Examining Diversity Recruitment Efforts in US-Based Physical Medicine and Rehabilitation Residencies: A Survey of Program Directors on Gender, Ethnic and Racial Diversity, and Implications for Recruitment Efforts.

As the population of the United States continues to grow and diversify, it is critical that the medical profession follows. This study aimed to evaluate aspects of the current landscape of diversity within physical medicine and rehabilitation by surveying program directors of US-based physical medicine and rehabilitation residencies. The secondary aim was to identify program characteristics that correlate with more diverse residency classes. An online, cross-sectional 17-question survey was distributed to program directors of all US-based physical medicine and rehabilitation residencies with known contact information (95/100), with a completed survey response rate of 53% (50/95). Race and ethnicity categories of the survey were based on those used by the US Census. The percentages of individuals identifying as women or those underrepresented in medicine in this survey were below those of the general US population, a trend also seen within the field of physical medicine and rehabilitation overall. Linear regression revealed no statistically significant association between the percentage of underrepresented in medicine residents and commonly used diversity recruitment initiatives. There was a significant association between the presence of a departmental role for diversity, equity and inclusion and the percentage of women residents (odds ratio, 1.13; P = 0.017). Continued research is required to identify additional strategies with demonstrated efficacy in recruiting diverse residency applicants.

Predictors of Prolonged Length of Stay After Pituitary Adenoma Resection: A Large Cohort Analysis Using the National Inpatient Sample (2016-2019).

OBJECTIVE: In this nationwide retrospective study, the authors aimed to identify demographic, clinical, and baseline health risk factors predictive of a prolonged length of stay (PLOS) for patients with pituitary adenomas (PAs). METHODS: The National Inpatient Sample dataset from 2016 to 2019 was utilized to identify all included hospitalizations for PA resection as identified by the appropriate diagnosis-related group code. Comorbidities were classified based on the Charlson Comorbidity Index mapping of ICD-10 codes, and PLOS was identified as any stay longer than 3 days. Univariable and multivariable logistic regression models, accounting for the sample design, were built to determine factors associated with PLOS and emergent surgery. RESULTS: Overall, 30 945 patients were included in this study with 10 535 patients having PLOS. Female patients experienced an increased odds of PLOS (odds ratio [OR]: 1.29; P < .001). Black patients (OR: 1.49; P < .001) and Hispanic patients (OR: 1.30; P = .003) had 1.49 times and 1.30 times the odds of PLOS compared to White patients, respectively. Compared to patients insured by Medicare, patients insured by Medicaid had an increased odds of PLOS (OR: 1.36; P = .007) as well as emergent surgery (OR: 5.40; P < .001). When stratified by emergent surgeries, Black patients (OR: 1.89; P < .001), Hispanic patients, (OR: 2.14; P < .001), and patients on Medicaid insurance (OR: 1.71; P < .001) were at an increased risk of emergent procedures. However, female sex (OR: 0.65; P < .001), upper third quartile (OR: 0.73; P = .017), and fourth quartile (OR: 0.69; P = .014) of patients categorized by zip code income were at decreased odds of an emergent procedure. CONCLUSIONS: Black and Hispanic patients, patients with Medicaid insurance, and patients of low socioeconomic status patients are at significantly higher risk of emergent PA resection and PLOS. Efforts to prevent emergent surgeries and shorten hospitalization after pituitary surgery may need to primarily focus on patient groups with select sociodemographic characteristics.

An interdisciplinary student-led multifaceted intervention addressing overuse of broad-spectrum antibiotics for patients with penicillin allergies.

