The purpose of this study was to evaluate the effect of Reiki as an alternative and complementary approach to treating community-dwelling older adults who experience pain, depression, and/or anxiety. Participants (N = 20) were randomly assigned to either an experimental or wait list control group. The pre- and posttest measures included the Hamilton Anxiety Scale, Geriatric Depression Scale-Short Form, Faces Pain Scale, and heart rate and blood pressure. The research design included an experimental component to examine changes in these measures and a descriptive component (semi-structured interview) to elicit information about the experience of having Reiki treatments. Significant differences were observed between the experimental and treatment groups on measures of pain, depression, and anxiety; no changes in heart rate and blood pressure were noted. Content analysis of treatment notes and interviews revealed five broad categories of responses: Relaxation; Improved Physical Symptoms, Mood, and Well-Being; Curiosity and a Desire to Learn More; Enhanced Self-Care; and Sensory and Cognitive Responses to Reiki.

Aims Almost half of African American (AA) men and women have cardiovascular disease (CVD). Detection of prevalent CVD in community settings would facilitate secondary prevention of CVD. We sought to develop a tool for automated CVD detection. Methods and Results Participants from the Jackson Heart Study (JHS) with analyzable ECGs (n = 3,679; age, 62±12 years; 36% men) were included. Vectorcardiographic (VCG) metrics QRS, T, and spatial ventricular gradient (SVG) vectors’ magnitude and direction, and traditional ECG metrics were measured on 12-lead ECG. Random forests, convolutional neural network (CNN), lasso, adaptive lasso, plugin lasso, elastic net, ridge, and logistic regression models were developed in 80% and validated in 20% samples. We compared models with demographic, clinical, and VCG input (43 predictors) and those after the addition of ECG metrics (695 predictors). Prevalent CVD was diagnosed in 411 out of 3,679 participants (11.2%). Machine-learning models detected CVD with ROC AUC 0.69-0.74. There was no difference in CVD detection accuracy between models with VCG and VCG+ECG input. Models with VCG input were better calibrated than models with ECG input. Plugin-based lasso model consisting of only two predictors (age and peak QRS-T angle) detected CVD with AUC 0.687 (95%CI 0.625-0.749), which was similar (P = 0.394) to the CNN (0.660; 95%CI 0.597-0.722) and better (P < 0.0001) than random forests (0.512; 95% CI 0.493-0.530). Conclusions Simple model (age and QRS-T angle) can be used for prevalent CVD detection in limited-resources community settings, which opens an avenue for secondary prevention of CVD in underserved communities.

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ObjectivesWe hypothesised that (1) the prevalent cardiovascular disease (CVD) is associated with global electrical heterogeneity (GEH) after adjustment for demographic, anthropometric, socioeconomic and traditional cardiovascular risk factors, (2) there are sex differences in GEH and (3) sex modifies an association of prevalent CVD with GEH.DesignCross-sectional, cohort study.SettingProspective African-American The Jackson Heart Study (JHS) with a nested family cohort in 2000–2004 enrolled residents of the Jackson, Mississippi metropolitan area.ParticipantsParticipants from the JHS with analysable ECGs recorded in 2009–2013 (n=3679; 62±12 y; 36% men; 863 family units). QRS, T and spatial ventricular gradient (SVG) vectors’ magnitude and direction, spatial QRS-T angle and sum absolute QRST integral (SAI QRST) were measured.OutcomePrevalent CVD was defined as the history of (1) coronary heart disease defined as diagnosed/silent myocardial infarction, or (2) revascularisation procedure defined as prior coronary/peripheral arterial revascularisation, or (3) carotid angioplasty/carotid endarterectomy, or (4) stroke.ResultsIn adjusted mixed linear models, women had a smaller spatial QRS-T angle (−12.2 (95% CI −19.4 to -5.1)°; p=0.001) and SAI QRST (−29.8 (−39.3 to −20.3) mV*ms; p<0.0001) than men, but larger SVG azimuth (+16.2(10.5–21.9)°; p<0.0001), with a significant random effect between families (+20.8 (8.2–33.5)°; p=0.001). SAI QRST was larger in women with CVD as compared with CVD-free women or men (+15.1 (3.8–26.4) mV*ms; p=0.009). Men with CVD had a smaller T area (by 5.1 (95% CI 1.2 to 9.0) mV*ms) and T peak magnitude (by 44 (95%CI 16 to 71) µV) than CVD-free men. T vectors pointed more posteriorly in women as compared with men (peak T azimuth + 17.2(8.9–25.6)°; p<0.0001), with larger sex differences in T azimuth in some families by +26.3(7.4–45.3)°; p=0.006.ConclusionsThere are sex differences in the electrical signature of CVD in African-American men and women. There is a significant effect of unmeasured genetic and environmental factors on cardiac repolarisation.

