Intelligent real-time therapy: Harnessing the power of machine learning to optimise the delivery of momentary cognitive–behavioural interventions

Applying the Just-In-Time Adaptive Intervention Framework to the Development of Gambling Interventions

Just-In-Time Adaptive Interventions (JITAIs) are emerging "push" mHealth interventions that provide the right type, timing, and amount of support to address the dynamically-changing needs for each individual. Although JITAIs are well-suited to the delivery of interventions for the addictions, few are available to support gambling behaviour change. We therefore developed GamblingLess: In-The-Moment and Gambling Habit Hacker, two smartphone-delivered JITAIs that differ with respect to their target populations, theoretical underpinnings, and decision rules. We aim to describe the decisions, methods, and tools we used to design these two treatments, with a view to providing guidance to addiction researchers who wish to develop JITAIs in the future. Specifically, we describe how we applied a comprehensive, organising scientific framework to define the problem, define just-in-time in the context of the identified problem, and formulate the adaptation strategies. While JITAIs appear to be a promising design in addiction intervention science, we describe several key challenges that arose during development, particularly in relation to applying micro-randomised trials to their evaluation, and offer recommendations for future research. Issues including evaluation considerations, integrating on-demand intervention content, intervention optimisation, combining active and passive assessments, incorporating human facilitation, adding cost-effectiveness evaluations, and redevelopment as transdiagnostic interventions are discussed.

A Personalized, Transdiagnostic Smartphone Intervention (Mello) Targeting Repetitive Negative Thinking in Young People With Depression and Anxiety: Pilot Randomized Controlled Trial

BACKGROUND

Repetitive negative thinking (RNT) is a key transdiagnostic mechanism underpinning depression and anxiety. Using "just-in-time adaptive interventions" via smartphones may disrupt RNT in real time, providing targeted and personalized intervention.

OBJECTIVE

This pilot randomized controlled trial evaluates the feasibility, acceptability, and preliminary clinical outcomes and mechanisms of Mello-a fully automated, personalized, transdiagnostic, and mechanistic smartphone intervention targeting RNT in young people with depression and anxiety.

METHODS

Participants with heightened depression, anxiety, and RNT were recruited via social media and randomized to receive Mello or a nonactive control over a 6-week intervention period. Assessments were completed via Zoom sessions at baseline and at 3 and 6 weeks after baseline.

RESULTS

The findings supported feasibility and acceptability, with high rates of recruitment (N=55), uptake (55/64, 86% of eligible participants), and retention (52/55, 95% at 6 weeks). Engagement was high, with 90% (26/29) and 59% (17/29) of the participants in the Mello condition still using the app during the third and sixth weeks, respectively. Greater reductions in depression (Cohen d=0.50), anxiety (Cohen d=0.61), and RNT (Cohen d=0.87) were observed for Mello users versus controls. Mediation analyses suggested that changes in depression and anxiety were accounted for by changes in RNT.

CONCLUSIONS

The results indicate that mechanistic, targeted, and real-time technology-based solutions may provide scalable and effective interventions that advance the treatment of youth mental ill health.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12621001701819; http://tinyurl.com/4d3jfj9f.

Using a complexity science approach to evaluate the effectiveness of just-in-time adaptive interventions: A meta-analysis

Objective

Just-in-time adaptive interventions (JITAIs), which allow individuals to receive the right amount of tailored support at the right time and place, hold enormous potential for promoting behavior change. However, research on JITAIs' implementation and evaluation is still in its early stages, and more empirical evidence is needed. This meta-analysis took a complexity science approach to evaluate the effectiveness of JITAIs that promote healthy behaviors and assess whether key design principles can increase JITAIs' impacts.

Methods

We searched five databases for English-language papers. Study eligibility required that interventions objectively measured health outcomes, had a control condition or pre-post-test design, and were conducted in the real-world setting. We included randomized and non-randomized trials. Data extraction encompassed interventions' features, methodologies, theoretical foundations, and delivery modes. RoB 2 and ROBINS-I were used to assess risk of bias.

Results

The final analysis included 21 effect sizes with 592 participants. All included studies used pre- and post-test design. A three-level random meta-analytic model revealed a medium effect of JITAIs on objective behavior change (g = 0.77 (95% confidence interval (CI); 0.32 to 1.22), p < 0.001). The summary effect was robust to bias. Moderator analysis indicated that design principles, such as theoretical foundations, targeted behaviors, and passive or active assessments, did not moderate JITAIs' effects. Passive assessments were more likely than a combination of passive and active assessments to relate to higher intervention retention rates.

Conclusions

This review demonstrated some evidence for the efficacy of JITAIs. However, high-quality randomized trials and data on non-adherence are needed.

Microrandomized Trials: Developing Just-in-Time Adaptive Interventions for Better Public Health

Just-in-time adaptive interventions (JITAIs) represent an intervention design that adapts the provision and type of support over time to an individual's changing status and contexts, intending to deliver the right support on the right occasion. As a novel strategy for delivering mobile health interventions, JITAIs have the potential to improve access to quality care in underserved communities and, thus, alleviate health disparities, a significant public health concern. Valid experimental designs and analysis methods are required to inform the development of JITAIs. Here, we briefly review the cutting-edge design of microrandomized trials (MRTs), covering both the classical MRT design and its outcome-adaptive counterpart. Associated statistical challenges related to the design and analysis of MRTs are also discussed. Two case studies are provided to illustrate the aforementioned concepts and designs throughout the article. We hope our work leads to better design and application of JITAIs, advancing public health research and practice. (Am J Public Health. 2023;113(1):60-69. https://doi.org/10.2105/AJPH.2022.307150).

Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review

Emotional disorders are the most common mental disorders globally. Psychological treatments have been found to be useful for a significant number of cases, but up to 40% of patients do not respond to psychotherapy as expected. Artificial intelligence (AI) methods might enhance psychotherapy by providing therapists and patients with real- or close to real-time recommendations according to the patient’s response to treatment. The goal of this investigation is to systematically review the evidence on the use of AI-based methods to enhance outcomes in psychological interventions in real-time or close to real-time. The search included studies indexed in the electronic databases Scopus, Pubmed, Web of Science, and Cochrane Library. The terms used for the electronic search included variations of the words “psychotherapy”, “artificial intelligence”, and “emotional disorders”. From the 85 full texts assessed, only 10 studies met our eligibility criteria. In these, the most frequently used AI technique was conversational AI agents, which are chatbots based on software that can be accessed online with a computer or a smartphone. Overall, the reviewed investigations indicated significant positive consequences of using AI to enhance psychotherapy and reduce clinical symptomatology. Additionally, most studies reported high satisfaction, engagement, and retention rates when implementing AI to enhance psychotherapy in real- or close to real-time. Despite the potential of AI to make interventions more flexible and tailored to patients’ needs, more methodologically robust studies are needed.

The temporal relationships between defeat, entrapment and suicidal ideation: ecological momentary assessment study

BACKGROUND

Psychological models of suicidal experiences are largely based on cross-sectional or long-term prospective data with follow-up intervals typically greater than 1 year. Recent time-series analyses suggest that these models may not account for fluctuations in suicidal thinking that occur within a period of hours and/or days.

AIMS

We explored whether previously posited causal relationships between defeat, entrapment and suicidal ideation accounted for temporal associations between these experiences at small time intervals from 3 to 12 h.

METHOD

Participants (N = 51) completed an ecological momentary assessment (EMA) study, comprising repeated assessments at semi-random time points up to six times per day for 1 week, resulting in 1852 completed questionnaires. Multilevel vector autoregression was used to calculate temporal associations between variables at different time intervals (i.e. 3 to 12 h between measurements).

RESULTS

The results showed that entrapment severity was temporally associated with current and later suicidal ideation, consistently over these time intervals. Furthermore, entrapment had two-way temporal associations with defeat and suicidal ideation at time intervals of approximately 3 h. The residual and contemporaneous network revealed significant associations between all variables, of which the association between entrapment and defeat was the strongest.

CONCLUSIONS

Although entrapment is key in the pathways leading to suicidal ideation over time periods of months, our results suggest that entrapment may also account for the emergence of suicidal thoughts across time periods spanning a few hours.

'Care co-ordinator in my pocket': a feasibility study of mobile assessment and therapy for psychosis (TechCare)

OBJECTIVES

The aim of the project was to examine the acceptability and feasibility of a mobile phone application-based intervention 'TechCare', for individuals with psychosis in the North West of England. The main objectives were to determine whether appropriate individuals could be identified and recruited to the study and whether the TechCare App would be an acceptable intervention for individuals with psychosis.

METHODS

This was a mixed methods feasibility study, consisting of a test-run and feasibility evaluation of the TechCare App intervention.

SETTING

Early Intervention Services (EIS) for psychosis, within an NHS Trust in the North West of England.

PARTICIPANTS

Sixteen participants (test-run n=4, feasibility study n=12) aged between 18 and 65 years recruited from the East, Central and North Lancashire EIS.

INTERVENTION

A 6-week intervention, with the TechCare App assessing participants' symptoms and responses in real-time and providing a personalised-guided self-help-based psychological intervention based on the principles of Cognitive Behaviorual Therapy (CBT).

RESULTS

A total of 83.33% (n=10) of participants completed the 6-week feasibility study, with 70% of completers achieving the set compliance threshold of ≥33% engagement with the TechCare App system. Analysis of the qualitative data suggested that participants held the view that the TechCare was both an acceptable and feasible means of delivering interventions in real-time.

CONCLUSION

Innovative digital clinical technologies, such as the TechCare App, have the potential to increase access to psychological interventions, reduce health inequality and promote self-management with a real-time intervention, through enabling access to mental health resources in a stigma-free, evidence-based and time-independent manner.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Identifier: NCT02439619.

Just-in-the-Moment Adaptive Interventions (JITAI): A Meta-Analytical Review

A just-in-time, adaptive intervention (JITAI) is an emerging type of intervention that provides tailored support at the exact time of need. It does so using enabling new technologies (e.g., mobile phones, sensors) that capture the changing states of individuals. Extracting effect sizes of primary outcomes produced by 33 empirical studies that used JITAIs, we found moderate to large effect sizes of JITAI treatments compared to (1) waitlist-control conditions (k = 9), Hedges's g = 1.65 and (2) non-JITAI treatments (k = 21), g = 0.89. Also, participants of JITAI interventions showed significant changes (k = 13) in the positive direction (g = 0.79). A series of sensitivity tests suggested that those effects persist. Those effects also persist despite differences in the behaviors of interests (e.g., blood glucose control, recovering alcoholics), duration of the treatments, and participants' age. Two aspects of tailoring, namely: (1) tailoring to what (i.e., both people's previous behavioral patterns and their current need states; with these effects additive) and (2) approach to tailoring (i.e., both using a human agent and an algorithm to decide tailored feedback; with these effects additive), are significantly associated with greater JITAI efficacy.

Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective

Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) and big data analytics have been applied within the m-health for providing an effective healthcare system. Various types of data such as electronic health records (EHRs), medical images, and complicated text which are diversified, poorly interpreted, and extensively unorganized have been used in the modern medical research. This is an important reason for the cause of various unorganized and unstructured datasets due to emergence of mobile applications along with the healthcare systems. In this paper, a systematic review is carried out on application of AI and the big data analytics to improve the m-health system. Various AI-based algorithms and frameworks of big data with respect to the source of data, techniques used, and the area of application are also discussed. This paper explores the applications of AI and big data analytics for providing insights to the users and enabling them to plan, using the resources especially for the specific challenges in m-health, and proposes a model based on the AI and big data analytics for m-health. Findings of this paper will guide the development of techniques using the combination of AI and the big data as source for handling m-health data more effectively.

A Systematic Review of the Evidence Supporting Mobile- and Internet-Based Psychological Interventions For Self-Harm

OBJECTIVES

Internet- and mobile phone-based psychological interventions have the potential to overcome many of the barriers associated with accessing traditional face-to-face therapy. Self-injurious thoughts and behaviors (STB) are prevalent global health problems that may benefit from Internet- and mobile-based interventions. We provide a systematic review and meta-analysis of studies evaluating mobile- and Internet-based interventions for STB, including nonsuicidal self-injury (NSSI).

