Predicting Barriers to Treatment for Depression in a U.S. National Sample: A Cross-Sectional, Proof-of-Concept Study

Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review

OBJECTIVES

This study underscores the importance of exploring AI's creative applications in treating depressive disorders to revolutionize mental health care. Through innovative integration of AI technologies, the research confirms their positive effects on preventing, diagnosing, and treating depression. The systematic review establishes an evidence base for AI in depression management, offering directions for effective interventions.

METHODS

This systematic literature review investigates the effectiveness of AI in depression management by analyzing studies from January 1, 2017, to May 31, 2022. Utilizing search engines like IEEE Xplore, PubMed, and Web of Science, the review focused on keywords such as Depression/Mental Health, Machine Learning/Artificial Intelligence, and Prediction/Diagnosis. The analysis of 95 documents involved classification based on use, data type, and algorithm type.

RESULTS

The study revealed that AI in depression management excelled in accuracy, particularly in monitoring and prediction. Biomarker-derived data demonstrated the highest accuracy, with the CNN algorithm proving most effective. The findings affirm the therapeutic benefits of AI, including treatment, detection, and disease prediction, highlighting its potential in analyzing monitored data for depression management.

LIMITATIONS

This study exclusively examined the application of AI in individuals with depressive disorders. Interpretation should be cautious due to the limited scope of subjects to this specific population.

CONCLUSIONS

To introduce digital healthcare and therapies for ongoing depression management, it's crucial to present empirical evidence on the medical fee payment system, safety, and efficacy. These findings support enhanced medical accessibility through digital healthcare, offering personalized disease management for patients seeking non-face-to-face treatment.

Symptom Reduction and Engagement in a Cognitive-Behavioral Mobile Phone App: A Study of User Profiling to Determine Prognostic Indicators

OBJECTIVE

We investigated the presence of latent transition profiles in a sample of users of a cognitive-behavioral mental health app for the general population. Users' baseline characteristics were used as predictors of the profiles. The role of engagement with the app in the transition profiles was examined.

METHOD

A total of 541 users completed the Patient Health Questionnaire-9 and the General Anxiety Disorder-7 when started using the app and 30 days after. Random-Intercept Latent Transition Analysis was implemented to identify users' profiles and transition patterns as classes. The age of the users and the Emotional Self-Awareness Scale-Revised (ESAS-R) were used as predictors of class membership at baseline. The Homework Rating Scale-Mobile Application (HRS-MA; as a measure of engagement) was used as a predictor of class membership at 30 days of app use.

RESULTS

A 3-class solution was obtained according to the severity of symptoms (from mild to moderately severe). Age and ESAS-R predicted class membership initially; the higher the age and ESAS-R, the higher the probability of starting using the app with lower distress levels. The HRS-MA predicted class membership at 30 days of app use; the higher the engagement for more symptomatic and younger users, the higher the probability of improvement. However, older users tended to engage less.

CONCLUSION

Our findings underpin the relevance of easily accessible digital interventions for young adults with mild to moderate mental health problems. Further studies and developments are required to enhance these apps for older cohorts.

Longitudinal outcomes of a therapist-supported digital mental health intervention for depression and anxiety symptoms: A retrospective cohort study

PURPOSE

This study examined treatment outcomes (depression and anxiety symptoms) up to 24 months after completion of a therapist-supported digital mental health intervention (DMHI).

METHODS

The sample consisted of 380 participants who participated in an eight-week DMHI from February 6, 2017 to May 20, 2019. Participants reported depression and anxiety symptoms at eight timepoints from baseline to 24 months. Mixed-effects modelling was used to investigate symptom changes over time. The proportion of participants meeting criteria for treatment response, clinically significant change, and remission of depression and anxiety symptoms were calculated, including proportions demonstrating each outcome sustained up to each timepoint.

RESULTS

Multivariate analyses yielded statistically significant reductions in depression (β = -5.40) and anxiety (β = -3.31) symptoms from baseline to end of treatment (8 weeks). Symptom levels remained significantly reduced from baseline through 24 months. The proportion of participants meeting criteria for clinical treatment outcomes remained constant over 24 months, although there were linear decreases in the proportions experiencing sustained clinical outcomes.

