Adverse Childhood Experiences, Resilience, and Cardiovascular Disease in Adult Survivors of Childhood Cancer: A Report from the Childhood Cancer Survivor Study.

BACKGROUND: The impact of Adverse Childhood Experiences (ACEs: e.g., abuse, neglect and/or household dysfunction experienced before age 18) and resilience on risk for cardiovascular disease (CVD) has not previously been investigated in adult survivors of childhood cancer. METHODS: We conducted a nested case-control study among long-term, adult-aged survivors of childhood cancer from the Childhood Cancer Survivor Study (CCSS). Self-report questionnaires ascertained ACEs and resilience, and scores were compared between cases with serious/life-threatening CVD and controls without CVD matched on demographic and cardiotoxic treatment factors. RESULTS: Among 95 cases and 261 controls, the mean ACE score was 1.4 for both groups; 53.4% of survivors endorsed ≥1 ACE. There was no association between ACEs or resilience and CVD in adjusted models. CONCLUSIONS: ACEs and resilience do not appear to contribute to CVD risk for adult survivors of childhood cancer with cardiotoxic treatment exposures. IMPACT: Although not associated with CVD in this population, ACEs are associated with serious health issues in other populations. Therefore, future studies could investigate effects of ACEs on other health outcomes affecting childhood cancer survivors.

Consumer preferences for sleep-tracking wearables: The role of scientific evaluation and endorsement.

OBJECTIVES: Accuracy and relevance to health outcomes are important to researchers and clinicians who use consumer sleep technologies, but economic demand motivates consumer sleep technology design. This report quantifies the value of scientific relevance to the general consumer in a dollar amount to convey the importance of device accuracy in terms that consumer sleep technology manufacturers can appreciate. METHODS: Survey data were collected from 368 participants on Amazon mTurk. Participants ranked sleep metrics, evaluation methods, and scientific endorsement by perceived level of importance. Participants indicated their likelihood of purchasing a hypothetical consumer sleep technology that had either (1) not been evaluated or endorsed; (2) had been evaluated but not endorsed, and; (3) had been evaluated and endorsed by a sleep science authority. Demand curves determined the relative value of each consumer sleep technology. RESULTS: Devices that were evaluated and endorsed had the most value, followed by those only evaluated, and then those with no evaluation. The unit price at which there was 50% probability of purchase increased by $30 or $48 for evaluation or endorsement, respectively, relative to a nonvalidated device. Respondents indicated the most valuable sleep metric was sleep duration, the most important evaluation method was against laboratory/hospital standards for sleep, and that the highest value of endorsement came from a medical institution. CONCLUSIONS: Consumer demand is greatest for a device that has been evaluated by an independent laboratory and is endorsed by a medical institution. Consumer sleep technology manufacturers may be able to increase sales by partnering with sleep science authorities to produce a scientifically superior device.

The Association of Neighborhood Characteristics and Frailty in Childhood Cancer Survivors: A Report from the St. Jude Lifetime Cohort Study.

BACKGROUND: Childhood cancer survivors experience reduced physiologic reserve, or frailty, earlier and more frequently than peers. In other populations, frailty is impacted by one's neighborhood. This study's purpose was to evaluate associations between neighborhood characteristics and frailty in childhood cancer survivors. METHODS: Participants in the St. Jude Lifetime Cohort Study with geocoded residential addresses were analyzed. Pre-frailty/Frailty was defined as having 1-2/≥3 of sarcopenia, muscle weakness, poor endurance, slow walking speed, and exhaustion from direct assessments. Neighborhood characteristics [e.g., access to exercise opportunities and healthy food, neighborhood socioeconomic status (nSES), and rurality/urbanicity] were determined using publicly available geospatial data. Nested multivariable logistic regression models identified associations between neighborhood characteristics and pre-frailty/frailty, adjusting for chronic health conditions, individual health behaviors and socio-demographics, and high-risk cancer treatment exposures. RESULTS: For our cohort (N = 3,806, 46.79% female, 81.40% white, mean age 33.63±9.91 years), compared with non-frail survivors (n = 2,573; 67.6%), pre-frail (n = 900; 23.6%) and frail survivors (n = 333; 8.7%) were more likely to live in neighborhoods with decreased exercise opportunities (frail OR: 1.62, 1.26-2.09), reduced healthy food access (pre-frail OR: 1.28, 1.08-1.51; frail OR: 1.36, 1.06-1.75), and lower nSES (pre-frail OR: 1.31, 1.12-1.52; frail OR: 1.64, 1.30-2.07). Participants had 8% increased odds (95% confidence interval, 2%-14%) of being pre-frail/frail if they lived in "resource poor" neighborhoods as opposed to "resource rich" neighborhoods after adjusting for other pre-frailty/frailty risk factors. CONCLUSIONS: The neighborhood a childhood cancer survivor resides in as an adult is associated with pre-frailty/frailty. IMPACT: This study provides valuable information for creating interventions using neighborhood-level factors to mitigate frailty and improve health outcomes in survivors. See related commentary by Bhandari and Armenian, p. 997.

