Trace Analysis of C4F7N Insulating Gas Mixtures by Spontaneous Raman Spectroscopy and Gas Chromatography.

2,3,3,3-Tetrafluoro-2-(trifluoromethyl) propanenitrile (C4F7N) is being researched as an alternative to sulfur hexafluoride (SF6) for applications in gas-insulated switchgear. We independently assessed the effectiveness of gas chromatography-mass spectrometry (GC-MS) and a novel method of feedback-assisted multipass cavity spontaneous Raman spectroscopy (SRS) for the trace quantification of impurities in C4F7N and its related byproducts. A total of 14 gases were identified with estimated concentrations as low as 20 ppm (ppm) for C3F6 using GC-MS and 7.4 ppm for CH4 using SRS and as high as 500 ppm for CF4 using GC-MS and 1430 ppm for CO using SRS. While GC-MS is highly effective in selectively detecting and quantifying trace contaminants, it necessitates separate detectors for various gases, such as CH4 and H2. SRS succeeded in detecting CF4 and C2F6 at concentrations of 465 and 100 ppm, respectively, and in placing an upper bound of several hundred ppm for the other analytes. Crucially, SRS holds potential for portability-and thus for field applications-in gas-insulated switchgear equipment diagnostics.

The Trajectories of Online Mental Health Information Seeking: Modeling Search Behavior Before and After Completion of Self-report Screens.

There is an appreciable mental health treatment gap in the United States. Efforts to bridge this gap and improve resource accessibility have led to the provision of online, clinically-validated tools for mental health self-assessment. In theory, these screens serve as an invaluable component of information-seeking, representing the preparative and action-oriented stages of this process while altering or reinforcing the search content and language of individuals as they engage with information online. Accordingly, this work investigated the association of screen completion with mental health-related search behaviors. Three-year internet search histories from N=7,572 Microsoft Bing users were paired with their respective depression, anxiety, bipolar disorder, or psychosis online screen completion and sociodemographic data available through Mental Health America. Data was transformed into network representations to model queries as discrete steps with probabilities and times-to-transition from one search type to another. Search data subsequent to screen completion was also modeled using Markov chains to simulate likelihood trajectories of different search types through time. Differences in querying dynamics relative to screen completion were observed, with searches involving treatment, diagnosis, suicidal ideation, and suicidal intent commonly emerging as the highest probability behavioral information seeking endpoints. Moreover, results pointed to the association of low risk states of psychopathology with transitions to extreme clinical outcomes (i.e., active suicidal intent). Future research is required to draw definitive conclusions regarding causal relationships between screens and search behavior.

Predicting state level suicide fatalities in the united states with realtime data and machine learning.

Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states. Specifically, per state, we built a long short-term memory (LSTM) neural network model to combine signals from the real-time data sources and compared predicted values of suicide deaths from our model to observed values in the same state. Our LSTM model produced accurate estimates of state-specific suicide rates in all four states (percentage error in suicide rate of -2.768% for Utah, -2.823% for Louisiana, -3.449% for New York, and -5.323% for Colorado). Furthermore, our deep learning based approach outperformed current gold-standard baseline autoregressive models that use historical death data alone. We demonstrate an approach to incorporate signals from multiple proxy real-time data sources that can potentially provide more timely estimates of suicide trends at the state level. Timely suicide data at the state level has the potential to improve suicide prevention planning and response tailored to the needs of specific geographic communities.

Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health.

Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health non-profit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains.

Acquisition of Mandarin tones by Canadian first graders: Effect of prior exposure to tonal and non-tonal languages.

This study examines the tone productions of school-aged children with and without a tonal language background who are learning Mandarin as a second language (L2) or heritage language in Mandarin-English bilingual schools in Western Canada. Tones are frequently identified as one of the most challenging aspects of phonology for Mandarin L2 learners to acquire. In this study, tone productions of bilingual children from three home language backgrounds, English, Cantonese, and Mandarin Chinese, were compared for transcribed accuracy using mixed effects logistic regression. In addition, the fundamental frequency contours of correct tone productions were fitted with generalized additive mixed models to analyse the acoustic differences between groups. Error patterns were also analysed for possible Cantonese substitutions. Our results suggest that children with a Cantonese background are more accurate in tone productions than children with an English language background, but they also made more errors than their peers with a Mandarin language background. These findings suggest that a tonal language background could result in positive transfer among school-age children who are in the early stages of learning Mandarin as an L2.