CONCLUSIONS

Treatment gains were made for depression and anxiety symptoms at the end of treatment and up to 24 months. Future studies should determine the feasibility of integrating post-treatment programmes into DMHIs to address symptom deterioration.

Illusory generalizability of clinical prediction models

It is widely hoped that statistical models can improve decision-making related to medical treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically based on investigators observing a model's success in one or two datasets or clinical contexts. We scrutinized this optimism by examining how well a machine learning model performed across several independent clinical trials of antipsychotic medication for schizophrenia. Models predicted patient outcomes with high accuracy within the trial in which the model was developed but performed no better than chance when applied out-of-sample. Pooling data across trials to predict outcomes in the trial left out did not improve predictions. These results suggest that models predicting treatment outcomes in schizophrenia are highly context-dependent and may have limited generalizability.

Efficacy of stand-alone digital mental health applications for anxiety and depression: A meta-analysis of randomized controlled trials

BACKGROUND

Anxiety and depressive disorders affect 20% of the population, cause functional impairment, and represent a leading cause of disability. Although evidence-based treatments exist, the shortage of trained clinicians and high demand for mental health services have resulted in limited access to evidence-based care. Digital mental health applications (DMHA) present innovative, scalable, and sustainable solutions to address disparities in mental health care.

METHODS

The present study used meta-analytic techniques to evaluate the therapeutic effect of DMHAs in randomized controlled trials (RCTs) for individuals experiencing anxiety and/or depressive symptoms. Search terms were selected based on concepts related to digital mental health applications, mental health/wellness, intervention type, trial design, and anxiety and/or depression symptoms/diagnosis outcomes to capture all potentially eligible results. Potential demographic, DMHA, and trial design characteristics were examined as moderators of therapeutic effects.

RESULTS

Random effects meta-analyses found that stand-alone DMHAs produced a modest reduction in anxiety (g = 0.31) and depressive (g = 0.35) symptom severity. Several moderators influenced the therapeutic effects of DMHAs for anxiety and/or depressive symptoms including treatment duration, participant inclusion criteria, and outcome measures.

LIMITATIONS

Minimal information was available on DMHA usability and participant engagement with DMHAs within RCTs.

CONCLUSIONS

While DMHAs have the potential to be scalable and sustainable solutions to improve access and availability of evidence-based mental healthcare, moderator analyses highlight the considerations for implementation of DMHAs in practice. Further research is needed to understand factors that influence therapeutic effects of DMHAs and investigate strategies to optimize its implementation and overcome the extant research-to-practice gap.

Framing Effects of Cognitive Behavioural Therapy for Depression on Perceptions of Believability, Acceptability, and Credibility

While CBT is an effective treatment for depression, uptake can be low. This is largely due to attitudinal barriers. Accordingly, the goals of the current investigation were to (a) tailor and develop persuasive psychoeducational materials to match dominant cultural beliefs about the causes of depression and (b) examine the effectiveness of tailored CBT descriptions in improving CBT perceptions. We examined the believability of CBT mechanisms by invoking commonly endorsed etiological models of depression and investigated whether tailoring CBT descriptions to match etiological beliefs about depression influences perceptions of CBT. Participants were recruited using TurkPrime. In Study 1, participants (n = 425) read a CBT description that was generic or framed to match an etiological model of depression (biological, stress/environmental, or relationship/interpersonal). The participants indicated believability of each model as adopted by CBT. In study 2, the participants (n = 449) selected what they believed was the most important cause of depression. Subsequently, the participants were randomised to receive either a CBT description tailored to their endorsed model or a generic CBT description, and they provided ratings for CBT's acceptability, credibility, and expectancy. In Study 1, the believability of biological CBT mechanisms was low across conditions, but participants reported greater believability when receiving a biological description than when receiving other mechanistic descriptions. Participants who received the stress- and relationship-focused descriptions did not rate the respective models as more believable than those who received a generic description. In study 2, there were no differences in the perceptions of acceptability, credibility and expectancy between participants who received a tailored description and those who received a generic description. Our findings suggest that CBT is believed to be a psychologically appropriate treatment; however, the believability of biological mechanisms is improved by presenting a biology-focused description.

