Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis.

BACKGROUND: Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings. OBJECTIVE: This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts. METHODS: Data were sourced from a large longitudinal mHealth study, wherein participants' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score. RESULTS: Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (ß=-93.61, P<.001), increased sleep variability (ß=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: ß=0.55, P=.001; sleep offset: ß=1.12, P<.001; M10 onset: ß=0.73, P=.003; HR acrophase: ß=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (ß of PHQ-8 × spring = -31.51, P=.002) and summer (ß of PHQ-8 × summer = -42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (ß of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer. CONCLUSIONS: Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression.

Study protocol of a systematic review and metaanalysis of stress and burnout among general surgery residents.

Introduction: Chronic workplace stress and burnout are impediments to physicians' professional fulfillment, healthcare organizations' efficiency, and patient care quality/safety. General surgery residents are especially at risk due to the complexity of their training. We report the protocol of a metaanalysis of chronic stress and burnout among Accreditation Council for Graduate Medical Education (ACGME)-affiliated general surgery residents in the era after duty-hour reforms, plus downstream effects on their health and clinical performance. Methods: The proposed systematic review and metaanalysis (PROSPERO registration CRD42021277626) will synthesize/pool data from studies of chronic stress and burnout among general surgery residents at ACGME-affiliated programs. The timeframe under review is subdivided into three intervals: (a) after the 2003 duty-hour restrictions but before 2011 reforms, (b) after the 2011 reforms but before the coronavirus pandemic, and (c) the first 3 years after the pandemic's outbreak. Only studies reporting outcomes based on validated instruments will be included. Qualitative studies, commentaries/editorials, narrative reviews, and studies not published in English will be excluded. Multivariable analyses will adjust for sample characteristics and the methodological quality of included studies. Conclusions: The metaanalysis will yield evidence reflecting experiences of North American-based general surgery residents in the years after ACGME-mandated duty-hour restructuring.

Management of Depression in Adults: A Review.

Importance: Approximately 9% of US adults experience major depression each year, with a lifetime prevalence of approximately 17% for men and 30% for women. Observations: Major depression is defined by depressed mood, loss of interest in activities, and associated psychological and somatic symptoms lasting at least 2 weeks. Evaluation should include structured assessment of severity as well as risk of self-harm, suspected bipolar disorder, psychotic symptoms, substance use, and co-occurring anxiety disorder. First-line treatments include specific psychotherapies and antidepressant medications. A network meta-analysis of randomized clinical trials reported cognitive therapy, behavioral activation, problem-solving therapy, interpersonal therapy, brief psychodynamic therapy, and mindfulness-based psychotherapy all had at least medium-sized effects in symptom improvement over usual care without psychotherapy (standardized mean difference [SMD] ranging from 0.50 [95% CI, 0.20-0.81] to 0.73 [95% CI, 0.52-0.95]). A network meta-analysis of randomized clinical trials reported 21 antidepressant medications all had small- to medium-sized effects in symptom improvement over placebo (SMD ranging from 0.23 [95% CI, 0.19-0.28] for fluoxetine to 0.48 [95% CI, 0.41-0.55] for amitriptyline). Psychotherapy combined with antidepressant medication may be preferred, especially for more severe or chronic depression. A network meta-analysis of randomized clinical trials reported greater symptom improvement with combined treatment than with psychotherapy alone (SMD, 0.30 [95% CI, 0.14-0.45]) or medication alone (SMD, 0.33 [95% CI, 0.20-0.47]). When initial antidepressant medication is not effective, second-line medication treatment includes changing antidepressant medication, adding a second antidepressant, or augmenting with a nonantidepressant medication, which have approximately equal likelihood of success based on a network meta-analysis. Collaborative care programs, including systematic follow-up and outcome assessment, improve treatment effectiveness, with 1 meta-analysis reporting significantly greater symptom improvement compared with usual care (SMD, 0.42 [95% CI, 0.23-0.61]). Conclusions and Relevance: Effective first-line depression treatments include specific forms of psychotherapy and more than 20 antidepressant medications. Close monitoring significantly improves the likelihood of treatment success.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals; specifically the sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from behavior should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

The Impact of Involvement in Whole Health System for Veterans or Participating in It for Oneself on Job Attitudes in VA Employees.

