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Background More than 1 in 5 US adults live with mental illness. Novel therapies as complements to standard therapies are needed to improve patient well-being. Reiki is a biofield therapy that may improve well-being in mental health. Objective The primary aim of this study was to evaluate the feasibility of providing Reiki at a behavioral health clinic serving a low-income population. The secondary aim was to evaluate outcomes in terms of patients’ symptoms, emotions, and feelings before and after Reiki. Methods This was a mixed-methods, feasibility pilot study with a pre-post experimental design. Reiki was offered to adult outpatients at a community behavioral health center in Rochester, Minnesota. Patients with a stable mental health diagnosis seen between July 22, 2021, and May 18, 2023, completed surveys before and after the Reiki intervention and provided qualitative feedback. Patients were asked to report their ratings of pain, anxiety, fatigue, and feelings (eg, happy, calm) on 0- to 10-point numeric rating scales. Data were analyzed with Wilcoxon signed rank tests. Results Among 91 patients who completed a Reiki session during the study period, 74 (81%) were women. Major depressive disorder (71%), posttraumatic stress disorder (47%), and generalized anxiety disorder (43%) were the most common diagnoses. The study was feasible in terms of recruitment, retention, data quality, acceptability, and fidelity of the intervention. Patient ratings of pain, fatigue, anxiety, stress, sadness, and agitation were significantly lower, and ratings of happiness, energy levels, relaxation, and calmness were significantly higher after a single Reiki session. Conclusion The results of this study suggest that Reiki is feasible and could be fit into the flow of clinical care in an outpatient behavioral health clinic. It improved positive emotions and feelings and decreased negative measures. Implementing Reiki in clinical practice should be further explored to improve mental health and well-being.

AbstractBackgroundPopulation dementia prevalence is traditionally estimated using cohort studies, surveys, routinely‐collected administrative data, and registries. Hospital Electronic Health Records (EHRs) are comprised of rich structured and unstructured (text) clinical data that are underutilised for this purpose.We aimed to develop a suite of algorithms using routinely‐collected EHR data to reliably identify cases of dementia, as a key step towards incorporating such data in prevalence estimation. Towards this, we developed a novel predictive framework integrating data‐science and biostatistical methods.MethodTraining data were sourced via the National Centre for Healthy Ageing (NCHA) Data Platform, a linked, curated, EHR‐derived data warehouse. Individuals within the platform catchment aged >60 years with confirmed dementia were identified through hospital specialist dementia clinics. A comparison group of individuals aged >60years with EHR records without dementia was recruited from the community.A panel of clinical experts (Neurology, Geriatric Medicine) informed variable and concept selection and guided data cleaning efforts within both streams. Algorithms were developed via two work‐streams; a traditional biostatistical approach to fit logistic regression models using structured data elements, and a data science stream used Natural Language Processing (NLP) to fit models to the unstructured (text) parts of the EHR, for the same individuals.ResultOf 568 individuals (362 with dementia), 434 had clinical notes available. In the data science stream using unstructured data, among a range of NLP derived models, the Random Forest classifier performed best in assigning dementia status, with Area Under the Curve (AUC) 0.95, specificity 90.2% and sensitivity 88.4%. In the biostatistics stream, 15 structured variables were included in the final model, covering demographics, health service attendance, medications, and ICD‐10 Codes, with AUC 0.94, specificity 85.9% and sensitivity 85.6%.ConclusionArtificial intelligence techniques applied to unstructured electronic health data and guided by human clinical expertise may be powerful tools in capturing the presence of dementia, at least comparable to traditional techniques using structured data, and conferring practical and scientific advantages for dementia prevalence estimation. Future validation is required in less crisply delineated real‐world settings.