Public mental health during and after the SARS-CoV-2 pandemic: Opportunities for intervention via emotional self-efficacy and resilience

Importance

During the pandemic, the number of United States adults reporting clinically significant symptoms of anxiety and depression sky-rocketed, up from 11% in 2020 to more than 40% in 2021. Our current mental healthcare system cannot adequately accommodate the current crisis; it is therefore important to identify opportunities for public mental health interventions.

Objective

Assess whether modifiable emotional factors may offer a point of intervention for the mental health crisis.

Design setting and participants

From January 13 to 15, 2022, adults living in the United States were recruited via Amazon Mechanical Turk to complete an anonymous survey.

Main outcomes and measures

Linear regressions tested whether the primary outcomes during the SARS-CoV-2 pandemic (depressive and anxiety symptoms, burnout) were associated with hypothesized modifiable risk factors (loneliness and need for closure) and hypothesized modifiable protective factors (the ability to perceive emotions and connect with others emotionally; emotion-regulation efficacy; and resilience, or the ability to "bounce back" after negative events).

Results

The sample included 1,323 adults (mean [SD] age 41.42 [12.52] years; 636 women [48%]), almost half of whom reported clinically significant depressive (29%) and/or anxiety (15%) symptoms. Approximately 90% of participants indicated feeling burned out at least once a year and nearly half of participants (45%) felt burned out once a week or more. In separate analyses, depressive symptoms (Model A), anxiety symptoms (Model B), and burnout (Model C) were statistically significantly associated with loneliness (βModel A, 0.38; 95% CI, 0.33-0.43; βModel B, 0.30; 95% CI, 0.26-0.36; βModel C, 0.34; 95% CI, 0.28-0.41), need for closure (βModel A, 0.09; 95% CI, 1.03-1.06; βModel B, 0.13; 95% CI, 0.97-0.17; βModel C, 0.11; 95% CI, 0.07-0.16), recent stressful life events (βModel A, 0.14; 95% CI, 0.10-0.17; βModel B, 0.14; 95% CI, 0.11-0.18; βModel C, 0.10; 95% CI, 0.06-0.15), and resilience (βModel A, -0.10; 95% CI, -0.15 to -0.05; βModel B, -0.18; 95% CI, -0.23 to -0.13; βModel C, -0.11; 95% CI, -0.17 to -0.05). In addition, depressive and anxiety symptoms were associated with emotional self-efficacy (βModel A, -0.17; 95% CI, -0.22 to -0.12; βModel B, -0.11; 95% CI, -0.17 to -0.06), and beliefs about the malleability of emotions (βModel A, -0.08; 95% CI, -0.12 to -0.03; βModel B, -0.09; 95% CI, -0.13 to -0.04). Associations between loneliness and symptoms were weaker among those with more emotional self-efficacy, more endorsement of emotion malleability beliefs, and greater resilience, in separate models. Analyses controlled for recent stressful life events, optimism, and social desirability.

Conclusion and relevance

Public mental health interventions that teach resilience in response to negative events, emotional self-efficacy, and emotion-regulation efficacy may protect against the development of depressive symptoms, anxiety, and burnout, particularly in the context of a collective trauma. Emotional self-efficacy and regulation efficacy may mitigate the association between loneliness and mental health, but loneliness prevention research is also needed to address the current mental health crisis.

The Seeking Mental Health Care model: prediction of help-seeking for depressive symptoms by stigma and mental illness representations

BACKGROUND

Only about half the people with depression seek professional health care services. To constitute the different predictors and associating variables of health care utilisation, we model the process and aim to test our hypothesised Seeking Mental Health Care Model. The model includes empirical influences on the help-seeking process to predict actual behaviour and incorporates superordinate (stigma, treatment experiences) as well as intermediate attitudinal variables (continuum and causal beliefs, depression literacy and self-efficacy).

METHOD

All variables are examined in an online study (baseline, three- and six-month follow-up). The sample consisted of adults with depressive symptoms (PHQ-9 sum score ≥ 8), currently not receiving mental health care treatment. To examine the prediction of variables explaining help-seeking behaviour, a path model analysis was carried out (lavaan package, software R).

