Introduction: Digital technologies, including text messaging and mobile phone apps, can be leveraged to increase people's physical activity and manage health. Chatbots, powered by artificial intelligence, can automatically interact with individuals through natural conversation. They may be more engaging than one-way messaging interventions. To our knowledge, physical activity chatbots have not been developed with low-income participants, nor in Spanish—the second most dominant language in the U.S. We recommend best practices for physical activity chatbots in English and Spanish for low-income women. Methods: We designed a prototype physical activity text-message based conversational agent based on various psychotherapeutic techniques. We recruited participants through SNAP-Ed (Supplemental Nutrition Assistance Program Education) in California (Alameda County) and Tennessee (Shelby County). We conducted qualitative interviews with participants during testing of our prototype chatbot, held a Wizard of Oz study, and facilitated a co-design workshop in Spanish with a subset of our participants. Results: We included 10 Spanish- and 8 English-speaking women between 27 and 41 years old. The majority was Hispanic/Latina (n = 14), 2 were White and 2 were Black/African American. More than half were monolingual Spanish speakers, and the majority was born outside the US (>50% in Mexico). Most participants were unfamiliar with chatbots and were initially skeptical. After testing our prototype, most users felt positively about health chatbots. They desired a personalized chatbot that addresses their concerns about privacy, and stressed the need for a comprehensive system to also aid with nutrition, health information, stress, and involve family members. Differences between English and monolingual Spanish speakers were found mostly in exercise app use, digital literacy, and the wish for family inclusion. Conclusion: Low-income Spanish- and English-speaking women are interested in using chatbots to improve their physical activity and other health related aspects. Researchers developing health chatbots for this population should focus on issues of digital literacy, app familiarity, linguistic and cultural issues, privacy concerns, and personalization. Designing and testing this intervention for and with this group using co-creation techniques and involving community partners will increase the probability that it will ultimately be effective.@en

Digital health, including the use of mobile health apps, telemedicine, and data analytics to improve health systems, has surged during the COVID-19 pandemic. The social and economic fallout from COVID-19 has further exacerbated gender inequities, through increased domestic violence against women, soaring unemployment rates in women, and increased unpaid familial care taken up by women—all factors that can worsen women's health. Digital health can bolster gender equity through increased access to health care, empowerment of one's own health data, and reduced burden of unpaid care work. Yet, digital health is rarely designed from a gender equity perspective. In this Viewpoint, we show that because of lower access and exclusion from app design, gender imbalance in digital health leadership, and harmful gender stereotypes, digital health is disadvantaging women—especially women with racial or ethnic minority backgrounds. Tackling digital health's gender inequities is more crucial than ever. We explain our feminist intersectionality framework to tackle digital health's gender inequities and provide recommendations for future research.@en

Introduction: Cognitive behavioral therapy (CBT) is an established treatment for depression, but its success is often impeded by low attendance. Supportive text messages assessing participants' mood in between sessions might increase attendance to in-clinic CBT, although it is not fully understood who benefits most from these interventions and how. This study examined (1) user groups showing different profiles of study engagement and (2) associations between increased response rates to mood texts and psychotherapy attendance. Methods: We included 73 participants who attended Group CBT (GCBT) in a primary care clinic and participated in a supportive automated text-messaging intervention. Using unsupervised machine learning, we identified and characterized subgroups with similar combinations of total texting responsiveness and total GCBT attendance. We used mixed-effects models to explore the association between increased previous week response rate and subsequent week in-clinic GCBT attendance and, conversely, response rate following attendance. Results: Participants could be divided into four clusters of overall study engagement, showing distinct profiles in age and prior texting knowledge. The response rate to texts in the week before GCBT was not associated with GCBT attendance, although the relationship was moderated by age; there was a positive relationship for younger, but not older, participants. Attending GCBT was, however, associated with higher response rate the week after an attended session. Conclusion: User groups of study engagement differ in texting knowledge and age. Younger participants might benefit more from supportive texting interventions when their purpose is to increase psychotherapy attendance. Our results have implications for tailoring digital interventions to user groups and for understanding therapeutic effects of these interventions. @en