End-of-Life Care Transitions in Assisted Living: Associations With State Staffing and Training Regulations.

OBJECTIVE: We examined the frequency and categories of end-of-life care transitions among assisted living community decedents and their associations with state staffing and training regulations. DESIGN: Cohort study. SETTING AND PARTICIPANTS: Medicare beneficiaries who resided in assisted living facilities and had validated death dates in 2018-2019 (N = 113,662). METHODS: We used Medicare claims and assessment data for a cohort of assisted living decedents. Generalized linear models were used to examine the associations between state staffing and training requirements and end-of-life care transitions. The frequency of end-of-life care transitions was the outcome of interest. State staffing and training regulations were the key covariates. We controlled for individual, assisted living, and area-level characteristics. RESULTS: End-of-life care transitions were observed among 34.89% of our study sample in the last 30 days before death, and among 17.25% in the last 7 days. Higher frequency of care transitions in the last 7 days of life was associated with higher regulatory specificity of licensed [incidence risk ratio (IRR) = 1.08; P = .002] and direct care worker staffing (IRR = 1.22; P < .0001). Greater regulatory specificity of direct care worker training (IRR = 0.75; P < .0001) was associated with fewer transitions. Similar associations were found for direct care worker staffing (IRR = 1.15; P < .0001) and training (IRR = 0.79; P < .001) and transitions within 30 days of death. CONCLUSIONS AND IMPLICATIONS: There were significant variations in the number of care transitions across states. The frequency of end-of-life care transitions among assisted living decedents during the last 7 or 30 days of life was associated with state regulatory specificity for staffing and staff training. State governments and assisted living administrators may wish to set more explicit guidelines for assisted living staffing and training to help improve end-of-life quality of care.

COVID-19 in Assisted Living: Protecting a Critical Long-Term Care Resource.

The COVID-19 pandemic had a big impact on assisted living (AL), a vital setting in long-term care (LTC). Understanding the strengths and opportunities for improvement through practice, policy, and research are essential for AL to be prepared for the next pandemic and other challenges. AL communities experienced the pandemic in unique ways, because of varying regulatory environments, differences in familiarity with using and procuring personal protective equipment not typically used in AL (such as N95 masks), loss of family involvement, the homelike environment, and lower levels of licensed clinical staff. Being state rather than federally regulated, much less national data are available about the COVID-19 experience in AL. This article reviews what is known about cases and deaths, infection control, and the impact on residents and staff. For each, we suggest actions that could be taken and link them to the Assisted Living Workgroup Report (ALW) recommendations. Using the Center for Excellence in Assisted Living (CEAL) 15-year ALW report, we also review which of these recommendations have and have not been implemented by states in the preceding decade and half, and how their presence or absence may have affected AL pandemic preparedness. Finally, we provide suggestions for policy, practice, and research moving forward, including improving state-level reporting, staff vaccine requirements, staff training and work-life, levels of research-provider partnerships, dissemination of research, and uptake of a holistic model of care for AL.

Adverse childhood experiences and resilience in childhood and adolescent and young adult cancer patients.

