Understanding suicidal transitions in Australian adults: protocol for the LifeTrack prospective longitudinal cohort study.

BACKGROUND: The factors that influence transition from suicidal ideation to a suicide attempt or remission of suicidal thoughts are poorly understood. Despite an abundance of research on risk factors for suicidal ideation, no large-scale longitudinal population-based studies have specifically recruited people with suicidal ideation to examine the mechanisms underlying critical transitions to either suicide attempt or recovery from suicidal ideation. Without longitudinal data on the psychological, behavioural, and social determinants of suicide attempt and the remission of suicidal ideation, we are unlikely to see major gains in the prevention of suicide. AIM: The LifeTrack Project is a population-based longitudinal cohort study that aims to identify key modifiable risk and protective factors that predict the transition from suicidal ideation to suicide attempt or remission of suicidal ideation. We will assess theory-informed risk and protective factors using validated and efficient measures to identify distinct trajectories reflecting changes in severity of suicidal ideation and transition to suicide attempt over three years. METHODS: A three-year prospective population-based longitudinal cohort study will be conducted with adults from the general Australian population who initially report suicidal ideation (n = 842). Eligibility criteria include recent suicidal ideation (past 30 days), aged 18 years or older, living in Australia and fluent in English. Those with a suicide attempt in past 30 days or who are unable to participate in a long-term study will be excluded. Participants will be asked to complete online assessments related to psychopathology, cognition, psychological factors, social factors, mental health treatment use, and environmental exposures at baseline and every six months during this three-year period. One week of daily measurement bursts (ecological momentary assessments) at yearly intervals will also capture short-term fluctuations in suicidal ideation, perceived burdensomeness, thwarted belongingness, capability for suicide, and distress. CONCLUSION: This study is intended to identify potential targets for novel and tailored therapies for people experiencing suicidal ideation and improve targeting of suicide prevention programs. Even modest improvements in current treatments may lead to important reductions in suicide attempts and deaths. STUDY REGISTRATION: Australian New Zealand Clinical Trials Registry identifier: ACTRN12623000433606.

Understanding and detecting behaviours prior to a suicide attempt: A mixed-methods study.

OBJECTIVE: Prior research suggests there are observable behaviours preceding suicide attempts in public places. However, there are currently no ways to continually monitor such sites, limiting the potential to intervene. In this mixed-methods study, we examined the acceptability and feasibility of using an automated computer system to identify crisis behaviours. METHODS: First, we conducted a large-scale acceptability survey to assess public perceptions on research using closed-circuit television and artificial intelligence for suicide prevention. Second, we identified crisis behaviours at a frequently used cliff location by manual structured analysis of closed-circuit television footage. Third, we configured a computer vision algorithm to identify crisis behaviours and evaluated its sensitivity and specificity using test footage. RESULTS: Overall, attitudes were positive towards research using closed-circuit television and artificial intelligence for suicide prevention, including among those with lived experience. The second study revealed that there are identifiable behaviours, including repetitive pacing and an extended stay. Finally, the automated behaviour recognition algorithm was able to correctly identify 80% of acted crisis clips and correctly reject 90% of acted non-crisis clips. CONCLUSION: The results suggest that using computer vision to detect behaviours preceding suicide is feasible and well accepted by the community and may be a feasible method of initiating human contact during a crisis.

The application of spatial analysis to understanding the association between area-level socio-economic factors and suicide: a systematic review.