Background: Regular physical activity (PA) is crucial for well-being; however, healthy habits are difficult to create and maintain. Interventions delivered via conversational agents (eg, chatbots or virtual agents) are a novel and potentially accessible way to promote PA. Thus, it is important to understand the evolving landscape of research that uses conversational agents. Objective: This mixed methods systematic review aims to summarize the usability and effectiveness of conversational agents in promoting PA, describe common theories and intervention components used, and identify areas for further development. Methods: We conducted a mixed methods systematic review. We searched seven electronic databases (PsycINFO, PubMed, Embase, CINAHL, ACM Digital Library, Scopus, and Web of Science) for quantitative, qualitative, and mixed methods studies that conveyed primary research on automated conversational agents designed to increase PA. The studies were independently screened, and their methodological quality was assessed using the Mixed Methods Appraisal Tool by 2 reviewers. Data on intervention impact and effectiveness, treatment characteristics, and challenges were extracted and analyzed using parallel-results convergent synthesis and narrative summary. Results: In total, 255 studies were identified, 7.8% (20) of which met our inclusion criteria. The methodological quality of the studies was varied. Overall, conversational agents had moderate usability and feasibility. Those that were evaluated through randomized controlled trials were found to be effective in promoting PA. Common challenges facing interventions were repetitive program content, high attrition, technical issues, and safety and privacy concerns. Conclusions: Conversational agents hold promise for PA interventions. However, there is a lack of rigorous research on long-term intervention effectiveness and patient safety. Future interventions should be based on evidence-informed theories and treatment approaches and should address users' desires for program variety, natural language processing, delivery via mobile devices, and safety and privacy concerns.@en

Physical activity (PA) is an effective depression treatment. However, knowledge on how variation in day-to-day PA relates to depression in women is lacking. The purposes of this study were to 1) compare overall objectively measured baseline daily steps and duration of moderate to vigorous PA (MVPA) and 2) examine differences in steps and MVPA on days of the week between women aged 25–65 years, who were physically inactive, with high and low depressive symptoms, enrolled in a run-in period of the mobile phone based physical activity education (mPED) trial. The Center for Epidemiological Studies Depression Scale was used to categorize low/high depressive symptom groups. We used linear mixed-effects models to examine the associations between steps and MVPA and depression-status overall and by day of the week, adjusting for selected demographic variables and their interactions with day of the week. 274 women were included in the final analysis, of which 58 had high depressive symptoms. Overall physical activity levels did not differ. However, day of the week modified the associations of depression with MVPA (p = 0.015) and daily steps (p = 0.08). Women with high depression were characterized by reduced activity at the end of the week (Posthoc: Friday: 791 fewer steps, 95% CI: 73–1509, p = 0.03; 8.8 lower MVPA, 95% CI: 2.16–15.5, p = 0.0098) compared to women with low depression, who showed increased activity. Day of the week might be an important target for personalization of physical activity interventions. Future work should evaluate potential causes of daily activity alterations in depression in women.@en

Background: The COVID-19 pandemic has propelled patient-facing research to shift to digital and telehealth strategies. If these strategies are not adapted for minority patients of lower socioeconomic status, health inequality will further increase. Patient-centered models of care can successfully improve access and experience for minority patients. Objective: This study aims to present the development process and preliminary acceptability of altering in-person onboarding procedures into internet-based, remote procedures for a mobile health (mHealth) intervention in a population with limited digital literacy. Methods: We actively recruited safety-net patients (English- and Spanish-speaking adults with diabetes and depression who were receiving care at a public health care delivery system in San Francisco, United States) into a randomized controlled trial of text messaging support for physical activity. Because of the COVID-19 pandemic, we modified the in-person recruitment and onboarding procedures to internet-based, remote processes with human support. We conducted a preliminary evaluation of how the composition of the recruited cohort might have changed from the pre-COVID-19 period to the COVID-19 enrollment period. First, we analyzed the digital profiles of patients (n=32) who had participated in previous in-person onboarding sessions prior to the COVID-19 pandemic. Next, we documented all changes made to our onboarding processes to account for remote recruitment, especially those needed to support patients who were not very familiar with downloading apps onto their mobile phones on their own. Finally, we used the new study procedures to recruit patients (n=11) during the COVID-19 social distancing period. These patients were also asked about their experience enrolling into a fully digitized mHealth intervention. Results: Recruitment across both pre-COVID-19 and COVID-19 periods (N=43) demonstrated relatively high rates of smartphone ownership but lower self-reported digital literacy, with 32.6% (14/43) of all patients reporting they needed help with using their smartphone and installing apps. Significant changes were made to the onboarding procedures, including facilitating app download via Zoom video call and/or a standard phone call and implementing brief, one-on-one staff-patient interactions to provide technical assistance personalized to each patient's digital literacy skills. Comparing recruitment during pre-COVID-19 and COVID-19 periods, the proportion of patients with digital literacy barriers reduced from 34.4% (11/32) in the pre-COVID-19 cohort to 27.3% (3/11) in the COVID-19 cohort. Differences in digital literacy scores between both cohorts were not significant (P=.49). Conclusions: Patients of lower socioeconomic status have high interest in using digital platforms to manage their health, but they may require additional upfront human support to gain access. One-on-one staff-patient partnerships allowed us to provide unique technical assistance personalized to each patient's digital literacy skills, with simple strategies to troubleshoot patient barriers upfront. These additional remote onboarding strategies can mitigate but not eliminate digital barriers for patients without extensive technology experience.@en