Childhood and adolescent and young adult (AYA) cancer survivors experience poor health outcomes in adulthood. Adverse childhood experiences (ACEs) also portend poor health outcomes for the general population. Resilience can mitigate effects of ACEs. We examined the feasibility of assessing ACEs and resilience in childhood and AYA cancer patients. We also described occurrences of ACEs, resilience, and poor health outcomes. Of 52 participants, most rated their study experience favorably, with privacy in answering sensitive questions. Half reported ACEs, and those with ACEs had lower resilience; X2 (3, N = 52) = 9.4, p = .02. Further investigations of ACEs and resilience in larger cohorts are warranted to delineate associations with long-term health outcomes.

A general model of demand and discounting.

OBJECTIVE: A simulation using a two-commodity demand model, with time as the constraint, replicated the primary findings from delay discounting experiments and introduces explicit terms for time elasticity and cross-price substitution into the delay discounting paradigm. METHOD: A two-commodity temporal demand equation based on Hursh and Silberberg (2008) and Hursh (2014) was used to emulate delay discounting experiments. The own-price and cross-price demand curves intersected and plotting those indifference points emulated the usual hyperbolic discount function for substitutes. Simulations examined delay discounting in relation to (a) time elasticity of demand, (b) substitution between the delayed and immediate alternatives, and (c) amplitude of demand for the delayed alternative. RESULTS: The simulated discount functions with substitutes were hyperbolic. The discount rate was a direct function of increasing time elasticity and substitutability of delayed alternative demand, shifting the function toward an exponential model. Amplitude of demand for the delayed alternative was inversely proportional to discount rate and supported a hyperboloid model with a power function of time (Killeen, 2015; Rachlin, 2006). The emulation of cross-commodity discounting involving drugs points to amplitude and persistence of time-dependent demand and cross-commodity substitution as primary factors. CONCLUSIONS: This report describes the first general model of time-dependent demand and delay discounting. The model implicates cross-commodity substitution as a potential factor in delay discounting. In the context of substance use disorder, the model underscores the importance of defining the properties of multicommodity demand (time elasticity, substitution, and amplitude) specific to the commodities and context. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Psychomotor Vigilance Performance in Participants with Excessive Daytime Sleepiness in Obstructive Sleep Apnea or Narcolepsy Compared with SAFTE-FAST Model Predictions.

INTRODUCTION: Excessive daytime sleepiness (EDS) associated with narcolepsy or obstructive sleep apnea (OSA) can impair vigilance/attention. Solriamfetol, a dopamine/norepinephrine reuptake inhibitor, is approved to treat EDS associated with narcolepsy (75-150 mg/day) or OSA (37.5-150 mg/day). The analysis reported here explored the use of the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model (used in transport industries to model performance based on accumulated sleep and circadian variability) as a substitute for healthy controls using psychomotor vigilance task (PVT) data collected during clinical studies. METHODS: Data were analyzed from two phase 2 studies of solriamfetol in adults with OSA (NCT02806895, EudraCT 2015-003930-28) or narcolepsy (NCT02806908, EudraCT 2015-003931-36). Participants were randomly assigned 1:1 to solriamfetol 150 mg/day (3 days) followed by 300 mg/day (4 days), or placebo (7 days), then crossed over to the other treatment. Actual task effectiveness scores were calculated from average PVT inverse reaction time (pre-dose; 2 h post-dose; 6 h post-dose). Actigraphy-derived sleep intervals were used in SAFTE to determine modeled healthy control task effectiveness scores. RESULTS: In participants with OSA (N = 31) on placebo or solriamfetol, actual and modeled healthy control task effectiveness did not differ at any time point. In participants with narcolepsy (N = 20) on placebo, actual task effectiveness at 2 h post-dose was lower than modeled healthy control task effectiveness (nominal P = 0.03), a difference not present with solriamfetol. There was no main effect of solriamfetol on actual or modeled healthy control task effectiveness across time points. CONCLUSION: This study represents a novel application of the SAFTE biomathematical model to approximate healthy controls in sleep disorder research and provides valuable lessons that may optimize future research. Future studies should perform a priori power analyses for model-tested outcomes and use sleep measures that capture sleep fragmentation characteristic of sleep disorders for sleep input (e.g., total sleep time rather than time in bed). TRIAL REGISTRATION: NCT02806895, EudraCT 2015-003930-28: A Randomized, Double-Blind, Placebo-Controlled, Crossover On-Road Driving Study Assessing the Effect of JZP-110 on Driving Performance in Subjects With Excessive Sleepiness Due to Obstructive Sleep Apnea. NCT02806908, EudraCT 2015-003931-36: A Randomized, Double-Blind, Placebo-Controlled, Crossover On-Road Driving Study Assessing the Effect of JZP-110 on Driving Performance in Subjects With Excessive Sleepiness Due to Narcolepsy.

