Community participation disparities among people with disabilities during the COVID-19 pandemic.

PURPOSE: To describe disparities in community participation during the COVID-19 pandemic among people with disabilities. METHODS: Respondents to Phase 3.3 of the COVID Household Pulse Survey (US Census Bureau) were classified by disability status. Risk ratios and risk differences were computed to compare the risk of poor outcomes on economic participation, community service use, and community activities by disability status - both overall (compared to the nondisabled reference) and by race/ethnicity (each subgroup compared to the White nondisabled reference). RESULTS: At least one type of disability was reported by 59.6% of respondents. People with disabilities were more likely to report in-person medical appointments but were at greater risk of poor outcomes across all other outcomes [risk ratio range = 1.01(1.01-1.02) to 1.91(1.80-2.01), risk difference range = 1.0(0.5-1.5) to 13.4(12.6-14.2)]. The disabled Black and disabled Hispanic/Latino groups experienced disproportionately high risk of poor outcomes across all indicators [risk ratio range = 1.0 (1.0-1.1) to 6.1 (5.0-7.1), risk difference range = 3.2 (1.9-4.4) to 33.1 (30.1-35.4)]. CONCLUSIONS: The high number of people reporting disability, along with the notable disparities in community participation outcomes among those reporting disability, suggest the need for expanded rehabilitation services and community supports to enhance participation.

People with disabilities experienced disparities in community participation outcomes during the pandemic, particularly in indicators of economic participation (paid employment, income, and education).Disabled people from racial and ethnic minority groups experienced the most severe disparities in outcomes.Stronger rehabilitation services are critical to address new disability or pandemic-related changes in the experience or severity of existing disability.Stronger community and social supports (employment supports, accessible assistive technology, and safe transportation options) may also reduce the disparities in community participation experienced by people with disabilities.

Similarities and differences in factors associated with high and low sedentary behavior after stroke: a mixed methods study.

PURPOSE: To identify similarities and differences in factors affecting activity engagement between adults with stroke who are more and less sedentary. MATERIALS AND METHODS: Data were pooled from two studies of adults with stroke (N = 36). Sedentary time was measured activPAL micro3. Participants completed activPAL interviews, which were analyzed using framework analysis. Participants were stratified into more and less sedentary groups based on activPAL data. Between-group similarities and differences were identified. RESULTS: Adults with stroke (mean [SD] age = 65.8 [13.6] years, stroke chronicity = 40.5 [SD = 38.3] months, 36.1% female) were more sedentary (785.5 [64.7] sedentary minutes/day) and less sedentary (583.6 [87.4] sedentary minutes/day). Those who were more sedentary: engaged in basic activities of daily living, avoided activities, received assistance from other people, and did not use strategies to overcome barriers. Those who were less sedentary: engaged in instrumental and community activities, embraced new strategies, did activities with other people, and used strategies to overcome environmental barriers. CONCLUSIONS: Factors affecting activity engagement differed between people who are more and less sedentary. Interventions that aim to reduce post-stroke sedentary behavior should consider the: (1) types of activities, (2) role of other people, and (2) application of strategies to overcome activity and environment-related barriers.

People with lower levels of post-stroke sedentary behavior engaged in instrumental and community activities, relative to those with higher levels who primarily engaged in basic activities of daily living.Applying models of social support that emphasize doing with rather than doing for may be important for facilitating non-sedentary lifestyles.Facilitating the use of strategies to overcome environmental barriers to activities may be an important aspect of facilitating non-sedentary lifestyles.

Stakeholders' Experiences Using Videoconferencing for a Group-Based Stroke Intervention During COVID-19: A Thematic Analysis.