BACKGROUND: Little is known about what impact the use of different spatial methodological approaches may have on understanding the relationship between area-level socio-economic factors and suicide. METHODS: In this systematic review, we searched PubMed, Embase, CINAHL and PsycInfo for original empirical studies examining the relationship between socio-economic factors and suicide with a spatial lens, published up to January 22, 2022. Data on applied spatial methods, indicators of socio-economic factors, and risk of suicide related to socio-economic factors were extracted. The protocol for this systematic review was registered with PROSPERO (CRD42021251387). RESULTS: A systematic search yielded 6290 potentially relevant results; 58 studies met the inclusion criteria for review. Of the 58 included studies, more than half of the studies (n = 34; 58.6%) used methods that accounted for spatial effects in analyses of the association between socio-economic factors and suicide or examined spatial autocorrelation, while 24 (41.4%) studies applied univariate and multivariate models without considering spatial effects. Bayesian hierarchical models and spatial regression models were commonly used approaches to correct for spatial effects. The risk of suicide relating to socio-economic factors varied greatly by local areas and between studies using various socio-economic indicators. Areas with higher deprivation, higher unemployment, lower income, and lower education level were more likely to have higher suicide risk. There was no significant difference in results between studies using conventional versus spatial statistic methods. CONCLUSION: An increasing number of studies have applied spatial methods, including Bayesian spatial models and spatial regression models, to explore the relationship between area-level socio-economic factors and suicide. This review of spatial studies provided further evidence that area-level socio-economic factors are generally inversely associated with suicide risk, with or without accounting for spatial autocorrelation.

A Spatial Analysis of Suicide Displacement at a High-Risk Cliff-Based Location Following Installation of a Means Restriction Initiative.

Means restriction interventions are recognised as highly effective for the deterrence of suicide attempts by jumping. While such interventions can lead to significant reductions in suicide, it is unclear whether these reductions represent a displacement effect, whereby individuals are instead choosing to attempt suicide at other nearby locations which offer the same means. The potential displacement of suicides as an unintended consequence of means restriction has been relatively unexplored to date. The only studies exploring displacement effects have focused on bridges, which are relatively easily contained sites; no studies have yet explored displacement effects at cliff-based high risk suicide locations (hotspots). Using Australian coronial data for the period of 2006-2019, we undertook joinpoint and kernel density analysis of suicides by jumping at a well-known cliff-based hotspot in Sydney, Australia, to determine whether there was evidence of displacement to local and broader surrounding cliffs following the installation of a multi-component harm minimization intervention (the Gap Park Masterplan). While slight decreases were noted in the immediate area subject to the structural intervention in the post-implementation period, alongside slight increases in the surrounding cliffs, there was no evidence for statistically significant changes. While kernel density analyses did not identify the emergence of any new hotspot locations in the post-implementation period, three existing hotspot sites of concern were found in our total area of interest, with greater than expected growth in the density of one of the hotspots. While we found no persuasive evidence of displacement, ongoing monitoring of the cliff-based location where the structural interventions were implemented is needed to ensure the ongoing safety of the area.

Promoting engagement with self-guided digital therapeutics for mental health: Insights from a cross-sectional survey of end-users.

BACKGROUND: Poor engagement has hindered the dissemination of self-guided digital therapeutics for mental health. Evidence on the efficacy of technology-supported strategies designed for increasing engagement with these interventions is currently inconclusive. Few studies have examined user perceptions of such strategies, which could yield key insights for enhancing their effectiveness. METHODS: Two hundred and eleven participants completed a cross-sectional online survey. All participants were aged 18 years and above, living in Australia, fluent in English, and self-reported prior use of digital therapeutics for mental health needs. Survey items elicited participants' views on factor(s) contributing to their engagement/disengagement with digital therapeutics, and perceptions of engagement strategies, including: automated electronic reminders, fun facts, quizzes, and social media platforms. χ2 tests of independence were used to analyse nominal data. Inductive thematic analysis was used to analyse free-text data. RESULTS: Factors facilitating engagement included digestible content, assurance of confidentiality, and esthetically appealing design. Factors hindering engagement included lack of time, forgetfulness, and lack of perceived intervention efficacy. Participants had mixed views over the feasibility and acceptability of the strategies explored toward promoting engagement. CONCLUSIONS: Regardless of strategy type, succinct and easily applicable content was identified as an important feature for promoting engagement. Conversely, content that could trigger psychological distress among intervention users should be avoided. Overall, the diverse range of user perspectives highlighted that engagement strategies for any specific digital intervention should be co-designed with individuals with lived experience of mental health difficulties.