Recurrent major depressive disorder (rMDD) is a relapsing-remitting disease with high morbidity and a 5-year risk of recurrence of up to 80%. This was a prospective pilot study to examine the potential diagnostic and prognostic value of targeted plasma metabolomics in the care of patients with rMDD in remission. We used an established LC-MS/MS platform to measure 399 metabolites in 68 subjects with rMDD (n = 45 females and 23 males) in antidepressant-free remission and 59 age- and sex-matched controls (n = 40 females and 19 males). Patients were then followed prospectively for 2.5 years. Metabolomics explained up to 43% of the phenotypic variance. The strongest biomarkers were gender specific. 80% of the metabolic predictors of recurrence in both males and females belonged to 6 pathways: (1) phospholipids, (2) sphingomyelins, (3) glycosphingolipids, (4) eicosanoids, (5) microbiome, and (6) purines. These changes traced to altered mitochondrial regulation of cellular redox, signaling, energy, and lipid metabolism. Metabolomics identified a chemical endophenotype that could be used to stratify rrMDD patients at greatest risk for recurrence with an accuracy over 0.90 (95%CI = 0.69–1.0). Power calculations suggest that a validation study of at least 198 females and 198 males (99 cases and 99 controls each) will be needed to confirm these results. Although a small study, these results are the first to show the potential utility of metabolomics in assisting with the important clinical challenge of prospectively identifying the patients at greatest risk of recurrence of a depressive episode and those who are at lower risk.@en

Background: Social distancing is a crucial intervention to slow down person-to-person transmission of COVID-19. However, social distancing has negative consequences, including increases in depression and anxiety. Digital interventions, such as text messaging, can provide accessible support on a population-wide scale. We developed text messages in English and Spanish to help individuals manage their depressive mood and anxiety during the COVID-19 pandemic. Objective: In a two-arm randomized controlled trial, we aim to examine the effect of our 60-day text messaging intervention. Additionally, we aim to assess whether the use of machine learning to adapt the messaging frequency and content improves the effectiveness of the intervention. Finally, we will examine the differences in daily mood ratings between the message categories and time windows. Methods: The messages were designed within two different categories: behavioral activation and coping skills. Participants will be randomized into (1) a random messaging arm, where message category and timing will be chosen with equal probabilities, and (2) a reinforcement learning arm, with a learned decision mechanism for choosing the messages. Participants in both arms will receive one message per day within three different time windows and will be asked to provide their mood rating 3 hours later. We will compare self-reported daily mood ratings; self-reported depression, using the 8-item Patient Health Questionnaire; and self-reported anxiety, using the 7-item Generalized Anxiety Disorder scale at baseline and at intervention completion. Results: The Committee for the Protection of Human Subjects at the University of California Berkeley approved this study in April 2020 (No. 2020-04-13162). Data collection began in April 2020 and will run to April 2021. As of August 24, 2020, we have enrolled 229 participants. We plan to submit manuscripts describing the main results of the trial and results from the microrandomized trial for publication in peer-reviewed journals and for presentations at national and international scientific meetings. Conclusions: Results will contribute to our knowledge of effective psychological tools to alleviate the negative effects of social distancing and the benefit of using machine learning to personalize digital mental health interventions. Trial Registration: ClinicalTrials.gov NCT04473599; https://clinicaltrials.gov/ct2/show/NCT04473599 International Registered Report Identifier (IRRID): DERR1-10.2196/23592@en

