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BackgroundDiabetes is a leading cause of years of life lost and accounts for approximately one-fourth of health care dollars spent in the United States. Many of these costs are related to poor medication adherence and lack of self-care behaviors and are thus preventable. Depression, which is more prevalent among people with diabetes than in the general population, predicts poorer management of one’s diabetes, whereas positive affect predicts engaging in more positive health behaviors. Consequently, interventions that improve depression and positive affect may also improve diabetes-related outcomes among people with diabetes. Although preliminary research on the impact of such interventions among people with diabetes is promising, these studies focused primarily on in-person interventions, have had small samples, and lack long-term follow-up. ObjectiveThis study aims to examine the short- and long-term effects of a digital therapeutic platform focused on mental health among adults with poorly managed type 2 diabetes and elevated levels of depression. MethodsThis is a randomized controlled trial in which adults with a type 2 diabetes diagnosis, elevated hemoglobin A1c (HbA1c) levels (≧7), and moderate to severe depressive symptoms will be randomly assigned to a positive emotion regulation skills intervention group or a sham digital intervention with only psychoeducational content. The study will take place over 14 months, including the 8-week intervention (or control) delivered via a digital therapeutic platform (Happify Health) and follow-up assessments at 3, 6, and 12 months postintervention. Throughout the intervention and for 1 week at each postintervention follow-up, participants will complete daily assessments of diabetes-related distress, diabetes regimen adherence, and mood. Our primary outcome, HbA1c, will be self-reported every 3 months throughout the study. Secondary and exploratory outcomes will be assessed at baseline; at 8 weeks; and at 3, 6, and 12 months postintervention. ResultsRecruitment is expected to begin in June 2020. Participants will begin the study as they are recruited and will finish in waves. The final wave of data collection from the 8-week intervention is expected for winter 2020, with the completion of the 12-month follow-up in winter 2021. ConclusionsAlthough previous research suggests that in-person psychological interventions have promising effects on both psychological and physical outcomes among adults with diabetes, digital interventions can be advantageous because they are easily scalable and reduce many barriers that prevent people from seeking treatment. This trial will provide important information about the effects of a digital mental health intervention among adults with type 2 diabetes, assessing both short- and long-term effects of this intervention on HbA1c, depressive symptoms, and other diabetes-specific outcomes. If successful, this may introduce a scalable intervention that would help reduce some of the preventable costs associated with diabetes. Trial RegistrationClinicalTrials.gov NCT04068805; https://clinicaltrials.gov/ct2/show/NCT04068805. International Registered Report Identifier (IRRID)PRR1-10.2196/18578

OBJECTIVE:. Pulse oximetry, a ubiquitous vital sign in modern medicine, has inequitable accuracy that disproportionately affects minority Black and Hispanic patients, with associated increases in mortality, organ dysfunction, and oxygen therapy. Previous retrospective studies used self-reported race or ethnicity as a surrogate for skin tone which is believed to be the root cause of the disparity. Our objective was to determine the utility of skin tone in explaining pulse oximetry discrepancies. DESIGN:. Prospective cohort study. SETTING:. Patients were eligible if they had pulse oximetry recorded up to 5 minutes before arterial blood gas (ABG) measurements. Skin tone was measured using administered visual scales, reflectance colorimetry, and reflectance spectrophotometry. PARTICIPANTS:. Admitted hospital patients at Duke University Hospital. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. Sao2–Spo2 bias, variation of bias, and accuracy root mean square, comparing pulse oximetry, and ABG measurements. Linear mixed-effects models were fitted to estimate Sao2–Spo2 bias while accounting for clinical confounders. One hundred twenty-eight patients (57 Black, 56 White) with 521 ABG-pulse oximetry pairs were recruited. Skin tone data were prospectively collected using six measurement methods, generating eight measurements. The collected skin tone measurements were shown to yield differences among each other and overlap with self-reported racial groups, suggesting that skin tone could potentially provide information beyond self-reported race. Among the eight skin tone measurements in this study, and compared with self-reported race, the Monk Scale had the best relationship with differences in pulse oximetry bias (point estimate: –2.40%; 95% CI, –4.32% to –0.48%; p = 0.01) when comparing patients with lighter and dark skin tones. CONCLUSIONS:. We found clinical performance differences in pulse oximetry, especially in darker skin tones. Additional studies are needed to determine the relative contributions of skin tone measures and other potential factors on pulse oximetry discrepancies.