OBJECTIVE: The aim of the study is to examine how involvement in the Whole Health System of care, clinically and personally (through employee-focused activities), would affect employee satisfaction, engagement, burnout, and turnover intent in the Veterans Health Administration. METHODS: Multivariate logistic regression analysis of cross-sectional survey from Veterans Health Administration employees was used to determine the influence of Whole Health System involvement and Employee Whole Health participation on job attitudes. RESULTS: Whole Health System involvement was associated higher job satisfaction, higher levels of engagement, lower burnout, and lower turnover intent. A similar pattern of results was identified when looking specifically at Employee Whole Health participation and associated job attitudes. CONCLUSIONS: Employees who are either directly involved in delivering Whole Health services to veterans or who have participated in Whole Health programming for their own benefit may experience a meaningful positive impact on their well-being and how they experience the workplace.

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.

Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study.

While studies show links between smartphone data and affective symptoms, we lack clarity on the temporal scale, specificity (e.g., to depression vs. anxiety), and person-specific (vs. group-level) nature of these associations. We conducted a large-scale (n = 1013) smartphone-based passive sensing study to identify within- and between-person digital markers of depression and anxiety symptoms over time. Participants (74.6% female; M age = 40.9) downloaded the LifeSense app, which facilitated continuous passive data collection (e.g., GPS, app and device use, communication) across 16 weeks. Hierarchical linear regression models tested the within- and between-person associations of 2-week windows of passively sensed data with depression (PHQ-8) or generalized anxiety (GAD-7). We used a shifting window to understand the time scale at which sensed features relate to mental health symptoms, predicting symptoms 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Spending more time at home relative to one's average was an early signal of PHQ-8 severity (distal ß = 0.219, p = 0.012) and continued to relate to PHQ-8 at medial (ß = 0.198, p = 0.022) and proximal (ß = 0.183, p = 0.045) windows. In contrast, circadian movement was proximally related to (ß = -0.131, p = 0.035) but did not predict (distal ß = 0.034, p = 0.577; medial ß = -0.089, p = 0.138) PHQ-8. Distinct communication features (i.e., call/text or app-based messaging) related to PHQ-8 and GAD-7. Findings have implications for identifying novel treatment targets, personalizing digital mental health interventions, and enhancing traditional patient-provider interactions. Certain features (e.g., circadian movement) may represent correlates but not true prospective indicators of affective symptoms. Conversely, other features like home duration may be such early signals of intra-individual symptom change, indicating the potential utility of prophylactic intervention (e.g., behavioral activation) in response to person-specific increases in these signals.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

A Digital Intervention to Promote Self-Management Self-Efficacy Among Community-Dwelling Individuals With Stroke: Pilot Randomized Controlled Trial.