Towards Building a Visual Behaviour Analysis Pipeline for Suicide Detection and Prevention.

Understanding human behaviours through video analysis has seen significant research progress in recent years with the advancement of deep learning. This topic is of great importance to the next generation of intelligent visual surveillance systems which are capable of real-time detection and analysis of human behaviours. One important application is to automatically monitor and detect individuals who are in crisis at suicide hotspots to facilitate early intervention and prevention. However, there is still a significant gap between research in human action recognition and visual video processing in general, and their application to monitor hotspots for suicide prevention. While complex backgrounds, non-rigid movements of pedestrians and limitations of surveillance cameras and multi-task requirements for a surveillance system all pose challenges to the development of such systems, a further challenge is the detection of crisis behaviours before a suicide attempt is made, and there is a paucity of datasets in this area due to privacy and confidentiality issues. Most relevant research only applies to detecting suicides such as hangings or jumps from bridges, providing no potential for early prevention. In this research, these problems are addressed by proposing a new modular design for an intelligent visual processing pipeline that is capable of pedestrian detection, tracking, pose estimation and recognition of both normal actions and high risk behavioural cues that are important indicators of a suicide attempt. Specifically, based on the key finding that human body gestures can be used for the detection of social signals that potentially precede a suicide attempt, a new 2D skeleton-based action recognition algorithm is proposed. By using a two-branch network that takes advantage of three types of skeleton-based features extracted from a sequence of frames and a stacked LSTM structure, the model predicts the action label at each time step. It achieved good performance on both the public dataset JHMDB and a smaller private CCTV footage collection on action recognition. Moreover, a logical layer, which uses knowledge from a human coding study to recognise pre-suicide behaviour indicators, has been built on top of the action recognition module to compensate for the small dataset size. It enables complex behaviour patterns to be recognised even from smaller datasets. The whole pipeline has been tested in a real-world application of suicide prevention using simulated footage from a surveillance system installed at a suicide hotspot, and preliminary results confirm its effectiveness at capturing crisis behaviour indicators for early detection and prevention of suicide.

Lhb-/-Lhr-/- Double Mutant Mice Phenocopy Lhb-/- or Lhr-/- Single Mutants and Display Defects in Leydig Cells and Steroidogenesis.

In the mouse, two distinct populations of Leydig cells arise during testis development. Fetal Leydig cells arise from a stem cell population and produce T required for masculinization. It is debated whether they persist in the adult testis. A second adult Leydig stem cell population gives rise to progenitor-immature-mature adult type Leydig cells that produce T in response to LH to maintain spermatogenesis. In testis of adult null male mice lacking either only LH (Lhb-/-) or LHR (Lhr-/-), mature Leydig cells are absent but fetal Leydig cells persist. Thus, it is not clear whether other ligands signal via LHRs in Lhb null mice or LH signals via other receptors in the absence of LHR in Lhr null mice. Moreover, it is not clear whether truncated LHR isoforms generated from the same Lhr gene promoter encode functionally relevant LH receptors. To determine the in vivo roles of LH-LHR signaling pathway in the Leydig cell lineage, we generated double null mutant mice lacking both LH Ligand and all forms of LHR. Phenotypic analysis indicated testis morpho-histological characteristics are identical among double null and single mutants which all showed poorly developed interstitium with a reduction in Leydig cell number and absence of late stage spermatids. Gene expression analyses confirmed that the majority of the T biosynthesis pathway enzyme-encoding mRNAs expressed in Leydig cells were all suppressed. Expression of thrombospondin-2, a fetal Leydig cell marker gene was upregulated in single and double null mutants indicating that fetal Leydig cells originate and develop independent of LH-LHR signaling pathway in vivo. Serum and intratesticular T levels were similarly suppressed in single and double mutants. Consequently, expression of AR-regulated genes in Sertoli and germ cells were similarly affected in single and double mutants without any evidence of any additive effect in the combined absence of both LH and LHR. Our studies unequivocally provide genetic evidence that in the mouse testis, fetal Leydig cells do not require LH-LHR signaling pathway and a one-to-one LH ligand-LHR signaling pathway exists in vivo to regulate adult Leydig cell lineage and spermatogenesis.