BACKGROUND: Though 15% of hospitalized patients have a documented penicillin (PCN) allergy, fewer than 1% have an IgE-mediated reaction that necessitates avoidance of ß-lactam antibiotics. OBJECTIVE: Our interdisciplinary team of medical and nursing students led and executed a two-pronged quality improvement intervention to reduce prescribing of non-ß-lactam antibiotics (NBLs) for patients with reported PCN allergies. To the best of our knowledge, this is the first multidisciplinary student-led intervention aimed at educating providers on low-risk penicillin allergy and encouraging best antibiotic prescribing practices. DESIGN AND PARTICIPANTS: The intervention took place from June 2021 to February 2022. We developed and provided clinician education modules, including peer-to-peer information sharing and in-person small group discussions, as well as clinical decision support (CDS) strategies through the electronic medical record (EMR). The target population was attendings, residents, nurse practitioners, and physician assistants on the hospital medicine service at a large urban academic tertiary care center. We followed the SQUIRE 2.0 guidelines for reporting on quality improvement. MAIN MEASURES: Primary outcome measures included number of NBL prescriptions and use of nonspecific descriptors (e.g., "other" or "unknown") for PCN allergy reaction type, and were compared with a pre-intervention period. KEY RESULTS: The percent of ß-lactam prescriptions for patients with a PCN allergy after the intervention increased from 19 to 23% (p = 0.006). For patients with a low severity PCN allergy, the percent of ß-lactam prescriptions increased from 20 to 28% (p = 0.001). There was a significant decrease in nonspecific PCN allergy reaction type from 23% in the pre-intervention period to 20% post-intervention (p = 0.012). CONCLUSIONS: An intervention focused on educating prescribers and CDS strategies delivered through the EMR increased appropriate ß-lactam prescribing for patients with a documented low-risk PCN allergy and reduced the use of nonspecific PCN allergy reaction type in EMR documentation.

Analysis of physician recipients of recognition awards from the American Academy of Physical Medicine and Rehabilitation by Race and Ethnicity.

BACKGROUND: Medical society recognition awards are important resources for physicians in advancing their careers. There is a need to better understand the representation of physician recipients by race and ethnicity, especially in women with intersectional identities. OBJECTIVE: To assess the proportions of American Academy of Physical Medicine and Rehabilitation (AAPM&R) award recipients by race and ethnicity and the intersection of gender. DESIGN: Cross-sectional and retrospective study. SETTING AND METHODS: One hundred seven (n = 107) published online physician award recipients from 2011 to 2020 were categorized by race, ethnicity, and gender by two independent researchers. There was 100% interrater agreement on race and gender and 95% on ethnicity. Data were analyzed with descriptive analysis and multilinear regression. MAIN OUTCOME MEASUREMENTS: Awards given to physicians coded by race (White/Caucasian, Asian, and Black/African American), ethnicity (Hispanic/Latino), and the intersection of gender with race and ethnicity were analyzed. The primary comparator was proportions by race, ethnicity, and gender of academic physicians in physical medicine and rehabilitation (PM&R) using Association of American Medical Colleges (AAMC) data. A secondary aim was recipients' proportions compared to AAMC benchmarks for all practicing physiatrists. RESULTS: There were no significant differences in representation of award recipients by race or ethnicity compared to the primary comparator of their percentages in academic PM&R. Notably, 96.3% of awards were given to physicians identified as being in or having been in academic medicine. Secondary analysis of award recipients to all practicing physiatrists revealed significant underrepresentation of recipients who were coded as (1) White/Caucasian women, Asian men and women, Black/African American men and women (p = .016), and (2) Hispanic/Latino men and women (p = .028). CONCLUSIONS: This is a novel study assessing race and ethnicity in physician recognition awards presented by a medical society. No significant disparities were found among recipients as compared to representation in academic PM&R. However, there were significant disparities when compared to all practicing physiatrists. These findings deserve further investigation and consideration as medical societies strive to equitably support all members.

Seasonal Effects on Postoperative Complications After Spinal Surgery: A National Database Analysis.

