Dynamics of Perceived Social Isolation, Secondary Conditions, and Daily Activity Patterns Among Individuals With Stroke: A Network Analysis of Ecological Momentary Assessment Data.

OBJECTIVE: To assess contemporaneous and temporal dynamics of perceived social isolation (PSI), secondary conditions, and daily activity patterns in individuals post-stroke. DESIGN: Longitudinal observational study using ecological momentary assessment (EMA) as a real-time assessment of an individual's lived experiences. We conducted dynamic network analyses to examine longitudinal associations among EMA variables. SETTINGS: Home and Community. PARTICIPANTS: 202 individuals with mild-to-moderate chronic stroke (median age=60 years; 45% women; 44% black; 90% ischemic stroke; median NIHSS score=2; N=202). INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: EMA questions measured PSI, secondary conditions (pain, tiredness, stress, anxiety, worthlessness, difficulty concentrating, and cheerfulness), and daily activity patterns (being at home, being alone, and participating in productive activities). RESULTS: The median EMA response rate was 84%. The contemporaneous model showed that PSI was associated with being home, alone, and all symptoms except pain. The temporal model revealed a pathway indicating that feelings of worthlessness predicted PSI (regression coefficient=0.06, P=.019), followed by stress (regression coefficient=0.06, P=.024), and then by being not at home (regression coefficient=-0.04, P=.013). CONCLUSION: Implementing dynamic network analyses on EMA data can uncover dynamic connections among PSI, secondary conditions, and daily activity patterns after stroke. This study found a significant temporal association between PSI and negative emotions. Feeling isolated was followed by feeling stressed, which was followed by a tendency to be out of home, indicating adaptive behaviors in individuals with stroke. These findings highlight the importance of engaging in out-of-home or outdoor activities to mitigate PSI and negative emotions.

A Digital Intervention to Promote Self-Management Self-Efficacy Among Community-Dwelling Individuals With Stroke: Pilot Randomized Controlled Trial.

BACKGROUND: Digital interventions provided through smartphones or the internet that are guided by a coach have been proposed as promising solutions to support the self-management of chronic conditions. However, digital intervention for poststroke self-management is limited; we developed the interactive Self-Management Augmented by Rehabilitation Technologies (iSMART) intervention to address this gap. OBJECTIVE: This study aimed to examine the feasibility and initial effects of the iSMART intervention to improve self-management self-efficacy in people with stroke. METHODS: A parallel, 2-arm, nonblinded, randomized controlled trial of 12-week duration was conducted. A total of 24 participants with mild-to-moderate chronic stroke were randomized to receive either the iSMART intervention or a manual of stroke rehabilitation (attention control). iSMART was a coach-guided, technology-supported self-management intervention designed to support people managing chronic conditions and maintaining active participation in daily life after stroke. Feasibility measures included retention and engagement rates in the iSMART group. For both the iSMART intervention and active control groups, we used the Feasibility of Intervention Measure, Acceptability of Intervention Measure, and Intervention Appropriateness Measure to assess the feasibility, acceptability, and appropriateness, respectively. Health measures included the Participation Strategies Self-Efficacy Scale and the Patient-Reported Outcomes Measurement Information System's Self-Efficacy for Managing Chronic Conditions. RESULTS: The retention rate was 82% (9/11), and the engagement (SMS text message response) rate was 78% for the iSMART group. Mean scores of the Feasibility of Intervention Measure, Acceptability of Intervention Measure, and Intervention Appropriateness Measure were 4.11 (SD 0.61), 4.44 (SD 0.73), and 4.36 (SD 0.70), respectively, which exceeded our benchmark (4 out of 5), suggesting high feasibility, acceptability, and appropriateness of iSMART. The iSMART group showed moderate-to-large effects in improving self-efficacy in managing emotions (r=0.494), symptoms (r=0.514), daily activities (r=0.593), and treatments and medications (r=0.870), but the control group showed negligible-to-small effects in decreasing self-efficacy in managing emotions (r=0.252), symptoms (r=0.262), daily activities (r=0.136), and treatments and medications (r=0.049). In addition, the iSMART group showed moderate-to-large effects of increasing the use of participation strategies for management in the home (r=0.554), work (r=0.633), community (r=0.673), and communication activities (r=0.476). In contrast, the control group showed small-to-large effects of decreasing the use of participation strategies for management in the home (r=0.567), work (r=0.342, community (r=0.215), and communication activities (r=0.379). CONCLUSIONS: Our findings support the idea that iSMART was feasible to improve poststroke self-management self-efficacy. Our results also support using a low-cost solution, such as SMS text messaging, to supplement traditional therapeutic patient education interventions. Further evaluation with a larger sample of participants is still needed. TRIAL REGISTRATION: ClinicalTrials.gov 202004137; https://clinicaltrials.gov/study/NCT04743037?id=202004137&rank=1.