Ethics and Privacy in Social Media Research for Mental Health.

PURPOSE OF REVIEW: This review provides an overview of recent research which uses social media data in the context of mental health. It also provides an overview of challenges in relation to consent, privacy, and usage of such data. RECENT FINDINGS: A broad range of research has been conducted in recent years, using text-based and visual data from social media platforms, for purposes such as risk detection at the individual level, providing crisis outreach, and developing a better understanding of the lived experience of mental ill-health. Challenges remain in relation to obtaining truly informed consent for research using social media data-however platforms allowing data donation may address these concerns. There is an imperative need to ensure that privacy is preserved at all stages of the research process, from data collection, to analysis, and the responsible use of raw data in publications.

Medical Mobile App Classification Using the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies: Interrater Reliability Study.

BACKGROUND: Clinical governance of medical mobile apps is challenging, and there is currently no standard method for assessing the quality of such apps. In 2018, the National Institute for Health and Care Excellence (NICE) developed a framework for assessing the required level of evidence for digital health technologies (DHTs), as determined by their clinical function. The framework can potentially be used to assess mobile apps, which are a subset of DHTs. To be used reliably in this context, the framework must allow unambiguous classification of an app's clinical function. OBJECTIVE: The objective of this study was to determine whether mobile health apps could be reliably classified using the NICE evidence standards framework for DHTs. METHODS: We manually extracted app titles, screenshots, and content descriptions for all apps listed on the National Health Service (NHS) Apps Library website on July 12, 2019; none of the apps were downloaded. Using this information, 2 mobile health (mHealth) researchers independently classified each app to one of the 4 functional tiers (ie, 1, 2, 3a, and 3b) described in the NICE digital technologies evaluation framework. Coders also answered contextual questions from the framework to identify whether apps were deemed to be higher risk. Agreement between coders was assessed using Cohen κ statistic. RESULTS: In total, we assessed 76 apps from the NHS Apps Library. There was classification agreement for 42 apps. Of these, 0 apps were unanimously classified into Tier 1; 24, into Tier 2; 15, into Tier 3a; and 3, into Tier 3b. There was disagreement between coders in 34/76 cases (45%); interrater agreement was poor (Cohen κ=0.32, 95% CI 0.16-0.47). Further investigation of disagreements highlighted 5 main explanatory themes: apps that did not correspond to any tier, apps that corresponded to multiple tiers, ambiguous tier descriptions, ambiguous app descriptions, and coder error. CONCLUSIONS: The current iteration of the NICE evidence standards framework for DHTs did not allow mHealth researchers to consistently and unambiguously classify digital health mobile apps listed on the NHS app library according to their functional tier.

Clinicians' Concerns About Mobile Ecological Momentary Assessment Tools Designed for Emerging Psychiatric Problems: Prospective Acceptability Assessment of the MEmind App.