METHODS

Online databases (PsycINFO, Web of Science, Medline) were searched up to March 2019 for single-arm and controlled trials of Internet- or mobile-based interventions for STB. The potential for bias was assessed using the Cochrane risk of bias tool.

RESULTS

Twenty-two eligible trials were identified. The research was limited by a lack of controlled designs and small samples. Evidence supports the acceptability of interventions. There is preliminary evidence that these interventions are associated with a decline in STB. A meta-analysis suggested a positive treatment effect on suicidal ideation when compared to treatment as usual, but not when trials with active controls were also considered.

CONCLUSIONS

Overall, Internet- and mobile-based interventions show promise and further controlled trials are warranted, focusing on behavioral outcomes (NSSI, suicidal behavior). This review was preregistered with PROSPERO (CRD42017074065).

Evidence-Based Interventions for Youth Suicide Risk

PURPOSE OF REVIEW

To review and discuss recent advances in evidence-based interventions (EBIs) for youth suicide risk.

RECENT FINDINGS

There is a growing body of research on the effectiveness of interventions targeting suicidal ideation and behavior among adolescents. Dialectical Behavioral Therapy-Adolescent has shown effectiveness across two independent randomized controlled trials (RCTs). Several other interventions have shown effectiveness in only one trial and are in need of replication. New interventions are also being developed that incorporate developments in technology and adaptive intervention designs. It is recommended that future research focus on strategies for engaging underserved youth with interventions, consider the broader needs of youth living in poverty, and further tailor interventions to subgroups with distinct risk profiles. Limited EBIs exist for preadolescents, despite evidence for an increasing rate of suicidal behavior for these youth. Several interventions for youth suicide risk are highly promising, but further investigation is necessary. EBIs that are effective for preadolescents are needed, and greater efforts to tailor interventions for distinct subgroups of youth at risk are recommended.

Causal Inference in Generalizable Environments: Systematic Representative Design

Causal inference and generalizability both matter. Historically, systematic designs emphasize causal inference, while representative designs focus on generalizability. Here, we suggest a transformative synthesis - Systematic Representative Design (SRD) - concurrently enhancing both causal inference and "built-in" generalizability by leveraging today's intelligent agent, virtual environments, and other technologies. In SRD, a "default control group" (DCG) can be created in a virtual environment by representatively sampling from real-world situations. Experimental groups can be built with systematic manipulations onto the DCG base. Applying systematic design features (e.g., random assignment to DCG versus experimental groups) in SRD affords valid causal inferences. After explicating the proposed SRD synthesis, we delineate how the approach concurrently advances generalizability and robustness, cause-effect inference and precision science, a computationally-enabled cumulative psychological science supporting both "bigger theory" and concrete implementations grappling with tough questions (e.g., what is context?) and affording rapidly-scalable interventions for real-world problems.

Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support

Background

The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual's changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual's state can change rapidly, unexpectedly, and in his/her natural environment.

Purpose

Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap.

Methods

Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration.

Conclusions

As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention.

Feasibility and Acceptability of Using a Mobile Phone App for Characterizing Auditory Verbal Hallucinations in Adolescents With Early-Onset Psychosis: Exploratory Study

Background

Auditory verbal hallucinations (AVH) are the most frequent symptom in early-onset psychosis (EOP) and a risk factor for increased suicide attempts in adolescents. Increased knowledge of AVH characteristics can lead to better prediction of risk and precision of diagnosis and help identify individuals with AVH who need care. As 98% of Norwegian adolescents aged 12 to 16 years own a mobile phone, the use of mobile phone apps in symptom assessment and patient communication is a promising new tool. However, when introducing new technology to patients, their subjective experiences are crucial in identifying risks, further development, and potential integration into clinical care.

Objective

The objective was to explore the feasibility and acceptability of a newly developed mobile phone app in adolescents with EOP by examining compliance with the app and user experiences. Indication of validity was explored by examining associations between AVH dimensions, which were correlated and analyzed.

Methods

Three adolescents with EOP and active AVH were enrolled. Real-time AVH were logged on an iPod touch using the experience sampling method (ESM), for seven or more consecutive days. The app included five dimensions of AVH characteristics and was programmed with five daily notifications. Feasibility and acceptability were examined using the mean response rate of data sampling and by interviewing the participants. Validity was assessed by examining associations between the AVH dimensions using nonparametric correlation analysis and by visual inspection of temporal fluctuations of the AVH dimensions.

Results

One participant was excluded from the statistical analyses but completed the interview and was included in the examination of acceptability. The sampling period of the two participants was mean 12 (SD 6) days with overall completed sampling rate of 74% (SD 30%), indicating adequate to high compliance with the procedure. The user experiences from the interviews clustered into four categories: (1) increased awareness, (2) personal privacy, (3) design and procedure, and (4) usefulness and clinical care. One participant experienced more commenting voices during the sampling period, and all three participants had concerns regarding personal privacy when using electronic devices in symptom assessment. The AVH dimensions of content, control, and influence showed moderate to strong significant correlations with all dimensions (P<.001). Days of data sampling showed weak to moderate correlations with localization (P<.001) and influence (P=.03). Visual inspection indicated that the app was able to capture fluctuations within and across days for all AVH dimensions.

Conclusions

This study demonstrates the value of including patients’ experiences in the development and pilot-testing of new technology. Based on the small sample size, the use of mobile phones with ESM seems feasible for patients with EOP, but the acceptability of using apps should be considered. Further investigation with larger samples is warranted before definitive conclusions are made.

Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature

Background

Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals.

Objective

Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain.

Methods

We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction).

Results

Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes.

Conclusions

This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.

Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort

BACKGROUND

Early identification of probable post-traumatic stress disorder (PTSD) can lead to early intervention and treatment.

AIMS

This study aimed to evaluate supervised machine learning (ML) classifiers for the identification of probable PTSD in those who are serving, or have recently served in the United Kingdom (UK) Armed Forces.