Factors associated with perceived social isolation among veterans with spinal cord injury and disorders: Cross sectional survey.

CONTEXT/OBJECTIVE: Examine demographics, injury characteristics, objective measures of social isolation and health factors that are associated with perceived social isolation (PSI) among Veterans with spinal cord injury and disorders (SCI/D). DESIGN: Cross-sectional survey. SETTING: The Veterans Health Administrations (VHA) SCI/D system of care. PARTICIPANTS: Veterans with SCI/D who have used the VHA health care system. INTERVENTION: Not applicable. OUTCOMES MEASURES: We assessed unadjusted associations of high PSI (above population mean) vs low (normative/below population mean), and multivariable logistic regression for independent associations with PSI. RESULTS: Out of 1942 Veterans with SCI/D, 421 completed the survey (22% response rate). Over half (56%) had PSI mean scores higher than the general population. Among the objective measures, having a smaller social network size was associated with increased odds of high PSI (OR 3.59, P < .0001); additionally, for health factors, having depression (OR 3.98, P < 0.0001), anxiety (OR 2.29, P = 0.009), and post-traumatic stress (OR 2.56, P = 0.003) in the previous 6 months, and having 4 or more chronically occurring secondary conditions (OR 1.78, P = 0.045) was associated with increased odds of high PSI. The most commonly identified contributors to feelings of PSI included mobility concerns (63%), having a SCI/D (61%), and concerns about being a burden on others (57%). CONCLUSIONS: Factors such as social network size may be used to identify individuals with SCI/D at risk for PSI. Additionally, by identifying mental health problems, presence of multiple chronically occurring secondary conditions, and Veteran-identified contributors of PSI, we can target these factors in a patient-centered interventions to identify and reduce PSI.

Intraindividual variability in post-stroke cognition and its relationship with activities of daily living and social functioning: an ecological momentary assessment approach.

INTRODUCTION: Ecological momentary assessment (EMA) is a methodological approach to studying intraindividual variation over time. This study aimed to use EMA to determine the variability of cognition in individuals with chronic stroke, identify the latent classes of cognitive variability, and examine any differences in daily activities, social functioning, and neuropsychological performance between these latent classes. METHODS: Participants (N = 202) with mild-to-moderate stroke and over 3-month post-stroke completed a study protocol, including smartphone-based EMA and two lab visits. Participants responded to five EMA surveys daily for 14 days to assess cognition. They completed patient-reported measures and neuropsychological assessments during lab visits. Using latent class analysis, we derived four indicators to quantify cognitive variability and identified latent classes among participants. We used ANOVA and Chi-square to test differences between these latent classes in daily activities, social functioning, and neuropsychological performance. RESULTS: The latent class analysis converged on a three-class model. The moderate and high variability classes demonstrated significantly greater problems in daily activities and social functioning than the low class. They had significantly higher proportions of participants with problems in daily activities and social functioning than the low class. Neuropsychological performance was not statistically different between the three classes, although a trend approaching statistically significant difference was observed in working memory and executive function domains. DISCUSSION: EMA could capture intraindividual cognitive variability in stroke survivors. It offers a new approach to understanding the impact and mechanism of post-stroke cognitive problems in daily life and identifying individuals benefiting from self-regulation interventions.

Text messaging intervention for fatigue self-management in people with stroke, spinal cord injury, and multiple sclerosis: A pilot study.