BACKGROUND: Many mental disorders are preceded by a prodromal phase consisting of various attenuated and unspecific symptoms and functional impairment. Electronic health records are generally used to capture these symptoms during medical consultation. Internet and mobile technologies provide the opportunity to monitor symptoms emerging in patients' environments using ecological momentary assessment techniques to support preventive therapeutic decision making. OBJECTIVE: The objective of this study was to assess the acceptability of a Web-based app designed to collect medical data during appointments and provide ecological momentary assessment features. METHODS: We recruited clinicians at 4 community psychiatry departments in France to participate. They used the app to assess patients and to collect data after viewing a video of a young patient's emerging psychiatric consultation. We then asked them to answer a short anonymous self-administered questionnaire that evaluated their experience, the acceptability of the app, and their habit of using new technologies. RESULTS: Of 24 practitioners invited, 21 (88%) agreed to participate. Most of them were between 25 and 45 years old, and greater age was not associated with poorer acceptability. Most of the practitioners regularly used new technologies, and 95% (20/21) connected daily to the internet, with 70% (15/21) connecting 3 times a day or more. However, only 57% (12/21) reported feeling comfortable with computers. Of the clinicians, 86% (18/21) would recommend the tool to their colleagues and 67% (14/21) stated that they would be interested in daily use of the app. Most of the clinicians (16/21, 76%) found the interface easy to use and useful. However, several clinicians noted the lack of readability (8/21, 38%) and the need to improve ergonometric features (4/21, 19%), in particular to facilitate browsing through various subsections. Some participants (5/21, 24%) were concerned about the storage of medical data and most of them (11/21, 52%) seemed to be uncomfortable with this. CONCLUSIONS: We describe the first step of the development of a Web app combining an electronic health record and ecological momentary assessment features. This online tool offers the possibility to assess patients and to integrate medical data easily into face-to-face conditions. The acceptability of this app supports the feasibility of its broader implementation. This app could help to standardize assessment and to build up a strong database. Used in conjunction with robust data mining analytic techniques, such a database would allow exploration of risk factors, patterns of symptom evolution, and identification of distinct risk subgroups.

Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps.

PURPOSE OF REVIEW: As rates of suicide continue to rise, there is urgent need for innovative approaches to better understand, predict, and care for those at high risk of suicide. Numerous mobile and sensor technology solutions have already been proposed, are in development, or are already available today. This review seeks to assess their clinical evidence and help the reader understand the current state of the field. RECENT FINDINGS: Advances in smartphone sensing, machine learning methods, and mobile apps directed towards reducing suicide offer promising evidence; however, most of these innovative approaches are still nascent. Further replication and validation of preliminary results is needed. Whereas numerous promising mobile and sensor technology based solutions for real time understanding, predicting, and caring for those at highest risk of suicide are being studied today, their clinical utility remains largely unproven. However, given both the rapid pace and vast scale of current research efforts, we expect clinicians will soon see useful and impactful digital tools for this space within the next 2 to 5 years.

The Emerging Imperative for a Consensus Approach Toward the Rating and Clinical Recommendation of Mental Health Apps.

With over 10,000 mental health- and psychiatry-related smartphone apps available today and expanding, there is a need for reliable and valid evaluation of these digital tools. However, the updating and nonstatic nature of smartphone apps, expanding privacy concerns, varying degrees of usability, and evolving interoperability standards, among other factors, present serious challenges for app evaluation. In this article, we provide a narrative review of various schemes toward app evaluations, including commercial app store metrics, government initiatives, patient-centric approaches, point-based scoring, academic platforms, and expert review systems. We demonstrate that these different approaches toward app evaluation each offer unique benefits but often do not agree to each other and produce varied conclusions as to which apps are useful or not. Although there are no simple solutions, we briefly introduce a new initiative that aims to unify the current controversies in app elevation called CHART (Collaborative Health App Rating Teams), which will be further discussed in a second article in this series.

The Use of Virtual Reality in Patients with Eating Disorders: Systematic Review.

