001, per-protocol Cohen d = 2.58). Conclusions Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed. Trial Registration Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n)

Supportive Accountability: A Model for Providing Human Support to Enhance Adherence to eHealth Interventions

The effectiveness of and adherence to eHealth interventions is enhanced by human support. However, human support has largely not been manualized and has usually not been guided by clear models. The objective of this paper is to develop a clear theoretical model, based on relevant empirical literature, that can guide research into human support components of eHealth interventions. A review of the literature revealed little relevant information from clinical sciences. Applicable literature was drawn primarily from organizational psychology, motivation theory, and computer-mediated communication (CMC) research. We have developed a model, referred to as “Supportive Accountability.” We argue that human support increases adherence through accountability to a coach who is seen as trustworthy, benevolent, and having expertise. Accountability should involve clear, process-oriented expectations that the patient is involved in determining. Reciprocity in the relationship, through which the patient derives clear benefits, should be explicit. The effect of accountability may be moderated by patient motivation. The more intrinsically motivated patients are, the less support they likely require. The process of support is also mediated by the communications medium (eg, telephone, instant messaging, email). Different communications media each have their own potential benefits and disadvantages. We discuss the specific components of accountability, motivation, and CMC medium in detail. The proposed model is a first step toward understanding how human support enhances adherence to eHealth interventions. Each component of the proposed model is a testable hypothesis. As we develop viable human support models, these should be manualized to facilitate dissemination.

Comparative outcomes for individual cognitive-behavior therapy, supportive-expressive group psychotherapy, and sertraline for the treatment of Depression in multiple sclerosis

This study compared the efficacy of 3 16-week treatments for depression in 63 patients with multiple sclerosis (MS) and major depressive disorder (MDD): individual cognitive-behavioral therapy (CBT), supportive-expressive group therapy (SEG). and the antidepressant sertraline. Significant reductions were seen from pre- to posttreatment in all measures of depression. Intent-to-treat and completers analyses using the Beck Depression Inventory (BDI; A. T. Beck, C. H. Ward. M. Medelson. J. Mock, & J. Erbaugh, 1961) and MDD diagnosis found that CBT and sertraline were more effective than SEG at reducing depression. These results were largely supported by the BDI-18, which eliminates BDI items confounded with MS. However, the Hamilton Rating Scale for Depression (M. Hamilton, 1960) did not show consistent differences between treatments. Reasons for this inconsistency are discussed. These findings suggest that CBT or sertraline is more likely to be effective in treating MDD in MS compared with supportive group treatments.

Association between stressful life events and exacerbation in multiple sclerosis: a meta-analysis

Abstract Objective To quantify the association between stressful life events and exacerbations of multiple sclerosis. Data sources PubMed, PsychInfo, and Psychological Abstracts searched for empirical papers from 1965 to February 2003 with terms “stress”, “trauma”, and “multiple sclerosis”. Review methods Three investigators independently reviewed papers for inclusion/exclusion criteria and extracted the relevant data, including methods, sample statistics, and outcomes. Results Of 20 studies identified, 14 were included. The meta-analysis showed a significant increase in risk of exacerbation in multiple sclerosis after stressful life events, with a weighted average effect size of d = 0.53 (95% confidence interval 0.40 to 0.65), P < 0.0001. The studies were homogenous, q = 16.62, p = 0.22, i2 = 21.8%. Neither sampling nor study methods had any effect on study outcomes. Conclusions There is a consistent association between stressful life events and subsequent exacerbation in multiple sclerosis. However these data do not allow the linking of specific stressors to exacerbations nor should they be used to infer that patients are responsible for their exacerbations. Investigation of the psychological, neuroendocrine, and immune mediators of stressful life events on exacerbation may lead to new behavioural and pharmacological strategies targeting potential links between stress and exacerbation.

The psychosocial impact of multiple sclerosis: exploring the patient's perspective.

This study examined subjective patient experiences of the psychosocial consequences of multiple sclerosis (MS). Fifty patients were interviewed regarding the effects MS had on their lives and interpersonal relationships. These statements were collated and administered with a 5-point Likert scale to 94 MS patients. The responses were subjected to factor analysis. Three areas of subjective patient experience of the psychosocial consequences of MS emerged: demoralization, benefit-finding, and deteriorated relationships. Of particular interest was benefit-finding, which included a deepening of relationships, enhanced appreciation of life, and an increase in spiritual interests. Although benefit-finding was related to adaptive coping strategies such as positive reappraisal and seeking social support, it was unrelated to depression and was related to higher levels of anxiety and anger. These findings indicate that benefit-finding is a substantial and poorly understood part of the illness experience for MS patients.

Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health

OBJECTIVE A technical expert panel convened by the Agency for Healthcare Research and Quality and the National Institute of Mental Health was charged with reviewing the state of research on behavioral intervention technologies (BITs) in mental health and identifying the top research priorities. BITs refers to behavioral and psychological interventions that use information and communication technology features to address behavioral and mental health outcomes. METHOD This study on the findings of the technical expert panel. RESULTS Videoconferencing and standard telephone technologies to deliver psychotherapy have been well validated. Web-based interventions have shown efficacy across a broad range of mental health outcomes. Social media such as online support groups have produced disappointing outcomes when used alone. Mobile technologies have received limited attention for mental health outcomes. Virtual reality has shown good efficacy for anxiety and pediatric disorders. Serious gaming has received little work in mental health. CONCLUSION Research focused on understanding reach, adherence, barriers and cost is recommended. Improvements in the collection, storage, analysis and visualization of big data will be required. New theoretical models and evaluation strategies will be required. Finally, for BITs to have a public health impact, research on implementation and application to prevention is required.

The Goldman Consensus statement on depression in multiple sclerosis

Background. In January 2002 the New York City Chapter of the National Multiple Sclerosis Society convened a panel of experts to review the issue of depressive affective disorders associated with multiple sclerosis (MS). This Consensus Conference was supported by a grant from the Goldman family of New York City. Results. The panel reviewed summaries of current epidemiologic, neurobiologic, and therapeutic studies having to do with depressive disorders among MS patient populations. Depressive disorders occur at high rates among patients with MS, and there is reason to believe that the immunopathology of the disease is involved in the clinical expression of affective disorders. The depressive syndromes of MS have a major, negative impact on quality of life for MS sufferers, but are treatable. At the present time, most MS patients with depression do not receive adequate recognition and treatment. Conclusions. The Goldman Consensus Conference Study Group provides recommendations for improved screening, diagnosis, and clinical management for depressive affective disorders among patients suffering from MS.