RESULTS

Altogether, 1368 participants (M_age = 42.38, SD_age = 15.22, 65.6% female) were included, 983 participating in at least one follow-up. Model fit was excellent (i.e., RMSEA = 0.059, CFI = 0.989), and the model confirmed most of the hypothesised predictions. Intermediary variables were significantly associated with stigma and experiences. Depression literacy (ß = .28), continuum beliefs (ß = .11) and openness to a balanced biopsychosocial causal model (ß = .21) significantly influenced self-identification (R² = .35), which among the causal beliefs and self-efficacy influenced help-seeking intention (R² = .10). Intention (ß = .40) prospectively predicted help-seeking behaviour (R² = .16).

CONCLUSION

The Seeking Mental Health Care Model provides an empirically validated conceptualisation of the help-seeking process of people with untreated depressive symptoms as a comprehensive approach considering internal influences. Implications and open questions are discussed, e.g., regarding differentiated assessment of self-efficacy, usefulness of continuum beliefs and causal beliefs in anti-stigma work, and replication of the model for other mental illnesses.

TRIAL REGISTRATION

German Clinical Trials Register: DRKS00023557. Registered 11 December 2020. World Health Organization, Universal Trial Number: U1111-1264-9954. Registered 16 February 2021.

Alternative metrics for characterizing longer-term clinical outcomes in difficult-to-treat depression: I. Association with change in quality of life

BACKGROUND

In difficult-to-treat depression (DTD) the outcome metrics historically used to evaluate treatment effectiveness may be suboptimal. Metrics based on remission status and on single end-point (SEP) assessment may be problematic given infrequent symptom remission, temporal instability, and poor durability of benefit in DTD.

METHODS

Self-report and clinician assessment of depression symptom severity were regularly obtained over a 2-year period in a chronic and highly treatment-resistant registry sample (N = 406) receiving treatment as usual, with or without vagus nerve stimulation. Twenty alternative metrics for characterizing symptomatic improvement were evaluated, contrasting SEP metrics with integrative (INT) metrics that aggregated information over time. Metrics were compared in effect size and discriminating power when contrasting groups that did (N = 153) and did not (N = 253) achieve a threshold level of improvement in end-point quality-of-life (QoL) scores, and in their association with continuous QoL scores.

RESULTS

Metrics based on remission status had smaller effect size and poorer discrimination of the binary QoL outcome and weaker associations with the continuous end-point QoL scores than metrics based on partial response or response. The metrics with the strongest performance characteristics were the SEP measure of percentage change in symptom severity and the INT metric quantifying the proportion of the observation period in partial response or better. Both metrics contributed independent variance when predicting end-point QoL scores.

CONCLUSIONS

Revision is needed in the metrics used to quantify symptomatic change in DTD with consideration of INT time-based measures as primary or secondary outcomes. Metrics based on remission status may not be useful.

Depression, anxiety, and stress among university students in Selangor, Malaysia during COVID-19 pandemics and their associated factors

INTRODUCTION

This study aims to assess the impacts of COVID-19 pandemics among university students in Malaysia, by identifying the prevalence of depression, anxiety and stress among them and their respective predictors.

METHODOLOGY

An online cross-sectional study was conducted via non-probabilistic convenience sampling. Data were collected on sociodemographic characteristics, lifestyle, COVID-19 related influences. Mental health status was assessed with depression, anxiety, and stress scale (DASS-21).

RESULTS

388 students participated this study (72.4% female; 81.7% Bachelor's student). The prevalence of moderate to severe depression, anxiety and stress among university students are 53.9%, 66.2% and 44.6%, respectively. Multivariable logistic regression analysis found that the odds of depression were lower among students who exercise at least 3 times per week (OR: 0.380, 95% CI: 0.203-0.711). The odd ratio of student who had no personal history of depression to had depression, anxiety and stress during this pandemic was also lower in comparison (OR: 0.489, 95% CI: 0.249-0.962; OR: 0.482, 95% CI: 0.241-0.963; OR: 0.252, 95% CI: 0.111-0.576). Surprisingly, students whose are currently pursuing Master study was associated with lower stress levels (OR: 0.188, 95% CI: 0.053-0.663). However, student who had poorer satisfaction of current learning experience were more likely to experience stress (OR: 1.644, 95% CI: 1.010-2.675).

LIMITATIONS

It is impossible to establish causal relationships between variables on mental health outcomes, and there is a risk of information bias.

CONCLUSION

The prevalence of mental health issues among university students is high. These findings present essential pieces of predictive information when promoting related awareness among them.

