Reducing Physical Therapy Consults for Patients With High Functional Mobility in the Acute Medical Inpatient Setting: A Difference-in-Difference Analysis.

OBJECTIVE: To evaluate the effectiveness of clinical decision support for reducing misallocation of physical therapy (PT) consults. DESIGN: A prospective quasi-experimental study. Between October 2018 and November 2021, routinely documented data on functional status and physical therapy referrals were collected from electronic medical records. SETTING: Hospital Medicine and General Internal Medicine service lines at a large quaternary academic medical center. PARTICIPANTS: 20,810 adult patients hospitalized on any of the included treatment (hospital medicine) or control (general internal medicine) service lines. MAIN OUTCOME MEASURE: The primary outcome was "change in proportion of misallocated PT consults" measured as likelihood of PT consults for patients admitted with high functional mobility scores. Changes in the primary outcome from the pre-intervention to post-intervention period were compared in the control and treatment groups using propensity score-weighted difference-in-differences multivariable logit regression adjusting for clinically relevant covariates. INTERVENTION: The intervention period was measured for 20 months and consisted of a clinical decision support tool embedded in the daily note templates for hospital medicine providers. The tool provided education on patient mobility scores and their relation to need for PT consult. The tool was rolled out without any further announcements or education. RESULTS: Our cohort included 20,810 unique admissions (mean age 58.9, 55% women, 83% Black). Post-intervention, the likelihood of PT referrals for patients with high baseline mobility (AM-PAC >18) decreased by 7.3% (P<.001) for the treatment group compared with control, adjusted for age, sex, race, ethnicity, length-of-stay, and mobility change. CONCLUSION: Mobility score-based clinical decision support can decrease unneeded PT consults in the inpatient setting. This could help allocate therapy time for at-risk patients while also having a positive effect on health care systems.

Trends in Before Medically Advised Discharges for Patients With Opioid Use Disorder, 2016-2020.

This study examines discharge trends for opioid-related admissions from 2016-2020 with a focus on admissions with opioid use disorder and an injection-related infection.

A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure.

Supply-demand mismatch of ward resources ("ward capacity strain") alters care and outcomes. Narrow strain definitions and heterogeneous populations limit strain literature. Evaluate the predictive utility of a large set of candidate strain variables for in-hospital mortality and discharge destination among acute respiratory failure (ARF) survivors. In a retrospective cohort of ARF survivors transferred from intensive care units (ICUs) to wards in five hospitals from 4/2017-12/2019, we applied 11 machine learning (ML) models to identify ward strain measures during the first 24 hours after transfer most predictive of outcomes. Measures spanned patient volume (census, admissions, discharges), staff workload (medications administered, off-ward transports, transfusions, isolation precautions, patients per respiratory therapist and nurse), and average patient acuity (Laboratory Acute Physiology Score version 2, ICU transfers) domains. The cohort included 5,052 visits in 43 wards. Median age was 65 years (IQR 56-73); 2,865 (57%) were male; and 2,865 (57%) were white. 770 (15%) patients died in the hospital or had hospice discharges, and 2,628 (61%) were discharged home and 964 (23%) to skilled nursing facilities (SNFs). Ward admissions, isolation precautions, and hospital admissions most consistently predicted in-hospital mortality across ML models. Patients per nurse most consistently predicted discharge to home and SNF, and medications administered predicted SNF discharge. In this hypothesis-generating analysis of candidate ward strain variables' prediction of outcomes among ARF survivors, several variables emerged as consistently predictive of key outcomes across ML models. These findings suggest targets for future inferential studies to elucidate mechanisms of ward strain's adverse effects.

Geographic cohorting by clinical care team: a narrative review.