Objective: Providing behavioral health interventions via smartphones allows these interventions to be adapted to the changing behavior, preferences, and needs of individuals. This can be achieved through reinforcement learning (RL), a sub-area of machine learning. However, many challenges could affect the effectiveness of these algorithms in the real world. We provide guidelines for decision-making. Materials and Methods: Using thematic analysis, we describe challenges, considerations, and solutions for algorithm design decisions in a collaboration between health services researchers, clinicians, and data scientists. We use the design process of an RL algorithm for a mobile health study "DIAMANTE"for increasing physical activity in underserved patients with diabetes and depression. Over the 1.5-year project, we kept track of the research process using collaborative cloud Google Documents, Whatsapp messenger, and video teleconferencing. We discussed, categorized, and coded critical challenges. We grouped challenges to create thematic topic process domains. Results: Nine challenges emerged, which we divided into 3 major themes: 1. Choosing the model for decision-making, including appropriate contextual and reward variables; 2. Data handling/collection, such as how to deal with missing or incorrect data in real-time; 3. Weighing the algorithm performance vs effectiveness/implementation in real-world settings. Conclusion: The creation of effective behavioral health interventions does not depend only on final algorithm performance. Many decisions in the real world are necessary to formulate the design of problem parameters to which an algorithm is applied. Researchers must document and evaulate these considerations and decisions before and during the intervention period, to increase transparency, accountability, and reproducibility.@en

Purpose: Depressive disorders are common and have a considerable impact on patients and societies. Several treatments are available, but their effects are modest and reduce the burden only to a limited extent. Preventing the onset of depressive disorders may be one option to further reduce the global disease burden. Methods: We conducted a meta-analysis of randomized controlled trials in participants without a diagnosis of depression at baseline, who were assigned to a preventive psychological intervention, or a care-as-usual, or comparable control group and in which incident cases of depression at follow-up were ascertained with a diagnostic interview. Results: Our systematic searches resulted in 50 trials (14,665 participants) with relatively high quality, in high risk groups of all ages. The psychological interventions were mostly based on cognitive behavioral interventions. One year after the preventive interventions, the relative risk of developing a depressive disorder was RR = 0.81 (95% CI: 0.72–0.91), indicating that those who had received the intervention had 19% less chance to develop a depressive disorder. Given the average control event rate of 30%, twenty-one people had to participate in the intervention to prevent one depressive disorder compared to people in the control conditions. Conclusions: Prevention is a promising approach to reduce the global disease burden of depression in addition to treatments.@en

Objective: Omega-3 (n-3) and omega-6 (n-6) polyunsaturated fatty acid (PUFA) alterations in patients with major depressive disorder (MDD) have been shown to persist after remission. Whether these alterations are risk factors for MDD recurrence remains unknown. Here, we examined whether fatty acids predict time until MDD recurrence in remitted MDD patients. Methods: Data were used from remitted MDD patients of the Netherlands Study of Depression and Anxiety (n = 356) and the Depression Evaluation Longitudinal Therapy Assessment studies (n = 118). Associations of FAs with time until MDD recurrence up to 8-year follow-up were analyzed using Cox regression analyses. Study-specific estimates were pooled using mega- and meta-analysis techniques. Results: 27.5% (NESDA) and 56.8% (DELTA) participants had an MDD recurrence. Pooled results showed that no FA was significantly associated with time until MDD recurrence (n-3 PUFAs: hazard ratio (HR) = 1.17, 95% confidence interval (CI) = 0.98–1.41, P = 0.082; n-6 PUFAs: HR = 1.08, 95% CI = 0.84–1.38, P = 0.55). Conclusion: In remitted MDD patients, circulating PUFAs were not associated with prospective risk of MDD recurrence. Consequently, circulating PUFAs are unlikely to reflect a vulnerability marker for recurrence, so correcting n-3 PUFA ‘deficits’ through supplementation does not seem a promising option to prevent MDD recurrence.