OBJECTIVE: To investigate the role of seasonality on postoperative complications after spinal surgery. METHODS: Data were obtained from the American College of Surgeons National Surgical Quality Improvement Program database from 2011 to 2018. Current Procedural Terminology codes were used to identify the following procedures: posterior cervical decompression and fusion, cervical laminoplasty, posterior lumbar fusion, lumbar laminectomy, and spinal deformity surgery. The database was queried for deep vein thrombosis (DVT), pulmonary embolism, pneumonia, sepsis, septic shock, Clostridium difficile infection, stroke, cardiac arrest, myocardial infarction, urinary tract infection (UTI), and early unplanned hospital readmission (readmission). Warm season was defined as April-September, whereas cold season was defined as October-March. Statistical analysis included computing overall complication rates and comparison between seasons using univariate analysis and multivariable logistic regression. RESULTS: A total of 208,291 individuals underwent spinal surgery from 2011 to 2018. There was a statistically significant increase in UTI (odds ratio [OR], 1.16; 95% confidence interval [CI], 1.07-1.26; P = 0.0002) and readmission (OR, 1.06; 95% CI, 1.02-1.11, P = 0.007) in the warm season compared with the cold season. An investigation into the July effect showed increases in DVT (OR, 1.24; 95% CI, 1.03-1.48; P = 0.020) and thromboembolic events (OR 1.17; 95% CI, 1.01-1.35; P = 0.032) in July-September compared with the preceding 3 months. CONCLUSIONS: The results showed a higher incidence of UTI and readmission among spine surgery patients in the warm season and a higher incidence of DVT and thromboembolic events from July to September. In both cases, the effect of seasonality is statistically significant, but the absolute difference is small and may not suggest policy changes.

Screw-only fibular construct for Weber B ankle fractures: A retrospective clinical and cost comparison to assess feasibility for resource-limited settings.

INTRODUCTION: Ankle fractures are one of the most common injuries sustained worldwide, with the majority being isolated lateral malleolus fractures. The majority of the world's population live in Low and Middle Income Countries (LMIC), where implant cost may limit surgical treatment of ankle fractures. We investigate if Weber B ankle fractures could be effectively treated with a lower-cost technique using two screws between the fibula and the tibia to neutralize an interfragmentary lag screw. METHODS: After IRB approval, consecutive patients from January 1, 2020 to December 31, 2020 with Weber-B ankle fractures were treated using AO technique (AOT) with plate osteosynthesis neutralizing an interfragmentary screw. Syndesmotic injuries, as well as injuries to the medial malleolus or foot were treated according to the surgeon's preferences. From January 1, 2021 to December 31, 2021 these injuries were treated with a screw-only technique (SOT) with two fibula pro tibia screws to neutralize an interfragmentary screw. Patient demographics including age, sex, BMI, smoking status, associated rheumatoid arthritis, and associated diabetes mellitus were recorded. The primary outcome variable was a stable radiographic mortise at six weeks post-surgery, secondary outcome variables included clinical union, infection, hardware removal, and implant cost for lateral malleolar fixation charged to the hospital. RESULTS: Seventeen AOT and 10 SOT constructs were included. Demographic characteristics were similar between groups. All fractures maintained a stable mortise with clinical union at 6 weeks without infection. There was a statistically significant difference in hardware removal (17.6% AOT, 50% SOT, p = 0.012). The average implant cost to the hospital of the lateral malleolar fixation was significantly less in the SOT group ($592 (SD $229)), compared to the AOT group ($1,949.97 (SD $562)), (p < 0.0001). CONCLUSION: We introduce proof of concept of a novel lower-cost fixation strategy for Weber B ankle fractures that maintained a stable mortise with clinical union at six weeks post-surgery. However, there was a significantly higher rate of hardware removal following fixation with a screw-only construct.

Identification of Anterior Cervical Spinal Instrumentation Using a Smartphone Application Powered by Machine Learning.

STUDY DESIGN: Cross-sectional study. OBJECTIVE: The purpose of this study is to develop and validate a machine learning algorithm for the automated identification of anterior cervical discectomy and fusion (ACDF) plates from smartphone images of anterior-posterior (AP) cervical spine radiographs. SUMMARY OF BACKGROUND DATA: Identification of existing instrumentation is a critical step in planning revision surgery for ACDF. Machine learning algorithms that are known to be adept at image classification may be applied to the problem of ACDF plate identification. METHODS: A total of 402 smartphone images containing 15 different types of ACDF plates were gathered. Two hundred seventy-five images (∼70%) were used to train and validate a convolution neural network (CNN) for classification of images from radiographs. One hundred twenty-seven (∼30%) images were held out to test algorithm performance. RESULTS: The algorithm performed with an overall accuracy of 94.4% and 85.8% for top-3 and top-1 accuracy, respectively. Overall positive predictive value, sensitivity, and f1-scores were 0.873, 0.858, and 0.855, respectively. CONCLUSION: This algorithm demonstrates strong performance in the classification of ACDF plates from smartphone images and will be deployed as an accessible smartphone application for further evaluation, improvement, and eventual widespread use.Level of Evidence: 3.