BACKGROUND AND OBJECTIVE: Geographic cohorting refers to localization of inpatients to designated hospital areas (typically a unit or a set of beds) based on specified criteria. One such criterion that has been commonly discussed and studied since the early days of the hospitalist movement in the US is a patient's assigned clinical care team. Because implementing cohorting of this type requires substantial operational investment, it is important to understand the benefits and the tradeoffs associated with bringing patients into closer spatial proximity with their full team of providers and allowing clinicians to work within a defined clinical space. METHODS: We conducted a narrative review of the evidence around geographic cohorting of patients by clinical care team. We performed a comprehensive search of the PubMed, Embase, Cinahl and Scopus databases, identifying relevant English language articles. We used an inductive approach to developing thematic domains for categorization of article content. KEY CONTENT AND FINDINGS: We reviewed eighteen articles published between 2008 and 2022, and identified four thematic outcomes domains: patient-centered outcomes, communication, efficiency, and satisfaction. The existing literature demonstrates associations with improved communication and staff satisfaction. The data regarding patient outcomes and overall work efficiency, on the other hand, are equivocal and, in general, limited by study methodology. CONCLUSIONS: Geographic cohorting of inpatients according to clinical care team offers some promise for improved workplace culture. More rigorously designed studies are needed, however, to understand its impact on patient outcomes, and there should be added attention paid to throughput metrics and tradeoffs that often limit its implementation.

Prevalence and Causes of Diagnostic Errors in Hospitalized Patients Under Investigation for COVID-19.

BACKGROUND: The COVID-19 pandemic required clinicians to care for a disease with evolving characteristics while also adhering to care changes (e.g., physical distancing practices) that might lead to diagnostic errors (DEs). OBJECTIVE: To determine the frequency of DEs and their causes among patients hospitalized under investigation (PUI) for COVID-19. DESIGN: Retrospective cohort. SETTING: Eight medical centers affiliated with the Hospital Medicine ReEngineering Network (HOMERuN). TARGET POPULATION: Adults hospitalized under investigation (PUI) for COVID-19 infection between February and July 2020. MEASUREMENTS: We randomly selected up to 8 cases per site per month for review, with each case reviewed by two clinicians to determine whether a DE (defined as a missed or delayed diagnosis) occurred, and whether any diagnostic process faults took place. We used bivariable statistics to compare patients with and without DE and multivariable models to determine which process faults or patient factors were associated with DEs. RESULTS: Two hundred and fifty-seven patient charts underwent review, of which 36 (14%) had a diagnostic error. Patients with and without DE were statistically similar in terms of socioeconomic factors, comorbidities, risk factors for COVID-19, and COVID-19 test turnaround time and eventual positivity. Most common diagnostic process faults contributing to DE were problems with clinical assessment, testing choices, history taking, and physical examination (all p < 0.01). Diagnostic process faults associated with policies and procedures related to COVID-19 were not associated with DE risk. Fourteen patients (35.9% of patients with errors and 5.4% overall) suffered harm or death due to diagnostic error. LIMITATIONS: Results are limited by available documentation and do not capture communication between providers and patients. CONCLUSION: Among PUI patients, DEs were common and not associated with pandemic-related care changes, suggesting the importance of more general diagnostic process gaps in error propagation.

Participants' Perspectives on Payment for Research Participation: A Qualitative Study.

Investigators commonly offer payments to research participants to promote recruitment and retention. Yet the ethics of offering monetary incentives to research participants continues to be debated. Prior conceptual work has addressed some of these concerns; there is, however, also a need for empirical evidence to understand the effects of payment on participants. Here, we report the results of a qualitative study comprising (1) discourse analysis of recruitment conversations between study coordinators and potential participants for an actual clinical trial and (2) semistructured interviews with participants addressing the effects of an incentive on their decision-making. Many participants reported that money had been a motivation for enrolling in the clinical trial but did not use reasoning that suggested undue influence or unjust inducement. These findings add to a growing body of literature suggesting that payment is an ethically acceptable tool for promoting recruitment and retention in clinical trials.

An accelerated hospital observation pathway to reduce length of stay for patients with COVID-19.

OBJECTIVES: Strategies to maintain hospital capacity during the COVID-19 pandemic included reducing hospital length of stay (LOS) for infected patients. We sought to evaluate the association between LOS and enrollment in the COVID Accelerated Care Pathway, which consisted of a hospital observation protocol and postdischarge automated text message-based monitoring. STUDY DESIGN: Retrospective matched cohort study of patients hospitalized from December 14, 2020, to January 31, 2021. METHODS: Participants were patients who presented to the emergency department with acute infection due to COVID-19, required hospitalization, and met pathway inclusion criteria. Participants were compared with a propensity score-matched cohort of patients with COVID-19 admitted to the same hospital during the 7 weeks preceding and following pathway implementation. RESULTS: There were 44 patients in the intervention group and 83 patients in the propensity score-matched cohort. The mean (SD) hospital LOS for patients in the intervention group was 1.7 (2.6) days compared with 3.9 (2.3) days for patients in the matched cohort (difference, -2.2 days; 95% CI, -3.3 to -1.1). In the intervention group, 2 patients (5%; 95% CI, 0%-15%) were rehospitalized within 14 days compared with 8 (10%; 95% CI, 4%-17%) in the matched cohort. CONCLUSIONS: Patients with COVID-19 who were managed through an accelerated hospital observation protocol and postdischarge monitoring service had reduced hospital LOS compared with patients receiving standard care. Hospital preparedness for future public health emergencies may involve the design of pathways that reduce the time that patients spend in the hospital, lower cost, and ensure continued recovery upon discharge.