BACKGROUND: Patients with eating disorders are characterized by pathological eating habits and a tendency to overestimate their weight and body shape. Virtual reality shows promise for the evaluation and management of patients with eating disorders. This technology, when accepted by this population, allows immersion in virtual environments, assessment, and therapeutic approaches, by exposing users to high-calorie foods or changes in body shape. OBJECTIVE: To better understand the value of virtual reality, we conducted a review of the literature, including clinical studies proposing the use of virtual reality for the evaluation and management of patients with eating disorders. METHODS: We searched PubMed, PsycINFO, ScienceDirect, the Cochrane Library, Scopus, and Web of Science up to April 2017. We created the list of keywords based on two domains: virtual reality and eating disorders. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify, select, and critically appraise relevant research while minimizing bias. RESULTS: The initial database searches identified 311 articles, 149 of which we removed as duplicates. We analyzed the resulting set of 26 unique studies that met the inclusion criteria. Of these, 8 studies were randomized controlled trials, 13 were nonrandomized studies, and 5 were clinical trials with only 1 participant. Most articles focused on clinical populations (19/26, 73%), with the remainder reporting case-control studies (7/26, 27%). Most of the studies used visual immersive equipment (16/26, 62%) with a head-mounted display (15/16, 94%). Two main areas of interest emerged from these studies: virtual work on patients' body image (7/26, 27%) and exposure to virtual food stimuli (10/26, 38%). CONCLUSIONS: We conducted a broad analysis of studies on the use of virtual reality in patients with eating disorders. This review of the literature showed that virtual reality is an acceptable and promising therapeutic tool for patients with eating disorders.

From eHealth to iHealth: Transition to Participatory and Personalized Medicine in Mental Health.

Clinical assessment in psychiatry is commonly based on findings from brief, regularly scheduled in-person appointments. Although critically important, this approach reduces assessment to cross-sectional observations that miss essential information about disease course. The mental health provider makes all medical decisions based on this limited information. Thanks to recent technological advances such as mobile phones and other personal devices, electronic health (eHealth) data collection strategies now can provide access to real-time patient self-report data during the interval between visits. Since mobile phones are generally kept on at all times and carried everywhere, they are an ideal platform for the broad implementation of ecological momentary assessment technology. Integration of these tools into medical practice has heralded the eHealth era. Intelligent health (iHealth) further builds on and expands eHealth by adding novel built-in data analysis approaches based on (1) incorporation of new technologies into clinical practice to enhance real-time self-monitoring, (2) extension of assessment to the patient's environment including caregivers, and (3) data processing using data mining to support medical decision making and personalized medicine. This will shift mental health care from a reactive to a proactive and personalized discipline.

Issues for eHealth in Psychiatry: Results of an Expert Survey.

BACKGROUND: Technology has changed the landscape in which psychiatry operates. Effective, evidence-based treatments for mental health care are now available at the fingertips of anyone with Internet access. However, technological solutions for mental health are not necessarily sought by consumers nor recommended by clinicians. OBJECTIVE: The objectives of this study are to identify and discuss the barriers to introducing eHealth technology-supported interventions within mental health. METHODS: An interactive polling tool was used to ask "In this brave new world, what are the key issues that need to be addressed to improve mental health (using technology)?" Respondents were the multidisciplinary attendees of the "Humans and Machines: A Quest for Better Mental Health" conference, held in Sydney, Australia, in 2016. Responses were categorized into 10 key issues using team-based qualitative analysis. RESULTS: A total of 155 responses to the question were received from 66 audience members. Responses were categorized into 10 issues and ordered by importance: access to care, integration and collaboration, education and awareness, mental health stigma, data privacy, trust, understanding and assessment of mental health, government and policy, optimal design, and engagement. In this paper, each of the 10 issues are outlined, and potential solutions are discussed. Many of the issues were interrelated, having implications for other key areas identified. CONCLUSIONS: As many of the issues identified directly related to barriers to care, priority should be given to addressing these issues that are common across mental health delivery. Despite new challenges raised by technology, technology-supported mental health interventions represent a tremendous opportunity to address in a timely way these major concerns and improve the receipt of effective, evidence-based therapy by those in need.

Mobile Apps for Bipolar Disorder: A Systematic Review of Features and Content Quality.