BACKGROUND: Fatigue negatively impacts the function and quality of life of people with disabilities (PwD). Mobile health (mHealth) platforms are recognized as effective and accessible approaches to delivering health interventions and may show higher satisfaction by tailoring the information toward personalized needs for PwD. OBJECTIVE: To evaluate the acceptability, feasibility, and participant engagement with a Short Message Service (SMS) text messaging intervention for fatigue self-management and to explore the pre- and post-score health changes in PwD. METHODS: A total of 27 PwD (multiple sclerosis = 9, spinal cord injury = 9, or stroke = 9) experiencing fatigue in their daily lives participated in a 12-week self-management text messaging intervention. Participants completed a demographic survey and health outcome measures, including patient activation, self-efficacy for managing symptoms, fatigue, sleep, and satisfaction with participation in social roles before and after the intervention. Participants also completed a client satisfaction questionnaire after the intervention. We also tracked the program retention and SMS response rates over the 12-week intervention period. RESULTS: Twenty-five participants completed the entire intervention (93% retention rate), and the overall SMS response rate was 84.67%, indicating high acceptability and adherence to the intervention. The mean satisfaction score was 3.18, indicating high satisfaction with the intervention. Despite finding a negligible effect on patient activation, we found a small intervention effect on self-efficacy for managing symptoms (η2 = 0.04) and moderate effects on fatigue (η2 = 0.06-0.12), sleep (η2 = 0.11), and satisfaction with participation in social roles (η2 = 0.08). CONCLUSIONS: This study provides initial feasibility and health outcome change evidence to support an SMS text messaging intervention to manage fatigue in PwD.

Post-stroke Depressive Symptoms and Cognitive Performances: A Network Analysis.

OBJECTIVE: To examine the relationships between post-stroke depression and cognition using network analysis. In particular, we identified central depressive symptoms, central cognitive performances, and bridge components that connect these 2 constructs. DESIGN: An observational study. We applied network analysis to analyze baseline data to visualize and quantify the relationships between depression and cognition. SETTING: Home and Community. PARTICIPANTS: 202 participants with mild-to-moderate stroke (N=202; mean age: 59.7 years; 55% men; 55% Whites; 90% ischemic stroke). INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: Patient Health Questionnaire (PHQ-8) for depressive symptoms and the NIH Toolbox Cognitive Battery for cognitive performances. RESULTS: Depressive symptoms were positively intercorrelated with the network, with symptoms from similar domains clustered together. Mood (expected influence=1.58), concentration (expected influence=0.67), and guilt (expected influence=0.63) were the top 3 central depressive symptoms. Cognitive performances also showed similar network patterns, with executive function (expected influence=0.89), expressive language (expected influence=0.68), and processing speed (expected influence=0.48) identified as the top 3 central cognitive performances. Psychomotor functioning (bridge expected influence=2.49) and attention (bridge expected influence=1.10) were the components connecting depression and cognition. CONCLUSIONS: The central and bridge components identified in this study might serve as targets for interventions against these deficits. Future trials are needed to compare the effectiveness of interventions targeting the central and bridge components vs general interventions treating depression and cognitive impairment as a homogenous clinical syndrome.

Using Intervention Mapping and Behavior Change Techniques to Develop a Digital Intervention for Self-Management in Stroke: Development Study.

BACKGROUND: Digital therapeutics, such as interventions provided via smartphones or the internet, have been proposed as promising solutions to support self-management in persons with chronic conditions. However, the evidence supporting self-management interventions through technology in stroke is scarce, and the intervention development processes are often not well described, creating challenges in explaining why and how the intervention would work. OBJECTIVE: This study describes a specific use case of using intervention mapping (IM) and the taxonomy of behavior change techniques (BCTs) in designing a digital intervention to manage chronic symptoms and support daily life participation in people after stroke. IM is an implementation science framework used to bridge the gap between theories and practice to ensure that the intervention can be implemented in real-world settings. The taxonomy of BCTs consists of a set of active ingredients designed to change self-management behaviors. METHODS: We used the first 4 steps of the IM process to develop a technology-supported self-management intervention, interactive Self-Management Augmented by Rehabilitation Technologies (iSMART), adapted from a face-to-face stroke-focused psychoeducation program. Planning group members were involved in adapting the intervention. They also completed 3 implementation measures to assess the acceptability, appropriateness, and feasibility of iSMART. RESULTS: In step 1, we completed a needs assessment consisting of assembling a planning group to codevelop the intervention, conducting telephone surveys of people after stroke (n=125) to identify service needs, and performing a systematic review of randomized controlled trials to examine evidence of the effectiveness of digital self-management interventions to improve patient outcomes. We identified activity scheduling, symptom management, stroke prevention, access to care resources, and cognitive enhancement training as key service needs after a stroke. The review suggested that digital self-management interventions, especially those using cognitive behavioral theory, effectively reduce depression, anxiety, and fatigue and enhance self-efficacy in neurological disorders. Step 2 identified key determinants, objectives, and strategies for self-management in iSMART, including knowledge, behavioral regulation, skills, self-efficacy, motivation, negative and positive affect, and social and environmental support. In step 3, we generated the intervention components underpinned by appropriate BCTs. In step 4, we developed iSMART with the planning group members. Especially, iSMART simplified the original psychoeducation program and added 2 new components: SMS text messaging and behavioral coaching, intending to increase the uptake by people after stroke. iSMART was found to be acceptable (mean score 4.63, SD 0.38 out of 5), appropriate (mean score 4.63, SD 0.38 out of 5), and feasible (mean score 4.58, SD 0.34 out of 5). CONCLUSIONS: We describe a detailed example of using IM and the taxonomy of BCTs for designing and developing a digital intervention to support people after stroke in managing chronic symptoms and maintaining active participation in daily life.

