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Objective:The purpose of this exploratory study was to describe associations between NIH Toolbox-Cognition Battery subtests and legacy measures of neurocognitive function in two samples with neurological conditions (stroke and sickle cell disease (SCD)).Method:This exploratory secondary analysis uses data from two studies that assessed cognition at one time point using the NIH Toolbox-Cognition Battery, the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), and subtests from the Delis-Kaplan Executive Functions System (DKEFS). People with stroke (n = 26) and SCD (n = 64) were included. Associations between the NIH Toolbox-Cognition Battery subtests and corresponding legacy measures were examined using linear correlations, Bland–Altman analysis, and Lin’s Concordance Correlation Coefficient.Results:Linear correlations and Lin’s Concordance Correlation Coefficient were poor to strong in both samples on NIH Toolbox-CB subtests: Flanker Inhibitory Control and Attention (r = .35 to .48, Lin CCC = .27 to .37), Pattern Comparison Processing Speed (r = .40 to .65, Lin CCC = .37 to .62), Picture Sequence Memory (r = .19 to .55, Lin CCC = .18 to .48), Dimensional Change Card Sort (r = .39 to .77, Lin CCC = .38 to .63), Fluid Cognition Composite (r = .88 to .90, Lin CCC = .60 to .79), and Total Cognition Composite (r = .64 to .83, Lin CCC = .60 to .78). Bland–Altman analyses demonstrated wide limits of agreement across all subtests (–3.17 to 3.78).Conclusions:The NIH Toolbox-Cognition Battery subtests may behave similarly to legacy measures as an overall assessment of cognition across samples at risk for neurological impairment. Findings should be replicated across additional clinical samples.

Health care delivery shifted and adapted with the COVID-19 pandemic caused by the novel severe acute respiratory syndrome coronavirus 2. Stroke care was negatively affected across the care continuum and may lead to poor community living outcomes in those who survived a stroke during the ongoing pandemic. For instance, delays in seeking care, changes in length of stays, and shifts in discharge patterns were observed during the pandemic. Those seeking care were younger and had more severe neurologic effects from stroke. Increased strain was placed on caregivers and public health efforts, and community-wide lockdowns, albeit necessary to reduce the spread of COVID-19, had detrimental effects on treatment and recommendations to support community living outcomes. The American Congress of Rehabilitation Medicine Stroke Interdisciplinary Special Interest Group Health and Wellness Task Force convened to (1) discuss international experiences in stroke care and rehabilitation and (2) review recently published literature on stroke care and outcomes during the pandemic. Based on the findings in the literature, the task force proposes recommendations and interdisciplinary approaches at the (1) institutional and societal level; (2) health care delivery level; and (3) individual and interpersonal level spanning across the care continuum and into the community.

Psychotherapy is a standard depression treatment; however, determining a patient's prognosis with therapy relies on clinical judgment that is subject to trial-and-error and provider variability.To develop machine learning (ML) algorithms to predict depression remission for patients undergoing 6 months of problem-solving therapy (PST).Using data from the treatment arm of 2 randomized trials, ML models were trained and validated on ENGAGE-2 (ClinicalTrials.gov, #NCT03841682) and tested on RAINBOW (ClinicalTrials.gov, #NCT02246413) for predictions at baseline and at 2-months. Primary outcome was depression remission using the Depression Symptom Checklist (SCL-20) score 0.5 at 6 months. Predictor variables included baseline characteristics (sociodemographic, behavioral, clinical, psychosocial) and intervention engagement through 2-months.Of the 26 candidate variables, 8 for baseline and 11 for 2-months were predictive of depression remission, and used to train the models. The best-performing model predicted remission with an accuracy significantly greater than chance in internal validation using the ENGAGE-2 cohort, at baseline [72.6% (SD = 3.6%), p 0.0001] and at 2-months [72.3% (5.1%), p 0.0001], and in external validation with the RAINBOW cohort at baseline [58.3% (0%), p 0.0001] and at 2-months [62.3% (0%), p 0.0001]. Model-agnostic explanations highlighted key predictors of depression remission at the cohort and patient levels, including female sex, lower self-reported sleep disturbance, lower sleep-related impairment, and lower negative problem orientation.ML models using clinical and patient-reported data can predict depression remission for patients undergoing PST, affording opportunities for prospective identification of likely responders, and for developing personalized early treatment optimization along the patient care trajectory.