Time Cost and Demand: Implications for Public Policy.

The success of policy involves not only good design but a good understanding of how the public will respond behaviorally to the benefits or detriments of that policy. Behavioral science has greatly contributed to how we understand the impact of monetary costs on behavior and has therefore contributed to policy design. Consumption taxes are a direct result of this; for example, cigarette taxes that aim to reduce cigarette consumption. In addition to monetary costs, time may also be conceptualized as a constraint on consumption. Time costs may therefore have policy implications, for example, long waiting times could deter people from accessing certain benefits. Recent data show that behavioral economic demand curve methods used to understand monetary cost may also be used to understand time costs. In this article we discuss how the impact of time cost can be conceptualized as a constraint on demand for public benefits utilization and public health when there are delays to receiving the benefits. Policy examples in which time costs may be relevant and demand curve methods may be useful are discussed in the areas of government benefits, public health, and transportation design.

Latent factor structure of behavioral economic heroin and cocaine demand curves.

Research has shown that behavioral economic demand curve indices can be characterized by a two-factor latent structure and that these factors can predict dimensions of substance use. No study to date has examined the latent factor structure of heroin and cocaine demand curves. The objective of this study was to use exploratory factor analysis to examine the underlying factor structure of the facets of heroin and cocaine reinforcement derived from heroin and cocaine demand curves. Participants were 143 patients from two samples that met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5; American Psychiatric Association [APA], 2013) criteria for opioid dependance and were undergoing medication-assisted treatment (methadone or buprenorphine). Heroin and cocaine demand curves were generated via hypothetical purchase tasks (HPT) that assessed consumption at 9 or 17 levels of prices from $0 to $500. Five facets of demand were generated from the tasks (Q0, 1/α, Pmax, Omax, and break point). Principal components analysis was used to examine the latent structure among the variables. The results revealed a two-factor solution for both heroin and cocaine demand. These factors were interpreted as persistence, consisting of 1/α, Pmax, Omax, and break point, and amplitude, consisting of Q0 and Omax, and in one case, 1/α. Heroin factors had some predictive power for future substance use, but cocaine factors did not. These findings suggest that heroin and cocaine demand indices can be reduced to two factors indicating sensitivity and volume of consumption, and that these factors may be able to predict substance use for heroin. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Recommendations for Medical and Mental Health Care in Assisted Living Based on an Expert Delphi Consensus Panel: A Consensus Statement.

Importance: Assisted living (AL) is the largest provider of residential long-term care in the US, and the morbidity of AL residents has been rising. However, AL is not a health care setting, and concern has been growing about residents' medical and mental health needs. No guidance exists to inform this care. Objective: To identify consensus recommendations for medical and mental health care in AL and determine whether they are pragmatic. Evidence Review: A Delphi consensus statement study was conducted in 2021; as a separate effort, the extent to which the recommendations are reflected in practice was examined in data obtained from 2016 to 2021 (prepandemic). In the separate effort, data were from a 7-state study (Arkansas, Louisiana, New Jersey, New York, Oklahoma, Pennsylvania, Texas). The 19 Delphi panelists constituted nationally recognized experts in medical, nursing, and mental health needs of and care for older adults; dementia care; and AL and long-term care management, advocacy, regulation, and education. One invitee was unavailable and nominated an alternate. The primary outcome was identification of recommended practices based on consensus ratings of importance. Panelists rated 183 items regarding importance to care quality and feasibility. Findings: Consensus identified 43 recommendations in the areas of staff and staff training, nursing and related services, resident assessment and care planning, policies and practices, and medical and mental health clinicians and care. To determine the pragmatism of the recommendations, their prevalence was examined in the 7-state study and found that most were in practice. The items reflected the tenets of AL, the role of AL in providing dementia care, the need for pragmatism due to the diversity of AL, and workforce needs. Conclusions and Relevance: In this consensus statement, 43 recommendations important to medical and mental health care in AL were delineated that are highly pragmatic as a guide for practice and policy.