METHODS

Supervised ML classification techniques were applied to a military cohort of 13,690 serving and ex-serving UK Armed Forces personnel to identify probable PTSD based on self-reported service exposures and a range of validated self-report measures. Data were collected between 2004 and 2009.

RESULTS

The predictive performance of supervised ML classifiers to detect cases of probable PTSD were encouraging when compared to a validated measure, demonstrating a capability of supervised ML to detect the cases of probable PTSD. It was possible to identify which variables contributed to the performance, including alcohol misuse, gender and deployment status. A satisfactory sensitivity was obtained across a range of supervised ML classifiers, but sensitivity was low, indicating a potential for false negative diagnoses.

CONCLUSIONS

Detection of probable PTSD based on self-reported measurement data is feasible, may greatly reduce the burden on public health and improve operational efficiencies by enabling early intervention, before manifestation of symptoms.

Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study

BACKGROUND

Each year, approximately 800,000 people die by suicide worldwide, accounting for 1-2 in every 100 deaths. It is always a tragic event with a huge impact on family, friends, the community and health professionals. Unfortunately, suicide prevention and the development of risk assessment tools have been hindered by the complexity of the underlying mechanisms and the dynamic nature of a person's motivation and intent. Many of those who die by suicide had contact with health services in the preceding year but identifying those most at risk remains a challenge.

OBJECTIVE

To explore the feasibility of using artificial neural networks with routinely collected electronic health records to support the identification of those at high risk of suicide when in contact with health services.

METHODS

Using the Secure Anonymised Information Linkage Databank UK, we extracted the data of those who died by suicide between 2001 and 2015 and paired controls. Looking at primary (general practice) and secondary (hospital admissions) electronic health records, we built a binary feature vector coding the presence of risk factors at different times prior to death. Risk factors included: general practice contact and hospital admission; diagnosis of mental health issues; injury and poisoning; substance misuse; maltreatment; sleep disorders; and the prescription of opiates and psychotropics. Basic artificial neural networks were trained to differentiate between the suicide cases and paired controls. We interpreted the output score as the estimated suicide risk. System performance was assessed with 10x10-fold repeated cross-validation, and its behavior was studied by representing the distribution of estimated risk across the cases and controls, and the distribution of factors across estimated risks.

RESULTS

We extracted a total of 2604 suicide cases and 20 paired controls per case. Our best system attained a mean error rate of 26.78% (SD 1.46; 64.57% of sensitivity and 81.86% of specificity). While the distribution of controls was concentrated around estimated risks < 0.5, cases were almost uniformly distributed between 0 and 1. Prescription of psychotropics, depression and anxiety, and self-harm increased the estimated risk by ~0.4. At least 95% of those presenting these factors were identified as suicide cases.

CONCLUSIONS

Despite the simplicity of the implemented system, the proposed methodology obtained an accuracy like other published methods based on specialized questionnaire generated data. Most of the errors came from the heterogeneity of patterns shown by suicide cases, some of which were identical to those of the paired controls. Prescription of psychotropics, depression and anxiety, and self-harm were strongly linked with higher estimated risk scores, followed by hospital admission and long-term drug and alcohol misuse. Other risk factors like sleep disorders and maltreatment had more complex effects.

Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data

OBJECTIVE

Suicide is a significant public health concern worldwide. Social media data have a potential role in identifying high suicide risk individuals and also in predicting suicide rate at the population level. In this study, we report an advanced daily suicide prediction model using social media data combined with economic/meteorological variables along with observed suicide data lagged by 1 week.

METHODS

The social media data were drawn from weblog posts. We examined a total of 10,035 social media keywords for suicide prediction. We made predictions of national suicide numbers 7 days in advance daily for 2 years, based on a daily moving 5-year prediction modeling period.

RESULTS

Our model predicted the likely range of daily national suicide numbers with 82.9% accuracy. Among the social media variables, words denoting economic issues and mood status showed high predictive strength. Observed number of suicides one week previously, recent celebrity suicide, and day of week followed by stock index, consumer price index, and sunlight duration 7 days before the target date were notable predictors along with the social media variables.

CONCLUSION

These results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events.

Economic evaluation of an experience sampling method intervention in depression compared with treatment as usual using data from a randomized controlled trial

BACKGROUND

Experience sampling, a method for real-time self-monitoring of affective experiences, holds opportunities for person-tailored treatment. By focussing on dynamic patterns of positive affect, experience sampling method interventions (ESM-I) accommodate strategies to enhance personalized treatment of depression-at potentially low-costs. This study aimed to investigate the cost-effectiveness of an experience sampling method intervention in patients with depression, from a societal perspective.

METHODS

Participants were recruited between January 2010 and February 2012 from out-patient mental health care facilities in or near the Dutch cities of Eindhoven and Maastricht, and through local advertisements. Out-patients diagnosed with major depression (n = 101) receiving pharmacotherapy were randomized into: (i) ESM-I consisting of six weeks of ESM combined with weekly feedback regarding the individual's positive affective experiences, (ii) six weeks of ESM without feedback, or (iii) treatment as usual only. Alongside this randomised controlled trial, an economic evaluation was conducted consisting of a cost-effectiveness and a cost-utility analysis, using Hamilton Depression Rating Scale (HDRS) and quality adjusted life years (QALYs) as outcome, with willingness-to-pay threshold for a QALY set at €50,000 (based on Dutch guidelines for moderate severe to severe illnesses).

RESULTS

The economic evaluation showed that ESM-I is an optimal strategy only when willingness to pay is around €3000 per unit HDRS and around €40,500 per QALY. ESM-I was the least favourable treatment when willingness to pay was lower than €30,000 per QALY. However, at the €50,000 willingness-to-pay threshold, ESM-I was, with a 46% probability, the most favourable treatment (base-case analysis). Sensitivity analyses confirmed the robustness of these results.

CONCLUSIONS

We may tentatively conclude that ESM-I is a cost-effective add-on intervention to pharmacotherapy in outpatients with major depression.

TRIAL REGISTRATION

Netherlands Trial register, NTR1974 .