To examine the association between COVID-19 impact and clinical outcomes of an integrated collaborative care intervention for adults with obesity and comorbid depression.Latent class analysis identified clusters of self-reported COVID-19 impact. Cluster characteristics were examined using Fishers' least significant difference method and canonical discriminant analysis. Intervention vs. usual care effects on primary (body mass index [BMI], depressive symptoms) and secondary (anxiety symptoms and other psychosocial) outcomes stratified by cluster were examined using linear mixed models.Three clusters were identified: mental health and sleep impact (cluster 1, n = 37), economic impact (cluster 2, n = 18), and less overall impact (cluster 3, n = 20). Clusters differed in age, income, diet, and baseline coping skills. The intervention led to improvements across several health outcomes compared with usual care, with medium to large effects on functional impairments (standardized mean difference, -0.7 [95% CI: -1.3, -0.1]) in cluster 1, depressive symptoms (-1.1 [95% CI: -2.0, -0.1]) and obesity-related problems (-1.6 [95% CI: -2.8, -0.4]) in cluster 2, and anxiety (-1.1 [95% CI: -1.9, -0.3]) in cluster 3.People with obesity and comorbid depression may have varied intervention responses based on COVID-19 impact. Interventions tailored to specific COVID-19 impact clusters may restore post-pandemic health.

OBJECTIVE: To describe patterns of sedentary behavior over 6 weeks among ambulatory people with subacute and chronic stroke. DESIGN: Observational longitudinal study with assessments at baseline (T0) and week 6 (T1). METHODS: Community-dwelling people with stroke (n=39) pooled from 2 studies who were ≥18 years of age were assessed for sedentary behavior at 2 timepoints (T0, T1). Sedentary behavior was measured with the activPAL micro3 following a 7-day wear protocol to obtain mean daily: total sitting time, sitting time accumulated in bouts ≥30 minutes, number of sit-to-stand transitions, and fragmentation index (sit-to-stand transitions/total sitting hours). Paired samples t-tests were used to calculate mean group differences in sedentary behavior metrics between T0 and T1 (α=.05). Cohen’s d was calculated to describe the magnitude of within-person change between T0 and T1. RESULTS: There were no statistically significant within-person differences between T0 and T1 on mean daily sitting time (Cohen’s d=−0.21, p=.19), sitting time accumulated in bouts ≥30 minutes (d=−0.27, p=.11), number of sit-to-stand transitions (d=−0.02, p=.53), or the fragmentation index (d=−0.11, p=.92). CONCLUSIONS: Sedentary behavior metrics were stable over 6 weeks. The number of sit-to-stand transitions per day and the fragmentation index appeared to be the most stable indicators over 6 weeks. Future research should confirm these findings and identify correlates of sedentary behavior among people with stroke.

To synthesize evidence from randomized controlled trials on the effects of integrated behavioral interventions for comorbid obesity and depression in adults.Seven trials (n = 33 to 409) were included. The quality of evidence was mixed. In 2 trials, integrated interventions led to greater improvements in both obesity and depression over 12 months, compared with usual care. Of 4 trials comparing integrated interventions with a standalone obesity intervention, 2 showed incremental effects on depression only, and 2 did not detect a significant effect for either outcome. One 3-arm trial compared an integrated intervention with standalone obesity and depression interventions and only detected incremental effects on obesity when compared with a standalone depression intervention. The effects of integrated interventions for comorbid obesity and depression are varied but promising. Implications for future research to guide intervention optimization and implement integrated interventions in clinical practice are provided.