Behavioral economic methods predict future COVID-19 vaccination.

Increasing vaccine utilization is critical for numerous diseases, including COVID-19, necessitating novel methods to forecast uptake. Behavioral economic methods have been developed as rapid, scalable means of identifying mechanisms of health behavior engagement. However, most research using these procedures is cross-sectional and evaluates prediction of behaviors with already well-established repertories. Evaluation of the validity of hypothetical tasks that measure behaviors not yet experienced is important for the use of these procedures in behavioral health. We use vaccination during the COVID-19 pandemic to test whether responses regarding a novel, hypothetical behavior (COVID-19 vaccination) are predictive of later real-world response. Participants (N = 333) completed a behavioral economic hypothetical purchase task to evaluate willingness to receive a hypothetical COVID-19 vaccine based on efficacy. This was completed in August 2020, before clinical trial data on COVID-19 vaccines. Participants completed follow-up assessments approximately 1 year later when the COVID-19 vaccines were widely available in June 2021 and November 2021 with vaccination status measured. Prediction of vaccination was made based on data collected in August 2020. Vaccine demand was a significant predictor of vaccination after controlling for other significant predictors including political orientation, delay discounting, history of flu vaccination, and a single-item intent to vaccinate. These findings show predictive validity of a behavioral economic procedure explicitly designed to measure a behavior for which a participant has limited-to-no direct prior experience or exposure. Positive correspondence supports the validity of these hypothetical arrangements for predicting vaccination utilization and advances behavioral economic methods.

A goal of behavioral science is to develop methods that can predict future behavior to inform preventive health efforts and identify ways people engage in positive health behaviors. Behavioral economic methods apply easy to use and rapid assessment tools to evaluate these mechanisms of health behavior engagement. Here, we show how similar methods can be applied to novel behaviors yet experienced like intentions to vaccinate against COVID-19. We find that responses on a behavioral economic task designed to measure vaccination likelihood closely corresponded to the likelihood of being vaccinated 1 year later. This prediction was above and beyond common predictors of vaccination including demographics like political orientation and age. These findings provide support for these novel methods in the context of the COVID-19 pandemic, specifically, and behavioral health, broadly.

End-Of-Life Care In Assisted Living Communities: Race And Ethnicity, Dual Enrollment Status, And State Regulations.

Assisted living communities are the final home for many of their residents, most of whom are older, frail, and cognitively or functionally impaired. Yet little is known about end-of-life care in this setting. We examined associations of both death at home and home hospice care with individual characteristics, such as race or ethnicity and dual Medicare-Medicaid enrollment; community characteristics; and the stringency of state-level assisted living regulations. Of the 100,783 fee-for-service Medicare beneficiaries residing in 16,560 assisted living communities who died in 2018-19, almost 60 percent died at home, 84 percent of them with home hospice. In predicting the likelihood of death at home, dual Medicare-Medicaid enrollment was more important than race or ethnicity; in contrast, race was a stronger predictor than dual enrollment for hospice care at death. Residents were less likely to die at home or with home hospice in states with lower regulatory stringency regarding assisted living communities. These findings may help inform efforts to ensure equitable access to desired end-of-life care in this setting and suggest an important role for state-level regulation.

Technical, Regulatory, Economic, and Trust Issues Preventing Successful Integration of Sensors into the Mainstream Consumer Wearables Market.

Sensors that track physiological biomarkers of health must be successfully incorporated into a fieldable, wearable device if they are to revolutionize the management of remote patient care and preventative medicine. This perspective article discusses logistical considerations that may impede the process of adapting a body-worn laboratory sensor into a commercial-integrated health monitoring system with a focus on examples from sleep tracking technology.