Small Steps over time: A longitudinal usability test of an automated interactive text messaging intervention to support self-management of depression and anxiety symptoms.

BACKGROUND: Digital mental health interventions (DMHIs) offer potential solutions for addressing mental health care gaps, but often suffer from low engagement. Text messaging is one promising medium for increasing access and sustaining user engagement with DMHIs. This paper examines the Small Steps SMS program, an 8-week, automated, adaptive text message-based intervention for depression and anxiety. METHODS: We conducted an 8-week longitudinal usability test of the Small Steps SMS program, recruiting 20 participants who met criteria for major depressive disorder and/or generalized anxiety disorder. Participants used the automated intervention for 8 weeks and completed symptom severity and usability self-report surveys after 4 and 8 weeks of intervention use. Participants also completed individual interviews to provide feedback on the intervention. RESULTS: Participants responded to automated messages on 70 % of study days and with 85 % of participants sending responses to messages in the 8th week of use. Usability surpassed established cutoffs for software that is considered acceptable. Depression symptom severity decreased significantly over the usability test, but reductions in anxiety symptoms were not significant. Participants noted key areas for improvement including addressing message volume, aligning message scheduling to individuals' availability, and increasing the customizability of content. LIMITATIONS: This study does not contain a control group. CONCLUSIONS: An 8-week automated interactive text messaging intervention, Small Steps SMS, demonstrates promise with regard to being a feasible, usable, and engaging method to deliver daily mental health support to individuals with symptoms of anxiety and depression.

Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain Health: Smartphone Keyboard and Accelerometer as Window into Brain Health.

We examine the feasibility of using accelerometer data exclusively collected during typing on a custom smartphone keyboard to study whether typing dynamics are associated with daily variations in mood and cognition. As part of an ongoing digital mental health study involving mood disorders, we collected data from a well-characterized clinical sample (N = 85) and classified accelerometer data per typing session into orientation (upright vs. not) and motion (active vs. not). The mood disorder group showed lower cognitive performance despite mild symptoms (depression/mania). There were also diurnal pattern differences with respect to cognitive performance: individuals with higher cognitive performance typed faster and were less sensitive to time of day. They also exhibited more well-defined diurnal patterns in smartphone keyboard usage: they engaged with the keyboard more during the day and tapered their usage more at night compared to those with lower cognitive performance, suggesting a healthier usage of their phone.

A text messaging intervention to support the mental health of young adults: User engagement and feedback from a field trial of an intervention prototype.

Background: Young adults have high rates of mental health conditions, but most do not want or cannot access treatment. By leveraging a medium that young adults routinely use, text messaging programs have potential to keep young adults engaged with content supporting self-management of mental health issues and can be delivered inexpensively at scale. We designed an intervention that imparts strategies for self-managing mental health symptoms through interactive text messaging dialogues and engages users through novelty and variety in strategies (from cognitive behavioral therapy, acceptance and commitment therapy, and positive psychology) and styles of interaction (e.g., prompts, peer stories, writing tasks). Methods: The aim of this mixed-methods study was to pilot 1- and 2-week versions of an interactive text messaging intervention among young adults (ages 18-25), and to obtain feedback to guide intervention refinements. Young adults were recruited via a mental health advocacy website and snowball sampling at a North American University. We used Wizard-of-Oz methods in which study staff sent messages based on a detailed script. Transcripts of interviews were subject to qualitative analysis to identify aspects of the program that need improvements, and to gather participant perspectives on possible solutions. Results: Forty-eight individuals ages 18-25 participated in the study (mean age: 22.0). 85 % responded to the program at least once. Among those who ever responded, they replied to messages on 85 % of days, and with engagement sustained over the study period. Participants endorsed the convenience of text messaging, the types of interactive dialogues, and the variety of content. They also identified needed improvements to message volume, scheduling, and content. Conclusions: Young adults showed high levels of engagement and satisfaction with a texting program supporting mental health self-management. The program may be improved through refining personalization, timing, and message volume, and extending content to support use over a longer timeframe. If shown to be effective in randomized trials, this program has potential to help address a substantial treatment gap in young adults' mental health.

