Jessica Barth

Background: The VA Hospital in Home (HIH) model of care delivers acute‐level hospital care to Veterans at home across the country. We aim to examine the similarities and differences in the structure and implementation of existing HIH programs across sites to identify best practices that optimize resources, ensure high‐quality care, and enhance sustainability and scalability of the program. Methods: A total of 6 out of 11 existing VA HIH programs reported on their program through structured presentations at Community‐of‐Practice meetings in 2022. Additional site‐level characteristics were retrieved from the VA Corporate Data Warehouse to describe context at each program and to identify HIH encounters, types of stays, and numbers of HIH patients for each site. Results: All sites offered transfer (from hospital‐based inpatient service) and substitutive (from other than inpatient) HIH stays, though the proportion of patients for each varied (transfer patients ranging from 6% to 85%). Referral sources for substitutive stays varied across sites including Home‐Based Primary Care (HBPC) (67%), outpatient primary care (83%), specialty care clinics (67%), and emergency departments (83%). Each program had interdisciplinary teams, though composition and staff size varied (4–7 full‐time equivalent [FTE] RNs; 1.0–2.0 FTE physicians). Programs varied in the number of unique patients served per program (63–146 unique HIH Veterans annually). Programs reported similar procedures in clinical evaluation, informed consent, and maintaining 24/7 access to the HIH team. Outcome metrics tracked include readmission (67%), cost avoidance (50%), and length of stay (50%). Additionally, 50% of sites reported integrating ancillary services and introducing program adaptations during the COVID‐19 pandemic period such as virtual follow‐up visits. Conclusion: Our study identified similarities and differences across the existing HIH programs, adapting to local contexts while maintaining quality of care. These characteristics may reflect differences in population and site characteristics, patient needs, and program goals.

Background and Objectives: Understanding the factors that enable or hinder successful implementation is essential for improving patient outcomes. Hospital in Home (HiH) programs provide hospital-level care in patients’ homes to reduce hospitalization, enhance satisfaction, and improve resource use. Within the Veterans Health Administration (VHA), 14 sites have implemented HiH programs, yet limited research has examined implementation within a large integrated system. This study identifies factors that can support or hinder HiH implementation. Research Design and Methods: We conducted interviews with 48 key informants across 14 VHA HiH sites. Guided by the (Consolidated Framework for Implementation Research (CFIR), 2022) 2.0, we applied directed content analysis using a priori constructs, then developed site-level matrices to summarize findings and compare patterns across sites. Results: We identified 18 CFIR constructs—nine that primarily facilitated implementation and nine mixed factors that acted as both facilitators and barriers. Key facilitators included two Individual-level factors (HiH lead capability and motivation, team motivation), one Innovation factor (relative advantage of HiH), and four Implementation Process factors (planning, teaming, reflecting and evaluating, and adapting). Mixed factors were mostly within the inner setting, reflecting the medical center context, along with two individual-level mixed factors (leader and recipient motivation). Discussion and Implications: HiH programs hold strong potential to transform care for older adults, but success depends on addressing barriers and leveraging facilitators, particularly within the inner setting. Understanding key implementation determinants can enhance adoption, improve outcomes, and strengthen healthcare delivery.

Dissemination and implementation (D&I) theories, models, and frameworks (TMFs) guide the translation of evidence into practice, yet their use in international aging research remains understudied. To examine which D&I TMFs are most frequently used in international aging research and how they are applied across different settings and implementation stages. We conducted a citation review of articles citing 83 D&I TMFs identified from a public repository. Articles focusing on adults aged ≥ 60 years published through January 2022 in non-US settings were included. We assessed TMF citation patterns, application across implementation processes, application across article sections, and meaningful use. Of 451 included articles from 41 countries, most originated from: Canada (21%), England/Great Britain (17%), Australia (15%), and the Netherlands (12%), representing 69% of articles. Publications increased ten-fold from 2012 to 2021, with 49 unique TMFs utilized. Five TMFs accounted for 51% of citations: Behavior Change Wheel (16%); Consolidated Framework for Implementation Research (11%); Promoting Action on Research Implementation in Health Services (9%); Reach, Effectiveness, Adoption, Implementation, Maintenance 1.0 (9%); and Explaining Behavior Change in Evidence-Based Practice (8%). Most articles (81%) used only one TMF and 39% demonstrated meaningful use. Most studies focused on pre-implementation (41%) or implementation (46%) stages, with fewer addressing intervention adaptation (4%) or sustainment (9%). While TMF use in international aging research is growing, meaningful application remains limited. The concentration in specific countries and dominance of few frameworks reveal areas for future work. Improving TMF selection, adaptation for international contexts, and application across all implementation stages has the potential to enhance global translation of aging research into practice.