Facility characteristics and costs associated with meeting proposed minimum staffing levels in skilled nursing facilities.

BACKGROUND: Federal minimum nurse staffing levels for skilled nursing facilities (SNFs) were proposed in 2019 U.S. Congressional bills. We estimated costs and personnel needed to meet the proposed staffing levels, and examined characteristics of SNFs not meeting these thresholds. METHODS: This was a cross-sectional analysis of 2019Q4 payroll data, the Hospital Wage Index, and other administrative data for 14,964 Medicare and Medicaid-certified SNFs. We examined characteristics of SNFs not meeting proposed minimum thresholds: 4.1 total nursing hours per resident day (HPRD); 0.75 registered nurse (RN) HPRD; 0.54 licensed practical nurse (LPN) HPRD; and 2.81 certified nursing assistant (CNA) HPRD. For SNFs falling below the thresholds, we calculated the additional HPRD needed, along with the associated full-time equivalent (FTE) personnel and salary costs. RESULTS: In 2019, 25.0% of SNFs met the minimum 4.1 total nursing HPRD, while 31.0%, 84.5%, and 10.7% met the RN, LPN, and CNA thresholds, respectively. Only 5.0% met all four categories. In adjusted analyses, factors most strongly associated with SNFs not meeting the proposed minimums were: higher Medicaid census, larger bed size, for-profit ownership, higher county SNF competition; and, for RNs specifically, higher community poverty and lower Medicare census. Rural SNFs were less likely to meet all categories and this was explained primarily by county SNF competition. We estimate that achieving the proposed federal minimums across SNFs nationwide would require an estimated additional 35,804 RN, 3509 LPN, and 116,929 CNA FTEs at $7.25 billion annually in salary costs based on current wage rates and prepandemic resident census levels. CONCLUSIONS: Achieving proposed minimum nurse staffing levels in SNFs will require substantial financial investment in the workforce and targeted support of low-resource facilities. Extensive recruitment and retention efforts are needed to overcome supply constraints, particularly in the aftermath of the COVID-19 pandemic.

A Survivorship Educational Tool for Latino Adolescent and Young Adult Cancer Survivors.

BACKGROUND: Low receipt of survivorship care by Latino adolescent and young adult (AYA) cancer survivors necessitates development of age-appropriate and culturally tailored interventions aimed at increasing their perceived need for survivorship care. METHOD: This study describes the development and acceptability testing of a culturally tailored intervention, a photonovela, as part of a community-partnered participatory research (CPPR) project. A four-step approach to the photonovela's development was implemented: (a) literature review, (b) RAND-modified Delphi method, (c) photonovela booklet development, and (d) photonovela acceptability testing through focus groups. Using the CPPR approach, community and academic experts and members worked together at all stages of this project to identify educational domains for the photonovela and ensure that community views and scientific knowledge were equally represented. RESULTS: Cancer survivors and their families described the photonovela as entertaining and relatable. Its story positively reflected their own experiences, and they connected strongly with its characters. Acceptability testing of the photonovela played a significant role in its final script and content, and provided additional new insights into understanding survivorship care perspectives for Latino AYA survivors and their families. CONCLUSION: Equal and shared community and academic involvement through CPPR is essential in identifying unique needs and developing culturally acceptable educational interventions for Latino AYA cancer survivors. The photonovela was seen as an important educational resource in enhancing knowledge and increasing perceived need for survivorship care in this population.

Behavioral economic methods to inform infectious disease response: Prevention, testing, and vaccination in the COVID-19 pandemic.