Evolution of cognitive-behavioral therapy for eating disorders

The evolution of cognitive-behavioral therapy (CBT) for the treatment of bulimic disorders is described in this review. The impacts of successive attempts to enhance CBT such as the addition of exposure and response prevention; the development of enhanced CBT; and broadening the treatment from bulimia nervosa to binge eating disorder are considered. In addition to developing advanced forms of CBT, shortening treatment to guided self-help was the first step in broadening access to treatment. The use of technology such as computer-based therapy and more recently the Internet, promises further broadening of access to self-help and to therapist guided treatment. Controlled studies in this area are reviewed, and the balance of risks and benefits that accompany the use of technology and lessened therapist input are considered. Looking into the future, more sophisticated forms of treatment delivered as mobile applications ("apps") may lead to more personalized and efficacious treatments for bulimic disorders, thus enhancing the delivery of treatments for eating disorders.

The Efficacy of Internet-Based Mindfulness Training and Cognitive-Behavioral Training With Telephone Support in the Enhancement of Mental Health Among College Students and Young Working Adults: Randomized Controlled Trial

Background

College students and working adults are particularly vulnerable to stress and other mental health problems, and mental health promotion and prevention are needed to promote their mental health. In recent decades, mindfulness-based training has demonstrated to be efficacious in treating physical and psychological conditions.

Objective

The aim of our study was to examine the efficacy of an Internet-based mindfulness training program (iMIND) in comparison with the well-established Internet-based cognitive-behavioral training program (iCBT) in promoting mental health among college students and young working adults.

Methods

This study was a 2-arm, unblinded, randomized controlled trial comparing iMIND with iCBT. Participants were recruited online and offline via mass emails, advertisements in newspapers and magazines, announcement and leaflets in primary care clinics, and social networking sites. Eligible participants were randomized into either the iMIND (n=604) or the iCBT (n=651) condition. Participants received 8 Web-based sessions with information and exercises related to mindfulness or cognitive-behavioral principles. Telephone or email support was provided by trained first tier supporters who were supervised by the study’s research team. Primary outcomes included mental and physical health-related measures, which were self-assessed online at preprogram, postprogram, and 3-month follow-up.

Results

Among the 1255 study participants, 213 and 127 completed the post- and 3-month follow-up assessment, respectively. Missing data were treated using restricted maximum likelihood estimation. Both iMIND (n=604) and iCBT (n=651) were efficacious in improving mental health, psychological distress, life satisfaction, sleep disturbance, and energy level.

Conclusions

Both Internet-based mental health programs showed potential in improving the mental health from pre- to postassessment, and such improvement was sustained at the 3-month follow-up. The high attrition rate in this study suggests the need for refinement in future technology-based psychological programs. Mental health professionals need to team up with experts in information technology to increase personalization of Web-based interventions to enhance adherence.

Trial Registration

Chinese Clinical Trial Registry (ChiCTR): ChiCTR-TRC-12002623; https://www2.ccrb.cuhk.edu.hk/ registry/public/191 (Archived by WebCite at http://www.webcitation.org/6kxt8DjM4).

Emotional response to a therapeutic technique: The social Broad Minded Affective Coping

OBJECTIVES

It has been suggested that savouring positive memories can generate positive emotions. Increasing positive emotion can have a range of benefits including reducing attention to and experiences of threat. This study investigated individuals' emotional reactions to a guided mental imagery task focussing on positive social memory called the 'social Broad Minded Affective Coping (BMAC)' technique. The study examined possible predictors of individuals' responses to this intervention.

METHOD

An internet-based, within-group, repeated-measures design was used. One hundred and twenty-three participants completed self-report measures of self-attacking and social safeness/pleasure. They were then guided through the social BMAC. Participants completed state measures of positive and negative affect and social safeness/pleasure before and after the intervention. Forty-nine participants took part in a 2-week follow-up.

RESULTS

It was found that safe/warm positive affect, relaxed positive affect and feelings of social safeness increased following the social BMAC, whilst negative affect decreased. In addition, it was found that people scoring higher on inadequate self-attacking benefited most from this intervention. Changes in affect were not maintained at the 2-week follow-up.

CONCLUSION

The results provide preliminary support for the efficacy of the social BMAC in activating specific types of mood (those associated with safeness rather than drive/reward). This task has potential as part of therapeutic interventions directed at clinical groups, but further evaluation is needed.

PRACTITIONER POINTS

The social Broad Minded Affective Coping (BMAC) was related to improvements in forms of positive affect linked to the affiliative system. This task may be helpful in inducing these positive mood states within therapy. Further evaluation comparing the BMAC to a control task is needed. Individuals with a greater fear of compassion or more hated-self-criticism may gain less from the task, although effects were small.

"Just Take a Moment and Breathe and Think": Young Women with Depression Talk about the Development of an Ecological Momentary Intervention to Reduce Their Sexual Risk

STUDY OBJECTIVE

Depressed young women are at increased risk for adverse outcomes related to sexual behavior, including unintended pregnancy, HIV, and other sexually transmitted infections. Brief sexual risk reduction interventions have not targeted depressed young women's specific needs for affect management and impulse control. DESIGN, SETTING, PARTICIPANTS, INTERVENTIONS, AND MAIN OUTCOME MEASURES: We interviewed depressed young women ages 15-23 years engaging in sexual risk behavior about a proposed intervention approach. The approach was described as in-person counseling and cognitive-behavioral skills training, followed by an ecological momentary intervention (EMI) delivered via smartphone application for 4 weeks. The EMI would include reporting multiple times a day on affective states, self-efficacy for safer sex behavior, and sexual behavior, and receiving responsive messages to provide support and prompt use of cognitive-behavioral skills. Participants provided their perspectives on comfort, usability, burden, confidentiality, and potential efficacy of the EMI and recommended message content. Interviews were audio-recorded, transcribed, and analyzed using thematic analysis.

RESULTS

Thematic saturation was reached with 16 interviews. Participants expressed positive opinions about the EMI. They believed that reporting at random times would help them to recognize their feelings, receiving the messages would be reassuring, and overall the smartphone application would be experienced as therapeutic. They desired a high degree of personalization of the message quality, style, and voice, and provided a wide variety of message content.