Clinical Need, Perceived Need, and Treatment Use: Estimating Unmet Need for Mental Health Services in the Adult Population

Estimates of unmet need for mental health services in the adult population are too high because many recover without treatment. Untreated recovery suggests that individuals accurately perceive professional help as unnecessary and do not pursue it. If so, perceived need for treatment should predict service use/nonuse more strongly than the presence or seriousness of disorder. With National Comorbidity Survey-Replication data, respondents who recovered from prior disorder by the current year (N = 1,054) were compared to currently unrecovered respondents with less serious (N = 999) and more serious disorders (N = 294). Perceived need covaried positively with the presence and seriousness of disorder and linked to far higher odds of treatment use than disorder seriousness, supporting perceptual accuracy. Two-thirds of respondents who perceived a treatment need obtained care; only one-third had unmet need. Need perceptions may better estimate a treatment gap and prompt research on individuals' self-assessments and treatment decision-making.

Predictors of Dropout in a Digital Intervention for the Prevention and Treatment of Depression in Patients With Chronic Back Pain: Secondary Analysis of Two Randomized Controlled Trials

Background

Depression is a common comorbid condition in individuals with chronic back pain (CBP), leading to poorer treatment outcomes and increased medical complications. Digital interventions have demonstrated efficacy in the prevention and treatment of depression; however, high dropout rates are a major challenge, particularly in clinical settings.

Objective

This study aims to identify the predictors of dropout in a digital intervention for the treatment and prevention of depression in patients with comorbid CBP. We assessed which participant characteristics may be associated with dropout and whether intervention usage data could help improve the identification of individuals at risk of dropout early on in treatment.

Methods

Data were collected from 2 large-scale randomized controlled trials in which 253 patients with a diagnosis of CBP and major depressive disorder or subclinical depressive symptoms received a digital intervention for depression. In the first analysis, participants’ baseline characteristics were examined as potential predictors of dropout. In the second analysis, we assessed the extent to which dropout could be predicted from a combination of participants’ baseline characteristics and intervention usage variables following the completion of the first module. Dropout was defined as completing <6 modules. Analyses were conducted using logistic regression.

Results

From participants’ baseline characteristics, lower level of education (odds ratio [OR] 3.33, 95% CI 1.51-7.32) and both lower and higher age (a quadratic effect; age: OR 0.62, 95% CI 0.47-0.82, and age²: OR 1.55, 95% CI 1.18-2.04) were significantly associated with a higher risk of dropout. In the analysis that aimed to predict dropout following completion of the first module, lower and higher age (age: OR 0.60, 95% CI 0.42-0.85; age²: OR 1.59, 95% CI 1.13-2.23), medium versus high social support (OR 3.03, 95% CI 1.25-7.33), and a higher number of days to module completion (OR 1.05, 95% CI 1.02-1.08) predicted a higher risk of dropout, whereas a self-reported negative event in the previous week was associated with a lower risk of dropout (OR 0.24, 95% CI 0.08-0.69). A model that combined baseline characteristics and intervention usage data generated the most accurate predictions (area under the receiver operating curve [AUC]=0.72) and was significantly more accurate than models based on baseline characteristics only (AUC=0.70) or intervention usage data only (AUC=0.61). We found no significant influence of pain, disability, or depression severity on dropout.

Conclusions

Dropout can be predicted by participant baseline variables, and the inclusion of intervention usage variables may improve the prediction of dropout early on in treatment. Being able to identify individuals at high risk of dropout from digital health interventions could provide intervention developers and supporting clinicians with the ability to intervene early and prevent dropout from occurring.

Feasibility, engagement, and preliminary clinical outcomes of a digital biodata-driven intervention for anxiety and depression

Hypothesis

The main hypothesis is that a digital, biodata-driven, and personalized program would exhibit high user retention and engagement, followed by more effective management of their depressive and anxiety symptoms.

Objective

This pilot study explores the feasibility, acceptability, engagement, and potential impact on depressive and anxiety and quality of life outcomes of the 16-week Feel Program. Additionally, it examines potential correlations between engagement and impact on mental health outcomes.