BACKGROUND: Digital interventions provided through smartphones or the internet that are guided by a coach have been proposed as promising solutions to support the self-management of chronic conditions. However, digital intervention for poststroke self-management is limited; we developed the interactive Self-Management Augmented by Rehabilitation Technologies (iSMART) intervention to address this gap. OBJECTIVE: This study aimed to examine the feasibility and initial effects of the iSMART intervention to improve self-management self-efficacy in people with stroke. METHODS: A parallel, 2-arm, nonblinded, randomized controlled trial of 12-week duration was conducted. A total of 24 participants with mild-to-moderate chronic stroke were randomized to receive either the iSMART intervention or a manual of stroke rehabilitation (attention control). iSMART was a coach-guided, technology-supported self-management intervention designed to support people managing chronic conditions and maintaining active participation in daily life after stroke. Feasibility measures included retention and engagement rates in the iSMART group. For both the iSMART intervention and active control groups, we used the Feasibility of Intervention Measure, Acceptability of Intervention Measure, and Intervention Appropriateness Measure to assess the feasibility, acceptability, and appropriateness, respectively. Health measures included the Participation Strategies Self-Efficacy Scale and the Patient-Reported Outcomes Measurement Information System's Self-Efficacy for Managing Chronic Conditions. RESULTS: The retention rate was 82% (9/11), and the engagement (SMS text message response) rate was 78% for the iSMART group. Mean scores of the Feasibility of Intervention Measure, Acceptability of Intervention Measure, and Intervention Appropriateness Measure were 4.11 (SD 0.61), 4.44 (SD 0.73), and 4.36 (SD 0.70), respectively, which exceeded our benchmark (4 out of 5), suggesting high feasibility, acceptability, and appropriateness of iSMART. The iSMART group showed moderate-to-large effects in improving self-efficacy in managing emotions (r=0.494), symptoms (r=0.514), daily activities (r=0.593), and treatments and medications (r=0.870), but the control group showed negligible-to-small effects in decreasing self-efficacy in managing emotions (r=0.252), symptoms (r=0.262), daily activities (r=0.136), and treatments and medications (r=0.049). In addition, the iSMART group showed moderate-to-large effects of increasing the use of participation strategies for management in the home (r=0.554), work (r=0.633), community (r=0.673), and communication activities (r=0.476). In contrast, the control group showed small-to-large effects of decreasing the use of participation strategies for management in the home (r=0.567), work (r=0.342, community (r=0.215), and communication activities (r=0.379). CONCLUSIONS: Our findings support the idea that iSMART was feasible to improve poststroke self-management self-efficacy. Our results also support using a low-cost solution, such as SMS text messaging, to supplement traditional therapeutic patient education interventions. Further evaluation with a larger sample of participants is still needed. TRIAL REGISTRATION: ClinicalTrials.gov 202004137; https://clinicaltrials.gov/study/NCT04743037?id=202004137&rank=1.

Intraindividual variability in post-stroke cognition and its relationship with activities of daily living and social functioning: an ecological momentary assessment approach.

INTRODUCTION: Ecological momentary assessment (EMA) is a methodological approach to studying intraindividual variation over time. This study aimed to use EMA to determine the variability of cognition in individuals with chronic stroke, identify the latent classes of cognitive variability, and examine any differences in daily activities, social functioning, and neuropsychological performance between these latent classes. METHODS: Participants (N = 202) with mild-to-moderate stroke and over 3-month post-stroke completed a study protocol, including smartphone-based EMA and two lab visits. Participants responded to five EMA surveys daily for 14 days to assess cognition. They completed patient-reported measures and neuropsychological assessments during lab visits. Using latent class analysis, we derived four indicators to quantify cognitive variability and identified latent classes among participants. We used ANOVA and Chi-square to test differences between these latent classes in daily activities, social functioning, and neuropsychological performance. RESULTS: The latent class analysis converged on a three-class model. The moderate and high variability classes demonstrated significantly greater problems in daily activities and social functioning than the low class. They had significantly higher proportions of participants with problems in daily activities and social functioning than the low class. Neuropsychological performance was not statistically different between the three classes, although a trend approaching statistically significant difference was observed in working memory and executive function domains. DISCUSSION: EMA could capture intraindividual cognitive variability in stroke survivors. It offers a new approach to understanding the impact and mechanism of post-stroke cognitive problems in daily life and identifying individuals benefiting from self-regulation interventions.

Longitudinal associations of daily affective dynamics with depression, generalized anxiety, and social anxiety symptoms.