A Mobile Phone Text Messaging Intervention to Manage Fatigue for People With Multiple Sclerosis, Spinal Cord Injury, and Stroke: Development and Usability Testing.

BACKGROUND: Fatigue significantly affects daily functioning in persons with disabilities. Fatigue management can be challenging, and the information provided during routine physician visits to manage fatigue can be overwhelming. One way to address fatigue is to increase knowledge, skills, and confidence for self-management (ie, patient activation). Self-management programs have shown promising effects in targeting fatigue in persons with disabilities. However, satisfaction with self-management programs is low for persons with disabilities, and tailoring interventions to personalized needs has been recommended. SMS text messaging is increasingly being used to implement health behavior change interventions in a person's natural environment. Little has been done to link mobile health approaches with patient activation and self-management to address fatigue in persons with disabilities. OBJECTIVE: This study aimed to develop and test a mobile phone-based fatigue self-management SMS text messaging intervention targeting patient activation in 3 groups of persons with disabilities: persons with multiple sclerosis, persons who had a stroke, and persons with a spinal cord injury. METHODS: We used evidence-based resources and input from a consumer advisory board (CAB; composed of 2 participants from each of the 3 disability groups) and a neurologist to develop the intervention. The study was conducted using a 4-step process: development of the initial SMS text messaging library and categorization of the content into 9 content areas, review and modification of the SMS text messages by the neurologist and CAB, integration of the content library into a digital platform, and utility testing by CAB members. RESULTS: A total of 6 CAB participants rated SMS text messages covering 9 domain areas of fatigue self-management with good clarity (mean ratings=3.5-5.0 out of 5) and relevance (mean ratings=3.2-5.0 out of 5). Overall, SMS text messaging content was reported by CAB participants as helpful, clear, and well suited for a mobile health intervention. The CAB reached consensus on the time of day that SMS text messages should be sent (morning) and their frequency (once per day). This feedback led the research team to narrow down the program to deliver 48 SMS text messages, 1 per day, Monday through Thursday only, a total of 4 SMS text messages per week, over a 12-week period. The final set of SMS text messages was programmed into a digital platform with a predefined delivery schedule. The usability of the intervention was high, with 55 (83%) out of 66 responses endorsing the highest rating. CONCLUSIONS: This study demonstrates a step-by-step process for developing a fatigue self-management SMS text messaging intervention for persons with disabilities. For this population, whose access to health services is often limited, this intervention provides an alternative delivery model to increase access to fatigue information and deliver content that aligns with the person's needs.

Theory-Based Self-Management Interventions for Community-Dwelling Stroke Survivors: A Systematic Review and Meta-Analysis.