IMPORTANCE: Guidance is limited for training protocols that support stakeholders who are new to participating in telerehabilitation interventions using videoconferencing software. OBJECTIVE: To explore stakeholders' experiences participating in a group-based intervention during the coronavirus disease 2019 (COVID-19) pandemic using a videoconferencing software (Zoom). DESIGN: Ad hoc exploratory thematic analysis. SETTING: Community-based telerehabilitation. PARTICIPANTS: Stakeholders included group members (n = 8) who were low-income adults with chronic stroke (≥3 mo) and mild to moderate disability (National Institutes of Health Stroke Scale ≤ 16), group leaders (n = 4), and study staff (n = 4). INTERVENTION: Group-based intervention, ENGAGE, delivered using videoconferencing technology. ENGAGE blends social learning and guided discovery to facilitate community and social participation. OUTCOMES AND MEASURES: Semistructured interviews. RESULTS: Stakeholders included group members (ages 26-81 yr), group leaders (ages 32-71 yr), and study staff (ages 23-55 yr). Group members characterized ENGAGE as learning, doing, and connecting with others who shared their experience. Stakeholders identified social advantages and disadvantages to the videoconferencing environment. Attitudes toward technology, past technology experiences, the amount of time allotted for training, group size, physical environments, navigation of technology disruptions, and design of the intervention workbook were facilitators for some and barriers for others. Social support facilitated technology access and intervention engagement. Stakeholders recommended training structure and content. CONCLUSIONS AND RELEVANCE: Tailored training protocols may support stakeholders who are participating in telerehabilitation interventions using new software or devices. Future studies that identify specific tailoring variables will advance the development of telerehabilitation training protocols. What This Article Adds: These findings provide stakeholder-identified barriers and facilitators, in addition to stakeholder-informed recommendations, for technology training protocols that may support uptake of telerehabilitation in occupational therapy.

Associations between daily step count trajectories and clinical outcomes among adults with comorbid obesity and depression.

Purpose: To examine the relationship between features of daily measured step count trajectories and clinical outcomes among people with comorbid obesity and depression in the ENGAGE-2 Trial. Methods: This post hoc analysis used data from the ENGAGE-2 trial where adults (n=106) with comorbid obesity (BMI ≥30.0 or 27.0 if Asian) and depressive symptoms (Patient Health Questionnaire-9 score ≥10) were randomized (2:1) to receive the experimental intervention or usual care. Daily step count trajectories over the first 60 days (Fitbit Alta HR) were characterized using functional principal component analyses. 7-day and 30-day trajectories were also explored. Functional principal component scores that described features of step count trajectories were entered into linear mixed models to predict weight (kg), depression (Symptom Checklist-20), and anxiety (Generalized Anxiety Disorder Questionnaire-7) at 2-months (2M) and 6-months (6M). Results: Features of 60-day step count trajectories were interpreted as overall sustained high, continuous decline, and disrupted decline. Overall sustained high step count was associated with low anxiety (2M, ß=-0.78, p<.05; 6M, ß=-0.80, p<.05) and low depressive symptoms (6M, ß=-0.15, p<.05). Continuous decline in step count was associated with high weight (2M, ß=0.58, p<.05). Disrupted decline was not associated with clinical outcomes at 2M or 6M. Features of 30-day step count trajectories were also associated with weight (2M, 6M), depression (6M), and anxiety (2M, 6M); Features of 7-day step count trajectories were not associated with weight, depression, or anxiety at 2M or 6M. Conclusions: Features of step count trajectories identified using functional principal component analysis were associated with depression, anxiety, and weight outcomes among adults with comorbid obesity and depression. Functional principal component analysis may be a useful analytic method that leverages daily measured physical activity levels to allow for precise tailoring of future behavioral interventions.

Effects of a virtual voice-based coach delivering problem-solving treatment on emotional distress and brain function: a pilot RCT in depression and anxiety.