AbstractBackgroundPeople living with dementia have high rates of emergency department (ED) use and hospitalizations. Identifying factors that influence acute care utilization is important. We examined the influence of physical activity levels on risk of hospitalization and emergency department (ED) use in a population of older people living with mild cognitive impairment (MCI) or dementia.MethodsWe included people with a diagnosis of MCI or dementia followed in Community Internal Medicine at Mayo Clinic, age 55 years and older, who had a clinic visit between June 1, 2019 and June 30, 2021 and had completed a social determinants of health questionnaire about physical activity levels. Physical activity was classified based on responses to questions about time spent exercising per week and rated as sufficiently active (≥150 minutes per week), insufficiently active (10‐140 minutes) or physically inactive (0 minutes). The risk of hospitalization and ED visits across physical activity levels was examined using quasi‐Poisson regression and presented as rate ratios (95% confidence interval (CI)), with adjustment for demographics, Charlson Comorbidity Index, marital status, living arrangement and body mass index.ResultsA total of 3090 persons living with MCI (n = 1308) or dementia (n = 1782) were included. People who were physically inactive were more likely to be older, female, not married, obese, have higher comorbid burden and a dementia diagnosis (p<0.001). Hospitalization and ED visit rates in those who were physically inactive were approximately double the rates for the sufficiently active group (p<0.001). Compared with those who were sufficiently active, people who were physically inactive had a significantly higher risk of hospitalization (rate ratio 1.73, 95% CI 1.30‐2.31) and ED visits (rate ratio 1.59, 95% CI 1.26‐2.01).ConclusionsPhysical inactivity in older people living with MCI or dementia is associated with an increased risk of ED visits and hospitalization. Targeted physical activity interventions could potentially impact on acute care utilization in this population. Our findings emphasize an area for public health promotion in people living with MCI or dementia.

Abstract Objectives The aims of the study were to identify conditions diagnosed in at least 10% of midlife women living in the US upper midwest and to assess prevalence by age, race, ethnicity, and sociodemographic status. Methods The Rochester Epidemiology Project was used to conduct a cross-sectional prevalence study of 86,946 women between 40 and 59 years residing in a 27-county region of the United States on January 1, 2020. Diagnostic billing codes were extracted and grouped into broader condition categories using the Clinical Classification System Refined. The prevalence of 424 conditions was calculated by age, race, ethnicity, and area deprivation index quartiles. Logistic regression was used to examine associations between participant characteristics and conditions that affected 10% or more of the study population. Results Twenty-eight conditions affected ≥10% of women, and eight conditions increased by ≥45% between the ages of 40 and 59 (disorders of lipid metabolism, hypertension, sleep/wake disorders, thyroid disorders, esophageal disorders, osteoarthritis, tendon and synovial disorders, and menopausal disorders; all test for trend P < 0.01). Black women had a significantly higher prevalence of hypertension and esophageal disorders at all ages (adjusted P values <0.05). Women living in more deprived areas had a significantly higher prevalence of hyperlipidemia, hypertension, sleep/wake disorders, and esophageal disorders (adjusted P values <0.05). Women living in less deprived areas had a significantly higher prevalence of thyroid disorders at age 40 to 44 and menopausal disorders at ages 50 to 59 (adjusted P values <0.05). Conclusions These data suggest that additional attention should focus on Black women and women with a lower socioeconomic status to ensure that common midlife conditions are diagnosed and treated.