Factors associated with upper limb prosthesis adoption are not well understood. In this study, we explored how prosthesis usability experience relates to the extent of prosthesis adoption through the development of a structural equation model (SEM). First, items related to prosthesis usability were developed and refined using cognitive testing and pilot testing and employed in a survey of 402 prosthesis users (mean age 61.7 (sd 14.4), 77.1% Veterans). The SEM examined two unidimensional latent constructs: Prosthesis Usability Experience and Prosthesis Adoption–and each had multiple measured indicators. SEMs tested direct as well as moderating and mediating effects between the latent constructs and covariates related to demographics and prosthesis type. SEM found a significant positive association between Prosthesis Usability Experience and Extent of Prosthesis Adoption. Several covariates had direct effects on prosthesis adoption: 1) Extent of Prosthesis Adoption was lower for those with transhumeral and shoulder amputation, and higher for those with bilateral amputation, compared to the reference group with unilateral transradial amputation and 2) Myoelectric multiple degree of freedom (multi-DOF) prosthesis use was associated with lower Extent of Prosthesis Adoption, compared to body-powered prosthesis use. Myoelectric multi-DOF use also modified the effect of Prosthesis Usability Experience on Extent of Prosthesis Adoption. For those with bilateral ULA, the strength of the relationship between Prosthesis Usability Experience and Extent of Prosthesis Adoption was reduced. Findings suggest that in order to increase prosthesis adoption, prosthetics developers and rehabilitation providers should focus on implementing strategies to improve prosthesis usability experience. New Prosthesis Usability Experience measures could be used to identify persons at greater risk for poor prosthesis adoption and target interventions to increase prosthesis use.

Trials for therapies after an upper limb amputation (ULA) require a focus on the real-world use of the upper limb prosthesis. In this paper, we extend a novel method for identifying upper extremity functional and nonfunctional use to a new patient population: upper limb amputees. We videotaped five amputees and 10 controls performing a series of minimally structured activities while wearing sensors on both wrists that measured linear acceleration and angular velocity. The video data was annotated to provide ground truth for annotating the sensor data. Two different analysis methods were used: one that used fixed-size data chunks to create features to train a Random Forest classifier and one that used variable-size data chunks. For the amputees, the fixed-size data chunk method yielded good results, with 82.7% median accuracy (range of 79.3–85.8) on the 10-fold cross-validation intra-subject test and 69.8% in the leave-one-out inter-subject test (range of 61.4–72.8). The variable-size data method did not improve classifier accuracy compared to the fixed-size method. Our method shows promise for inexpensive and objective quantification of functional upper extremity (UE) use in amputees and furthers the case for use of this method in assessing the impact of UE rehabilitative treatments.

The Critical Periods After Stroke Study (CPASS, n = 72) showed that, compared to controls, an additional 20 hours of intensive upper limb therapy led to variable gains on the Action Research Arm Test depending on when therapy was started post-stroke: the subacute group (2-3 months) improved beyond the minimal clinically important difference and the acute group (0-1 month) showed smaller but statistically significant improvement, but the chronic group (6-9 months) did not demonstrate improvement that reached significance. Some have misinterpreted CPASS results to indicate that all inpatient motor therapy should be shifted to outpatient therapy delivered 2 to 3 months post-stroke. Instead, however, CPASS argues for a large dose of motor therapy delivered continuously and cumulatively during the acute and subacute phases. When interpreting trials like CPASS, one must consider the substantial dose of early usual customary care (UCC) motor therapy that all participants received. CPASS participants averaged 27.9 hours of UCC occupational therapy (OT) during the first 2 months and 9.8 hours of UCC OT during the third and fourth months post-stroke. Any recovery experienced would therefore result not just from CPASS intensive motor therapy but the combined effects of experimental therapy plus UCC. Statistical limitations also did not allow direct comparisons of the acute and subacute group outcomes in CPASS. Instead of shifting inpatient therapy hours to the subacute phase, CPASS argues for preserving inpatient UCC. We also recommend conducting multi-site dosing trials to determine whether additional intensive motor therapy delivered in the first 2 to 3 months following inpatient rehabilitation can further improve outcomes.