BACKGROUND: Low average affect, measured using ecological momentary assessment (EMA), has been consistently linked with depression, generalized anxiety, and social anxiety, supporting trait-like negative affect as a shared underlying feature. However, while theoretical models of emotion regulation would also implicate greater variability in daily affect in these conditions, empirical evidence linking EMA of mood variability with affective disorders is mixed. We used multilevel modeling to test relationships of daily mood and mood variability with depression, generalized anxiety, and social anxiety symptoms. METHODS: Participants (N = 1004; 72.31 % female; Mage = 40.85) responded to EMA of mood 2-3×/day and completed measures of depression (PHQ-8), generalized anxiety (GAD-7), and social anxiety (SPIN) every three weeks. RESULTS: Lower mean affect predicted all symptoms at both the between-person (PHQ-8: ß = -0.486, p < 0.001; GAD-7: ß = -0.429, p < 0.001; SPIN: ß = -0.284, p < 0.001) and within-person (PHQ-8: ß = -0.219, p < 0.001; GAD-7: ß = -0.196, p < 0.001; SPIN: ß = -0.049, p < 0.001) levels. Similarly, at the between-person level, greater affective variability was linked with all three clinical symptoms (PHQ-8: ß = 0.617, p < 0.001; GAD-7: ß = 0.703, p < 0.001; SPIN: ß = 0.449, p < 0.001). However, within-person, affective variability related to depression (ß = 0.144, p < 0.001) and generalized anxiety (ß = 0.150, p < 0.001), but not social anxiety (ß = 0.006, p = 0.712). LIMITATIONS: The COVID-19 pandemic lockdown period occurred midway through the study. CONCLUSION: Findings point to common and specific emotion dynamics that characterize affective symptoms severity, with implications for affective monitoring in a clinical context.

Effect of early medication adherence on behavioral treatment utilization and smoking cessation among individuals with current or past major depressive disorder.

SIGNIFICANCE: Little is known about the mechanisms by which medication adherence promotes smoking cessation among adults with MDD. We tested the hypothesis that early adherence promotes abstinence by increasing behavioral treatment (BT) utilization. METHODS: Data for this post-hoc analysis were from a randomized trial of 149 adults with current or past MDD treated with BT and either varenicline (n = 81) or placebo (n = 68). Arms were matched on medication regimen. Early medication adherence was measured by the number of days in which medication was taken at the prescribed dose during the first six of 12 weeks of pharmacological treatment (weeks 2-7). BT consisted of eight 45-minute sessions (weeks 1-12). Bioverified abstinence was assessed at end-of-treatment (week 14). A regression-based approach was used to test whether the effect of early medication adherence on abstinence was mediated by BT utilization. RESULTS: Among 141 participants who initiated the medication regimen, BT utilization mediated the effect of early medication adherence on abstinencea) an interquartile increase in early medication days from 20 to 42 predicted a 4.2 times increase in abstinence (Total Risk Ratio (RR) = 4.24, 95% CI = 2.32-13.37; p <.001); b) increases in BT sessions predicted by such an increase in early medication days were associated with a 2.7 times increase in abstinence (Indirect RR = 2.73, 95% CI = 1.54-7.58; p <.001); and c) early medication adherence effects on abstinence were attenuated, controlling for BT (Direct RR = 1.55, 95% CI = 0.83-4.23, p =.17). CONCLUSIONS: The effect of early medication adherence on abstinence in individuals with current or past MDD is mediated by intensive BT utilization.

LiveWell, a smartphone-based self-management intervention for bipolar disorder: Intervention participation and usability analysis.

BACKGROUND: Understanding how individuals utilize and perceive digital mental health interventions may improve engagement and effectiveness. To support intervention improvement, participant feedback was obtained and app use patterns were examined for a randomized clinical trial evaluating a smartphone-based intervention for individuals with bipolar disorder. METHODS: App use and coaching engagement were examined (n = 124). Feedback was obtained via exit questionnaires (week 16, n = 81) and exit interviews (week 48, n = 17). RESULTS: On average, over 48 weeks, participants used the app for 4.4 h and engaged with the coach for 3.9 h. Participants spent the most time monitoring target behaviors and receiving adaptive feedback and the least time viewing self-assessments and skills. Participants reported that the daily check in helped increase awareness of target behaviors but expressed frustration with repetitiveness of monitoring and feedback content. Participants liked personalizing their wellness plan, but its use did not facilitate skills practice. App use declined over time which participants attributed to clinical stability, content mastery, and time commitment. Participants found the coaching supportive and motivating for app use. LIMITATIONS: App engagement based on viewing time may overestimate engagement. The delay between intervention delivery and the exit interviews and low exit interview participation may introduce bias. CONCLUSION: Utilization patterns and feedback suggest that digital mental health engagement and efficacy may benefit from adaptive personalization of targets monitored combined with adaptive monitoring and feedback to support skills practice and development. Increasing engagement with supports may also be beneficial.

