Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach

BACKGROUND: Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. METHODS: The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. RESULTS: The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. CONCLUSION: The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed

Adherence to the MoodGYM program: Outcomes and predictors for an adolescent school-based population

Background Program adherence has been associated with improved intervention outcomes for mental and physical conditions. The aim of the current study is to investigate adolescent adherence to an Internet-based depression prevention program in schools to identify the effect of adherence on outcomes and to ascertain the predictors of program adherence. Methods Data for the current study (N=1477) was drawn from the YouthMood Project, which was conducted to test the effectiveness of the MoodGYM program in reducing and preventing symptoms of anxiety and depression in an adolescent school-based population. The current study compares intervention effects across three sub-groups: high adherers, low adherers and the wait-list control condition. Results When compared to the control condition, participants in the high adherence intervention group reported stronger intervention effects at post-intervention and 6-month follow-up than participants in the low adherence group for anxiety (d=0.34–0.39 vs. 0.11–0.22), and male (d=0.43–0.59 vs. 0.26–0.35) and female depression (d=0.13–0.20 vs. 0.02–0.04). No significant intervention effects were identified between the high and low adherence groups. Being in Year 9, living in a rural location and having higher pre-intervention levels of depressive symptoms or self-esteem were predictive of greater adherence to the MoodGYM program. Limitations The program trialled is Internet-based and therefore the predictors of adherence identified may not generalise to face-to-face interventions. Conclusions The current study provides preliminary support for the positive relationship between program adherence and outcomes in a school environment. The identification of significant predictors of adherence will assist in identifying the type of user who will engage most with an online depression prevention program.ALC is supported by National Health and Medical Research Council (NHMRC)Fellowship 1013199, HC is supported by NHMRC Fellowship 525411, and KMG is supported by NHMRC Fellowship 42541

Internet-based CBT for depression with and without telephone tracking in a national helpline: randomised controlled trial

BACKGROUND Telephone helplines are frequently and repeatedly used by individuals with chronic mental health problems and web interventions may be an effective tool for reducing depression in this population. AIM To evaluate the effectiveness of a 6 week, web-based cognitive behaviour therapy (CBT) intervention with and without proactive weekly telephone tracking in the reduction of depression in callers to a helpline service. METHOD 155 callers to a national helpline service with moderate to high psychological distress were recruited and randomised to receive either Internet CBT plus weekly telephone follow-up; Internet CBT only; weekly telephone follow-up only; or treatment as usual. RESULTS Depression was lower in participants in the web intervention conditions both with and without telephone tracking compared to the treatment as usual condition both at post intervention and at 6 month follow-up. Telephone tracking provided by a lay telephone counsellor did not confer any additional advantage in terms of symptom reduction or adherence. CONCLUSIONS A web-based CBT program is effective both with and without telephone tracking for reducing depression in callers to a national helpline. TRIAL REGISTRATION Controlled-Trials.comISRCTN93903959.Funding for the trial was provided by an Australian Research Council Linkage Project Grant (LP0667970) (http://www.arc.gov.au/). LF is supported by an Australian Postgraduate Award Industry scholarship. KG is supported by a National Health and Medical Research Council Fellowship (No. 525413) and HC is supported by a National Health and Medical Research Council Fellowship (No. 525411)

Validation of a smartphone app to map social networks of proximity

Social network analysis is a prominent approach to investigate interpersonal relationships. Most studies use self-report data to quantify the connections between participants and construct social networks. In recent years smartphones have been used as an alternative to map networks by assessing the proximity between participants based on Bluetooth and GPS data. While most studies have handed out specially programmed smartphones to study participants, we developed an application for iOS and Android to collect Bluetooth data from participants own smartphones. In this study, we compared the networks estimated with the smartphone app to those obtained from sociometric badges and self-report data. Participants (n=21) installed the app on their phone and wore a sociometric badge during office hours. Proximity data was collected for 4 weeks. A contingency table revealed a significant association between proximity data (rho = 0.17, p<0.0001), but the marginal odds were higher for the app (8.6%) than for the badges (1.3%), indicating that dyads were more often detected by the app. We then compared the networks that were estimated using the proximity and self-report data. All three networks were significantly correlated, although the correlation with self-reported data was lower for the app (rho = 0.25) than for badges (rho = 0.67). The scanning rates of the app varied considerably between devices and was lower on iOS than on Android. The association between the app and the badges increased when the network was estimated between participants whose app recorded more regularly. These findings suggest that the accuracy of proximity networks can be further improved by reducing missing data and restricting the interpersonal distance at which interactions are detected.Comment: 20 pages, 5 figure

Using Bluetooth Low Energy in smartphones to map social networks

Social networks have an important role in an individual's health, with the propagation of health-related features through a network, and correlations between network structures and symptomatology. Using Bluetooth-enabled smartphones to measure social connectivity is an alternative to traditional paper-based data collection; however studies employing this technology have been restricted to limited sets of homogenous handsets. We investigated the feasibility of using the Bluetooth Low Energy (BLE) protocol, present on users' own smartphones, to measure social connectivity. A custom application was designed for Android and iOS handsets. The app was configured to simultaneously broadcast via BLE and perform periodic discovery scans for other nearby devices. The app was installed on two Android handsets and two iOS handsets, and each combination of devices was tested in the foreground, background and locked states. Connectivity was successfully measured in all test cases, except between two iOS devices when both were in a locked state with their screens off. As smartphones are in a locked state for the majority of a day, this severely limits the ability to measure social connectivity on users' own smartphones. It is not currently feasible to use Bluetooth Low Energy to map social networks, due to the inability of iOS devices to detect another iOS device when both are in a locked state. While the technology was successfully implemented on Android devices, this represents a smaller market share of partially or fully compatible devices.Comment: 6 pages, 1 tabl