The objective of this study was to present lessons learned about engagement, delivery modality and pandemic impact while delivering a collaborative care intervention with a socioeconomically, racially and ethnically diverse sample. Participants completed a post-intervention survey (n = 41) on experiences and preferred intervention delivery modality, coronavirus 2019 (COVID-19) Impact Survey (n = 50) and provided open-ended feedback about the intervention (n = 27). Intervention process data included attendance, modality, and withdrawals. Data were analyzed using descriptive statistics and inductive content analyses. Of 71 intervention participants, 6 (8%) withdrew before session 1. Completers adhered to intervention timeline better than withdrawals. Participants liked the in-person interaction, efficient coach support, accountability of in-person and Zoom vs. phone sessions and the flexibility and convenience of phone and Zoom vs. in-person sessions. A majority of participants reported experiencing pandemic impacts such as heightened emotional distress, decreased activity engagement, poorer eating behaviors and being unable to meet basic needs. Participants deviating from intervention timelines may be re-engaged by targeted outreach attempts. Videoconference has the potential for providing as-needed coaching. Future interventions may be optimized to account for and address areas impacted by the pandemic. Findings revealed specific strategies that can be implemented in future interventions to improve emotional and physical health among diverse populations.

Purpose of Review Behavioral medicine is a multidisciplinary field that has a key role in reducing risk factors for cardiovascular disease (CVD). The purpose of this review is to describe the role of behavioral medicine for CVD prevention, using physical activity behaviors (e.g., sedentary behavior, daily physical activity, or exercise) as an exemplar. Application of behavioral medicine to improve dietary behaviors is also briefly discussed. Recent Findings Behavioral medicine interventions that address physical activity behaviors are associated with improved cardiovascular risk factors. Interventions framed in behavior change theory that integrate behavior change techniques to reduce sedentary behavior and promote daily physical activity and exercise have similarly been applied to improve certain dietary behaviors and show promise for reducing CVD risk factors. Summary Behavioral medicine has an important role in improving various physical activity behaviors for all populations, which is essential for preventing or managing CVD. Further investigation into behavioral medicine interventions that address personal, environmental, and social factors that influence participation in physical activity behaviors, as well as the adoption of a more optimal dietary pattern, is warranted.

BACKGROUND Evidence suggests that frequent engagement in daily activities requiring physical activity may influence risk factors for recurrent stroke. The effects of nonpharmacological interventions on daily physical activity levels and sedentary behavior are unclear. OBJECTIVE To describe the effects of interventions on levels of daily physical activity and sedentary behavior among people with stroke. METHODS OVID/Medline, CINAHL, PsycINFO, and the Cochrane Database were searched using the following search terms: stroke, rehabilitation, intervention, sedentary, physical activity, lifestyle, self-management, and exercise. Data extraction and risk of bias assessment were conducted by two authors. RESULTS Thirty-one interventions were identified that included exercise, behavior change techniques, and education components. These components were delivered alone and in varying combinations. At postintervention, between-group effects on change scores (Cohen's d = 0.17-0.75, P

Purpose: To define semi-supervised machine learning (SSML) and explore current and potential applications of this analytic strategy in rehabilitation research.Method: We conducted a scoping review using PubMed, GoogleScholar and Medline. Studies were included if they: (1) described a semi-supervised approach to apply machine learning algorithms during data analysis and (2) examined constructs encompassed by the International Classification of Functioning, Disability and Health (ICF). The first two authors reviewed identified articles and recorded study and participant characteristics. The ICF domain used in each study was also identified.Results: After combining information from the eight studies, we established that SSML was a feasible approach for analysis of complex data in rehabilitation research. We also determined that semi-supervised approaches may be more accurate than supervised machine learning approaches.Conclusions: A semi-supervised approach to machine learning has potential to enhance our understanding of complex data sets in rehabilitation science. SSML mirrors the iterative process of rehabilitation, making this approach ideal for calibrating devices, classifying activities or identifying just-in-time interventions. Rehabilitation scientists who are interested in conducting SSML should collaborate with data scientists to advance the application of this approach within our field.Implications for rehabilitationSemi-supervised machine learning applications may be a feasible approach for analyses of complex data sets in rehabilitation research.Semi-supervised machine learning approaches uses a combination of labelled and unlabelled data to produce accurate predictive models, thereby requiring less user-input data than other machine learning approaches (i.e., supervised, unsupervised), reducing resource cost and user-burden.Semi-supervised machine learning is an iterative process that, when applied to rehabilitation assessment and outcomes, could produce accurate personalized models for treatment.Rehabilitation researchers and data scientists should collaborate to implement semi-supervised machine learning approaches in rehabilitation research, optimizing the power of large datasets that are becoming more readily available within the field (e.g., EEG signals, sensors, smarthomes).