Patterns of utilization and evaluation of advanced practice providers on academic hospital medicine teams: A national survey.

This survey study aimed to provide a contemporary appraisal of advanced practice provider (APP) practice and to summarize perceptions of the benefits and challenges of integrating APPs into adult academic hospital medicine (HM) groups. We surveyed leaders of academic HM groups. We received responses from 43 of 86 groups (50%) surveyed. Thirty-four (79%) reported that they employed APPs. In most groups (85%), APPs were reported to perform daily tasks of patient care, including rounding and documentation. Less than half of the groups reported that APPs had completed HM-specific postgraduate training. The reported benefits of APPs included improved perceived quality of care and greater volume of patients that could be seen. Reported challenges included training requirements and support for new hires. Further investigation is needed to determine which APP team structures deliver the highest quality care. There may be a role for expanding standardized competency-based postgraduate training for APPs planning to practice HM.

Exploring Wearables to Focus on the "Sweet Spot" of Physical Activity and Sleep After Hospitalization: Secondary Analysis.

BACKGROUND: Inadequate sleep and physical activity are common during and after hospitalization, but their impact on patient-reported functional outcomes after discharge is poorly understood. Wearable devices that measure sleep and activity can provide patient-generated data to explore ideal levels of sleep and activity to promote recovery after hospital discharge. OBJECTIVE: This study aimed to examine the relationship between daily sleep and physical activity with 6 patient-reported functional outcomes (symptom burden, sleep quality, physical health, life space mobility, activities of daily living, and instrumental activities of daily living) at 13 weeks after hospital discharge. METHODS: This secondary analysis sought to examine the relationship between daily sleep, physical activity, and patient-reported outcomes at 13 weeks after hospital discharge. We utilized wearable sleep and activity trackers (Withings Activité wristwatch) to collect data on sleep and activity. We performed descriptive analysis of device-recorded sleep (minutes/night) with patient-reported sleep and device-recorded activity (steps/day) for the entire sample with full data to explore trends. Based on these trends, we performed additional analyses for a subgroup of patients who slept 7-9 hours/night on average. Differences in patient-reported functional outcomes at 13 weeks following hospital discharge were examined using a multivariate linear regression model for this subgroup. RESULTS: For the full sample of 120 participants, we observed a "T-shaped" distribution between device-reported physical activity (steps/day) and sleep (patient-reported quality or device-recorded minutes/night) with lowest physical activity among those who slept <7 or >9 hours/night. We also performed a subgroup analysis (n=60) of participants that averaged the recommended 7-9 hours of sleep/night over the 13-week study period. Our key finding was that participants who had both adequate sleep (7-9 hours/night) and activity (>5000 steps/day) had better functional outcomes at 13 weeks after hospital discharge. Participants with adequate sleep but less activity (<5000 steps/day) had significantly worse symptom burden (z-score 0.93, 95% CI 0.3 to 1.5; P=.02), community mobility (z-score -0.77, 95% CI -1.3 to -0.15; P=.02), and perceived physical health (z-score -0.73, 95% CI -1.3 to -0.13; P=.003), compared with those who were more physically active (≥5000 steps/day). CONCLUSIONS: Participants within the "sweet spot" that balances recommended sleep (7-9 hours/night) and physical activity (>5000 steps/day) reported better functional outcomes after 13 weeks compared with participants outside the "sweet spot." Wearable sleep and activity trackers may provide opportunities to hone postdischarge monitoring and target a "sweet spot" of recommended levels for both sleep and activity needed for optimal recovery. TRIAL REGISTRATION: ClinicalTrials.gov NCT03321279; https://clinicaltrials.gov/ct2/show/NCT03321279.