Assessment of interfraction dose variation in pancreas SBRT using daily simulation MR images.

Pancreatic Cancer is associated with poor treatment outcomes compared to other cancers. High local control rates have been achieved by using hypofractionated stereotactic body radiotherapy (SBRT) to treat pancreatic cancer. Challenges in delivering SBRT include close proximity of several organs at risk (OARs) and target volume inter and intra fraction positional variations. Magnetic resonance image (MRI) guided radiotherapy has shown potential for online adaptive radiotherapy for pancreatic cancer, with superior soft tissue contrast compared to CT. The aim of this study was to investigate the variability of target and OAR volumes for different treatment approaches for pancreatic cancer, and to assess the suitability of utilizing a treatment-day MRI for treatment planning purposes. Ten healthy volunteers were scanned on a Siemens Skyra 3 T MRI scanner over two sessions (approximately 3 h apart), per day over 5 days to simulate an SBRT daily simulation scan for treatment planning. A pretreatment scan was also done to simulate patient setup and treatment. A 4D MRI scan was taken at each session for internal target volume (ITV) generation and assessment. For each volunteer a treatment plan was generated in the Raystation treatment planning system (TPS) following departmental protocols on the day one, first session dataset (D1S1), with bulk density overrides applied to enable dose calculation. This treatment plan was propagated through other imaging sessions, and the dose calculated. An additional treatment plan was generated on each first session of each day (S1) to simulate a daily replan process, with this plan propagated to the second session of the day. These accumulated mock treatment doses were assessed against the original treatment plan through DVH comparison of the PTV and OAR volumes. The generated ITV showed large variations when compared to both the first session ITV and daily ITV, with an average magnitude of 22.44% ± 13.28% and 25.83% ± 37.48% respectively. The PTV D95 was reduced by approximately 23.3% for both plan comparisons considered. Surrounding OARs had large variations in dose, with the small bowel V30 increasing by 128.87% when compared to the D1S1 plan, and 43.11% when compared to each daily S1 plan. Daily online adaptive radiotherapy is required for accurate dose delivery for pancreas cancer in the absence of additional motion management and tumour tracking techniques.

Shake It Off! Acoustic Manipulation of Lipid Vesicles for Mass Spectrometric Analysis.

Analyzing lipid assemblies, including liposomes and extracellular vesicles (EVs), is challenging due to their size, diverse composition, and tendency to aggregate. Such vesicles form with a simple phospholipid bilayer membrane, and they play important roles in drug discovery and delivery. The use of mass spectrometry (MS) allows for broad analysis of lipids from different classes; however, their release from the higher order structural aggregates is typically achieved by chemical means. Mechanical disruption by high frequency surface acoustic waves (SAW) is presented as an appealing alternative to preparing lipid vesicles for MS sampling. In this work, SAWs used to disrupt liposomes allow for the direct analysis of their constituent lipids by employing SAW nebulization with corona discharge (CD) ionization. We explore the effects of duration, frequency, and incorporation of nonpolar lipids, including cholesterol, on the SAW's ability to disrupt the liposome. We also report on the successful MS analysis of liposome-derived lipids along with cytochrome C in solution, thus demonstrating applications to aqueous samples and native MS conditions.

People living in homeless hostels: a survey of health and care needs.