CONCLUSION

Depressed young women believed that a flexible, personalized approach to mobile momentary intervention for addressing the link between their symptoms and behavior would be acceptable, supportive, and effective in reducing sexual risk.

TechCare: mobile assessment and therapy for psychosis - an intervention for clients in the Early Intervention Service: A feasibility study protocol

Objectives:

Technological advances in healthcare have shown promise when delivering interventions for mental health problems such as psychosis. The aim of this project is to develop a mobile phone intervention for people with psychosis and to conduct a feasibility study of the TechCare App.

Methods:

The TechCare App will assess participant’s symptoms and respond with a personalised guided self-help-based psychological intervention with the aim of exploring feasibility and acceptability. The project will recruit 16 service users and 8–10 health professionals from the Lancashire Care NHS Foundation Trust Early Intervention Service.

Results:

In strand 1 of the study, we will invite people to discuss their experience of psychosis and give their opinions on the existing evidence-based treatment (cognitive behavioural therapy) and how the mobile app can be developed. In strand 2, we will complete a test run with a small number of participants (n = 4) to refine the mobile intervention (TechCare). Finally, in strand 3 of the study, the TechCare App will be examined in a feasibility study with 12 participants.

Conclusion:

It has been suggested that there is a need for a rapid increase in the efforts to develop the evidence base for the clinical effectiveness of digital technologies, considering mHealth research can potentially be helpful in addressing the demand on mental health services globally.

Effects of momentary self-monitoring on empowerment in a randomized controlled trial in patients with depression

Background

Interventions based on the experience sampling method (ESM) are ideally suited to provide insight into personal, contextualized affective patterns in the flow of daily life. Recently, we showed that an ESM-intervention focusing on positive affect was associated with a decrease in symptoms in patients with depression. The aim of the present study was to examine whether ESM-intervention increased patient empowerment.

Methods

Depressed out-patients (n = 102) receiving psychopharmacological treatment who had participated in a randomized controlled trial with three arms: (i) an experimental group receiving six weeks of ESM self-monitoring combined with weekly feedback sessions, (ii) a pseudo-experimental group participating in six weeks of ESM self-monitoring without feedback, and (iii) a control group (treatment as usual only). Patients were recruited in the Netherlands between January 2010 and February 2012. Self-report empowerment scores were obtained pre- and post-intervention.

Results

There was an effect of group × assessment period, indicating that the experimental (B = 7.26, P = 0.061, d = 0.44, statistically imprecise) and pseudo-experimental group (B = 11.19, P = 0.003, d = 0.76) increased more in reported empowerment compared to the control group. In the pseudo-experimental group, 29% of the participants showed a statistically reliable increase in empowerment score and 0% reliable decrease compared to 17% reliable increase and 21% reliable decrease in the control group. The experimental group showed 19% reliable increase and 4% reliable decrease.

Conclusions

These findings tentatively suggest that self-monitoring to complement standard antidepressant treatment may increase patients’ feelings of empowerment. Further research is necessary to investigate long-term empowering effects of self-monitoring in combination with person-tailored feedback.

Optimizing prediction of binge eating episodes: a comparison approach to test alternative conceptualizations of the affect regulation model

BACKGROUND

Although a wealth of studies have tested the link between negative mood states and likelihood of a subsequent binge eating episode, the assumption that this relationship follows a typical linear dose-response pattern (i.e., that risk of a binge episode increases in proportion to level of negative mood) has not been challenged. The present study demonstrates the applicability of an alternative, non-linear conceptualization of this relationship, in which the strength of association between negative mood and probability of a binge episode increases above a threshold value for the mood variable relative to the slope below this threshold value (threshold dose response model).

METHODS

A sample of 93 women aged 18 to 40 completed an online survey at random intervals seven times per day for a period of one week. Participants self-reported their current mood state and whether they had recently engaged in an eating episode symptomatic of a binge.

RESULTS

As hypothesized, the threshold approach was a better predictor than the linear dose-response modeling of likelihood of a binge episode. The superiority of the threshold approach was found even at low levels of negative mood (3 out of 10, with higher scores reflecting more negative mood). Additionally, severity of negative mood beyond this threshold value appears to be useful for predicting time to onset of a binge episode.

CONCLUSIONS

Present findings suggest that simple dose-response formulations for the association between negative mood and onset of binge episodes miss vital aspects of this relationship. Most notably, the impact of mood on binge eating appears to depend on whether a threshold value of negative mood has been breached, and elevation in mood beyond this point may be useful for clinicians and researchers to identify time to onset.

An experience sampling study of worry and rumination in psychosis

BACKGROUND

Increasing research effort is being dedicated to investigating the links between emotional processes and psychosis, despite the traditional demarcation between the two. Particular focus has alighted upon two specific anxious and depressive processes, worry and rumination, given the potential for links with aspects of delusions and auditory hallucinations. This study rigorously explored the nature of these links in the context of the daily life of people currently experiencing psychosis.

METHOD

Experience sampling methodology (ESM) was used to assess the momentary links between worry and rumination on the one hand, and persecutory delusional ideation and auditory hallucinations on the other. Twenty-seven participants completed the 6-day experience sampling period, which required repeated self-reports on thought processes and experiences. Multilevel modelling was used to examine the links within the clustered data.

RESULTS

We found that antecedent worry and rumination predicted delusional and hallucinatory experience, and the distress they elicited. Using interaction terms, we have shown that the links with momentary symptom severity were moderated by participants' trait beliefs about worry/rumination, such that they were reduced when negative beliefs about worry/rumination (meta-cognitions) were high.

CONCLUSIONS

The current findings offer an ecologically valid insight into the influence of worry and rumination on the experience of psychotic symptoms, and highlight possible avenues for future intervention strategies.

Perceived stops to suicidal thoughts, plans, and actions in persons experiencing psychosis

BACKGROUND

Suicide has been conceived as involving a continuum, whereby suicidal plans and acts emerge from thoughts about suicide. Suicide prevention strategies need to determine whether different responses are needed at these points on the continuum.

AIMS

This study investigates factors that were perceived to counter suicidal ideation, plans, and acts.