Introduction: Living in an area with high Area Deprivation Index (ADI) is linked to worse cardiovascular disease (CVD) outcomes. However, the association of ADI with type 2 diabetes (T2D), a major CVD risk factor, is incompletely described. This knowledge may guide primary CVD prevention. Method: Rochester Epidemiology Project is a medical record linkage system of persons in southeast Minnesota and southwest Wisconsin. We identified 253,890 persons (aged 4–98 yrs on July 1, 2015) in 9 counties with >90% population coverage. ADI scores, from 2015 home addresses, were grouped into quartiles of low (Q1) to high (Q4) deprivation. We excluded persons with type 1 diabetes (T1D) or history of T2D and identified new T2D cases (2015-2020) using ICD codes, HbA1c and medications. We examined the association between ADI quartile and risk of incident T2D using Cox regression with persons right censored at death, T2D, T1D or last visit. Models were adjusted for age and gender, and reported adjusted hazard ratios (aHR [95% confidence interval]). Results: Age, gender, and race statistically differed across ADI quartiles (Table). Age × ADI interaction was significant ( P <.001). T2D incidence ranged from 6.3 (Q1) to 9.0 (Q4) per 1000 person-years ( P <.001). Women vs. men/non-binary persons (combined due to limited non-binary persons) had 30% lower risk of incident T2D (aHR 0.70 [0.67–0.72]). Risk of incident T2D was higher in persons from higher ADI areas. At median age of 48.6 yrs, the risk was 30% higher in Q2 (aHR 1.3 [1.22–1.38]), 38% higher in Q3 (aHR 1.38 [1.30–1.47]) and 53% higher in Q4 (aHR 1.53 [1.43–1.62]) vs. Q1. Higher ADI was associated with higher incident T2D in younger persons, with risk attenuation with increasing age (Figure). Conclusion: In this population cohort, persons, particularly of younger age, living in more deprived areas had a higher risk of incident T2D. ADI may help identify areas for targeted T2D screening, with implications for CVD screening and prevention.

Abstract Obesity accelerates the onset and progression of age-related conditions. In preclinical models, obesity drives cellular senescence, a cell fate that compromises tissue health and function, in part through a robust and diverse secretome. In humans, components of the secretome have been used as senescence biomarkers that are predictive of age-related disease, disability, and mortality. Here, using biospecimens and clinical data from two large and independent cohorts of older adults, we tested the hypothesis that the circulating concentrations of senescence biomarkers are influenced by body mass index. After adjusting for age, sex, and race, we observed significant increases in activin A, Fas, MDC, PAI1, PARC, TNFR1, and VEGFA, and a significant decrease in RAGE, from normal weight, to overweight, to obesity body mass index categories by linear regression in both cohorts (all p &lt; .05). These results highlight the influence of body mass index on circulating concentrations of senescence biomarkers.

People living with dementia have high rates of hospitalizations. Identifying factors that influence hospitalization is important. This study examines the influence of physical activity levels on risk of hospitalization and emergency department (ED) use among older people living with mild cognitive impairment (MCI) or dementia followed in our primary care practice in Rochester, Minnesota, United States. We included those age 55 years and older, who had a clinic visit between June 1, 2019 and June 30, 2021 and completed a social determinants of health questionnaire about physical activity levels ( n = 3090). Physical activity was classified as sufficiently active, insufficiently active, or physically inactive. Risk of hospitalization and ED visits by physical activity levels were examined. People who were physically inactive were at higher risk of hospitalization and ED visits ( p < .001) compared with those who were sufficiently active. This highlights an area for health promotion in people living with MCI and dementia.

Abstract Background and Objectives Social isolation is commonly experienced by older people and is associated with adverse health outcomes. Little is known about the influence of social isolation on the risk of acute care utilization among people living with mild cognitive impairment (MCI) or dementia. Our objective was to investigate the impact of social isolation on the risk of death, hospitalization, and emergency department (ED) use among people living with MCI or dementia who are followed in our Community Internal Medicine practice at Mayo Clinic, Rochester, Minnesota. Research Design and Methods We included people living with MCI or dementia, 55 years and older, who had a clinic visit between June 1, 2019, and June 30, 2021, and who had completed questions about social connections. The risk of death, hospitalization, and ED use was examined by levels of social connection (socially isolated, moderately isolated, moderately integrated, or socially integrated). Results Of 2,320 people included (1,010 with MCI and 1,310 with dementia), 455 (19.6%) were classified as socially isolated and 591 (25.5%) were moderately isolated. Compared with those who were socially integrated, people who were socially isolated were at higher risk of death, hospitalization, and ED visits (p &lt; .001). Discussion and Implications Social isolation is associated with an increased risk of acute health care utilization and death in people living with MCI or dementia. Interventions to address social isolation in this population are needed.