Case studies from the digital clinic: integrating digital phenotyping and clinical practice into today's world.

The following case series provides several examples from the Digital Clinic, an outpatient mental health program which uses smartphone technology to augment traditional mental health care. The themes highlighted in this piece, expanding emotional-awareness, symptom tracking, and medication management, provide real-clinical examples of how the Digital Clinic offered remote mental health care to a diverse group of people. Furthermore, the following piece demonstrates to practicing clinicians how digital technologies, like smartphone apps, can diversify methods of clinical engagement, assist with collecting health metrics in a safe and ethical manner, and promote person centred care. With the COVID-19 pandemic forcing re-evaluation of how mental health services are provided, it is critical to ensure that digitally infused systems of care, like the Digital Clinic, are effective, accessible, and scalable.

Investigating Associations Between Screen Time and Symptomatology in Individuals With Serious Mental Illness: Longitudinal Observational Study.

BACKGROUND: Increasing screen time exposure from digital devices like smartphones has shown a variety of mixed associations with cognition, behavior, and well-being in adults and children but little is known about its associations with symptomatology in individuals with serious mental illness. OBJECTIVE: To determine the range of associations between screen time and symptoms of individuals with mental illness, we utilized a method called specification curve analysis. METHODS: In this observational study, we recruited smartphone-owning adults (≥18 years old) with schizophrenia and healthy controls. We installed 2 research-source smartphone apps, mindLAMP and Beiwe, to collect survey results, cognitive test results, and screen time metrics over a period of 3 months. Surveys were scheduled for twice a week, but participants were instructed to take the surveys naturally as much or as little as they wanted. Screen time was collected continuously in the background. A total of 140 participants was recruited from the outpatient clinic population as well as through general public advertising. Age-matched, smartphone-owning healthy controls were also part of the recruitment pool. A specification curve analysis was a priori designed to explore the relationship between every combination of independent variable and dependent variable in order to demonstrate to what degree screen time relates to symptoms in individuals with serious mental illness. RESULTS: The sample consisted of 88 participants (54 with schizophrenia and 34 healthy controls) who completed both the initial and follow-up visits, completed at least one self-reported survey, and had a minimum passive data cutoff of 5 consecutive days. While we found an association between smartphone screen time metrics and cognition (adjusted R2=0.107, P<.001), specification curve analysis revealed a wide range of heterogenous associations with screen time from very negative to very positive. The effects differed based on diagnostic group, age bracket, type of regression model used, and the specific independent and dependent variables selected for analysis. CONCLUSIONS: The associations between screen time and mental health in patients with schizophrenia are heterogenous when examined with methods that reduce analytical bias. The heterogeneity in associations suggests that complex and personalized potential effects must be understood in the greater context of an individual. This analysis of longitudinally collected screen time data shows potential for future research that could benefit from high resolution metrics on smartphone use.

Anomaly detection to predict relapse risk in schizophrenia.

The integration of technology in clinical care is growing rapidly and has become especially relevant during the global COVID-19 pandemic. Smartphone-based digital phenotyping, or the use of integrated sensors to identify patterns in behavior and symptomatology, has shown potential in detecting subtle moment-to-moment changes. These changes, often referred to as anomalies, represent significant deviations from an individual's baseline, may be useful in informing the risk of relapse in serious mental illness. Our investigation of smartphone-based anomaly detection resulted in 89% sensitivity and 75% specificity for predicting relapse in schizophrenia. These results demonstrate the potential of longitudinal collection of real-time behavior and symptomatology via smartphones and the clinical utility of individualized analysis. Future studies are necessary to explore how specificity can be improved, just-in-time adaptive interventions utilized, and clinical integration achieved.