Effectiveness and Ethics of Incentives for Research Participation: 2 Randomized Clinical Trials.

Importance: Incentivizing research participation is controversial and variably regulated because of uncertainty regarding whether financial incentives serve as undue inducements by diminishing peoples' sensitivity to research risks or unjust inducements by preferentially increasing enrollment among underserved individuals. Objective: To determine whether incentives improve enrollment in real randomized clinical trials (RCTs) or serve as undue or unjust inducements. Design, Setting, and Participants: Two RCTs of incentives that were embedded in 2 parent RCTs, 1 comparing smoking cessation interventions (conducted at smoking cessation clinics in 2 health systems) and 1 evaluating an ambulation intervention (conducted across wards of the Hospital of the University of Pennsylvania) included all persons eligible for the parent trials who did not have prior knowledge of the incentives trials. Recruitment occurred from September 2017 to August 2019 for the smoking trial and January 2018 through May 2019 for the ambulation trial; data were analyzed from January 2020 to July 2020. Interventions: Patients were randomly assigned to incentives of $0, $200, or $500 for participating in the smoking cessation trial and $0, $100, or $300 for the ambulation trial. Main Outcomes and Measures: The primary outcome of each incentive trial was the proportion of people assigned to each recruitment strategy that consented to participate. Each trial was powered to test the hypotheses that incentives served neither as undue inducements (based on the interaction between incentive size and perceived research risk, as measured using a 10-point scale, on the primary outcome), nor unjust inducements (based on the interaction between incentive size and participants' self-reported income). Noninferiority methods were used to test whether the data were compatible with these 2 effects of incentives and superiority methods to compare the primary and other secondary outcomes. Results: There were a total of 654 participants (327 women [50.0%]; mean [SD] age, 50.6 [12.1] years; 394 Black/African American [60.2%], 214 White [32.7%], and 24 multiracial individuals [3.7%]) in the smoking trial, and 642 participants (364 women [56.7%]; mean [SD] age, 46.7 [15.6] years; 224 Black/African American [34.9%], 335 White [52.2%], and 5 multiracial individuals [0.8%]) in the ambulation trial. Incentives significantly increased consent rates among those in the smoking trial in 47 of 216 (21.8%), 78 of 217 (35.9%), and 104 of 221 (47.1%) in the $0, $200, and $500 groups, respectively (adjusted odds ratio [aOR] for each increase in incentive, 1.70; 95% CI, 1.34-2.17; P < .001). Incentives did not increase consent among those in the ambulation trial: 98 of 216 (45.4%), 102 of 212 (48.1%), and 92 of 214 (43.0%) in the $0, $100, and $300 groups, respectively (aOR, 0.88; 95% CI, 0.64-1.22; P = .45). In neither trial was there evidence of undue or unjust inducement (upper confidence limits of ORs for undue inducement, 1.15 and 0.99; P < .001 showing noninferiority; upper confidence limits of ORs for unjust inducement, 1.21 and 1.26; P = .01 and P < .001, respectively). There were no significant effects of incentive size on the secondary outcomes in either trial, including time spent reviewing the risk sections of consent forms, perceived research risks, trial understanding, perceived coercion, or therapeutic misconceptions. Conclusions and Relevance: In these 2 randomized clinical trials, financial incentives increased trial enrollment in 1 of 2 trials and did not produce undue or unjust inducement or other unintended consequences in either trial. Trial Registration: ClinicalTrials.gov Identifier: NCT02697799.

Interventions to Reduce Hospital Length of Stay in High-risk Populations: A Systematic Review.