Improving e-therapy for mood disorders among lesbians and gay men

Introduction This toolkit provides the first comprehensive set of guidelines for tailoring mood-disorder e-therapies to the needs of same-sex attracted people. It gives developers of e-therapies a set of practical recommendations for adjusting e-therapies to more effectively accommodate lesbians and gay men. These recommendations are supported by in-depth research that was designed specifically to inform this toolkit. Summaries of this research are provided in the toolkit and detailed findings are available in published research articles. This toolkit also provides information on the mental health-related challenges that are often faced by same-sex attracted people and links readers to key resources and organisations for further information. Checklists and other tools are included as aids for developers to assess the inclusiveness and relevance of e-therapies to lesbians and gay men. In short, this toolkit contains an extensive set of tools and explains why and how they could be implemented

e-Mental health for mood and anxiety disorders in general practice

Familiarises general practitioners (GPs) with the range of online programs in Australia that have demonstrated efficacy and are currently available for use by patients with mental health problems. Background Australia is a world leader in the development of internetdelivered programs for the prevention and management of mood and anxiety disorders. Despite a strong evidence base of time- and cost-effectiveness, as well as clinical efficacy, the uptake of these programs in general practice remains low. Objective To familiarise general practitioners (GPs) with the range of online programs in Australia that have demonstrated efficacy and are currently available for use by patients with mental health problems. Discussion E-mental health programs provide an efficacious and accessible form of mental healthcare and have the potential to fill the gap for those for whom such care is inaccessible, unaffordable or unacceptable. Clinicians can also use it in a stepped-care manner to augment existing healthcare services. There are a number of online resources currently available to Australians who have mood or anxiety disorders. These resources have strong evidence to support their effectiveness. Online portals facilitate access to these programs. Recently the Australian Federal Government has funded an education program (eMHPrac) for GPs and mental health professionals, to outline what is available, indicate situations where recommending such resources is appropriate, and suggest ways in which they can be incorporated into general practice

Models in the delivery of depression care: a systematic review of randomised and controlled intervention trials

BACKGROUND: There is still debate as to which features, types or components of primary care interventions are associated with improved depression outcomes. Previous reviews have focused on components of collaborative care models in general practice settings. This paper aims to determine the effective components of depression care in primary care through a systematic examination of both general practice and community based intervention trials. METHODS: Fifty five randomised and controlled research trials which focused on adults and contained depression outcome measures were identified through PubMed, PsycInfo and the Cochrane Central Register of Controlled Trials databases. Trials were classified according to the components involved in the delivery of treatment, the type of treatment, the primary focus or setting of the study, detailed features of delivery, and the discipline of the professional providing the treatment. The primary outcome measure was significant improvement on the key depression measure. RESULTS: Components which were found to significantly predict improvement were the revision of professional roles, the provision of a case manager who provided direct feedback and delivered a psychological therapy, and an intervention that incorporated patient preferences into care. Nurse, psychologist and psychiatrist delivered care were effective, but pharmacist delivery was not. Training directed to general practitioners was significantly less successful than interventions that did not have training as the most important intervention. Community interventions were effective. CONCLUSION: Case management is important in the provision of care in general practice. Certain community models of care (education programs) have potential while others are not successful in their current form (pharmacist monitoring)

Detecting suicidality on Twitter

Twitter is increasingly investigated as a means of detecting mental health status, including depression and suicidality, in the population. However, validated and reliable methods are not yet fully established. This study aimed to examine whether the level of concern for a suicide-related post on Twitter could be determined based solely on the content of the post, as judged by human coders and then replicated by machine learning. From 18th February 2014 to 23rd April 2014, Twitter was monitored for a series of suicide-related phrases and terms using the public Application Program Interface (API). Matching tweets were stored in a data annotation tool developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). During this time, 14,701 suicide-related tweets were collected: 14% were randomly (n = 2000) selected and divided into two equal sets (Set A and B) for coding by human researchers. Overall, 14% of suicide-related tweets were classified as ‘strongly concerning’, with the majority coded as ‘possibly concerning’ (56%) and the remainder (29%) considered ‘safe to ignore’. The overall agreement rate among the human coders was 76% (average κ = 0.55). Machine learning processes were subsequently applied to assess whether a ‘strongly concerning’ tweet could be identified automatically. The computer classifier correctly identified 80% of ‘strongly concerning’ tweets and showed increasing gains in accuracy; however, future improvements are necessary as a plateau was not reached as the amount of data increased. The current study demonstrated that it is possible to distinguish the level of concern among suicide-related tweets, using both human coders and an automatic machine classifier. Importantly, the machine classifier replicated the accuracy of the human coders. The findings confirmed that Twitter is used by individuals to express suicidality and that such posts evoked a level of concern that warranted further investigation. However, the predictive power for actual suicidal behaviour is not yet known and the findings do not directly identify targets for intervention.This project was supported in part by funding from the NSW Mental Health Commission and the NHMRC John Cade Fellowship 1056964. PJB and ALC are supported by the NHMRC Early Career Fellowships 1035262 and 1013199