Digital Health Around Clinical High Risk and First-Episode Psychosis.

PURPOSE OF REVIEW: This review aims to examine relapse definitions and risk factors in psychosis as well as the role of technology in relapse predictions and risk modeling. RECENT FINDINGS: There is currently no standard definition for relapse. Therefore, there is a need for data models that can account for the variety of factors involved in defining relapse. Smartphones have the ability to capture real-time, moment-to-moment assessment symptomology and behaviors via their variety of sensors and have high potential to be used to create prediction and risk modeling. While there is still a need for further research on how technology can predict and model relapse, there are simple ways to begin incorporating technology for relapse prediction in clinical care.

Impact of dynamic greenspace exposure on symptomatology in individuals with schizophrenia.

There are currently many tools available for capturing and defining the context of one's environment. Digital phenotyping, the use of technology and sensors to capture moment-to-moment behavior, has shown potential in quantifying the lived experience of mental illness and in the identification of individualized targets related to recovery. Environmental data suggests that greenspace may have a restorative capacity on mental health. In this paper, we explore the relationship of greenspace derived from geolocation with self-reported symptomatology from individuals with schizophrenia as well as healthy controls. Individuals with schizophrenia had less exposure to greenspace than controls, but their exposure demonstrated a dosage effect: high greenspace environments were associated with lower symptoms for anxiety (Cohen's d = -0.70), depression (d = -0.97), and psychosis (d = -0.94), whereas effect sizes for healthy controls were all negligible or small (d < 0.38). The notion that greenspace may have a more pronounced effect on individuals with mental illness presents both potential areas for recovery as well as implications for health care policy, especially in cities with a broad range of greenspace environments.

Do neurobiological differences exist between paranoid and non-paranoid schizophrenia? Findings from the bipolar schizophrenia network on intermediate phenotypes study.

Subtypes of schizophrenia, constructed using clinical phenomenology to resolve illness heterogeneity, have faced criticism due to overlapping symptomatology and longitudinal instability; they were therefore dropped from the Diagnostic Statistical Manual-5. Cognitive and imaging findings comparing paranoid (P-SZ) and non-paranoid (disorganized, residual and undifferentiated; NP-SZ) schizophrenia have been limited due to small sample sizes. We assessed P-SZ and NP-SZ using symptomatology, cognition and brain structure and predicted that there would be few neurobiological differences. P-SZ (n = 237), NP-SZ (n = 127) and controls (n = 430) were included from a multi-site study. In a subset of this sample, structural imaging measures (P-SZ, n = 133; NP-SZ, n = 67; controls, n = 310) were calculated using Freesurfer 6.0. Group contrasts were run using analysis of covariance, controlling for age, sex, race and site, p-values were corrected using False Discovery Rate (FDR) and were repeated excluding the residual subtype. Compared to NP-SZ (with and without the residual subtype), P-SZ displayed fewer negative symptoms, faster speed of processing, larger bilateral hippocampus, right amygdala and their subfield volumes. Additionally, NP-SZ (with residual subtype) displayed fewer depressive symptoms and higher left transverse temporal cortical thickness (CT) but NP-SZ without residual subtype showed lower GAF scores and worse digit sequencing compared to P-SZ. No differences in positive symptoms and functioning (global or social) were detected. Subtle but significant differences were seen in cognition, symptoms, CT and subcortical volumes between P-SZ and NP-SZ. While the magnitude of these differences is not large enough to justify them as distinct categories, the paranoid- nonparanoid distinction in schizophrenia merits further investigation.

Technology Evaluation and Assessment Criteria for Health Apps (TEACH-Apps): Pilot Study.