People experiencing homelessness have extremely poor health outcomes and frequently die young. Many single homeless people live in hostels, the remit of which is to provide support to facilitate recovery out of homelessness. They are not usually designed to support people with high health or care needs. A cross-sectional survey was developed with, and completed by, hostel managers to explore and quantify the level of health and care needs of people living in their hostels. In total, 58 managers completed the survey, with information on 2,355 clients: 64% had substance use disorder, 56% had mental health issues, and 37.5% were in poor physical health. In addition, 5% had had more than three unplanned hospital visits in the previous month, and 11% had had safeguarding referrals submitted over the past year. Barriers to getting support and referrals accepted were highlighted, particularly for people with substance use disorder. Hostel managers identified 9% of clients as having needs too high for their service, while move-on options were scarce. Our study highlights significant unmet needs. Health and care services are not providing adequate support for many people living in hostels, who often have very poor health outcomes. This inequity needs to be considered and addressed as a matter of urgency.

Health and care needs of hospitalised people experiencing homelessness: an inpatient audit.

BACKGROUND: People experiencing homelessness frequently die young, from preventable and treatable conditions. They experience significant barriers to healthcare and are often critically ill when admitted to hospital. A hospital admission is an opportunity to intervene and prevent premature mortality by providing compassionate care and facilitating access to safe onward accommodation and support. METHODS: To quantify needs, a cross-sectional audit of inpatients experiencing homelessness across 15 acute hospital teams in London, was undertaken in February 2022. Integrated discharge and hospital homelessness teams were interviewed about each patient identified as homeless or vulnerably housed. Data was collected about patients' health, housing, support needs, and reasons for delayed discharges. RESULTS: Detailed information was gathered on 86 patients. There was a high level of clinical complexity and multimorbidity. For a safe discharge 60% of individuals were deemed to need accommodation providing high or medium level support and at the time of the audit, half were delayed discharges. CONCLUSION: There is an urgent need for a range of intermediate/step down and longer-term accommodation and support to enable safe appropriate discharge from hospital and start to address the huge inequity in health outcomes of this population. This paper includes recommendations for clinicians and commissioners.

A Review on Smartphone Keystroke Dynamics as a Digital Biomarker for Understanding Neurocognitive Functioning.

Can digital technologies provide a passive unobtrusive means to observe and study cognition outside of the laboratory? Previously, cognitive assessments and monitoring were conducted in a laboratory or clinical setting, allowing for a cross-sectional glimpse of cognitive states. In the last decade, researchers have been utilizing technological advances and devices to explore ways of assessing cognition in the real world. We propose that the virtual keyboard of smartphones, an increasingly ubiquitous digital device, can provide the ideal conduit for passive data collection to study cognition. Passive data collection occurs without the active engagement of a participant and allows for near-continuous, objective data collection. Most importantly, this data collection can occur in the real world, capturing authentic datapoints. This method of data collection and its analyses provide a more comprehensive and potentially more suitable insight into cognitive states, as intra-individual cognitive fluctuations over time have shown to be an early manifestation of cognitive decline. We review different ways passive data, centered around keystroke dynamics, collected from smartphones, have been used to assess and evaluate cognition. We also discuss gaps in the literature where future directions of utilizing passive data can continue to provide inferences into cognition and elaborate on the importance of digital data privacy and consent.

Understanding Users' Experiences of a Novel Web-Based Cognitive Behavioral Therapy Platform for Depression and Anxiety: Qualitative Interviews From Pilot Trial Participants.

