BackgroundMobile 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.ObjectiveThe 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.MethodsWe 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.ResultsPromising 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).ConclusionsMobilyze! 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)

Research suggests that individuals with Generalized Anxiety Disorder (GAD) show an attention bias for threat-relevant information. However, few studies have examined the causal role of attention bias in the maintenance of anxiety and whether modification of such biases may reduce pathological anxiety symptoms. In the current paper, we tested the hypothesis that an eight-session attention modification program would (a) decrease attention bias to threat and (b) reduce symptoms of GAD. Participants completed a probe detection task by identifying letters ("E" or "F") replacing one member of a pair of words. We trained attention by including a contingency between the location of the probe and the non-threat word in one group (Attention Modification program, AMP) and not in the other (Attention Control condition, ACC). Participants in the AMP showed change in attention bias and a decrease in anxiety, as indicated by both self report and interviewer measures. These effects were not present in the ACC group. These results are consistent with the hypothesis that attention plays a causal role in the maintenance of GAD and suggest that altering attention mechanisms may effectively reduce anxiety.Researchers have used a wide range of methods borrowed from cognitive psychology to examine attention bias to threat in individuals with Generalized Anxiety Disorder (GAD; Mathews & MacLeod, 1985MacLeod, Mathews, & Tata, 1986;Mogg, Millar, & Bradley, 2000). Research using these methods has consistently produced evidence that patients with GAD preferentially attend to threat relevant stimuli over neutral stimuli when the two compete for processing priority.In a seminal study, MacLeod, Mathews, and Tata (1986) developed the probe detection paradigm to measure attention bias to threat in GAD. In this paradigm, participants see two words, one above the other, on a computer screen. One word is neutral (e.g., table), and the other word has a threatening meaning (e.g., disease). Participants are asked to read the upper word and ignore the lower word. On critical trials (25%), either the upper or the lower word is replaced with a dot probe (·) and participants are asked to signal the presence of the probe by pressing a button. MacLeod et al. (1986) found that individuals with Generalized Anxiety Disorder detect probes that replace threat words, either in the upper or the lower portion of the screen, faster than probes that replace neutral words. Thus, clinically anxious individuals with GAD consistently showed an attention bias toward threat. On the other hand, non-anxious controls tended to demonstrate an attention bias away from threat in this paradigm. In a later replication of this study, MacLeod and Mathews (1988) threat stimuli from the mean response latency for trials where the probe replaced the neutral stimuli, such that larger numbers revealed greater bias for threat. Using this index, these authors again found that individuals with GAD show an attention bias toward threat.Two recent reviews of attention bias in anxiety ...

This paper reports on the findings of a technical expert panel convened by the Agency for Healthcare Research and Quality and the National Institute of Mental Health, charged with reviewing the state of research on behavioral intervention technologies (BITs) in mental health and identifying the top research priorities. BITs is the comprehensive term used to refer to behavioral and psychological interventions that use information and communication technology features to address behavioral and mental health outcomes. Mental health BITs using videoconferencing and standard telephone technologies to deliver psychotherapy have been wellvalidated. Web-based interventions have shown efficacy across a broad range of mental health outcomes, although outcomes vary widely. Social media such as online support groups have produced generally disappointing outcomes when used alone. Mobile technologies have received limited attention for mental health outcomes, although findings from behavioral health suggest they are promising. Virtual reality has shown good efficacy for anxiety and pediatric disorders. Serious gaming has received relatively little work in mental health. Recommendations for next step research in each of these are made. Research focused on understanding of reach, adherence, barriers and cost is recommended. As BITs can generate large amounts of data, improvements in the collection, storage, analysis, and visualization of big data will be required. Traditional psychological and behavioral theories have proven insufficient to understand how BITs produce behavioral change. Thus new theoretical models, as well as new evaluation strategies, will be required. Finally, for BITs to have a public health impact, research on implementation and application to prevention will be required.

We conducted a randomized, double-blind placebo-controlled trial to examine the efficacy of an attention training procedure in reducing symptoms of social anxiety in forty-four individuals diagnosed with Generalized Social Phobia (GSP). Attention training comprised a probe detection task where pictures of faces with either a threatening or neutral emotional expression cued different locations on the computer screen. In the Attention Modification Program (AMP), participants responded to a probe that always followed neutral faces when paired with a threatening face, thereby directing attention away from threat. In the Attention Control Condition (ACC), the probe appeared with equal frequency in the position of the threat and neutral faces. Results revealed that the AMP facilitated attention disengagement from threat from pre- to post-assessment, and reduced clinician- and self-reported symptoms of social anxiety relative to the ACC. Participants no longer meeting DSM-IV criteria for GSP at post-assessment were 50% in the AMP and 14% in the ACC. Symptom reduction in the AMP group was maintained during four-month follow-up assessment. These results suggest that computerized attention training procedures may be beneficial for treating social phobia.

A growing number of investigators have commented on the lack of models to inform the design of behavioral intervention technologies (BITs). BITs, which include a subset of mHealth and eHealth interventions, employ a broad range of technologies, such as mobile phones, the Web, and sensors, to support users in changing behaviors and cognitions related to health, mental health, and wellness. We propose a model that conceptually defines BITs, from the clinical aim to the technological delivery framework. The BIT model defines both the conceptual and technological architecture of a BIT. Conceptually, a BIT model should answer the questions why, what, how (conceptual and technical), and when. While BITs generally have a larger treatment goal, such goals generally consist of smaller intervention aims (the "why") such as promotion or reduction of specific behaviors, and behavior change strategies (the conceptual "how"), such as education, goal setting, and monitoring. Behavior change strategies are instantiated with specific intervention components or “elements” (the "what"). The characteristics of intervention elements may be further defined or modified (the technical "how") to meet the needs, capabilities, and preferences of a user. Finally, many BITs require specification of a workflow that defines when an intervention component will be delivered. The BIT model includes a technological framework (BIT-Tech) that can integrate and implement the intervention elements, characteristics, and workflow to deliver the entire BIT to users over time. This implementation may be either predefined or include adaptive systems that can tailor the intervention based on data from the user and the user’s environment. The BIT model provides a step towards formalizing the translation of developer aims into intervention components, larger treatments, and methods of delivery in a manner that supports research and communication between investigators on how to design, develop, and deploy BITs.