BACKGROUND: Despite the emergence of app evaluation tools, there remains no well-defined process receptive to diverse local needs, rigorous standards, and current content. The need for such a process to assist in the implementation of app evaluation across all medical fields is evident. Such a process has the potential to increase stakeholder engagement and catalyze interest and engagement with present-day app evaluation models. OBJECTIVE: This study aimed to develop and pilot test the Technology Evaluation and Assessment Criteria for Health apps (TEACH-apps). METHODS: Tailoring a well-known implementation framework, Replicating Effective Programs, we present a new process to approach the challenges faced in implementing app evaluation tools today. As a culmination of our experience implementing this process and feedback from stakeholders, we present the four-part process to aid the implementation of mobile health technology. This paper outlines the theory, evidence, and initial versions of the process. RESULTS: The TEACH-apps process is designed to be broadly usable and widely applicable across all fields of health. The process comprises four parts: (1) preconditions (eg, gathering apps and considering local needs), (2) preimplementation (eg, customizing criteria and offering digital skills training), (3) implementation (eg, evaluating apps and creating educational handouts), and (4) maintenance and evolution (eg, repeating the process every 90 days and updating content). TEACH-apps has been tested internally at our hospital, and there is growing interest in partnering health care facilities to test the system at their sites. CONCLUSIONS: This implementation framework introduces a process that equips stakeholders, clinicians, and users with the foundational tools to make informed decisions around app use and increase app evaluation engagement. The application of this process may lead to the selection of more culturally appropriate and clinically relevant tools in health care.

Towards clinically actionable digital phenotyping targets in schizophrenia.

Digital phenotyping has potential to quantify the lived experience of mental illness and generate real-time, actionable results related to recovery, such as the case of social rhythms in individuals with bipolar disorder. However, passive data features for social rhythm clinical targets in individuals with schizophrenia have yet to be studied. In this paper, we explore the relationship between active and passive data by focusing on temporal stability and variance at an individual level as well as large-scale associations on a population level to gain clinically actionable information regarding social rhythms. From individual data clustering, we found a 19% cluster overlap between specific active and passive data features for participants with schizophrenia. In the same clinical population, two passive data features in particular associated with social rhythms, "Circadian Routine" and "Weekend Day Routine," and were negatively associated with symptoms of anxiety, depression, psychosis, and poor sleep (Spearman ρ ranged from -0.23 to -0.30, p < 0.001). Conversely, in healthy controls, more stable social rhythms were positively correlated with symptomatology (Spearman ρ ranged from 0.20 to 0.44, p < 0.05). Our results suggest that digital phenotyping in schizophrenia may offer clinically relevant information for understanding how daily routines affect symptomatology. Specifically, negative correlations between smartphone reported anxiety, depression, psychosis, and poor sleep in individuals with schizophrenia, but not in healthy controls, offer an actionable clinical target and area for further investigation.

Feasibility and correlations of smartphone meta-data toward dynamic understanding of depression and suicide risk in schizophrenia.

OBJECTIVES: We investigate whether meta-data, specifically duration of responses to smartphone-delivered surveys, is correlated to elevated scores on the depression assessment PHQ-9 as well as the specific item around self-harm (item 9). METHODS: In this observational study, we recruited 92 smartphone-owning adults (≥ 18) with schizophrenia (45) and healthy controls (43). We installed an open-source smartphone app called mindLAMP to collect survey results and latencies (response times) over a period of 3 months. Surveys were scheduled for twice a week, but participants were instructed to take the surveys naturally as much or as little as they wanted. A total of 1,218 PHQ-9 surveys were completed across all participants over 3 months. RESULTS: A total of 75 participants (39 with schizophrenia and 36 healthy controls) completed both the initial visit and follow-up, as well as provided at least one self-reported PHQ-9 survey through the app. We found that depression symptom severity and response latencies were correlated for both individuals with schizophrenia (Spearman's ρ = .22, p = .037) and healthy controls (Spearman's ρ = .58, p < .001). Participants with schizophrenia scored higher (more severe) and took longer for every item of the PHQ-9 when compared to controls (p < .05 for each item). Item 9 response value and latency was slightly correlated for participants with schizophrenia (Spearman's ρ = .086, p = .035) but was not significant for controls (Spearman's ρ = .036, p = .37). CONCLUSIONS: Meta-data revealed group differences between individuals with schizophrenia and healthy controls based on individual depression symptoms completed on a smartphone. Correlation between suicide specific question latency and severity for participants with schizophrenia but not for controls indicates the clinical potential and need for further research.