BackgroundIn light of recent retrospective studies revealing evidence of disparities in access to medical technology and of bias in measurements, this narrative review assesses digital determinants of health (DDoH) in both technologies and medical formulae that demonstrate either evidence of bias or suboptimal performance, identifies potential mechanisms behind such bias, and proposes potential methods or avenues that can guide future efforts to address these disparities.ApproachMechanisms are broadly grouped into physical and biological biases (e.g., pulse oximetry, non-contact infrared thermometry [NCIT]), interaction of human factors and cultural practices (e.g., electroencephalography [EEG]), and interpretation bias (e.g, pulmonary function tests [PFT], optical coherence tomography [OCT], and Humphrey visual field [HVF] testing). This review scope specifically excludes technologies incorporating artificial intelligence and machine learning. For each technology, we identify both clinical and research recommendations.ConclusionsMany of the DDoH mechanisms encountered in medical technologies and formulae result in lower accuracy or lower validity when applied to patients outside the initial scope of development or validation. Our clinical recommendations caution clinical users in completely trusting result validity and suggest correlating with other measurement modalities robust to the DDoH mechanism (e.g., arterial blood gas for pulse oximetry, core temperatures for NCIT). Our research recommendations suggest not only increasing diversity in development and validation, but also awareness in the modalities of diversity required (e.g., skin pigmentation for pulse oximetry but skin pigmentation and sex/hormonal variation for NCIT). By increasing diversity that better reflects patients in all scenarios of use, we can mitigate DDoH mechanisms and increase trust and validity in clinical practice and research.

BackgroundThis manuscript describes the first two phases of pilot testing MARIGOLD, an online self-guided positive emotion skills intervention for adults with elevated depressive symptoms, along with enhancements to overcome retention and adherence problems reported in previous research.MethodsAdults with elevated depressive symptoms were recruited online and assessed at baseline, post-intervention, 1- and 3-month follow-up. Phase 1 participants (n = 58) were randomized to MARIGOLD, daily emotion reporting, or waitlist. Phase 2 participants (n = 79) were randomized to MARIGOLD plus one enhancement: online discussion board (ODB), virtual badges (VB), or facilitator contact (FC). Post-intervention interviews assessed acceptability. Intention-to-treat analyses examined retention, adherence, and preliminary efficacy.ResultsIn both phases, retention and adherence did not differ between groups. MARIGOLD skills were highly acceptable, but qualitative results indicate web-based features (e.g., log-in, ODB, VB) require refinement prior to larger testing. Neither phase demonstrated between-group differences in preliminary efficacy. In Phase 1 within-group analyses, MARIGOLD and emotion reporting control demonstrated a similar pattern of findings (stable depressive symptoms, increased positive emotion, decreased negative emotion and stress), whereas the waitlist group significantly increased in depressive mood. Most Phase 2 within-group analyses demonstrated the expected pattern of results (i.e., decreases in PHQ-8 and negative emotion, increases in positive emotion). However, CES-D scores were stable in FC; perceived stress was stable in FC and ODB.LimitationsThis pilot study is not powered to evaluate efficacy.ConclusionPositive emotion skills, plus enhancements for web-based, self-guided delivery, warrant additional study in people with elevated depressive symptoms.