BACKGROUND: Digital mental health interventions (DMHIs) can help bridge the gap between the demand for mental health care and availability of treatment resources. The affordances of DMHIs have been proposed to overcome barriers to care such as accessibility, cost, and stigma. Despite these proposals, most evaluations of the DMHI focus on clinical effectiveness, with less consideration of users' perspectives and experiences. OBJECTIVE: We conducted a pilot randomized controlled trial of "Overcoming Thoughts," a web-based platform that uses cognitive and behavioral principles to address depression and anxiety. The "Overcoming Thoughts" platform included 2 brief interventions-cognitive restructuring and behavioral experimentation. Users accessed either a version that included asynchronous interactions with other users ("crowdsourced" platform) or a completely self-guided version (control condition). We aimed to understand the users' perspectives and experiences by conducting a subset of interviews during the follow-up period of the trial. METHODS: We used purposive sampling to select a subset of trial participants based on group assignment (treatment and control) and symptom improvement (those who improved and those who did not on primary outcomes). We conducted semistructured interviews with 23 participants during the follow-up period that addressed acceptability, usability, and impact. We conducted a thematic analysis of the interviews until saturation was reached. RESULTS: A total of 8 major themes were identified: possible opportunities to expand the platform; improvements in mental health because of using the platform; increased self-reflection skills; platform being more helpful for certain situations or domains; implementation of skills into users' lives, even without direct platform use; increased coping skills because of using the platform; repetitiveness of platform exercises; and use pattern. Although no differences in themes were found among groups based on improvement status (all P values >.05, ranging from .12 to .86), there were 4 themes that differed based on conditions (P values from .01 to .046): helpfulness of self-reflection supported by an exercise summary (greater in control); aiding in slowing thoughts and feeling calmer (greater in control); overcoming patterns of avoidance (greater in control); and repetitiveness of content (greater in the intervention). CONCLUSIONS: We identified the different benefits that users perceived from a novel DMHI and opportunities to improve the platform. Interestingly, we did not note any differences in themes between those who improved and those who did not, but we did find some differences between those who received the control and intervention versions of the platform. Future research should continue to investigate users' experiences with DMHIs to better understand the complex dynamics of their use and outcomes.

Long-term outcomes of modern multidisciplinary management of sinonasal cancers: The M. D. Anderson experience.

PURPOSE: To report long-term outcomes of modern radiotherapy for sinonasal cancers. METHODS AND MATERIALS: A retrospective analysis of patients with sinonasal tumors treated with intensity-modulated radiotherapy or proton therapy. Multivariate analysis was used to determine predictive variables of progression free survival (PFS) and overall survival (OS). RESULTS: Three hundred and eleven patients were included, with median follow-up of 75 months. The most common histologies were squamous cell (42%), adenoid cystic (15%), and sinonasal undifferentiated carcinoma (15%). Induction chemotherapy was administered to 47% of patients; 68% had adjuvant radiotherapy. Ten-year local control, regional control, distant metastasis free survival, PFS, and overall survival rates were 73%, 88%, 47%, 32%, and 51%, respectively. Age, non-nasal cavity tumor site, T3-4 stage, neck dissection, and radiation dose were predictive of PFS, while age, non-nasal cavity tumor site, T3-4 stage, positive margins, neck dissection, and use of neoadjuvant chemotherapy were predictive of OS. There was a 13% rate of late grade ≥3 toxicities. CONCLUSION: This cohort of patients with sinonasal cancer treated with modern radiotherapy demonstrates favorable disease control rate and acceptable toxicity profile.

Digitally filling the access gap in mental health care: An investigation of the association between rurality and online engagement with validated self-report screens across the United States.

Mental health disorders are highly prevalent, yet few persons receive access to treatment; this is compounded in rural areas where mental health services are limited. The proliferation of online mental health screening tools are considered a key strategy to increase identification, diagnosis, and treatment of mental illness. However, research on real-world effectiveness, especially in hard to reach rural communities, is limited. Accordingly, the current work seeks to test the hypothesis that online screening use is greater in rural communities with limited mental health resources. The study utilized a national, online, population-based cohort consisting of Microsoft Bing search engine users across 18 months in the United States (representing approximately one-third of all internet searches), in conjunction with user-matched data of completed online mental health screens for anxiety, bipolar, depression, and psychosis (N = 4354) through Mental Health America, a leading non-profit mental health organization in the United States. Rank regression modeling was leveraged to characterize U.S. county-level screen completion rates as a function of rurality, health-care availability, and sociodemographic variables. County-level rurality and mental health care availability alone explained 42% of the variance in MHA screen completion rate (R2 = 0.42, p < 5.0 × 10-6). The results suggested that online screening was more prominent in underserved rural communities, therefore presenting as important tools with which to bridge mental health-care gaps in rural, resource-deficient areas.

The Prevalence and Determinants of NAFLD and MAFLD and Their Severity in the VA Primary Care Setting.

