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1. Perceived Balance, Balance Performance, and Falls Among Community-Dwelling Older Adults: A Retrospective, Cross-Sectional Study

   https://doi.org/10.1177/08982643241242518  

   Dolan, Hanne R; Pohl, Janet; Pituch, Keenan; Coon, David W (March 2025, Journal of Aging and Health)

   Objectives: To examine the extent to which older adults’ perceived balance, a balance performance test, and fear of falling (FOF) were associated with falls in the last month. Methods: The Health Belief Model served as the theoretical framework. A retrospective, cross-sectional, secondary analysis using data from the National Health and Aging Trends Study was conducted ( N = 7499). Results: Multiple logistic regression analysis revealed that the odds of reporting a fall in the past month were 3.3 times ( p < .001) greater for participants who self-reported having a balance problem compared to those who did not. The Short Physical Performance Battery and FOF were not uniquely associated with falls. Discussion: Our findings support limited evidence suggesting that older adults’ perceived balance is a better predictor of falls than balance performance. Assessing older adults’ perceived balance may be a new way to assess older adults’ fall risk to prevent future falls. 

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2. Prediction of fall risk among community-dwelling older adults using a wearable system

   https://doi.org/10.1038/s41598-021-00458-5  

   Lockhart, Thurmon E.; Soangra, Rahul; Yoon, Hyunsoo; Wu, Teresa; Frames, Christopher W.; Weaver, Raven; Roberto, Karen A. (December 2021, Scientific Reports)

   Abstract Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to determine their predictive ability for future falls using a wearable system. Participants wore a wearable sensor (inertial measurement unit, IMU) affixed to the sternum and performed a 10-m walking test. Measures of gait variability, complexity, and smoothness were extracted from each participant, and prospective fall incidence was evaluated over the following 6-months. Gait parameters were refined to better represent features for a random forest classifier for the fall-risk classification utilizing three experiments. The results show that the best-trained model for faller classification used both linear and nonlinear gait parameters and achieved an overall 81.6 ± 0.7% accuracy, 86.7 ± 0.5% sensitivity, 80.3 ± 0.2% specificity in the blind test. These findings augment the wearable sensor's potential as an ambulatory fall risk identification tool in community-dwelling settings. Furthermore, they highlight the importance of gait features that rely less on event detection methods, and more on time series analysis techniques. Fall prevention is a critical component in older individuals’ healthcare, and simple models based on gait-related tasks and a wearable IMU sensor can determine the risk of future falls. 

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3. Predicting Fall Risk Through Automatic Wearable Monitoring: A Systematic Review

   https://doi.org/10.36001/ijphm.2021.v12i4.2958  

   Olson, Markey C.; Lockhart, Thurmon E (January 2021, International journal of prognostics and health management)

   society as a whole. Technologies that are able to detect individuals at risk of fall before occurrence could help reduce this burden by targeting those individuals for rehabilitation to reduce risk of falls. Wearable technologies especially, which can continuously monitor aspects of gait, balance, vital signs, and other aspects of health known to be related to falls, may be useful and are in need of study. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematics Reviews and Meta-Analysis (PRISMA) 2009 guidelines to identify articles related to the use of wearable sensors to predict fall risk. Fifty four studies were analyzed. The majority of studies (98.0%) utilized inertial measurement units (IMUs) located at the lower back (58.0%), sternum (28.0%), and shins (28.0%). Most assessments were conducted in a structured setting (67.3%) instead of with free-living data. Fall risk was calculated based on retrospective falls history (48.9%), prospective falls reporting (36.2%), or clinical scales (19.1%). Measures of the duration spent walking and standing during free-living monitoring, linear measures such as gait speed and step length, and nonlinear measures such as entropy correlate with fall risk, and machine learning methods can distinguish between falls. However, because many studies generating machine learning models did not list the exact factors being considered, it is difficult to compare these models directly. Few studies to date have utilized results to give feedback about fall risk to the patient or to supply treatment or lifestyle suggestions to prevent fall, though these are considered important by end users. Wearable technology demonstrates considerable promise in detecting subtle changes in biomarkers of gait and balance related to an increase in fall risk. However, more large-scale studies measuring increasing fall risk before first fall are needed, and exact biomarkers and machine learning methods used need to be shared to compare results and pursue the most promising fall risk measurements. There is a great need for devices measuring fall risk also to supply patients with information about their fall risk and strategies and treatments for prevention. 