Research Objectives We explored stakeholders’ experiences using videoconferencing to participate in group-based social learning during the COVID-19 pandemic. Design Qualitative thematic analysis. Setting Community-based. Participants We interviewed 8 community-dwelling low-income adults with chronic stroke (≥ 3 months) and mild-to-moderate disability (NIH Stroke Scale ≤16) who enrolled in the ENGAGE Pilot Study (NCT04019275) during the COVID-19 pandemic. Research staff (n=4), peer facilitators (n=2), and occupational therapist facilitators (n=2) were also interviewed. Interventions ENGAGE blends social learning, guided discovery, and skills training to facilitate community and social participation. The 8-session curriculum was adapted for delivery via videoconferencing during the COVID-19 pandemic. Peer and occupational therapy facilitators led the groups. Main Outcome Measures Semi-structured interviews were conducted. Interviews were transcribed, annotated, and coded by 3 researchers. Results were member checked. Results Stakeholders reported that they valued participation in the intervention using videoconferencing and connectedness through shared experiences. Some participants felt more comfortable sharing experiences over videoconferencing than in person. The COVID-19 pandemic prompted participants and facilitators to identify creative approaches to community and social participation. All stakeholders highlighted technical and social components of using videoconferencing that should be included in training at the beginning of remotely delivered interventions. Learning occurred over the duration of the group sessions rather than during one training session. Group facilitators also described strategies for navigating technical challenges and engaging participants during online group sessions. Conclusions Group-based interventions delivered via videoconferencing may facilitate valuable social learning experiences after stroke. Videoconferencing training should address technical skills and social expectations. Future research should explore stakeholder preferences for virtual or face-to-face group interventions, virtual group processes, and optimal training strategies to facilitate technology uptake. Author(s) Disclosures This research was supported by the following: T32 Precision Lifestyle Medicine and Translational Research (PREMIER) Postdoctoral Training Program (NHLBI T32HL134634); NIH CTSA SPIRiT Award (University of Pittsburgh, UL1 TR001857; Washington University in St. Louis, UL1 TR002345).

Objectives: The purpose of this exploratory study was to identify clinical predictors that could distinguish clients’ level of engagement in inpatient rehabilitation following stroke. Methods: This is a secondary analysis of pooled data from three randomized controlled trials that examined the effects of a behavioral intervention. The sample (n=208) consisted of clients with stroke who had cognitive deficits (Quick-EXIT≥3) and were admitted to inpatient rehabilitation facilities associated with a university medical center. Individuals with pre-morbid dementia, aphasia and mood disorders were excluded. The Pittsburgh Rehabilitation Participation Scale was used to measure engagement. Clinical predictors were measured using the Functional Independence Measure, National Institutes of Health Stroke Scale, Repeatable Battery for the Assessment of Neuropsychological Status, selected subtests of the Delis-Kaplan Executive Function System, Patient Health Questionnaire-9, and Chedoke McMaster Stroke Assessment. Simple logistic regression identified individual clinical predictors associated with engagement. Hierarchical logistic regression identified the strongest predictors of engagement. Results: Impairments in executive functions [mean D-KEFS, odds ratio (OR)=4.062; 95% confidence interval (CI)=.866, 19.051], impairments in visuospatial skills (RBANS Visuospatial Index Score, OR=3.940; 95% CI=1.317, 11.785), impairments in mood (Patient Health Questionnaire-9, OR=2.059, 95% CI=.953, 4.449), and male gender (OR=2.474; 95% CI=1.145, 5.374) predicted levels of engagement in inpatient rehabilitation after controlling for study intervention group, baseline stroke severity, and baseline disability. Conclusions: Executive functions, visuospatial skills, mood, and gender distinguished individuals with high or low engagement in inpatient rehabilitation following stroke. Further studies should examine additional factors that may influence engagement (therapist-client relationship, treatment expectancy). (JINS, 2018, 24, 572–583)