BACKGROUND: With continued increases in smartphone ownership, researchers and clinicians are investigating the use of this technology to enhance the management of chronic illnesses such as bipolar disorder (BD). Smartphones can be used to deliver interventions and psychoeducation, supplement treatment, and enhance therapeutic reach in BD, as apps are cost-effective, accessible, anonymous, and convenient. While the evidence-based development of BD apps is in its infancy, there has been an explosion of publicly available apps. However, the opportunity for mHealth to assist in the self-management of BD is only feasible if apps are of appropriate quality. OBJECTIVE: Our aim was to identify the types of apps currently available for BD in the Google Play and iOS stores and to assess their features and the quality of their content. METHODS: A systematic review framework was applied to the search, screening, and assessment of apps. We searched the Australian Google Play and iOS stores for English-language apps developed for people with BD. The comprehensiveness and quality of information was assessed against core psychoeducation principles and current BD treatment guidelines. Management tools were evaluated with reference to the best-practice resources for the specific area. General app features, and privacy and security were also assessed. RESULTS: Of the 571 apps identified, 82 were included in the review. Of these, 32 apps provided information and the remaining 50 were management tools including screening and assessment (n=10), symptom monitoring (n=35), community support (n=4), and treatment (n=1). Not even a quarter of apps (18/82, 22%) addressed privacy and security by providing a privacy policy. Overall, apps providing information covered a third (4/11, 36%) of the core psychoeducation principles and even fewer (2/13, 15%) best-practice guidelines. Only a third (10/32, 31%) cited their information source. Neither comprehensiveness of psychoeducation information (r=-.11, P=.80) nor adherence to best-practice guidelines (r=-.02, P=.96) were significantly correlated with average user ratings. Symptom monitoring apps generally failed to monitor critical information such as medication (20/35, 57%) and sleep (18/35, 51%), and the majority of self-assessment apps did not use validated screening measures (6/10, 60%). CONCLUSIONS: In general, the content of currently available apps for BD is not in line with practice guidelines or established self-management principles. Apps also fail to provide important information to help users assess their quality, with most lacking source citation and a privacy policy. Therefore, both consumers and clinicians should exercise caution with app selection. While mHealth offers great opportunities for the development of quality evidence-based mobile interventions, new frameworks for mobile mental health research are needed to ensure the timely availability of evidence-based apps to the public.

A pilot study: dose adaptation of capecitabine using mobile phone toxicity monitoring - supporting patients in their homes.

PURPOSE: Real-time symptom monitoring using a mobile phone is potentially advantageous for patients receiving oral chemotherapy. We therefore conducted a pilot study of patient dose adaptation using mobile phone monitoring of specific symptoms to investigate relative dose intensity of capecitabine, level of toxicity and perceived supportive care. METHODS: Patients with breast or colorectal cancer receiving capecitabine completed a symptom, temperature and dose diary twice a day using a mobile phone application. This information was encrypted and automatically transmitted in real time to a secure server, with moderate levels of toxicity automatically prompting self-care symptom management messages on the screen of the patient's mobile phone or in severe cases, a call from a specialist nurse to advise on care according to an agreed protocol. RESULTS: Patients (n = 26) completed the mobile phone diary on 92.6 % of occasions. Twelve patients had a maximum toxicity grade of 3 (46.2 %). The average dose intensity for all patients as a percentage of standard dose was 90 %. In eight patients, the dose of capecitabine was reduced, and in eight patients, the dose of capecitabine was increased. Patients and healthcare professionals involved felt reassured by the novel monitoring system, in particular, during out of hours. CONCLUSION: It is possible to optimise the individual dose of oral chemotherapy safely including dose increase and to manage chemotherapy side effects effectively using real-time mobile phone monitoring of toxicity parameters entered by the patient.

Digital therapeutics in the hospital for suicide crisis - content and design recommendations from young people and hospital staff.

