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BackgroundHome Internet of Things (IoT) services and devices have the potential to aid older adults and people with disabilities in their living environments. IoT services and devices can also aid caregivers and health care providers in conveniently providing care to those in need. However, real-world data on the IoT needs of vulnerable people are lacking. ObjectiveThe objective of this study is to conduct a face-to-face survey on the demand for IoT services among older people and people with disabilities, their caregivers, and health care providers in a real-world setting and to see if there are any differences in the aspects of need. MethodsWe conducted a face-to-face survey with 500 participants between January 2019 and March 2019. A total of 300 vulnerable people (200 older adults aged ≥65 years and 100 physically disabled people aged 30-64 years) were randomly sampled from either a population-based, prospective cohort study of aging—the Aging Study of Pyeongchang Rural Area (ASPRA)—or from the outpatient clinics at the Asan Medical Center, Seoul, South Korea. Simultaneously, their caregivers (n=150) and health care providers (n=50) participated in the survey. Detailed socioeconomic status, digital literacy, health and physical function, and home IoT service needs were determined. Among all commercially available IoT services, 27 services were classified into five categories: emergency and security, safety, health care, convenience (information), and convenience (operation). The weighted-ranking method was used to rank the IoT needs in different groups. ResultsThere were discrepancies in the demand of IoT services among the vulnerable groups, their caregivers, and health care providers. The home IoT service category that was required the most by the vulnerable groups and their caregivers was emergency and security. However, health care providers indicated that the safety category was most needed by the older adults and disabled people. Home IoT service requirements differed according to the different types of disabilities among the vulnerable groups. Participants with fewer disabilities were more willing to use IoT services than those with more disabilities. ConclusionsOur survey study shows that there were discrepancies in the demand of IoT services among the vulnerable groups, their caregivers, and health care providers. IoT service requirements differed according to the various types of disabilities. Home IoT technology should be established by combining patients’ priorities and individualized functional assessments among vulnerable people. Trial RegistrationClinical Research Information Service (CRIS; KCT0004157); https://tinyurl.com/r83eyva

Background: The Centers for Medicare and Medicaid Services (CMS) introduced chronic care management (CCM) services in 2015 for patients with multiple chronic diseases. Few studies examine the utilization of CCM services by geographic region, sociodemographic, and clinical characteristics. Methods: We used 2014–2019 Medicare claims data from a 5% random sample of fee‐for‐service beneficiaries aged 65 years or over. We included beneficiaries potentially eligible for CCM services because they had multiple chronic conditions (1,073,729 in 2015 and 1,130,523 in 2019). We calculated the proportion of potentially eligible beneficiaries receiving CCM service each year for the total population and by geographic region, sociodemographic, and clinical characteristics. Results: The proportion of beneficiaries with two or more chronic conditions receiving CCM services increased from 1.1% in 2015 to 3.4% in 2019. The increase in CCM use was higher in the southern region, among dually eligible beneficiaries and beneficiaries with a greater burden of chronic conditions (2–5 conditions vs ≥10 conditions: 0.7% vs 2.0% in 2015; 2.1% vs 7.0% in 2019) and frailty (robust vs severely frail: 0.6% vs 3.3% in 2015; 1.9% vs 9.4% in 2019). Nearly one out of five recipients did not continue CCM service after the initial service. Conclusion: We found that CCM service is being used by a very small fraction of eligible patients. Barriers and facilitators to more effective CCM adoption should be identified and incorporated into strategies that encourage more widespread use of this Medicare benefit. [ABSTRACT FROM AUTHOR]

Background The relationship of claims-based frailty index (CFI), a validated measure to identify frail individuals using Medicare data, and frailty measures used in clinical practice has not yet been fully explored. Methods We identified community-dwelling participants of the 2015 National Health and Aging Trends Study (NHATS) whose CFI scores could be calculated using linked Medicare claims. We calculated 9 commonly used clinical frailty measures from their NHATS in-person examination: Study of Osteoporotic Fracture Index (SOF), FRAIL Scale, Frailty Phenotype, Clinical Frailty Scale (CFS), Vulnerable Elder Survey-13 (VES-13), Tilburg Frailty Indicator (TFI), Groningen Frailty Indicator (GFI), Edmonton Frail Scale (EFS), and 40-item Frailty Index (FI). Using equipercentile method, CFI scores were linked to clinical frailty measures. C-statistics and test characteristics of CFI to identify frailty as defined by each clinical frailty measure were calculated. Results Of the 3 963 older adults, 44.5% were ≥75 years, 59.4% were female, and 82.3% were non-Hispanic White. A CFI of 0.25 was equipercentile to the following clinical frailty measure scores: SOF 1.4, FRAIL 1.8, Phenotype 1.8, CFS 5.4, VES-13 5.7, TFI 4.6, GFI 5.0, EFS 6.0, and FI 0.26. The C-statistics of using CFI to identify frailty as defined by each clinical measure were ≥0.70, except for CFS and VES-13. The optimal CFI cutpoints to identify frailty per clinical frailty measure ranged from 0.212 to 0.242, with sensitivity and specificity of 0.37–0.83 and 0.66–0.84, respectively. Conclusions Understanding the relationship of CFI and commonly used clinical frailty measures can enhance the interpretability and potential utility of CFI. [ABSTRACT FROM AUTHOR]