Background and Objective: High levels of sedentary behavior are prevalent among people with stroke and contribute to elevated risk for recurrent stroke. Few interventions reduce sedentary behavior post-stroke. The ABLE intervention aims to reduce sedentary behavior using activity monitoring, activity scheduling, problem-solving, and self-assessment to promote engagement in meaningful daily activities. The purpose of this study was to assess the feasibility (tolerability, acceptability, reliability, safety) of the ABLE intervention after stroke and describe trends in sedentary behavior at baseline and 4 weeks. Clinical Presentation: Participants (n = 5) who were 6 months to 2 years post-stroke, ambulatory, and reported ≥6 h of daily sitting time. Intervention: Twelve ABLE intervention sessions (3x/week for 4 weeks) conducted in participants' homes. The ABLE intervention includes activity monitoring, activity scheduling, self-assessment, and collaborative problem-solving. Results: All feasibility benchmarks were met for three participants. Two participants met tolerability and safety benchmarks but did not meet acceptability and reliability benchmarks. Variability in feasibility and sedentary behavior outcomes may be related to baseline levels of sedentary behavior and social support. Conclusions: The ABLE intervention was tolerable and safe. The intervention protocol was refined to enhance reliability and acceptability. Future studies should estimate the effects of the ABLE intervention.

Importance: Reducing poststroke sedentary behavior is important for reducing recurrent stroke risk, yet interventions to achieve this are scant. Objective: To assess the feasibility of, and estimate change in sedentary behavior over time associated with, a behavioral intervention. Design: Single-arm delayed baseline with postintervention and 8-wk follow-up assessment. Setting: Community based. Participants: Ambulatory, community-dwelling people with chronic stroke and reported ≥6 hr daily sitting time (N = 21). Intervention: Activating Behavior for Lasting Engagement (ABLE) was delivered by an occupational therapist 3×/wk for 4 wk. ABLE involves activity monitoring, activity scheduling, self-assessment, and collaborative problem solving. Outcomes and Measures: Feasibility (participant safety, adherence, satisfaction, and reliable intervention delivery) was assessed against preestablished benchmarks. Changes over time in sedentary behavior (assessed with an ActivPAL micro3 device) and participation (Stroke Impact Scale–Participation subscale) were described. Results: ABLE was safe (0 serious adverse events), adhered to (11.95 sessions/participant), and reliably delivered (90.00%–97.50% adherence). Participant satisfaction was unmet (Client Satisfaction Questionnaire–8, M = 28.75, SD = 3.84). ABLE was associated with a mean group reduction in prolonged sitting of 54.95 min (SD = 81.10) at postintervention and 14.08 (SD = 58.95) at follow-up. ABLE was associated with a negligible mean group increase over time in participation at postintervention (M = 1.48%, SD = 8.52) and follow-up (M = 1.33%, SD = 15.38). Conclusions and Relevance: The ABLE intervention is feasible and may be associated with within-group reduction in sedentary behavior over time. Further refinement is indicated. What This Article Adds: The ABLE intervention uses engagement in meaningful daily activities to reduce sedentary behavior after stroke. These findings suggest that ABLE can be delivered safely and consistently. Further research is required to enhance participant satisfaction and determine the effects of ABLE on stroke survivors’ sedentary behavior.

Date Presented 04/05/19 Stroke leads to high levels of sedentary behavior and may be associated with risk for recurrent stroke. The ABLE intervention was developed to reduce sedentary behavior by promoting engagement in meaningful occupations using a behavioral activation framework. This study examined the feasibility of the ABLE intervention among people with chronic stroke. Primary Author and Speaker: Emily Kringle Contributing Authors: Grace Campbell, Michael McCue, Bethany Barone Gibbs, Lauren Terhorst, Elizabeth Skidmore