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Introduction Depression and diabetes are highly disabling diseases with a high prevalence and high rate of comorbidity, particularly in low-income ethnic minority patients. Though comorbidity increases the risk of adverse outcomes and mortality, most clinical interventions target these diseases separately. Increasing physical activity might be effective to simultaneously lower depressive symptoms and improve glycaemic control. Self-management apps are a cost-effective, scalable and easy access treatment to increase physical activity. However, cutting-edge technological applications often do not reach vulnerable populations and are not tailored to an individual's behaviour and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention. Methods and analysis In a three-arm randomised controlled trial, we will examine the effect of a text-messaging smartphone application to encourage physical activity in low-income ethnic minority patients with comorbid diabetes and depression. The adaptive intervention group receives messages chosen from different messaging banks by a reinforcement learning algorithm. The uniform random intervention group receives the same messages, but chosen from the messaging banks with equal probabilities. The control group receives a weekly mood message. We aim to recruit 276 adults from primary care clinics aged 18-75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (Patient Health Questionnaire depression scale-8 (PHQ-8) >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent haemoglobin A1c from medical records at baseline and at intervention completion at 6-month follow-up. Ethics and dissemination The Institutional Review Board at the University of California San Francisco approved this study (IRB: 17-22608). We plan to submit manuscripts describing our user-designed methods and testing of the adaptive learning algorithm and will submit the results of the trial for publication in peer-reviewed journals and presentations at (inter)-national scientific meetings. Trial registration number NCT03490253; pre-results.

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Background: Alterations in hypothalamic–pituitary–adrenal (HPA)-axis activity, fatty acid metabolism, and their relation have been associated with (recurrent) major depressive disorder (MDD), although conflicting findings exist. Aims: To determine whether alterations in HPA-axis activity and fatty acids in recurrent MDD remain during remission (i.e. reflect a potential trait factor). Furthermore, to test the association between HPA-axis activity and fatty acids in patients versus controls. Methods: We cross-sectionally compared 73 remitted unmedicated recurrent MDD patients with 46 matched never-depressed controls. Measurements included salivary cortisol and dehydroepiandrosterone sulfate (DHEAS) (awakening, evening, and after sad mood induction) and erythrocyte fatty acid parameters: (I) three main fatty acids [omega-3 docosahexaenoic acid (DHA), and the omega-3 eicosapentaenoic acid/omega-6 arachidonic acid (EPA/AA)-ratio], and (II) structural fatty acid indices [chain length, unsaturation and peroxidation]. Results: Patients showed higher cortisol awakening responses (p = 0.006) and lower evening cortisol/DHEAS ratios (p = 0.044) compared to matched controls. Fatty acids did not differ between patients and controls, but HPA-axis indicators were significantly associated with fatty acid parameters in both groups (0.001 ≤ p ≤ 0.043). Patients and controls significantly differed in the relations between awakening DHEAS or cortisol/DHEAS ratios and fatty acid parameters, including unsaturation and peroxidation indices (0.001≤ p ≤ 0.034). Significance remained after correction for confounders. Conclusions: Our results further support alterations in HPA-axis activity, i.e. a lower baseline, but higher responsiveness of awakening cortisol, in remitted medication-free recurrent MDD patients. Furthermore, the relationship between HPA-axis and fatty acids showed significant differences in recurrent MDD patients versus controls. Prospective research is needed to determine the predictive value of this relationship for MDD recurrence.@en