BACKGROUND/OBJECTIVES: To evaluate the long‐term association between a multicomponent intervention program and disability in socioeconomically vulnerable older adults. DESIGN: This was a nonrandomized prospective intervention trial. SETTING: The setting was a community. PARTICIPANTS: Participants included older Koreans living alone or receiving government assistance from a low‐income program. INTERVENTION: The intervention was a 24‐week multicomponent program compromising group exercise, nutritional supplementation, management of depression, deprescribing, and home hazard reduction (n = 187) versus usual care (n = 196). MEASUREMENTS: The number of dependencies in 17 basic and instrumental activities of daily living was measured every 3 months for 30 months (range: 0–17; greater values indicated worse disability). Inverse probability weighting Poisson regression was used to model the number of dependencies to adjust for confounding bias and higher dropout rates of those with greater disability. RESULTS: The study population had a mean age of 76 years, and 26% were men. During the 30‐month follow up, 17 died (n = 8, intervention; n = 9, control), 62 (n = 16, intervention; n = 46, control) were institutionalized or received nursing home care, and 34 (n = 15, intervention; n = 19, control) were lost to follow up. After inverse probability weighting, the mean number of dependencies at baseline was 1.21 and 1.29 for the intervention group and the control group, respectively (P =.80). The intervention group had fewer dependencies than the control group, but the difference was attenuated over time: 1.08 versus 1.60 at 6 months (P =.04), 1.29 versus 1.87 at 12 months (P =.03), 1.62 versus 2.17 at 18 months (P =.06), 2.08 versus 2.51 at 24 months (P =.18), and 2.73 versus 2.90 at 30 months (P =.67). CONCLUSION: A 24‐week multicomponent intervention was associated with a slower progression of disability; however, the diminishing association from 24 months and beyond suggests that reassessment and intervention may be necessary. Due to a lack of randomization, our findings should be interpreted with caution. [ABSTRACT FROM AUTHOR]

Background: Categorizing clinical risk amidst heterogeneous multimorbidity in older people living with HIV/AIDS (PLWH) may help prioritize and optimize health care engagements., Methods: PLWH and their prevalent conditions in 8 health domains diagnosed before January 1, 2015 were identified using 2014-2016 Medicare claims and the Chronic Conditions Data Warehouse. Latent profile analysis identified 4 distinct clinical subgroups based on the likelihood of conditions occurring together [G1: healthy, G2: substance use (SU), G3: pulmonary (PULM), G4: cardiovascular conditions (CV)]. Restricted mean survival time regression estimated the association of each subgroup with the 365 day mean event-free days until death, first hospitalization, and nursing home admission. Zero-inflated Poisson regression estimated hospitalization frequency in 2-year follow-up., Results: Of 11,196 older PLWH, 71% were male, and the average age was 61 (SD 9.2) years. Compared with healthy group, SU group had a mean of 30 [95% confidence interval: (19.0 to 40.5)], PULM group had a mean of 28 (22.1 to 34.5), and CV group had a mean of 22 (15.0 to 22.0) fewer hospitalization-free days over 1 year. Compared with healthy group (2.8 deaths/100 person-years), CV group (8.4) had a mean of 4 (3.8 to 6.8) and PULM group (7.9) had a mean of 3 (0.7 to 5.5) fewer days alive; SU group (6.0) was not different. There was no difference in restricted mean survival time for nursing home admission. Compared with healthy group, SU group had 1.42-fold [95% confidence interval: (1.32 to 1.54)], PULM group had 1.71-fold (1.61 to 1.81), and CV group had 1.28-fold (1.20 to 1.37) higher rates of hospitalization., Conclusion: Identifying clinically distinct subgroups with latent profile analysis may be useful to identify targets for interventions and health care optimization in older PLWH., Competing Interests: B. Olivieri-Mui and S. Shi received funding through T32 AG023480 from National Institute on Aging. D. H. Kim was supported by R01AG071809 and other National Institute on Aging grants. The remaining authors have no conflicts of interest to disclose., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)