Implementation of Direct-to-Patient Mobile Teledermatology in VA.

BACKGROUND: Innovative technology can enhance patient access to healthcare but must be successfully implemented to be effective. OBJECTIVE: We evaluated Department of Veterans Affairs' (VA's) implementation of My VA Images, a direct-to-patient asynchronous teledermatology mobile application enabling established dermatology patients to receive follow-up care remotely instead of in-person. DESIGN /PARTICIPANTS/APPROACH: Following pilot testing at 3 facilities, the app was introduced to 28 facilities (4 groups of 7) every 3 months using a stepped-wedge cluster-randomized design. Using the Organizational Theory of Implementation Effectiveness, we examined the app's implementation using qualitative and quantitative data consisting of encounter data from VA's corporate data warehouse; app usage from VA's Mobile Health database; bi-monthly reports from facility representatives; phone interviews with clinicians; and documented communications between the operational partner and facility staff. KEY RESULTS: Implementation policies and practices included VA's vision to expand home telehealth and marketing/communication strategies. The COVID-19 pandemic dominated the implementation climate by stressing staffing, introducing competing demands, and influencing stakeholder attitudes to the app, including its fit to their values. These factors were associated with mixed implementation effectiveness, defined as high quality consistent use. Nineteen of 31 exposed facilities prepared to use the app; 10 facilities used it for actual patient care, 7 as originally intended. Residents, nurse practitioners, and physician assistants were more likely than attendings to use the app. Facilities exposed to the app pre-pandemic were more likely to use and sustain the new process. CONCLUSIONS: Considerable heterogeneity existed in implementing mobile teledermatology, despite VA's common mission, integrated healthcare system, and stakeholders' broad interest. Identifying opportunities to target favorable facilities and user groups (such as teaching facilities and physician extenders, respectively) while addressing internal implementation barriers including incomplete integration with the electronic health record as well as inadequate staffing may help optimize the initial impact of direct-to-patient telehealth. The COVID pandemic was a notable extrinsic barrier. CLINICAL TRIALS REGISTRATION: NCT03241589.

Standardizing Patient Safety Event Reporting between Care Delivered or Purchased by the Veterans Health Administration (VHA).

BACKGROUND: Increasing community care (CC) use by veterans has introduced new challenges in providing integrated care across the Veterans Health Administration (VHA) and CC. VHA's well-recognized patient safety program has been particularly challenging for CC staff to adopt and implement. To standardize VHA safety practices across both settings, VHA implemented the Patient Safety Guidebook in 2018. The authors compared national- and facility-level trends in VHA and CC safety event reporting post-Guidebook implementation. METHODS: In this retrospective study using patient safety event data from VHA's event reporting system (2020-2022), the research team examined trends in patient safety events, adverse events, close calls (near misses), and recovery rates (ratio of close calls to adverse events plus close calls) in VHA and CC using linear regression models to determine whether the average changes in VHA and CC safety events at the national and facility levels per quarter were significant. RESULTS: A total of 499,332 safety events were reported in VHA and CC. Although VHA patient safety event trends were not significant (p > 0.05), there was a significant negative trend for adverse events (p = 0.02) and positive trends for close calls (p = 0.003) and recovery rates (p = 0.004). In CC there were significant negative trends for patient safety events and adverse events (p = 0.02) and a significant positive trend for recovery rates (p = 0.03). There was less variation in VHA than in CC facilities with significant decreases (for example, interquartile ranges in VHA and CC were 0.03 vs. 0.05, respectively). CONCLUSION: Fluctuations in different safety events over time were likely due to the disruption of care caused by COVID-19 as well as organizational factors. Notably, the increases in recovery rates reflect less staff focus on harmful events and more attention to close calls (preventable events). Although safety practice adoption from VHA to CC was feasible, additional implementation strategies are needed to sustain standardized safety reporting across settings.