METHOD

The 36 participants, all of whom had had experiences of psychosis and some level of suicidality, were presented with a vignette describing a protagonist with psychotic symptoms. They were asked to indicate what would counter the suicidal thoughts, plans, and acts of the protagonist described in the vignette. Qualitative techniques were first used to code these free responses into themes/categories. Correspondence analysis was then applied to the frequency of responses in each of these categories.

RESULTS

Social support was identified as a strong counter to suicidal ideation but not as a counter to suicidal plans or acts. Help from health professionals was strongly related to the cessation of suicidal plans as were the opinions of the protagonist's children. Changing cognitions and strengthening psychological resources were more weakly associated with the cessation of suicidal ideation and plans. The protagonist's children were considered potentially helpful in addressing suicidal acts.

CONCLUSION

These results suggest that both overlapping and nonoverlapping factors need to be considered in understanding suicide prevention, dependent on whether individuals are thinking about, planning, or attempting suicide.

A therapeutic application of the experience sampling method in the treatment of depression: a randomized controlled trial

In depression, the ability to experience daily life positive affect predicts recovery and reduces relapse rates. Interventions based on the experience sampling method (ESM-I) are ideally suited to provide insight in personal, contextualized patterns of positive affect. The aim of this study was to examine whether add-on ESM-derived feedback on personalized patterns of positive affect is feasible and useful to patients, and results in a reduction of depressive symptomatology. Depressed outpatients (n=102) receiving pharmacological treatment participated in a randomized controlled trial with three arms: an experimental group receiving add-on ESM-derived feedback, a pseudo-experimental group participating in ESM but receiving no feedback, and a control group. The experimental group participated in an ESM procedure (three days per week over a 6-week period) using a palmtop. This group received weekly standardized feedback on personalized patterns of positive affect. Hamilton Depression Rating Scale - 17 (HDRS) and Inventory of Depressive Symptoms (IDS) scores were obtained before and after the intervention. During a 6-month follow-up period, five HDRS and IDS assessments were completed. Add-on ESM-derived feedback resulted in a significant and clinically relevant stronger decrease in HDRS score relative to the control group (p<0.01; -5.5 point reduction in HDRS at 6 months). Compared to the pseudo-experimental group, a clinically relevant decrease in HDRS score was apparent at 6 months (B=-3.6, p=0.053). Self-reported depressive complaints (IDS) yielded the same pattern over time. The use of ESM-I was deemed acceptable and the provided feedback easy to understand. Patients attempted to apply suggestions from ESM-derived feedback to daily life. These data suggest that the efficacy of traditional passive pharmacological approach to treatment of major depression can be enhanced by using person-tailored daily life information regarding positive affect.

Dynamic treatment regimes: technical challenges and applications

Dynamic treatment regimes are of growing interest across the clinical sciences because these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. Formally, a dynamic treatment regime is a sequence of decision rules, one per stage of clinical intervention. Each decision rule maps up-to-date patient information to a recommended treatment. We briefly review a variety of approaches for using data to construct the decision rules. We then review a critical inferential challenge that results from nonregularity, which often arises in this area. In particular, nonregularity arises in inference for parameters in the optimal dynamic treatment regime; the asymptotic, limiting, distribution of estimators are sensitive to local perturbations. We propose and evaluate a locally consistent Adaptive Confidence Interval (ACI) for the parameters of the optimal dynamic treatment regime. We use data from the Adaptive Pharmacological and Behavioral Treatments for Children with ADHD Trial as an illustrative example. We conclude by highlighting and discussing emerging theoretical problems in this area.

A comparison of two delivery modalities of a mobile phone-based assessment for serious mental illness: native smartphone application vs text-messaging only implementations

BACKGROUND

Mobile phone-based assessment may represent a cost-effective and clinically effective method of monitoring psychotic symptoms in real-time. There are several software options, including the use of native smartphone applications and text messages (short message service, SMS). Little is known about the strengths and limitations of these two approaches in monitoring symptoms in individuals with serious mental illness.

OBJECTIVE

The objective of this study was to compare two different delivery modalities of the same diagnostic assessment for individuals with non-affective psychosis-a native smartphone application employing a graphical, touch user interface against an SMS text-only implementation. The overall hypothesis of the study was that patient participants with sewrious mental illness would find both delivery modalities feasible and acceptable to use, measured by the quantitative post-assessment feedback questionnaire scores, the number of data points completed, and the time taken to complete the assessment. It was also predicted that a native smartphone application would (1) yield a greater number of data points, (2) take less time, and (3) be more positively appraised by patient participant users than the text-based system.

METHODS

A randomized repeated measures crossover design was employed. Participants with currently treated Diagnostic and Statistical Manual (Fourth Edition) schizophrenia or related disorders (n=24) were randomly allocated to completing 6 days of assessment (four sets of questions per day) with a native smartphone application or the SMS text-only implementation. There was then a 1-week break before completing a further 6 days with the alternative delivery modality. Quantitative feedback questionnaires were administered at the end of each period of sampling.

RESULTS

A greater proportion of data points were completed with the native smartphone application in comparison to the SMS text-only implementation (β = -.25, SE=.11, P=.02), which also took significantly less time to complete (β =.78, SE= .09, P<.001). Although there were no significant differences in participants' quantitative feedback for the two delivery modalities, most participants reported preferring the native smartphone application (67%; n=16) and found it easier to use (71%; n=16). 33% of participants reported that they would be willing to complete mobile phone assessment for 5 weeks or longer.

CONCLUSIONS

Native smartphone applications and SMS text are both valuable methods of delivering real-time assessment in individuals with schizophrenia. However, a more streamlined graphical user interface may lead to better compliance and shorter entry times. Further research is needed to test the efficacy of this technology within clinical services, to assess validity over longer periods of time and when delivered on patients' own phones.

Integrating mobile-phone based assessment for psychosis into people's everyday lives and clinical care: a qualitative study

BACKGROUND

Over the past decade policy makers have emphasised the importance of healthcare technology in the management of long-term conditions. Mobile-phone based assessment may be one method of facilitating clinically- and cost-effective intervention, and increasing the autonomy and independence of service users. Recently, text-message and smartphone interfaces have been developed for the real-time assessment of symptoms in individuals with schizophrenia. Little is currently understood about patients' perceptions of these systems, and how they might be implemented into their everyday routine and clinical care.