Background: The deployment of Artificial Intelligence (AI) in healthcare has the potential to transform patient care through improved diagnostics, personalized treatment plans, and more efficient resource management. However, the effectiveness and fairness of AI are critically dependent on the data it learns from. Biased datasets can lead to AI outputs that perpetuate disparities, particularly affecting social minorities and marginalized groups. Objective: This paper introduces the "Data Artifacts Glossary", a dynamic, open-source framework designed to systematically document and update potential biases in healthcare datasets. The aim is to provide a comprehensive tool that enhances the transparency and accuracy of AI applications in healthcare and contributes to understanding and addressing health inequities. Methods: Utilizing a methodology inspired by the Delphi method, a diverse team of experts conducted iterative rounds of discussions and literature reviews. The team synthesized insights to develop a comprehensive list of bias categories and designed the glossary's structure. The Data Artifacts Glossary was piloted using the MIMIC-IV dataset to validate its utility and structure. Results: The Data Artifacts Glossary adopts a collaborative approach modeled on successful open-source projects like Linux and Python. Hosted on GitHub, it utilizes robust version control and collaborative features, allowing stakeholders from diverse backgrounds to contribute. Through a rigorous peer review process managed by community members, the glossary ensures the continual refinement and accuracy of its contents. The implementation of the Data Artifacts Glossary with the MIMIC-IV dataset illustrates its utility. It categorizes biases, and facilitates their identification and understanding. Conclusion: The Data Artifacts Glossary serves as a vital resource for enhancing the integrity of AI applications in healthcare by providing a mechanism to recognize and mitigate dataset biases before they impact AI outputs. It not only aids in avoiding bias in model development but also contributes to understanding and addressing the root causes of health disparities. [ABSTRACT FROM AUTHOR]

Racial and ethnic disparities in malnutrition are well-known, but it is unknown if there are disparities in early nutrition delivery for intensive care unit (ICU) patients, which is associated with better outcomes. We investigated the timing of enteral nutrition (EN) and parenteral nutrition (PN) initiation in the ICU, examining for racial differences. Using the eICU-Collaborative Research Database (eICU-CRD) from 2014 to 2015, we analyzed patients eligible for EN and PN from 208 hospitals. EN and PN delivery was captured through intake/output entries. Exclusions included pre-existing EN/PN and short (<4 d) mechanical ventilation. Severity-of-illness was assessed using the sequential organ failure assessment (SOFA) score. Self-identified race was defined as the primary exposure, and Cox proportional hazards models were used to examine the association between race and time to EN and PN initiation, adjusting for patient, ICU, and hospital characteristics. Of 1914 patients from 14 hospitals with EN data (5.3% Black, 42.4% female, median age 65 y), 888 received EN. Among Black and White patients, the median [Q1, Q3] time from mechanical ventilation to EN was 1.5 [1.0, 2.8] d. Race was not associated with time until EN initiation (hazard ratio = 0.961, 95% confidence interval 0.693, 1.333). Rather, other variables including sex, SOFA score, hospital characteristics and ICU unit type appeared to account for variation in EN initiation. Among 31,551 patients from 59 hospitals with PN data (11.3% Black, 45.1% female, median age 67 y), 1140 received PN, with a median [Q1, Q3] time to PN initiation of 7.4 [4.2, 12.4] d amongst Black and White patients. Race was not associated with time until PN initiation (hazard ratio = 1.095, 95% confidence interval = 0.901, 1.331). Instead, sex, body mass index, SOFA score, hospital characteristics and ICU unit type appeared to account for variation in PN initiation. Disparities in EN and PN delivery in the eICU-CRD dataset from 2014 to 2015 were not associated with race, but rather with sex, body mass index, SOFA, hospital characteristics and ICU unit type. Further investigations using more current data are needed. [ABSTRACT FROM AUTHOR]