Methods

This single-arm study included 48 adult participants with mild or moderate depressive or anxiety symptoms who joined the 16-week Feel Program, a remote biodata-driven mental health support program created by Feel Therapeutics. The program uses a combination of evidence-based approaches and psychophysiological data. Candidates completed an online demographics and eligibility survey before enrolment. Depressive and anxiety symptoms were measured using the Patient Health Questionnaire and Generalized Anxiety Disorder Scale, respectively. The Satisfaction with Life Scale and the Life Satisfaction Questionnaire were used to assess quality of life. User feedback surveys were employed to evaluate user experience and acceptability.

Results

In total, 31 participants completed the program with an overall retention rate of 65%. Completed participants spent 60 min in the app, completed 13 Mental Health Actions, including 5 Mental Health Exercises and 4.9 emotion logs on a weekly basis. On average, 96% of the completed participants were active and 76.8% of them were engaged with the sensor during the week. Sixty five percent of participants reported very or extremely high satisfaction, while 4 out of 5 were very likely to recommend the program to someone. Additionally, 93.5% of participants presented a decrease in at least one of the depressive or anxiety symptoms, with 51.6 and 45% of participants showing clinically significant improvement, respectively. Finally, our findings suggest increased symptom improvement for participants with higher engagement throughout the program.

Conclusions

The findings suggest that the Feel Program may be feasible, acceptable, and valuable for adults with mild or moderate depressive and/or anxiety symptoms. However, controlled trials with bigger sample size, inclusion of a control group, and more diverse participant profiles are required in order to provide further evidence of clinical efficacy.

Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study

Background

In 2017, an estimated 17.3 million adults in the United States experienced at least one major depressive episode, with 35% of them not receiving any treatment. Underdiagnosis of depression has been attributed to many reasons, including stigma surrounding mental health, limited access to medical care, and barriers due to cost.

Objective

This study aimed to determine if low-burden personal health solutions, leveraging person-generated health data (PGHD), could represent a possible way to increase engagement and improve outcomes.

Methods

Here, we present the development of PSYCHE-D (Prediction of Severity Change-Depression), a predictive model developed using PGHD from more than 4000 individuals, which forecasts the long-term increase in depression severity. PSYCHE-D uses a 2-phase approach. The first phase supplements self-reports with intermediate generated labels, and the second phase predicts changing status over a 3-month period, up to 2 months in advance. The 2 phases are implemented as a single pipeline in order to eliminate data leakage and ensure results are generalizable.

Results

PSYCHE-D is composed of 2 Light Gradient Boosting Machine (LightGBM) algorithm–based classifiers that use a range of PGHD input features, including objective activity and sleep, self-reported changes in lifestyle and medication, and generated intermediate observations of depression status. The approach generalizes to previously unseen participants to detect an increase in depression severity over a 3-month interval, with a sensitivity of 55.4% and a specificity of 65.3%, nearly tripling sensitivity while maintaining specificity when compared with a random model.

Conclusions

These results demonstrate that low-burden PGHD can be the basis of accurate and timely warnings that an individual’s mental health may be deteriorating. We hope this work will serve as a basis for improved engagement and treatment of individuals experiencing depression.

Effects of decision aids for depression treatment in adults: systematic review

AIM AND METHOD

To determine the effect on decisional-related and clinical outcomes of decision aids for depression treatment in adults in randomised clinical trials. In January 2019, a systematic search was conducted in five databases. Study selection and data extraction were performed in duplicate. Meta-analyses were performed, and standardised and weighted mean differences were calculated, with corresponding 95% confidence intervals. The certainty of the evidence was evaluated with GRADE methodology.

RESULTS

Six randomised clinical trials were included. The pooled estimates showed that decision aids for depression treatment had a beneficial effect on patients' decisional conflict, patient knowledge and information exchange between patient and health professional. However, no statistically significant effect was found for doctor facilitation, treatment adherence or depressive symptoms. The certainty of the evidence was very low for all outcomes.

CLINICAL IMPLICATIONS

Using decision aids to choose treatment in patients with depression may have a a beneficial effect on decisional-related outcomes, but it may not translate into an improvement in clinical outcomes.