Objective: Hospital emergency departments lack the resources to adequately support young people who present for suicidal crisis. Digital therapeutics could fill this service gap by providing psychological support without creating additional burden on hospital staff. However, existing research on what is needed for successful integration of digital therapeutics in hospital settings is scant. Thus, this study sought to identify key considerations for implementing digital therapeutics to manage acute suicidal distress in hospitals. Method: Participants were 17 young people who recently presented at the hospital for suicide-related crisis, and 12 hospital staff who regularly interacted with young people experiencing mental ill-health in their day-to-day work. Interviews were conducted via videoconference. Framework analysis and reflexive thematic analysis were used to interpret the data obtained. Results: Qualitative insights were centred around three major themes: hospital-specific content, therapeutic content, and usability. Digital therapeutics were seen as a useful means for facilitating hospital-based assessment and treatment planning, and for conducting post-discharge check-ins. Therapeutic content should be focused on helping young people self-manage suicide-related distress while they wait for in-person services. Features to promote usability, such as the availability of customisable features and the use of inclusive design or language, should be considered in the design of digital therapeutics. Conclusions: Digital therapeutics in hospital settings need to benefit both patients and staff. Given the unique context of the hospital setting and acute nature of suicidal distress, creating specialty digital therapeutics may be more viable than integrating existing ones.

Attitudes among the Australian public toward AI and CCTV in suicide prevention research: A mixed methods study.

Research is underway exploring the use of closed-circuit television (CCTV) cameras and artificial intelligence (AI) for suicide prevention research in public locations where suicides occur. Given the sensitive nature and potential implications of this research, this study explored ethical concerns the public may have about research of this nature. Developed based on the principle of respect, a survey was administered to a representative sample of 1,096 Australians to understand perspectives on the research. The sample was aged 18 and older, 53% female, and 9% ethnic minority. Following an explanatory mixed methods approach, interviews and a focus group were conducted with people with a lived experience of suicide and first responders to contextualize the findings. There were broad levels of acceptance among the Australian public. Younger respondents, females, and those declining to state their ethnicity had lower levels of acceptance of CCTV research using AI for suicide prevention. Those with lived experience of suicide had higher acceptance. Qualitative data indicated concern regarding racial bias in AI and police response to suicidal crises and the need for lived experience involvement in the development and implementation of any resulting interventions. Broad public acceptance of the research aligns with the principle of respect for persons. Beneficence emerged in the context of findings emphasizing the importance of meaningfully including people with lived experience in the development and implementation of interventions resulting from this research, while justice emerged in themes expressing concerns about racial bias in AI and police response to mental health crises. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Efficacy of BrighterSide, a Self-Guided App for Suicidal Ideation: Randomized Controlled Trial.

Background: Self-guided digital interventions can reduce the severity of suicidal ideation, although there remain relatively few rigorously evaluated smartphone apps targeting suicidality. Objective: This trial evaluated whether the BrighterSide smartphone app intervention was superior to a waitlist control group at reducing the severity of suicidal ideation. Methods: A total of 550 adults aged 18 to 65 years with recent suicidal ideation were recruited from the Australian community. In this randomized controlled trial, participants were randomly assigned to receive either the BrighterSide app or to a waitlist control group that received treatment as usual. The app was self-guided, and participants could use the app at their own pace for the duration of the study period. Self-report measures were collected at baseline, 6 weeks, and 12 weeks. The primary outcome was severity and frequency of suicidal ideation, and secondary outcomes included psychological distress and functioning and recovery. Additional data were collected on app engagement and participant feedback. Results: Suicidal ideation reduced over time for all participants, but there was no significant interaction between group and time. Similar improvements were observed for self-harm, functioning and recovery, days out of role, and coping. Psychological distress was significantly lower in the intervention group at the 6-week follow-up, but this was not maintained at 12 weeks. Conclusions: The BrighterSide app did not lead to a significant improvement in suicidal ideation relative to a waitlist control group. Possible reasons for this null finding are discussed.