IMPORTANCE: Self-management is a critical component of stroke rehabilitation. A better understanding of the use of theory and behavior change techniques (BCTs) informs the development of more effective stroke self-management interventions. OBJECTIVE: To examine what theories and BCTs have been applied in stroke self-management interventions; investigate the extent to which these interventions encourage implementation of behavior changes; and appraise their effectiveness to enhance self-efficacy, quality of life, and functional independence. DATA SOURCES: Ovid MEDLINE, Embase, Scopus, CINAHL, Cochrane Library, and ClinicalTrials.gov were searched from inception to May 26, 2020. STUDY SELECTION AND DATA COLLECTION: Randomized controlled trials (RCTs) in six databases were reviewed for inclusion and analysis. We included trials that involved community-dwelling adult stroke survivors, assessed the effectiveness of self-management interventions, and explicitly mentioned the use of theory in the development of the intervention. We assessed use of theory and BCTs using the Theory Coding Scheme and BCT taxonomy v1, respectively. FINDINGS: A total of 3,049 studies were screened, and 13 RCTs were included. The predominant theory and BCT categories were Social Cognitive Theory (7 studies) and goals and planning (12 studies), respectively. Significant and small effect sizes were found for self-efficacy (0.27) and functional independence (0.19). CONCLUSIONS AND RELEVANCE: Theory-based self-management interventions have the potential to enhance stroke outcomes. Systematic reporting on the use of theory and BCTs is recommended to enhance clarity and facilitate evaluations of future interventions. What This Article Adds: This review supports and guides occupational therapy practitioners to use theory-based self-management intervention as a routine part of stroke rehabilitation to improve stroke survivors' experience in the community.

Using Implementation Science to Guide the Process of Adapting a Patient Engagement Intervention for Inpatient Spinal Cord Injury/Disorder Rehabilitation.

OBJECTIVES: This study aimed to describe the process of adapting an evidence-based patient engagement intervention, enhanced medical rehabilitation (E-MR), for inpatient spinal cord injury/disease (SCI/D) rehabilitation using an implementation science framework. DESIGN: We applied the collaborative intervention planning framework and included a community advisory board (CAB) in an intervention mapping process. SETTING: A rehabilitation hospital. PARTICIPANTS: Stakeholders from inpatient SCI/D rehabilitation (N=7) serving as a CAB and working with the research team (N=7) to co-adapt E-MR. INTERVENTIONS: E-MR. MAIN OUTCOME MEASURES: Logic model and matrices of change used in CAB meetings to identify areas of intervention adaptation. RESULTS: The CAB and research team implemented adaptations to E-MR, including (1) identifying factors influencing patient engagement in SCI/D rehabilitation (eg, therapist training); (2) revising intervention materials to meet SCI/D rehabilitation needs (eg, modified personal goals interview and therapy trackers to match SCI needs); (3) incorporating E-MR into the rehabilitation hospital's operations (eg, research team coordinated with CAB to store therapy trackers in the hospital system); and (4) retaining fidelity to the original intervention while best meeting the needs of SCI/D rehabilitation (eg, maintained core E-MR principles while adapting). CONCLUSIONS: This study demonstrated that structured processes guided by an implementation science framework can help researchers and clinicians identify adaptation targets and modify the E-MR program for inpatient SCI/D rehabilitation.

Ecological Momentary Assessment of Real-World Functional Behaviors in Individuals With Stroke: A Longitudinal Observational Study.

OBJECTIVE: To validate and characterize real-world functional behaviors in individuals after stroke. DESIGN: Longitudinal observational study using ecological momentary assessment (EMA) as a real-time assessment of functional behaviors in natural contexts. Wilcoxon rank-sum tests, Fisher exact tests, and Spearman correlations were used to analyze data. SETTING: Community. PARTICIPANTS: Individuals with mild to moderate stroke (N=212). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Individuals were assessed 5 times daily for 14 days with EMA surveys to determine what, with whom, and where individuals were doing activities and appraise mental, somatic, and cognitive symptoms. Individuals also completed standardized assessments during laboratory visits, including Lawton Instrumental Activities of Daily Living Scale, Activity Card Sort, Patient-Reported Outcome Measurement Information System, and Quality of Life in Neurological Disorders. RESULTS: Most individuals (median age, 60 years; 55% male) were ischemic stroke (90%) and had mild stroke severity (median National Institutes of Health Stroke Scale, 2). A total of 14,140 EMA surveys were analyzed. Individuals were home 78% of the time; primarily participated in passive, unproductive activities (27%), especially watching television and resting; and participated least in physical activities (4%). EMA was sensitive to indicators of poststroke disability; unemployed individuals reported fewer vocational activities but more activities of daily living (ADL) and passive activities than employed counterparts. Users of mobility devices and individuals with cognitive problems spent significantly less time on vocational activities and more on ADL than nonusers and those without cognitive problems. Our data supported the validity of EMA methods in stroke, with small to moderate correlations of EMA with in-laboratory measures of daily functioning (r=-0.30 to 0.35, P<.05) and very large correlations between EMA and in-laboratory measures of symptoms, especially those measuring same constructs (r=-0.64 to 0.79, P<.0001). CONCLUSIONS: Our findings reveal that EMA tracked poststroke functioning precisely. EMA may be beneficial in examining poststroke functional recovery, in monitoring patients for home-based interventions, and for longitudinal research.