Sociodemographic factors associated with treatment-seeking and treatment receipt: cross-sectional analysis of UK Biobank participants with lifetime generalised anxiety or major depressive disorder

Background

Anxiety and depressive disorders can be chronic and disabling. Although there are effective treatments, only a fraction of those impaired receive treatment. Predictors of treatment-seeking and treatment receipt could be informative for initiatives aiming to tackle the burden of untreated anxiety and depression.

Aims

To investigate sociodemographic characteristics associated with treatment-seeking and treatment receipt.

Method

Two binary retrospective reports of lifetime treatment-seeking (n = 44 810) and treatment receipt (n = 37 346) were regressed on sociodemographic factors (age, gender, UK ethnic minority background, educational attainment, household income, neighbourhood deprivation and social isolation) and alternative coping strategies (self-medication with alcohol/drugs and self-help) in UK Biobank participants with lifetime generalised anxiety or major depressive disorder. Analyses were also stratified by gender.

Results

Treatment access was more likely in those who reported use of self-help strategies, with university-level education and those from less economically advantaged circumstances (household income <£30 000 and greater neighbourhood deprivation). Treatment access was less likely in those who were male, from a UK ethnic minority background and with high household incomes (>£100 000). Men who self-medicated and/or had a vocational qualification were also less likely to seek treatment.

Conclusions

This work on retrospective reports of treatment-seeking and treatment receipt at any time of life replicates known associations with treatment-seeking and treatment receipt during time of treatment need. More work is required to understand whether improving rates of treatment-seeking improves prognostic outcomes for individuals with anxiety or depression.

A pilot study of participatory and rapid implementation approaches to increase depression screening in primary care

BACKGROUND

Most individuals with depression go unidentified and untreated. In 2016 the US Preventive Services Task Force released guidelines recommending universal screening in primary care to identify patients with depression and to link them to treatment. Feasible, acceptable, and effective strategies to implement these guidelines are needed.

METHODS

This three-phased study employed rapid participatory methods to design and test strategies to increase depression screening at Penn Medicine, a large health system with 90 primary care practices. First, researchers solicited ideas and barriers from stakeholders to increase screening using an innovation tournament-a crowdsourcing method that invites stakeholders to submit ideas to address a workplace challenge. Second, a panel of stakeholders and scientists deliberated over and ranked the tournament ideas. An instant runoff election was held to select the winning idea. Third, the research team piloted the winning idea in a primary care practice using rapid prototyping, an approach that quickly refines and iterates strategy designs.

RESULTS

The innovation tournament yielded 31 ideas and 32 barriers from diverse stakeholders (12 primary care physicians, 10 medical assistants, 4 nurse practitioners, 2 practice managers, and 4 patient support assistants). A panel of 6 stakeholders and scientists deliberated on the ideas and voted for patient self-report (i.e., through tablet computers, text message, or an online patient portal) as the winning idea. The research team rapid prototyped tablets in one primary care practice with one physician over 5 five-hour shifts to examine the feasibility, acceptability, and effectiveness of the strategy. Most patients, the physician, and medical assistants found the tablets acceptable and feasible. However, patient support assistants struggled to incorporate them in their workflow and expressed concerns about scaling up the process. Depression screening rates were higher using tablets compared to usual care; follow-up was comparable between tablets and usual care.

CONCLUSIONS

Rapid participatory methods engaged and amplified the voices of diverse stakeholders in primary care. These methods helped design an acceptable and feasible implementation strategy that showed promise for increasing depression screening in a primary care setting. The next step is to evaluate the strategy in a randomized controlled trial across primary care practices.

Cluster Analysis of Care Pathways in Adults with Major Depressive Disorder with Acute Suicidal Ideation or Behavior in the USA

BACKGROUND AND OBJECTIVE

Suicidal ideation or behavior are core symptoms of major depressive disorder (MDD). This study aimed to understand heterogeneity among patients with MDD and acute suicidal ideation or behavior.

METHODS

Adults with a diagnosis of MDD on the same day or 6 months before a claim for suicidal ideation or behavior (index date) were identified in the MarketScan^® Databases (10/01/2014-04/30/2019). A mathematical algorithm was used to cluster patients on characteristics of care measured pre-index. Patient care pathways were described by cluster during the 12-month pre-index period and up to 12 months post-index.

