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.

An Intervention to Reduce Sedentary Behavior in Adults with Chronic Kidney Disease: A Feasibility Study.

Adults with chronic kidney disease (CKD) tend to be extremely sedentary. We investigated the feasibility and acceptability of a sedentary-reducing intervention for adults with CKD. The intervention utilized telephone-delivered coaching and a consumer wearable device to support participants to reduce their sedentary time. The mean age of participants in the sample was 60.5 years; 72% were women, and 83% had CKD Stage 3. At baseline, participants spent 73% of their waking time sedentary. Inter vention phone call attendance was 100%, study retention was 82%, and the intervention was rated as enjoyable (9.1/10). A telephone-delivered, sedentary-reducing intervention is feasible and acceptable in adults with CKD. Future work is needed investigating the efficacy of sedentary-reducing interventions for adults with CKD.

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.

Rest-Activity Rhythm Characteristics Associated With Depression Symptoms in Stroke Survivors.

OBJECTIVE: To examine which 24-hour rest-activity rhythm (RAR) characteristics are associated with depression symptoms in stroke survivors. DESIGN: Cross-sectional observational study examining associations of RAR characteristics with the presence of depression symptoms adjusting for age, sex, race, and medical comorbidity. SETTING: Community setting. PARTICIPANTS: Stroke survivors: (1) recruited locally (N women=35, N men=28) and (2) a nationally representative probability sample (the National Health and Nutrition Examination Survey [NHANES]; N women=156, N men=124). INTERVENTIONS: None. MEASUREMENTS: Objective RAR characteristics derived from accelerometer recordings including activity onset/offset times and non-parametric measures of RAR strength (relative amplitude), stability (interdaily stability), and fragmentation (intradaily variability). The presence of depression symptoms was categorized using Patient Health Questionnaire scores. RESULTS: In both samples, the only RAR characteristic associated with depression symptoms was intradaily variability (fragmentation): local sample, odds ratio=1.96 [95% confidence interval=1.05-3.63]; NHANES sample, odds ratio=1.34, [95% confidence interval=1.01-1.78]). In the NHANES sample, which included both mild and moderate/severe depression, the association between 24-hour sleep-wake fragmentation and depression symptoms was driven by moderate-to-severe cases. CONCLUSIONS: Stroke survivors with higher levels of RAR fragmentation were more likely to have depression symptoms in both samples. These findings have implications, given prior studies in general samples linking RAR fragmentation with future depression and dementia risk. Research is needed to establish the potential consequences, mechanisms, and modifiability of RAR fragmentation in stroke survivors.

Pilot RCT examining feasibility and disability outcomes of a mobile health platform for strategy training in inpatient stroke rehabilitation (iADAPT).

BACKGROUND: Strategy training is an intervention that may reduce disability when delivered in inpatient rehabilitation following stroke. However, shorter lengths of stay and challenges with continuity of care following discharge results in difficulties in achieving adequate intervention dosage and carryover of training. OBJECTIVE: We examined whether strategy training using a mobile health platform (iADAPT) is feasible during inpatient stroke rehabilitation and following discharge. METHODS: In this RCT, participants were randomized to receive strategy training using either the iADAPT application (n = 16) or a workbook (n = 15). Participants in both groups received 7 in-person sessions during inpatient rehabilitation and 3 remote sessions following discharge. We calculated descriptive statistics to examine acceptance, attendance, and adherence, and within-group effect sizes on satisfaction and disability. RESULTS: Participants in the iADAPT group attended fewer total intervention sessions (n = 5.5, workbook n = 9.0) but attempted a similar number of goals (n = 7.6, workbook n = 8.2). Both groups reported similar satisfaction with in-person intervention (Treatment Expectancy: iADAPT d = 0.60, workbook d = 0.47; Patient Provider Connection: iADAPT d = 0.18, workbook d = 0.31), but the mobile health group reported greater satisfaction with remote intervention (Treatment Expectancy: iADAPT d = -0.91, workbook d = -0.97; Patient Provider Connection: iADAPT d = 0.85, workbook d = -1.80). . CONCLUSIONS: Considering these promising feasibility metrics and the benefits of mobile health, it is worth continuing to explore the efficacy of strategy training using a mobile health platform.

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.

Behavioral Medicine for Sedentary Behavior, Daily Physical Activity, and Exercise to Prevent Cardiovascular Disease: A Review.

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. 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.

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.