Quality of Life and Psychosocial Factors as Predictors of Pain Relief Following Nerve Surgery.

Background: Peripheral nerve injuries may result in pain, disability, and decreased quality of life (QoL). Pain is an incompletely understood experience and is associated with emotional and behavioral qualities. We hypothesized that pain following peripheral nerve surgery could be predicted by changes in emotions or QoL postoperatively. Methods: Using prospectively collected data, a retrospective study design was used to evaluate the relationships among pain, QoL, and psychosocial factors in patients who underwent peripheral nerve surgery. Patients completed questionnaires rating pain; impact of pain on QoL, sadness, depression, frustration, anger, and hopefulness before surgery; and each postoperative follow-up visit. Multilevel modeling was used to assess the concurrent and lagged relationships between pain and psychosocial factors. Results: Increased pain was concurrently associated with decreased hopefulness (P = .001) and increased the impact on QoL, sadness, depression, and anger (P < .001). In lagged analyses, the impact on QoL and anger prospectively predicted pain (P < .001 and P = .02, respectively). Pain predicted subsequent scores of QoL, sadness, depression, anger, and hopefulness (P < .01). Having an upper limb nerve injury and self-report of "no comment for childhood trauma" were predictors of postsurgical pain. Conclusion: Psychosocial measures and pain are reciprocally related among patients who underwent surgery for peripheral nerve injuries or compression. Our study provides evidence of the important relationships among psychosocial factors, pain, and outcome and identifies treatment targets following nerve surgery.

Heart rate variability as a biomarker of functional outcomes in persons with acquired brain injury: Systematic review and meta-analysis.

This review aimed to quantify correlations between heart rate variability (HRV) and functional outcomes after acquired brain injury (ABI). We conducted a literature search from inception to January 2020 via electronic databases, using search terms with HRV, ABI, and functional outcomes. Meta-analyses included 16 studies with 906 persons with ABI. Results demonstrated significant associations: Low frequency (LF) (r = -0.28) and SDNN (r = -0.33) with neurological function; LF (r = -0.33), High frequency (HF) (r = -0.22), SDNN (r = -0.22), and RMSSD (r = -0.23) with emotional function; and LF (r = 0.34), HF (r = 0.41 to 0.43), SDNN (r = 0.43 to 0.51), and RMSSD (r = 0.46) with behavioral function. Results indicate that higher HRV is related to better neurological, emotional, and behavioral functions after ABI. In addition, persons with stroke showed lower HF (SMD = -0.50) and SDNN (SMD = -0.75) than healthy controls. The findings support the use of HRV as a biomarker to facilitate precise monitoring of post-ABI functions.

Psychometric Properties of the Infant Toddler Activity Card Sort.

The Infant Toddler Activity Card Sort (ITACS) is a new measure that engages caregivers in goal development by measuring participation in early childhood. Initial evaluation of the ITACS is required to assess its psychometric properties. To evaluate test-retest reliability, concurrent validity, and between-group differences of the ITACS. Caregivers (N = 208) of children 0-3 years, who were typically developing (TD) or had a developmental delay (DD), completed the ITACS and Pediatric Evaluation of Disability Inventory-Computer Adaptive Test (PEDI-CAT) with repeat assessment after 2 weeks later (n = 117). The ITACS demonstrated moderate concurrent validity with the PEDI-CAT. Children with DD had significantly more activity concerns on the ITACS than TD. Adequate test-retest reliability was not achieved. The ITACS is a useful tool for identifying caregiver concerns in activity engagement; interventions can be tailored to address concerns and increase participation in childhood routines.

Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study.