Longitudinal trends in the quality, effectiveness and attributes of highly rated smartphone health apps.

BACKGROUND: While there are numerous mental health apps on the market today, less is known about their safety and quality. This study aims to offer a longitudinal perspective on the nature of high visibility apps for common mental health and physical health conditions. METHODS: In July 2019, we selected the 10 top search-returned apps in the Apple App Store and Android Google Play Store using six keyword terms: depression, anxiety, schizophrenia, addiction, high blood pressure and diabetes. Each app was downloaded by two authors and reviewed by a clinician, and the app was coded for features, functionality, claims, app store properties, and other properties. RESULTS: Compared with 1 year prior, there were few statistically significant changes in app privacy policies, evidence and features. However, there was a high rate of turnover with only 34 (57%) of the apps from the Apple's App Store and 28 (47%) from the Google Play Store remaining in the 2019 top 10 search compared with the 2018 search. DISCUSSION: Although there was a high turnover of top search-returned apps between 2018 and 2019, we found that there were few significant changes in features, privacy, medical claims and other properties. This suggests that, although the highly visible and available apps are changing, there were no significant improvements in app quality or safety.

Digital Opportunities for Outcomes in Recovery Services (DOORS): A Pragmatic Hands-On Group Approach Toward Increasing Digital Health and Smartphone Competencies, Autonomy, Relatedness, and Alliance for Those With Serious Mental Illness.

Digital health technologies such as smartphones present the potential for increased access to care and on-demand services. However, many patients with serious mental illnesses (eg, schizophrenia) have not been offered the digital health training necessary to fully utilize these innovative approaches. To bridge this digital divide in knowledge and skills, we created a hands-on and interactive training program grounded in self-determination theory, technology use cases, and the therapeutic alliance. This article introduces the need and theoretical foundation for and the experience of running the resulting Digital Opportunities for Outcomes in Recovery Services (DOORS) group in the setting of 2 programs: a first-episode psychosis program and a clubhouse for individuals with serious mental illness. The experience of running these 2 DOORS groups resulted in 2 publicly available, free training manuals to empower others to run such groups and adapt them for local needs. Future work on DOORS will expand the curriculum to best support digital health needs and increase equity of access to and knowledge and skills related to technology use in serious mental illness.

Assessing Cognition Outside of the Clinic: Smartphones and Sensors for Cognitive Assessment Across Diverse Psychiatric Disorders.

Traditionally, the assessment of cognition and the administration of cognitive therapies has been performed in the clinic, but with modern technology, this clinic-centric view is changing. This article explores the landscape of digital devices used to measure cognition in settings outside the clinic. These devices range in mobility from user-friendly mobile devices to setting-specific devices able to provide powerful, robust cognitive therapy and living assistance in the comfort of a patient's home. Although these methods remain in early stages of developmental, initial studies suggest they may prove useful in treating patients with serious mental illnesses in a widespread clinical setting.

Using a Smartphone App to Identify Clinically Relevant Behavior Trends via Symptom Report, Cognition Scores, and Exercise Levels: A Case Series.

The use of smartphone apps for research and clinical care in mental health has become increasingly popular, especially within youth mental health. In particular, digital phenotyping, the monitoring of data streams from a smartphone to identify proxies for functional outcomes like steps, sleep, and sociability, is of interest due to the ability to monitor these multiple relevant indications of clinically symptomatic behavior. However, scientific progress in this field has been slow due to high heterogeneity among smartphone apps and lack of reproducibility. In this paper, we discuss how our division utilized a smartphone app to retrospectively identify clinically relevant behaviors in individuals with psychosis by measuring survey scores (symptom report), games (cognition scores), and step count (exercise levels). Further, we present specific cases of individuals and how the relevance of these data streams varied between them. We found that there was high variability between participants and that each individual's relevant behavior patterns relied heavily on unique data streams. This suggests that digital phenotyping has high potential to augment clinical care, as it could provide an efficient and individualized mechanism of identifying relevant clinical implications even if population-level models are not yet possible.