Quality Improvement Efforts in VA Community Living Centers Following Public Reporting of Performance.

For two decades, the U.S. government has publicly reported performance measures for most nursing homes, spurring some improvements in quality. Public reporting is new, however, to Department of Veterans Affairs nursing homes (Community Living Centers [CLCs]). As part of a large, public integrated healthcare system, CLCs operate with unique financial and market incentives. Thus, their responses to public reporting may differ from private sector nursing homes. In three CLCs with varied public ratings, we used an exploratory, qualitative case study approach involving semi-structured interviews to compare how CLC leaders (n = 12) perceived public reporting and its influence on quality improvement. Across CLCs, respondents said public reporting was helpful for transparency and to provide an "outside perspective" on CLC performance. Respondents described employing similar strategies to improve their public ratings: using data, engaging staff, and clearly defining staff roles vis-à-vis quality improvement, although more effort was required to implement change in lower performing CLCs. Our findings augment those from prior studies and offer new insights into the potential for public reporting to spur quality improvement in public nursing homes and those that are part of integrated healthcare systems.

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.

"Our Job is to be so Temporary": Designing Digital Tools that Meet the Needs of Care Managers and their Patients with Mental Health Concerns.

Digital tools have potential to support collaborative management of mental health conditions, but we need to better understand how to integrate them in routine healthcare, particularly for patients with both physical and mental health needs. We therefore conducted interviews and design workshops with 1) a group of care managers who support patients with complex health needs, and 2) their patients whose health needs include mental health concerns. We investigate both groups' views of potential applications of digital tools within care management. Findings suggest that care managers felt underprepared to play an ongoing role in addressing mental health issues and had concerns about the burden and ambiguity of providing support through new digital channels. In contrast, patients envisioned benefiting from ongoing mental health support from care managers, including support in using digital tools. Patients' and care managers' needs may diverge such that meeting both through the same tools presents a significant challenge. We discuss how successful design and integration of digital tools into care management would require reconceptualizing these professionals' roles in mental health support.

Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being.

Conversational artificial intelligence (AI), particularly AI-based conversational agents (CAs), is gaining traction in mental health care. Despite their growing usage, there is a scarcity of comprehensive evaluations of their impact on mental health and well-being. This systematic review and meta-analysis aims to fill this gap by synthesizing evidence on the effectiveness of AI-based CAs in improving mental health and factors influencing their effectiveness and user experience. Twelve databases were searched for experimental studies of AI-based CAs' effects on mental illnesses and psychological well-being published before May 26, 2023. Out of 7834 records, 35 eligible studies were identified for systematic review, out of which 15 randomized controlled trials were included for meta-analysis. The meta-analysis revealed that AI-based CAs significantly reduce symptoms of depression (Hedge's g 0.64 [95% CI 0.17-1.12]) and distress (Hedge's g 0.7 [95% CI 0.18-1.22]). These effects were more pronounced in CAs that are multimodal, generative AI-based, integrated with mobile/instant messaging apps, and targeting clinical/subclinical and elderly populations. However, CA-based interventions showed no significant improvement in overall psychological well-being (Hedge's g 0.32 [95% CI -0.13 to 0.78]). User experience with AI-based CAs was largely shaped by the quality of human-AI therapeutic relationships, content engagement, and effective communication. These findings underscore the potential of AI-based CAs in addressing mental health issues. Future research should investigate the underlying mechanisms of their effectiveness, assess long-term effects across various mental health outcomes, and evaluate the safe integration of large language models (LLMs) in mental health care.