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Objective: Vectorcardiographic (VCG) global electrical heterogeneity (GEH) metrics showed clinical usefulness. We aimed to assess the reproducibility of GEH metrics. Methods: GEH was measured on two 10-second 12-lead ECGs recorded on the same day in 4,316 participants of the Multi-Ethnic Study of Atherosclerosis (age 69.4 ± 9.4 y; 2317(54%) female, 1728 (40%) white, 1138(26%) African-American, 519(12%) Asian-American, 931(22%) Hispanic-American). GEH was measured on a median beat, comprised of the normal sinus (N), atrial fibrillation/flutter (S), and ventricular-paced (VP) beats. Spatial ventricular gradient's (SVG's) scalar was measured as sum absolute QRST integral (SAIQRST) and vector magnitude QT integral (VMQTi). Results: Two N ECGs with heart rate (HR) bias of -0.64 (95% limits of agreement [LOA] -5.68 to 5.21) showed spatial area QRS-T angle (aQRST) bias of -0.12 (95%LOA -14.8 to 14.5). Two S ECGs with HR bias of 0.20 (95%LOA -15.8 to 16.2) showed aQRST bias of 1.37 (95%LOA -33.2 to 35.9). Two VP ECGs with HR bias of 0.25 (95%LOA -3.0 to 3.5) showed aQRST bias of -1.03 (95%LOA -11.9 to 9.9). After excluding premature atrial or ventricular beat and two additional beats (before and after extrasystole), the number of cardiac beats included in a median beat did not affect the GEH reproducibility. Mean-centered log-transformed values of SAIQRST and VMQTi demonstrated perfect agreement (Bias 0; 95%LOA -0.092 to 0.092). Conclusion: GEH measurements on N, S, and VP median beats are reproducible. SVG's scalar can be measured as either SAIQRST or VMQTi. Significance: Satisfactory reproducibility of GEH metrics supports their implementation.

Background: Almost half of African American (AA) men and women have cardiovascular disease (CVD). Detection of prevalent CVD in barbershops would facilitate secondary prevention of CVD. We sought to investigate the cross-sectional association of prevalent CVD and sex with global electrical heterogeneity (GEH) and develop a tool for CVD detection. Methods: Participants from the Jackson Heart Study (JHS) with analyzable ECGs (n=3,679; age, 62 ± 12 years; 36% men) were included. QRS, T, and spatial ventricular gradient (SVG) vectors magnitude and direction, and traditional metrics were measured on 12-lead ECG. Linear regression and mixed linear models with random intercept were adjusted for cardiovascular risk factors, sociodemographic and anthropometric characteristics, type of median beat, and mean RR intervals. Random forests, convolutional neural network, and lasso models were developed in 80%, and validated in 20% samples. Results: In fully adjusted models, women had a smaller spatial QRS-T angle (-12.2(-19.4 to-5.1) ° ; P=0.001), SAI QRST (-29.8(-39.3 to -20.3) mV*ms; P<0.0001), and SVG elevation (-4.5(-7.5 to -1.4) ° ; P=0.004) than men, but larger SVG azimuth (+16.2(10.5-21.9) ° ; P<0.0001), with a significant random effect between families (+20.8(8.2-33.5) ° ; P=0.001). SAI QRST was larger in women with CVD as compared to CVD-free women or men (+15.1(3.8-26.4) mV*ms; P=0.009). Men with CVD had smaller T area [by 5.1 (95%CI 1.2-9.0) mV*ms] than CVD-free men, but there were no differences when comparing women with CVD to CVD-free women. Machine-learning detected CVD with ROC AUC 0.69-0.74; plug-in-based model included only age and QRS-T angle. Conclusions: GEH varies by sex. Sex modifies an association of GEH with CVD. Automated CVD detection is feasible.