OBJECTIVES:. The Centers for Disease Control has well-established surveillance programs to monitor preventable conditions in patients supported by mechanical ventilation (MV). The aim of the study was to develop a data-driven methodology to examine variations in the first tier of the ventilator-associated event surveillance definition, described as a ventilator-associated condition (VAC). Further, an interactive tool was designed to illustrate the effect of changes to the VAC surveillance definition, by applying different ventilator settings, time-intervals, demographics, and selected clinical criteria. DESIGN:. Retrospective, multicenter, cross-sectional analysis. SETTING:. Three hundred forty critical care units across 209 hospitals, comprising 261,910 patients in both the electronic Intensive Care Unit Clinical Research Database and Medical Information Mart for Intensive Care III databases. PATIENTS:. A total of 14,517 patients undergoing MV for 4 or more days. MEASUREMENTS AND MAIN RESULTS:. We designed a statistical analysis framework, complemented by a custom interactive data visualization tool to depict how changes to the VAC surveillance definition alter its prognostic performance, comparing patients with and without VAC. This methodology and tool enable comparison of three clinical outcomes (hospital mortality, hospital length-of-stay, and ICU length-of-stay) and provide the option to stratify patients by six criteria in two categories: patient population (dataset and ICU type) and clinical features (minimum Fio2, minimum positive end-expiratory pressure, early/late VAC, and worst first-day respiratory Sequential Organ Failure Assessment score). Patient population outcomes were depicted by heatmaps with mortality odds ratios. In parallel, outcomes from ventilation setting variations and clinical features were depicted with Kaplan-Meier survival curves. CONCLUSIONS:. We developed a method to examine VAC using information extracted from large electronic health record databases. Building upon this framework, we developed an interactive tool to visualize and quantify the implications of variations in the VAC surveillance definition in different populations, across time and critical care settings. Data for patients with and without VAC was used to illustrate the effect of the application of this method and visualization tool.

While technological innovations are the invariable crux of speculation about the future of critical care, they cannot replace the clinician at the bedside. This article summarizes the work of the Society of Critical Care Medicine–appointed multiprofessional task for the Future of Critical Care. The Task Force notes that critical care practice will be transformed by novel technologies, integration of artificial intelligence decision support algorithms, and advances in seamless data operationalization across diverse healthcare systems and geographic regions and within federated datasets. Yet, new technologies will be relevant and meaningful only if they improve the very human endeavor of caring for someone who is critically ill.

Background: eHealth interventions have proven to be valuable resources for users with diverse mental and behavioral health concerns. As these technologies continue to proliferate, both academic researchers and commercial app creators are leveraging the use of features that foster a sense of social connection on these digital platforms. Yet, the literature often insufficiently represents the functionality of these key social features, resulting in a lack of understanding of how they are being implemented. Objective: This study aimed to conduct a methodical review of commercially available eHealth apps to establish the SoCAP (social communication, affiliation, and presence) taxonomy of social features in eHealth apps. Our goal was to examine what types of social features are being used in eHealth apps and how they are implemented. Methods: A scoping review of commercially available eHealth apps was conducted to develop a taxonomy of social features. First, a shortlist of the 20 highest-rated eHealth apps was derived from One Mind PsyberGuide, a nonprofit organization with trained researchers who rate apps based on their (1) credibility, (2) user experience, and (3) transparency. Next, both mobile- and web-based versions of each app were double-coded by 2 trained raters to derive a list of social features. Subsequently, the social features were organized by category and tested on other apps to ensure their completeness. Results: Four main categories of social features emerged: (1) communication features (videoconferencing, discussion boards, etc), (2) social presence features (chatbots, reminders, etc), (3) affiliation and identity features (avatars, profiles, etc), and (4) other social integrations (social network and other app integrations). Our review shows that eHealth apps frequently use resource-intensive interactions (eg, videoconferencing with a clinician and phone calls from a facilitator), which may be helpful for participants with high support needs. Furthermore, among commercially available eHealth apps, there is a strong reliance on automated features (eg, avatars, personalized multimedia, and tailored content) that enhance a sense of social presence without requiring a high level of input from a clinician or staff member. Conclusions: The SoCAP taxonomy includes a comprehensive list of social features and brief descriptions of how these features work. This classification system will provide academic and commercial eHealth app creators with an understanding of the various social features that are commonly implemented, which will allow them to apply these features to enhance their own apps. Future research may include comparing the synergistic effects of various combinations of these social features. [ABSTRACT FROM AUTHOR]