RESULTS

Among 38,876 patients with MDD and acute suicidal ideation or behavior, three clusters were identified. Across clusters, pre-index exposure to mental healthcare was revealed as a key differentiator: Cluster 1 (N = 16,025) was least exposed, Cluster 2 (N = 5640) moderately exposed, and Cluster 3 (N = 17,211) most exposed. Patients whose MDD diagnosis was first observed during their index event comprised 86.0% and 72.8% of Clusters 1 and 2, respectively; in Cluster 3, all patients had an MDD diagnosis pre-index. Within 30 days post-index, in Clusters 1, 2, and 3, respectively, 79.3%, 85.2%, and 88.2% used mental health services, including outpatient visits for MDD. Within 12 months post-index, 61.5%, 91.5%, and 84.6% had one or more antidepressant claim, respectively. Per-patient index event costs averaged $5614, $6645, and $5853, respectively.

CONCLUSIONS

Patients with MDD and acute suicidal ideation or behavior least exposed to the healthcare system pre-index similarly received the least care post-index. An opportunity exists to optimize treatment and follow-up with mental health services.

Including Mental Health Screenings in Annual Wellness Programs

Mental health is an important component of overall wellness and is a growing concern in occupational settings. Approximately one half of Americans will experience a mental health disorder at some time in their life. The current descriptive correlational study used a convenience sample of manufacturing employees (N = 236) to examine the association of mental and physical health risks collected during an annual wellness program. A researcher-developed questionnaire was used to holistically screen for health risks. Pearson's r and chi-square tests were performed to determine the relationship among variables. Younger workers and individuals with higher body mass index had increased anxiety and depression scores (p = 0.005). Results suggest younger workers may have increased risk for mental health and biometabolic disorders. Due to the connections between mental and physical health, screening for anxiety and depression should be included in annual worker wellness programs to potentially improve overall health and wellness outcomes. [Journal of Psychosocial Nursing and Mental Health Services, xx(xx), xx-xx.].

Evaluating Commercially Available Mobile Apps for Depression Self-Management

Clinical depression affects 17.3 million adults in the U.S. However, 37% of these adults receive no treatment, and many symptoms remain unmanaged. Mobile health apps may complement in-person treatment and address barriers to treatment, yet their quality has not been systematically appraised. We conducted a systematic review of apps for depression by searching in three major app stores. Apps were selected using specific inclusion and exclusion criteria. The final apps were downloaded and independently evaluated using the Mobile Application Rating Scale (MARS), IMS Institute for Healthcare Informatics functionality score, and six features specific to depression self-management. Mobile health apps for depression self-management exhibit a wide range of quality, but more than half (74%) of the apps had acceptable quality, with 32% having MARS scores ≥ 4.0 out of 5.0. These high scoring apps indicate that mobile apps have the potential to improve patient self-management, treatment engagement, and mental health outcomes.

Digital Interventions for Mental Disorders: Key Features, Efficacy, and Potential for Artificial Intelligence Applications

Mental disorders are highly prevalent and often remain untreated. Many limitations of conventional face-to-face psychological interventions could potentially be overcome through Internet-based and mobile-based interventions (IMIs). This chapter introduces core features of IMIs, describes areas of application, presents evidence on the efficacy of IMIs as well as potential effect mechanisms, and delineates how Artificial Intelligence combined with IMIs may improve current practices in the prevention and treatment of mental disorders in adults. Meta-analyses of randomized controlled trials clearly show that therapist-guided IMIs can be highly effective for a broad range of mental health problems. Whether the effects of unguided IMIs are also clinically relevant, particularly under routine care conditions, is less clear. First studies on IMIs for the prevention of mental disorders have shown promising results. Despite limitations and challenges, IMIs are increasingly implemented into routine care worldwide. IMIs are also well suited for applications of Artificial Intelligence and Machine Learning, which provides ample opportunities to improve the identification and treatment of mental disorders. Together with methodological innovations, these approaches may also deepen our understanding of how psychological interventions work, and why. Ethical and professional restraints as well as potential contraindications of IMIs, however, should also be considered. In sum, IMIs have a high potential for improving the prevention and treatment of mental health disorders across various indications, settings, and populations. Therefore, implementing IMIs into routine care as both adjunct and alternative to face-to-face treatment is highly desirable. Technological advancements may further enhance the variability and flexibility of IMIs, and thus even further increase their impact in people's lives in the future.