Background. Wearable sensors allow for direct measurement of upper limb (UL) performance in daily life. Objective. To map the trajectory of UL performance and its relationships to other factors post-stroke. Methods . Participants (n = 67) with first stroke and UL paresis were assessed at 2, 4, 6, 8, 12, 16, 20, and 24 weeks after stroke. Assessments captured UL impairment (Fugl-Meyer), capacity for activity (Action Research Arm Test), and performance of activity in daily life (accelerometer variables of use ratio and hours of paretic limb activity), along with other potential modifying factors. We modeled individual trajectories of change for each measurement level and the moderating effects on UL performance trajectories. Results. Individual trajectories were best fit with a 3-parameter logistic model, capturing the rapid growth early after stroke within the longer data collection period. Plateaus (90% of asymptote) in impairment (bootstrap mean ± SE: 32 ± 4 days post-stroke) preceded those in capacity (41 ± 4 days). Plateau in performance, as measured by the use ratio (24 ± 5 days), tended to precede plateaus in impairment and capacity. Plateau in performance, as measured by hours of paretic activity (41 ± 6 days), occurred at a similar time to that of capacity and slightly lagged impairment. Modifiers of performance trajectories were capacity, concordance, UL rehabilitation, depressive symptomatology, and cognition. Conclusions. Upper limb performance in daily life approached plateau 3 to 6 weeks post-stroke. Individuals with stroke started to achieve a stable pattern of UL use in daily life early, often before neurological impairments and functional capacity started to stabilize.

Wrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results. Compare machine learning algorithms with standard methods (counts ratio) to improve accuracy in detecting functional activity. Healthy controls and individuals with stroke performed unstructured tasks in a simulated community environment (Test duration = 26 ± 8 minutes) while accelerometry and video were synchronously recorded. Human annotators scored each frame of the video as being functional or nonfunctional activity, providing ground truth. Several machine learning algorithms were developed to separate functional from nonfunctional activity in the accelerometer data. We also calculated the counts ratio, which uses a thresholding scheme to calculate the duration of activity in the paretic limb normalized by the less-affected limb. The counts ratio was not significantly correlated with ground truth and had large errors (r = 0.48; P = .16; average error = 52.7%) because of high levels of nonfunctional movement in the paretic limb. Counts did not increase with increased functional movement. The best-performing intrasubject machine learning algorithm had an accuracy of 92.6% in the paretic limb of stroke patients, and the correlation with ground truth was r = 0.99 (P < .001; average error = 3.9%). The best intersubject model had an accuracy of 74.2% and a correlation of r =0.81 (P = .005; average error = 5.2%) with ground truth. In our sample, the counts ratio did not accurately reflect functional activity. Machine learning algorithms were more accurate, and future work should focus on the development of a clinical tool.

While the promise of wearable sensor technology to transform physical rehabilitation has been around for a number of years, the reality is that wearable sensor technology for the measurement of human movement has remained largely confined to rehabilitation research labs with limited ventures into clinical practice. The purposes of this paper are to: (1) discuss the major barriers in clinical practice and available wearable sensing technology; (2) propose benchmarks for wearable device systems that would make it feasible to implement them in clinical practice across the world and (3) evaluate a current wearable device system against the benchmarks as an example. If we can overcome the barriers and achieve the benchmarks collectively, the field of rehabilitation will move forward towards better movement interventions that produce improved function not just in the clinic or lab, but out in peoples’ homes and communities.