Background: Depressive recurrence is highly prevalent and adds significantly to the burden of depressive disorder. Whilst some clinical predictors of recurrence have been clearly demonstrated (e.g. residual symptoms, previous episodes), the cognitive and psychological processes that may contribute to recurrence risk are less well established. In this study we examine whether cognitive flexibility deficits and rumination are related to recurrence in a remitted clinical sample. Method: We compared remitted patients with 2 or more previous depressive episodes (N = 69) to a matched group of healthy controls (N = 43). Cognitive flexibility was measured using the Internal Shift Task (IST) and a version of the Exogenous Cueing Task (ECT); rumination was assessed with the Ruminative Responses Scale. Results: IST and ECT performance did not differ between remitted patients and controls. Remitted patients had higher levels of rumination than controls. Within the remitted patient group, faster disengagement from angry and happy faces on the ECT was predictive of shorter time to recurrence (hazard ratio for 1 standard deviation, (HRSD) = 0.563 [CI, 0.381–0.832], p = 0.004, (HRSD) = 0.561 [CI, 0.389–0.808], p = 0.002, respectively). Rumination predicted recurrence (HRSD = 1.526 [CI, 1.152–2.202]; p = 0.003) but was not related to emotional disengagement. Limitations: We had low power to detect small effects for the analysis within remitted patients. Conclusions: Whilst cognitive flexibility in remitted patients was not impaired relative to controls, rapid disengagement from emotional stimuli and rumination were independently associated with time to recurrence. Cognitive flexibility may be an important indicator of recurrence risk, and a target for interventions to reduce recurrence.@en

An interesting factor explaining recurrence risk in Major Depressive Disorder (MDD) may be neuropsychological functioning, i.e., processing of emotional stimuli/information. Negatively biased processing of emotional stimuli/information has been found in both acute and (inconclusively) remitted states of MDD, and may be causally related to recurrence of depression. We aimed to investigate self-referent, memory and interpretation biases in recurrently depressed patients in remission and relate these biases to recurrence. We included 69 remitted recurrent MDD-patients (rrMDD-patients), 35–65 years, with ≥2 episodes, voluntarily free of antidepressant maintenance therapy for at least 4 weeks. We tested self-referent biases with an emotional categorization task, bias in emotional memory by free recall of the emotion categorization task 15 min after completing it, and interpretation bias with a facial expression recognition task. We compared these participants with 43 never-depressed controls matched for age, sex and intelligence. We followed the rrMDD-patients for 2.5 years and assessed recurrent depressive episodes by structured interview. The rrMDD-patients showed biases toward emotionally negative stimuli, faster responses to negative self-relevant characteristics in the emotional categorization, better recognition of sad faces, worse recognition of neutral faces with more misclassifications as angry or disgusting faces and less misclassifications as neutral faces (0.001 < p < 0.05). Of these, the number of misclassifications as angry and the overall performance in the emotional memory task were significantly associated with the time to recurrence (p ≤ 0.04), independent of residual symptoms and number of previous episodes. In a support vector machine data-driven model, prediction of recurrence-status could best be achieved (relative to observed recurrence-rate) with demographic and childhood adversity parameters (accuracy 78.1%; 1-sided p = 0.002); neuropsychological tests could not improve this prediction. Our data suggests a persisting (mood-incongruent) emotional bias when patients with recurrent depression are in remission. Moreover, these persisting biases might be mechanistically important for recurrence and prevention thereof.@en

Neurobiological models to explain vulnerability of major depressive disorder (MDD) are scarce and previous functional magnetic resonance imaging studies mostly examined “static” functional connectivity (FC). Knowing that FC constantly evolves over time, it becomes important to assess how FC dynamically differs in remitted-MDD patients vulnerable for new depressive episodes. Using a recently developed method to examine dynamic FC, we characterized re-emerging FC states during rest in 51 antidepressant-free MDD patients at high risk of recurrence (≥2 previous episodes), and 35 healthy controls. We examined differences in occurrence, duration, and switching profiles of FC states after neutral and sad mood induction. Remitted MDD patients showed a decreased probability of an FC state (p < 0.005) consisting of an extensive network connecting frontal areas—important for cognitive control—with default mode network, striatum, and salience areas, involved in emotional and self-referential processing. Even when this FC state was observed in patients, it lasted shorter (p < 0.005) and was less likely to switch to a smaller prefrontal–striatum network (p < 0.005). Differences between patients and controls decreased after sad mood induction. Further, the duration of this FC state increased in remitted patients after sad mood induction but not in controls (p < 0.05). Our findings suggest reduced ability of remitted-MDD patients, in neutral mood, to access a clinically relevant control network involved in the interplay between externally and internally oriented attention. When recovering from sad mood, remitted recurrent MDD appears to employ a compensatory mechanism to access this FC state. This study provides a novel neurobiological profile of MDD vulnerability.@en