Importance: Many strategies to reduce hospital length of stay (LOS) have been implemented, but few studies have evaluated hospital-led interventions focused on high-risk populations. The Agency for Healthcare Research and Quality (AHRQ) Learning Health System panel commissioned this study to further evaluate system-level interventions for LOS reduction. Objective: To identify and synthesize evidence regarding potential systems-level strategies to reduce LOS for patients at high risk for prolonged LOS. Evidence Review: Multiple databases, including MEDLINE and Embase, were searched for English-language systematic reviews from January 1, 2010, through September 30, 2020, with updated searches through January 19, 2021. The scope of the protocol was determined with input from AHRQ Key Informants. Systematic reviews were included if they reported on hospital-led interventions intended to decrease LOS for high-risk populations, defined as those with high-risk medical conditions or socioeconomically vulnerable populations (eg, patients with high levels of socioeconomic risk, who are medically uninsured or underinsured, with limited English proficiency, or who are hospitalized at a safety-net, tertiary, or quaternary care institution). Exclusion criteria included interventions that were conducted outside of the hospital setting, including community health programs. Data extraction was conducted independently, with extraction of strength of evidence (SOE) ratings provided by systematic reviews; if unavailable, SOE was assessed using the AHRQ Evidence-Based Practice Center methods guide. Findings: Our searches yielded 4432 potential studies. We included 19 systematic reviews reported in 20 articles. The reviews described 8 strategies for reducing LOS in high-risk populations: discharge planning, geriatric assessment, medication management, clinical pathways, interdisciplinary or multidisciplinary care, case management, hospitalist services, and telehealth. Interventions were most frequently designed for older patients, often those who were frail (9 studies), or patients with heart failure. There were notable evidence gaps, as there were no systematic reviews studying interventions for patients with socioeconomic risk. For patients with medically complex conditions, discharge planning, medication management, and interdisciplinary care teams were associated with inconsistent outcomes (LOS, readmissions, mortality) across populations. For patients with heart failure, clinical pathways and case management were associated with reduced length of stay (clinical pathways: mean difference reduction, 1.89 [95% CI, 1.33 to 2.44] days; case management: mean difference reduction, 1.28 [95% CI, 0.52 to 2.04] days). Conclusions and Relevance: This systematic review found inconsistent results across all high-risk populations on the effectiveness associated with interventions, such as discharge planning, that are often widely used by health systems. This systematic review highlights important evidence gaps, such as the lack of existing systematic reviews focused on patients with socioeconomic risk factors, and the need for further research.

Leveraging insights from behavioral economics to improve mobility for adults with stroke: Design and rationale of the BE Mobile clinical trial.

Physical inactivity post-stroke can negatively impact long-term health outcomes and contribute to cardiovascular deconditioning, muscle loss, and increased risk for recurrent stroke. The limited number of interventions designed to improve daily physical activity post-stroke have lacked precision in step goals, are resource intensive, and difficult to scale. The purpose of the Leveraging Insights from Behavioral Economics to Improve Mobility for Adults with Stroke (BE Mobile) trial is to examine the preliminary effectiveness of a novel gamification with social incentives intervention for improving physical activity post-stroke. This trial includes adults who have experienced an ischemic or hemorrhagic stroke ≥3 months prior to the time of recruitment who are randomized to a control or gamification arm. All participants receive a Fitbit Inspire 2 wearable device to quantify daily steps and complete a 2-week baseline run-in period followed by an 8-week intervention period. All participants select a daily step goal and the gamification arm is enrolled in a game with loss-framed points and levels to help participants achieve their daily step goal. Participants in the gamification arm also select a support partner who receives weekly updates on their progress in the game. The primary outcome is change in daily steps from baseline during the intervention period. The secondary outcome is difference in the proportion of days participants achieved their daily step goal. Results from this trial will inform future, larger studies that leverage insights from behavioral economics to help improve daily physical activity post-stroke. Trial registration: NCT #04607811.

Low hospital mobility-resurgence of an old epidemic within a new pandemic and future solutions.

Low mobility during hospitalisation poses risks of functional decline and other poor outcomes for older adults. Given the pervasiveness of this problem, low mobility during hospitalisation was first described as 'dangerous' in 1947 and later described as an epidemic. Hospitals have made considerable progress over the last half-century and the last two decades in particular, however, the COVID-19 pandemic presents serious new challenges that threaten to undermine recent efforts and progress towards a culture of mobility. In this special article, we address the question of how to confront an epidemic of immobility within a pandemic. We identify four specific problems for creating and advancing a culture of mobility posed by COVID-19: social distancing and policies restricting patient movement, personnel constraints, personal protective equipment shortages and increased patient hesitancy to ambulate. We also propose four specific solutions to address these problems. These approaches will help support a culture of healthy mobility during and after hospitalisation and help patients to keep moving during the pandemic and beyond.

Discharge Practices for COVID-19 Patients: Rapid Review of Published Guidance and Synthesis of Documents and Practices at 22 US Academic Medical Centers.