METHOD

24 community based individuals with non-affective psychosis completed a randomised repeated-measure cross-over design study, where they filled in self-report questions about their symptoms via text-messages on their own phone, or via a purpose designed software application for Android smartphones, for six days. Qualitative interviews were conducted in order to explore participants' perceptions and experiences of the devices, and thematic analysis was used to analyse the data.

RESULTS

Three themes emerged from the data: i) the appeal of usability and familiarity, ii) acceptability, validity and integration into domestic routines, and iii) perceived impact on clinical care. Although participants generally found the technology non-stigmatising and well integrated into their everyday activities, the repetitiveness of the questions was identified as a likely barrier to long-term adoption. Potential benefits to the quality of care received were seen in terms of assisting clinicians, faster and more efficient data exchange, and aiding patient-clinician communication. However, patients often failed to see the relevance of the systems to their personal situations, and emphasised the threat to the person centred element of their care.

CONCLUSIONS

The feedback presented in this paper suggests that patients are conscious of the benefits that mobile-phone based assessment could bring to clinical care, and that the technology can be successfully integrated into everyday routine. However, it also suggests that it is important to demonstrate to patients the personal, as well as theoretical, benefits of the technology. In the future it will be important to establish whether clinical practitioners are able to use this technology as part of a personalised mental health regime.

Does unstable mood increase risk of suicide? Theory, research and practice

BACKGROUND

Suicide represents a substantial problem, with significant societal and personal impact. The identification of factors influencing suicide risk is an important step in preventing self-harming behaviour. In this article the authors explore whether emotional instability increases risk of suicide, beyond that of mood intensity.

METHOD

This article provides a summary of existing theory and indirect evidence in support of an association between emotional instability and suicidality. A systematic literature search (Embase, Medline, PsychInfo) was carried out on literature conducted up to October, 2011. Meta-analysis was used to assess the strength of the proposed association.

RESULTS

The systematic search identified 20 journal articles meeting the inclusion criteria, including retrospective questionnaire design studies and research conducted across several time-points. Meta-analysis revealed a moderate association, which remained statistically significant even when only including studies conducted over multiple time-points. This effect was attenuated, but remained significant, when controlling for study selection bias.

LIMITATIONS

Retrospective questionnaire studies failed to adequately control for mood level. Little is still currently understood about the types of emotional instability (e.g., dysoria, anxiety) most associated with suicidality.

CONCLUSIONS

Future avenues of investigation include micro- to macro-longitudinal research and the differentiation of emotion subtypes and instability metrics. Momentary assessment techniques may help to detect subtle fluctuations in mood leading to more effective and immediate intervention. Psychosocial intervention strategies for treating unstable emotions are discussed.

The feasibility and validity of ambulatory self-report of psychotic symptoms using a smartphone software application

BACKGROUND

Semi-structured interview scales for psychosis are the gold standard approach to assessing psychotic and other symptoms. However, such assessments have limitations such as recall bias, averaging, insensitivity to change and variable interrater reliability. Ambulant, real-time self-report assessment devices may hold advantages over interview measures, but it needs to be shown that the data thus collected are valid, and the collection method is acceptable, feasible and safe. We report on a monitoring system for the assessment of psychosis using smartphone technology. The primary aims were to: i) assess validity through correlations of item responses with those on widely accepted interview assessments of psychosis, and ii) examine compliance to the procedure in individuals with psychosis of varying severity.

METHODS

A total of 44 participants (acute or remitted DSM-4 schizophrenia and related disorders, and prodromal) completed 14 branching self-report items concerning key psychotic symptoms on a touch-screen mobile phone when prompted by an alarm at six pseudo-random times, each day, for one week. Face to face PANSS and CDS interviews were conducted before and after the assessment period blind to the ambulant data.

RESULTS

Compliance as defined by completion of at least 33% of all possible data-points over seven days was 82%. In the 36 compliant participants, 5 items (delusions, hallucinations, suspiciousness, anxiety, hopelessness) showed moderate to strong (rho 0.6-0.8) associations with corresponding items from interview rating scales. Four items showed no significant correlation with rating scales: each was an item based on observable behaviour. Ambulant ratings showed excellent test-retest reliability and sensitivity to change.

CONCLUSIONS

Ambulatory monitoring of symptoms several times daily using smartphone software applications represents a feasible and valid way of assessing psychotic phenomena for research and clinical management purposes. Further evaluation required over longer assessment periods, in clinical trials and service settings.

Predictors of Self-Stigma in Schizophrenia: New Insights Using Mobile Technologies

OBJECTIVE

Self-stigma has significant negative impact on the recovery of individuals with severe mental illness, but its varying course is not well understood. Individual levels of self-stigma may vary over time and fluctuate in response to both external/contextual (i.e., location, activity, social company) and internal (i.e., psychiatric symptoms, mood) factors. The aim of this study was to examine the relationship between self-stigmatizing beliefs and these factors, as they occur in the daily life of individuals with schizophrenia.

METHODS

Mobile technologies were used to longitudinally track momentary levels of self-stigma, psychotic symptoms, negative affect, positive affect, activity, and immediate social and physical environment in twenty-four individuals with schizophrenia, multiple times daily, over a one-week period.

RESULTS

Multilevel modeling showed that participants' current activity was associated with changes in self-stigma (χ²= 10.53, p <0.05), but immediate location and social company were not. Time-lagged analyses found that increases in negative affect (β=0.11, p<0.01) and psychotic symptom severity (β=0.16, p<0.01) predicted increases in the intensity of self-stigmatizing beliefs. Psychotic symptoms were found to be both an antecedent and a consequence (β=0.08, p<0.01) of increased self-stigma.

CONCLUSIONS

Our findings support a framework for understanding self-stigma as an experience that changes based on alterations in internal states and external circumstances. Mobile technologies are an effective methodology to study self-stigma and have potential to be used to deliver clinical interventions.