BACKGROUND & AIMS: A recent panel of international experts proposed the disease acronym metabolic (dysfunction)-associated fatty liver disease (MAFLD) in lieu of nonalcoholic fatty liver disease (NAFLD). We aimed to estimate the burden of and risk factors for NAFLD and MAFLD, and to examine the concordance between definitions in a Veterans population. METHODS: We conducted a cross-sectional study among randomly selected patients within primary care at the Houston Veterans Affairs (VA) facility. Participants completed a survey, provided blood, and underwent Fibroscan. In the absence of heavy alcohol, hepatitis C virus and hepatitis B virus, a controlled attenuation parameter median ≥290 dB/m was used to define NAFLD, whereas MAFLD was defined as controlled attenuation parameter median ≥290 dB/m and either body mass index ≥25 kg/m2 or diabetes, or 2 or more of the following: hypertension, high triglycerides, low high-density lipoprotein cholesterol, and high low-density lipoprotein cholesterol. RESULTS: The mean age of participants was 50.9 years, 55.4% were women, 42.8% were white, and 43.8% were Black. The prevalence of NAFLD was 40.6% (82/202). All 82 patients with NAFLD had a body mass index ≥25 kg/m2, and therefore met our criteria for MAFLD (ie, 100% concordance). Compared with patients with no metabolic trait, patients with ≥3 traits had a 48-fold (adjusted odds ratio, 47.6; 95% confidence interval, 11.3-200) higher risk of NAFLD/MAFLD. Overall, 19 participants (9.4% of the total, 15.9% of those with NAFLD) had at least moderate fibrosis. CONCLUSIONS: NAFLD was present in 40% of Veterans registered in primary care; 9.4% of veterans had at least moderate hepatic fibrosis, with most having concurrent NAFLD. There was perfect concordance between NAFLD and the alternative MAFLD definition.

Missed Opportunities for Screening or Surveillance Among Patients with Newly Diagnosed Non-cardia Gastric Adenocarcinoma.

BACKGROUND: Screening for gastric cancer is not recommended despite rising rates in certain U.S. POPULATIONS: We determined possible missed opportunities for the detection and surveillance of preneoplastic lesions among gastric cancer patients in a VA hospital. METHODS: This retrospective cohort study included consecutive, newly diagnosed non-cardia gastric adenocarcinoma patients from 11/2007 to 10/2018 at the Houston VA Hospital. We identified missed opportunities for screening based on risk factors (non-White race, smoking, alcohol, Helicobacter pylori infection, gastric ulcers, family history of gastric cancer). We additionally determined missed opportunities for surveillance of known high-risk lesions. Associations between receipt of prior endoscopy for screening or surveillance and cancer-related outcomes (stage, treatment, survival) were determined using logistic regression models. RESULTS: Among 91 gastric cancer patients, 95.6% were men, 51.6% were black, 12.1% were Hispanic, with mean age of 68.0 years (standard deviation 10.8 years). The most common risk factors included non-white race (68.1%), smoking (76.9%), alcohol use (59.3%) and prior H. pylori (12.1%). Most patients had ≥ 1 risk factor for gastric cancer (92.6%), and 76.9% had ≥ 2 risk factors. Only 25 patients (27.5%) had undergone endoscopy prior to cancer diagnosis. Of 14 with known high-risk lesions (i.e., gastric intestinal metaplasia, dysplasia, ulcer), only 2 (14.3%) underwent surveillance endoscopy. Receipt of prior endoscopy was not associated with differences in cancer outcomes. CONCLUSIONS: Most patients with newly diagnosed gastric cancer had ≥ 2 known risk factors for gastric cancer but never received prior screening endoscopy. Among the few with known prior preneoplastic lesions, endoscopic surveillance was not consistently performed.

System design of a text messaging program to support the mental health needs of non-treatment seeking young adults.

Young adults (ages 18-25) experience the highest levels of mental health problems of any adult age group, but have the lowest mental health treatment rates. Text messages are the most used feature on the mobile phone and provide an opportunity to reach non-treatment engaged users throughout the day in a conversational manner. We present the design of an automated text message-based intervention for symptom self-management. The intervention comprises: (1) psychological strategies (i.e., types of evidence-based techniques leveraged to achieve symptom reduction) and (2) interaction types or the form that intervention content takes as it is delivered to and elicited from users.