BACKGROUND: There are currently no evidence-based guidelines that provide standardized criteria for the discharge of COVID-19 patients from the hospital. OBJECTIVE: To address this gap in practice guidance, we reviewed published guidance and collected discharge protocols and procedures to identify and synthesize common practices. DESIGN: Rapid review of existing guidance from US and non-US public health organizations and professional societies and qualitative review using content analysis of discharge documents collected from a national sample of US academic medical centers with follow-up survey of hospital leaders SETTING AND PARTICIPANTS: We reviewed 65 websites for major professional societies and public health organizations and collected documents from 22 Academic Medical Centers (AMCs) in the US participating in the HOspital MEdicine Reengineering Network (HOMERuN). RESULTS: We synthesized data regarding common practices around 5 major domains: (1) isolation and transmission mitigation; (2) criteria for discharge to non-home settings including skilled nursing, assisted living, or homeless; (3) clinical criteria for discharge including oxygenation levels, fever, and symptom improvement; (4) social support and ability to perform activities of daily living; (5) post-discharge instructions, monitoring, and follow-up. LIMITATIONS: We used streamlined methods for rapid review of published guidance and collected discharge documents only in a focused sample of US academic medical centers. CONCLUSION: AMCs studied showed strong consensus on discharge practices for COVID-19 patients related to post-discharge isolation and transmission mitigation for home and non-home settings. There was high concordance among AMCs that discharge practices should address COVID-19-specific factors in clinical, functional, and post-discharge monitoring domains although definitions and details varied.

Effect of Behaviorally Designed Gamification With a Social Support Partner to Increase Mobility After Hospital Discharge: A Randomized Clinical Trial.

Importance: Hospitalization is associated with decreased mobility and functional decline. Behaviorally designed gamification can increase mobility in community settings but has not been tested among patients at risk for functional decline during a high-risk transition period after hospitalization. Objective: To test a behaviorally designed gamification intervention with a social support partner to increase patient mobility after hospital discharge. Design, Setting, and Participants: This study is a randomized clinical trial of a 12-week intervention without follow-up. Enrollment occurred from January 2018 to June 2019 at a referral hospital with a remote at-home monitoring intervention among patients living predominantly in 3 states (Pennsylvania, New Jersey, and Delaware). Participants included adult patients discharged from general medicine and oncology units to home. Data analysis was performed from October 2019 to March 2020. Interventions: All participants received a wearable device to track daily steps. The control group received feedback from the device but no other interventions. The intervention group entered into a 12-week game informed by behavioral economics to assign points and levels for achieving step goals and reinforced by a support partner who received updates on participant progress. Main Outcomes and Measures: The primary outcome was change in mean daily steps from baseline through the 12-week intervention. Secondary measures were change in functional status and urgent care utilization (ie, emergency department visits and hospital readmissions) within this period. Results: A total of 232 participants were enrolled in the study (118 randomized to control and 114 randomized to the intervention). Participants had a mean (SD) age of 40 (14) years, 141 (61%) were female, 101 (43%) were White, and 103 (44%) had an annual household income less than $50 000. Daily step counts increased from 3795 to 4652 steps (difference, 857 steps; 95% CI, 488 to 1224 steps) among intervention participants and increased from 3951 to 4499 steps (difference, 548 steps; 95% CI, 193 to 903 steps) among control participants. The change in mean daily step count from baseline was not significantly different for participants in the intervention group vs the control group (adjusted difference, 270 steps; 95% CI, -214 to 754 steps; P = .27). Among the subgroup of 76 participants with higher levels of social engagement, post hoc exploratory analyses showed a significant increase in mobility for intervention vs control (adjusted difference, 1125 steps; 95% CI, 409 to 1841 steps; P = .002). Fewer participants in this subgroup experienced functional decline (1 of 36 participants [4%] in the intervention group vs 5 of 40 participants [12%] in the control group) and hospital readmission at 30 days (3 of 36 participants [8%] in the intervention group vs 6 of 40 participants [15%] in the control group), but the differences were not statistically significant. There were no significant differences in these secondary outcomes for the overall sample. Conclusions and Relevance: Gamification with social incentives did not affect mobility or functional decline in all participants, but post hoc analysis suggests positive findings for both outcomes for patients with higher social engagement. Trial Registration: ClinicalTrials.gov Identifier: NCT03321279.