Purpose of review: Digital mental health (DMH) interventions provide opportunities to alleviate mental health disparities among marginalized populations by overcoming traditional barriers to care and putting quality mental health services in the palm of one’s hand. While progress has been made towards realizing this goal, the potential for impactful change has yet to be realized. This paper reviews current examples of DMH interventions for certain marginalized and underserved groups, namely, ethnic and racial minorities including Latinx and African-Americans, rural populations, individuals experiencing homelessness, and sexual and gender minorities. Recent findings: Strengths and opportunities, along with the needs and considerations, of each group are discussed as they pertain to the development and dissemination of DMH interventions. Our review focuses on several DMH interventions that have been specifically designed for marginalized populations with a culturally sensitive approach along with other existing interventions that have been tailored to fit the needs of the target population. Overall, evidence is beginning to show promise for the feasibility and acceptability of DMH inter ventions for these groups, but large-scale efficacy testing and scaling potential are still lacking. Summary: These examples of how DMH can potentially positively impact marginalized populations should motivate developers, researchers, and practitioners to work collaboratively with stakeholders to deliver DMH interventions to these underserved populations in need.@en

Insufficient response to treatment is the main cause of prolonged suffering from major depressive disorder (MDD). Early identification of insufficient response could result in faster and more targeted treatment strategies to reduce suffering. We therefore explored whether baseline alterations within and between resting state functional connectivity networks could serve as markers of insufficient response to antidepressant treatment in two years of follow-up. We selected MDD patients (N = 17) from the NEtherlands Study of Depression and Anxiety (NESDA), who received ≥ two antidepressants, indicative for insufficient response, during the two year follow-up, a group of MDD patients who received only one antidepressant (N = 32) and a healthy control group (N = 19) matched on clinical characteristics and demographics. An independent component analysis (ICA) of baseline resting-state scans was conducted after which functional connectivity within the components was compared between groups. We observed lower connectivity of the right insula within the salience network in the group with ≥ two antidepressants compared to the group with one antidepressant. No difference in connectivity was found between the patient groups and healthy control group. Given the suggested role of the right insula in switching between task-positive mode (activation during attention-demanding tasks) and task-negative mode (activation during the absence of any task), we explored whether right insula activation differed during switching between these two modes. We observed that in the ≥2 antidepressant group, the right insula was less active compared to the group with one antidepressant, when switching from task-positive to task-negative mode than the other way around. These findings imply that lower right insula connectivity within the salience network may serve as an indicator for prospective insufficient response to antidepressants. This result, supplemented by the diminished insula activation when switching between task and rest related networks, could indicate an underlying mechanism that, if not sufficiently targeted by current antidepressants, could lead to insufficient response. When replicated, these findings may contribute to the identification of biomarkers for early detection of insufficient response.@en

One of the core symptoms of major depressive disorder is anhedonia, an inability to experience pleasure. In patients with major depressive disorder, a dysfunctional reward-system may exist, with blunted temporal difference reward-related learning signals in the ventral striatum and increased temporal difference-related (dopaminergic) activation in the ventral tegmental area. Anhedonia often remains as residual symptom during remission; however, it remains largely unknown whether the abovementioned reward systems are still dysfunctional when patients are in remission. We used a Pavlovian classical conditioning functional MRI task to explore the relationship between anhedonia and the temporal difference-related response of the ventral tegmental area and ventral striatum in medication-free remitted recurrent depression patients (n = 36) versus healthy control subjects (n = 27). Computational modelling was used to obtain the expected temporal difference errors during this task. Patients, compared to healthy controls, showed significantly increased temporal difference reward learning activation in the ventral tegmental area (PFWE,SVC = 0.028). No differences were observed between groups for ventral striatum activity. A group × anhedonia interaction [t(57) = -2.29, P = 0.026] indicated that in patients, higher anhedonia was associated with lower temporal difference activation in the ventral tegmental area, while in healthy controls higher anhedonia was associated with higher ventral tegmental area activation. These findings suggest impaired reward-related learning signals in the ventral tegmental area during remission in patients with depression. This merits further investigation to identify impaired reward-related learning as an endophenotype for recurrent depression. Moreover, the inverse association between reinforcement learning and anhedonia in patients implies an additional disturbing influence of anhedonia on reward-related learning or vice versa, suggesting that the level of anhedonia should be considered in behavioural treatments.@en