Consumer-based voice assistants have the ability to deliver evidence-based treatment, but their therapeutic potential is largely unknown. In a pilot trial of a virtual voice-based coach, Lumen, delivering problem-solving treatment, adults with mild-to-moderate depression and/or anxiety were randomized to the Lumen intervention (n = 42) or waitlist control (n = 21). The main outcomes included changes in neural measures of emotional reactivity and cognitive control, and Hospital Anxiety and Depression Scale [HADS] symptom scores over 16 weeks. Participants were 37.8 years (SD = 12.4), 68% women, 25% Black, 24% Latino, and 11% Asian. Activation of the right dlPFC (neural region of interest in cognitive control) decreased in the intervention group but increased in the control group, with an effect size meeting the prespecified threshold for a meaningful effect (Cohen's d = 0.3). Between-group differences in the change in activation of the left dlPFC and bilateral amygdala were observed, but were of smaller magnitude (d = 0.2). Change in right dlPFC activation was also meaningfully associated (r ≥ 0.4) with changes in self-reported problem-solving ability and avoidance in the intervention. Lumen intervention also led to decreased HADS depression, anxiety, and overall psychological distress scores, with medium effect sizes (Cohen's d = 0.49, 0.51, and 0.55, respectively), compared with the waitlist control group. This pilot trial showed promising effects of a novel digital mental health intervention on cognitive control using neuroimaging and depression and anxiety symptoms, providing foundational evidence for a future confirmatory study.

Validation of the NIH Toolbox-Cognition Battery against legacy neurocognitive measures in adults with cognitive impairments: An exploratory analysis.

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.

Design and Formative Evaluation of a Virtual Voice-Based Coach for Problem-solving Treatment: Observational Study.

BACKGROUND: Artificial intelligence has provided new opportunities for human interactions with technology for the practice of medicine. Among the recent artificial intelligence innovations, personal voice assistants have been broadly adopted. This highlights their potential for health care-related applications such as behavioral counseling to promote healthy lifestyle habits and emotional well-being. However, the use of voice-based applications for behavioral therapy has not been previously evaluated. OBJECTIVE: This study aimed to conduct a formative user evaluation of Lumen, a virtual voice-based coach developed as an Alexa skill that delivers evidence-based, problem-solving treatment for patients with mild to moderate depression and/or anxiety. METHODS: A total of 26 participants completed 2 therapy sessions-an introductory (session 1) and a problem-solving (session 2)-with Lumen. Following each session with Lumen, participants completed user experience, task-related workload, and work alliance surveys. They also participated in semistructured interviews addressing the benefits, challenges and barriers to Lumen use, and design recommendations. We evaluated the differences in user experience, task load, and work alliance between sessions using 2-tailed paired t tests. Interview transcripts were coded using an inductive thematic analysis to characterize the participants' perspectives regarding Lumen use. RESULTS: Participants found Lumen to provide high pragmatic usability and favorable user experience, with marginal task load during interactions for both Lumen sessions. However, participants experienced a higher temporal workload during the problem-solving session, suggesting a feeling of being rushed during their communicative interactions. On the basis of the qualitative analysis, the following themes were identified: Lumen's on-demand accessibility and the delivery of a complex problem-solving treatment task with a simplistic structure for achieving therapy goals; themes related to Lumen improvements included streamlining and improved personalization of conversations, slower pacing of conversations, and providing additional context during therapy sessions. CONCLUSIONS: On the basis of an in-depth formative evaluation, we found that Lumen supported the ability to conduct cognitively plausible interactions for the delivery of behavioral therapy. Several design suggestions identified from the study including reducing temporal and cognitive load during conversational interactions, developing more natural conversations, and expanding privacy and security features were incorporated in the revised version of Lumen. Although further research is needed, the promising findings from this study highlight the potential for using Lumen to deliver personalized and accessible mental health care, filling a gap in traditional mental health services.

Integrated collaborative care intervention for depression and obesity in primary care: translation from research to practice.

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.

Association of COVID-19 impact with outcomes of an integrated obesity and depression intervention: Posthoc analysis of an RCT.

OBJECTIVE: To examine the association between COVID-19 impact and clinical outcomes of an integrated collaborative care intervention for adults with obesity and comorbid depression. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Stroke Recovery During the COVID-19 Pandemic: A Position Paper on Recommendations for Rehabilitation.

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.

Cross-trial prediction of depression remission using problem-solving therapy: A machine learning approach.