The role of human behavior to thwart transmission of infectious diseases like COVID-19 is evident. Psychological and behavioral science are key areas to understand decision-making processes underlying engagement in preventive health behaviors. Here we adapt well validated methods from behavioral economic discounting and demand frameworks to evaluate variables (e.g., delay, cost, probability) known to impact health behavior engagement. We examine the contribution of these mechanisms within a broader response class of behaviors reflecting adherence to public health recommendations made during the COVID-19 pandemic. Four crowdsourced samples (total N = 1,366) completed individual experiments probing a response class including social (physical) distancing, facemask wearing, COVID-19 testing, and COVID-19 vaccination. We also measure the extent to which choice architecture manipulations (e.g., framing, opt-in/opt-out) may promote (or discourage) behavior engagement. We find that people are more likely to socially distance when specified activities are framed as high risk, that facemask use during social interaction decreases systematically with greater social relationship, that describing delay until testing (rather than delay until results) increases testing likelihood, and that framing vaccine safety in a positive valence improves vaccine acceptance. These findings collectively emphasize the flexibility of methods from diverse areas of behavioral science for informing public health crisis management.

The Imperative to Reimagine Assisted Living.

Assisted living (AL) has existed in the United States for decades, evolving in response to older adults' need for supportive care and distaste for nursing homes and older models of congregate care. AL is state-regulated, provides at least 2 meals a day, around-the-clock supervision, and help with personal care, but is not licensed as a nursing home. The key constructs of AL as originally conceived were to provide person-centered care and promote quality of life through supportive and responsive services to meet scheduled and unscheduled needs for assistance, an operating philosophy emphasizing resident choice, and a residential environment with homelike features. As AL has expanded to constitute half of all long-term care beds, the increasing involvement of the real estate, hospitality, and health care sectors has raised concerns about the variability of AL, the quality of AL, and standards for AL. Although the intent to promote person-centered care and quality of life has remained, those key constructs have become mired under tensions related to models of AL, regulation, financing, resident acuity, and the workforce. These tensions have resulted in a model of care that is not as intended, and which must be reimagined if it is to be an affordable care option truly providing quality, person-centered care in a suitable environment. Toward that end, 25 stakeholders representing diverse perspectives conferred during 2 half-day retreats to identify the key tensions in AL and discuss potential solutions. This article presents the background regarding those tensions, as well as potential solutions that have been borne out, paving the path to a better future of assisted living.

Addressing fatigue in medical residents with biomathematical fatigue modeling.

Fatigue in resident physicians has been identified as a factor that contributes to burnout and a decline in overall wellbeing. Fatigue risk exists because of poor sleep habits and demanding work schedules that have only increased due to the COVID-19 pandemic. At this time, it is important not to lose sight of how fatigue can impact residents and how fatigue risk can be mitigated. While fatigue mitigation is currently addressed by duty hour restrictions and education about fatigue, Fatigue Risk Management Systems (FRMSs) offer a more comprehensive strategy for addressing these issues. An important component of FRMS in other shiftwork industries, such as aviation and trucking, is the use of biomathematical models to prospectively identify fatigue risk in work schedules. Such an approach incorporates decades of knowledge of sleep and circadian rhythm research into shift schedules, taking into account not just duty hour restrictions but the temporal placement of work schedules. Recent research has shown that biomathematical models of fatigue can be adapted to a resident physician population and can help address fatigue risk. Such models do not require subject matter experts and can be applied in graduate medical education program shift scheduling. It is important for graduate medical education program providers to consider these alternative methods of fatigue mitigation. These tools can help reduce fatigue risk and may improve wellness as they allow for a more precise fatigue management strategy without reducing overall work hours.

Biomathematical Modeling Predicts Fatigue Risk in General Surgery Residents.

OBJECTIVE: To assess resident fatigue risk using objective and predicted sleep data in a biomathematical model of fatigue. DESIGN: 8-weeks of sleep data and shift schedules from 2019 for 24 surgical residents were assessed with a biomathematical model to predict performance ("effectiveness"). SETTING: Greater Washington, DC area hospitals RESULTS: As shift lengths increased, effectiveness scores decreased and the time spent below criterion increased. Additionally, 11.13% of time on shift was below the effectiveness criterion and 42.7% of shifts carried excess sleep debt. Sleep prediction was similar to actual sleep, and both predicted similar performance (p ≤ 0.001). CONCLUSIONS: Surgical resident sleep and shift patterns may create fatigue risk. Biomathematical modeling can aid the prediction of resident sleep patterns and performance. This approach provides an important tool to help educators in creating work-schedules that minimize fatigue risk.