Measuring activities of daily living (ADLs) using wearable technologies may offer higher precision and granularity than the current clinical assessments for patients after stroke. This study aimed to develop and determine the accuracy of detecting different ADLs using machine-learning (ML) algorithms and wearable sensors. Eleven post-stroke patients participated in this pilot study at an ADL Simulation Lab across two study visits. We collected blocks of repeated activity ("atomic" activity) performance data to train our ML algorithms during one visit. We evaluated our ML algorithms using independent semi-naturalistic activity data collected at a separate session. We tested Decision Tree, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) for model development. XGBoost was the best classification model. We achieved 82% accuracy based on ten ADL tasks. With a model including seven tasks, accuracy improved to 90%. ADL tasks included chopping food, vacuuming, sweeping, spreading jam or butter, folding laundry, eating, brushing teeth, taking off/putting on a shirt, wiping a cupboard, and buttoning a shirt. Results provide preliminary evidence that ADL functioning can be predicted with adequate accuracy using wearable sensors and ML. The use of external validation (independent training and testing data sets) and semi-naturalistic testing data is a major strength of the study and a step closer to the long-term goal of ADL monitoring in real-world settings. Further investigation is needed to improve the ADL prediction accuracy, increase the number of tasks monitored, and test the model outside of a laboratory setting.

Fronto-cerebellar connectivity mediating cognitive processing speed.

Processing speed is an important construct in understanding cognition. This study was aimed to control task specificity for understanding the neural mechanisms underlying cognitive processing speed. Forty young adult subjects performed attention tasks of two modalities (auditory and visual) and two levels of task rules (compatible and incompatible). Block-design fMRI captured BOLD signals during the tasks. Thirteen regions of interest were defined with reference to publicly available activation maps for processing speed tasks. Cognitive speed was derived from task reaction times, which yielded six sets of connectivity measures. Mixed-effect LASSO regression revealed six significant paths suggestive of a cerebello-frontal network predicting the cognitive speed. Among them, three are long range (two fronto-cerebellar, one cerebello-frontal), and three are short range (fronto-frontal, cerebello-cerebellar, and cerebello-thalamic). The long-range connections are likely to relate to cognitive control, and the short-range connections relate to rule-based stimulus-response processes. The revealed neural network suggests that automaticity, acting on the task rules and interplaying with effortful top-down attentional control, accounts for cognitive speed.

Rasch Analysis of Social Attitude Barriers and Facilitators to Participation for Individuals with Disabilities.

OBJECTIVES: To develop item banks of social attitude barriers and facilitators to participation and validate them with established instruments. DESIGN: We used the Rasch model to identify misfitting items and rating scale problems, calibrate items, and develop KeyForms and short forms. Correlations between the Social Attitude Barriers and Facilitators item banks with the Patient-Reported Outcomes Measurement Information System (PROMIS) Social Health domain and National Institutes of Health Toolbox Emotional Battery Social Relationships domain were computed to evaluate convergent and divergent validity. SETTING: Community-dwelling individuals traveled to 3 academic medical centers for testing. PARTICIPANTS: Participants (N=558) who had a primary impairment of stroke, spinal cord injury, or traumatic brain injury (mean age, 47.0±16.0y) completed 31 social attitude facilitator and 51 barrier items using a 5-point rating scale. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Item banks to measure social attitude barriers and facilitators for individuals with disabilities. RESULTS: After combining the "never" and "rarely" rating scale categories, 30 Facilitator items fit the Rasch model and demonstrated person reliability of 0.93. After collapsing the "never" and "rarely" rating scale categories, 45 Barrier items fit the Rasch model and demonstrated person reliability of 0.95. Ceiling and floor effects were negligible for both item banks. Facilitators and Barriers item banks were negatively correlated, and these banks were moderately correlated with PROMIS and Toolbox measures, providing evidence of convergent and divergent validity. CONCLUSIONS: Findings support the reliability and validity of the Social Attitude Facilitators and Barriers item banks. These item banks allow investigators and clinicians to measure perceptions of social attitudes, providing information that can guide individual interventions to reduce barriers and promote facilitators. Moderate correlations between the Social Attitude banks and PROMIS and Toolbox variables provide support for the measurement and theory of environmental influences on social health and participation.

Ecological Momentary Assessment of Social Interactions: Associations With Depression, Anxiety, Pain, and Fatigue in Individuals With Mild Stroke.