BACKGROUND: 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. PURPOSE: To develop machine learning (ML) algorithms to predict depression remission for patients undergoing 6 months of problem-solving therapy (PST). METHOD: 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. RESULTS: 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. CONCLUSIONS: 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.

Integrated Behavioral Interventions for Adults with Comorbid Obesity and Depression: a Systematic Review.

PURPOSE OF REVIEW: To synthesize evidence from randomized controlled trials on the effects of integrated behavioral interventions for comorbid obesity and depression in adults. RECENT FINDINGS: 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.

Sedentary behavior patterns over 6 weeks among ambulatory people with stroke.

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 two 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 for 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.

Activating Behavior to Reduce Sedentary Behavior After Stroke: A Nonrandomized Pilot Feasibility Study.

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.

Iterative processes: a review of semi-supervised machine learning in rehabilitation science.

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).

Feasibility of an iterative rehabilitation intervention for stroke delivered remotely using mobile health technology.

Background: Telehealth affords rehabilitation professionals opportunities to expand access to intervention for people in rural areas. Complex interventions have not been adapted for remote delivery using mobile health technologies. Strategy training is a complex intervention that teaches clients skills for identifying barriers and solutions to engagement in meaningful activities. Our goal was to adapt the delivery of strategy training for remote delivery using mobile health technology.Methods: We conducted a sequential descriptive case series study (n = 5) in which community-dwelling participants with chronic stroke and prior exposure to strategy training used the iADAPTS mobile health application for 5 weeks. Expert practitioners advised revisions to the intervention process. Safety was assessed via monitoring occurrence of adverse events and risk for adverse events. Acceptability was assessed via the Client Satisfaction Questionnaire-8 (CSQ-8) and the Patient-Provider Connection Short Form of the Healing Encounters and Attitudes Lists (HEAL PPC).Results: Revisions to the intervention process supported the delivery of strategy training using mobile health technology after stroke. No adverse events occurred and risk for adverse events was managed through the intervention process. Acceptability was high (CSQ-8, 25 to 32; HEAL PPC, 59.9 to 72.5).Conclusions: Strategy training can be adapted for delivery using mobile health technology, with careful consideration to methods for training participants on new technology and the intervention delivery. Future research should establish the efficacy and effectiveness of integrating mobile health in delivery of interventions that promote engagement in client-selected activities and community participation.Implications for rehabilitationTranslating the strategy training intervention from face-to-face to remote delivery required thoughtful adaptation of the intervention protocol.Strategies for training clients to use mobile health technology during intervention may be important when designing remotely delivered mHealth intervention protocols.Client safety should be considered within the design of the intervention protocol for a complex intervention designed to be delivered remotely.Future studies should examine the efficacy of complex rehabilitation interventions such as strategy training on clinical outcomes (e.g., community participation).

Influence of Interventions on Daily Physical Activity and Sedentary Behavior after Stroke: A Systematic Review.

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 < .05) or between-group differences in odds of participating in daily physical activity (odds ratio [OR] = 2.07, P < .05) were detected in six studies, and within-group effects in nine studies (Cohen's d = 0.21-3.97, P < .05). At follow-up, between-group differences in odds of participating in daily physical activity were detected in one study (OR = 2.64, P < .05), and within-group effects in two studies (Cohen's d = 0.25, P < .05). No effects (P < .05) were detected in 17 studies. CONCLUSION: It may be possible to modify daily physical activity levels and sedentary behavior poststroke; however, there is insufficient evidence to suggest the superiority of a particular intervention approach. Future studies should explore the unique contributions of individual intervention components to guide development of parsimonious multicomponent interventions that are effective for promoting daily physical activity and reducing sedentary behavior among people with stroke. LEVEL OF EVIDENCE: I.

Development and feasibility of a sedentary behavior intervention for stroke: a case series.

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.

Clinical Predictors of Engagement in Inpatient Rehabilitation Among Stroke Survivors With Cognitive Deficits: An Exploratory Study.

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).