OBJECTIVES: To examine real-time relationships between social interactions and poststroke mood and somatic symptoms in participants' daily environments. DESIGN: Prospective observational study using smartphone-based ecological momentary assessment (EMA) surveys 5 times a day for 2 weeks. Multilevel models were used to analyze data for concurrent and lagged associations. SETTING: Community. PARTICIPANTS: Adults (N=48) with mild stroke. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: EMA measures of self-appraisal of social interactions (confidence, satisfaction, and success), as well as mood (depression and anxiety) and somatic (pain and fatigue) symptoms. RESULTS: In concurrent associations, increased depressed mood was associated with reduced ratings of all aspects of social interactions. Fatigue was associated with reduced ratings of social satisfaction and success. In lagged associations, increased anxious mood preceded increased subsequent social confidence. Higher average social satisfaction, confidence, and success were related to lower momentary fatigue, anxious mood, and depressed mood at the next time point. Regarding clinicodemographic factors, being employed was concurrently related to increased social interactions. An increased number of comorbidities predicted higher somatic, but not mood, symptoms at the next time point. CONCLUSIONS: This study provides preliminary evidence of dynamic relationships between social interactions and somatic and mood symptoms in individuals with mild stroke. Interventions to not only address the sequelae of symptoms, but also to promote participation in social activities in poststroke life should be explored.

Effectiveness of non-pharmacological interventions for treating post-stroke depressive symptoms: Systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: To compare the effectiveness of non-pharmacological interventions on depressive symptoms in people after stroke. DATA SOURCES: A literature search was performed through databases from January 2000 to August 2018: MEDLINE; CINAHL Plus; Scopus; Academic Search Complete; Cochrane Central Register of Controlled Trials; Scopus; and Library, Information Science and Technology Abstracts. Search terms included depression, stroke, non-pharmacologic, and intervention. STUDY SELECTION: We included randomized controlled trials comparing non-pharmacological interventions to controls for depressive symptoms in people after stroke. Of 1703 identified articles, 22 trials were included in narrative synthesis, of which 13 were eligible for meta-analysis. DATA EXTRACTION: Two reviewers extracted characteristics of participants, interventions, and results from all included trials. DATA SYNTHESIS: Thirteen interventions were categorized into four types: complementary and alternative therapy (five trials, n=228), exercise (four trials, n=263), psychosocial therapy (two trials, n=216), and multifactorial therapy (two trials, n=358). Overall beneficial effects of non-pharmacological interventions on depressive symptoms were found both post-intervention (effect size [ES] = -0.24, 95% confidence Interval [CI]: -0.37 to -0.11, p < 0.05) and at follow-up (ES = -0.22, CI: -0.36 to -0.07, p< 0.05). We found individual beneficial effects for complementary and alternative therapy (ES = -0.29, CI: -0.55 to -0.02, p < 0.05) and psychosocial therapy (ES = - 0.33, CI: -0.60 to -0.06, p < 0.05) post-intervention. CONCLUSIONS: Complementary and alternative therapy and psychosocial therapy appear to be promising strategies for improving post-stroke depression. Future studies target  a personalized approach for people with specific conditions such as cognitive impairment.

Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders.

The goal of precision medicine (individually tailored treatments) is not being achieved for neurobehavioural conditions such as psychiatric disorders. Traditional randomized clinical trial methods are insufficient for advancing precision medicine because of the dynamic complexity of these conditions. We present a pragmatic solution: the precision clinical trial framework, encompassing methods for individually tailored treatments. This framework includes the following: (1) treatment-targeted enrichment, which involves measuring patients' response after a brief bout of an intervention, and then randomizing patients to a full course of treatment, using the acute response to predict long-term outcomes; (2) adaptive treatments, which involve adjusting treatment parameters during the trial to individually optimize the treatment; and (3) precise measurement, which involves measuring predictor and outcome variables with high accuracy and reliability using techniques such as ecological momentary assessment. This review summarizes precision clinical trials and provides a research agenda, including new biomarkers such as precision neuroimaging, transcranial magnetic stimulation-electroencephalogram digital phenotyping and advances in statistical and machine-learning models. Validation of these approaches - and then widespread incorporation of the precision clinical trial framework - could help achieve the vision of precision medicine for neurobehavioural conditions.