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4. Prevalence and Correlates of Long COVID Symptoms Among US Adults

   https://doi.org/10.1001/jamanetworkopen.2022.38804  

   Perlis, Roy H.; Santillana, Mauricio; Ognyanova, Katherine; Safarpour, Alauna; Lunz Trujillo, Kristin; Simonson, Matthew D.; Green, Jon; Quintana, Alexi; Druckman, James; Baum, Matthew A.; et al (October 2022, JAMA Network Open)

   ImportancePersistence of COVID-19 symptoms beyond 2 months, or long COVID, is increasingly recognized as a common sequela of acute infection. ObjectivesTo estimate the prevalence of and sociodemographic factors associated with long COVID and to identify whether the predominant variant at the time of infection and prior vaccination status are associated with differential risk. Design, Setting, and ParticipantsThis cross-sectional study comprised 8 waves of a nonprobability internet survey conducted between February 5, 2021, and July 6, 2022, among individuals aged 18 years or older, inclusive of all 50 states and the District of Columbia. Main Outcomes and MeasuresLong COVID, defined as reporting continued COVID-19 symptoms beyond 2 months after the initial month of symptoms, among individuals with self-reported positive results of a polymerase chain reaction test or antigen test. ResultsThe 16 091 survey respondents reporting test-confirmed COVID-19 illness at least 2 months prior had a mean age of 40.5 (15.2) years; 10 075 (62.6%) were women, and 6016 (37.4%) were men; 817 (5.1%) were Asian, 1826 (11.3%) were Black, 1546 (9.6%) were Hispanic, and 11 425 (71.0%) were White. From this cohort, 2359 individuals (14.7%) reported continued COVID-19 symptoms more than 2 months after acute illness. Reweighted to reflect national sociodemographic distributions, these individuals represented 13.9% of those who had tested positive for COVID-19, or 1.7% of US adults. In logistic regression models, older age per decade above 40 years (adjusted odds ratio [OR], 1.15; 95% CI, 1.12-1.19) and female gender (adjusted OR, 1.91; 95% CI, 1.73-2.13) were associated with greater risk of persistence of long COVID; individuals with a graduate education vs high school or less (adjusted OR, 0.67; 95% CI, 0.56-0.79) and urban vs rural residence (adjusted OR, 0.74; 95% CI, 0.64-0.86) were less likely to report persistence of long COVID. Compared with ancestral COVID-19, infection during periods when the Epsilon variant (OR, 0.81; 95% CI, 0.69-0.95) or the Omicron variant (OR, 0.77; 95% CI, 0.64-0.92) predominated in the US was associated with diminished likelihood of long COVID. Completion of the primary vaccine series prior to acute illness was associated with diminished risk for long COVID (OR, 0.72; 95% CI, 0.60-0.86). Conclusions and RelevanceThis study suggests that long COVID is prevalent and associated with female gender and older age, while risk may be diminished by completion of primary vaccination series prior to infection. 

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5. “It just isn’t the same”: altered routines among older Americans three years after the COVID-19 pandemic onset

   https://doi.org/10.3389/fpubh.2025.1573302  

   Milan, Rebecca A; Sagehorn, Mallory_A P; Perera, Rohini; Bowman, Grace I; Finlay, Jessica (July 2025, Frontiers in Public Health)

   IntroductionThe COVID-19 pandemic significantly disrupted civic life, particularly for older adults at increased risk for severe morbidity and mortality. Yet, little is known about the longer-term impacts on their daily routines and how this may affect health and wellbeing. MethodsThis qualitative study utilized data from older US adults who participated in the COVID-19 Coping Study’s three-year follow-up online survey, conducted in April–May 2023. The primary aim was to understandhowandwhydaily routines have changed among older Americans (N = 1,309). ResultsParticipants had an average age of 71 years, with approximately 74% female and 93% identifying as Non-Hispanic White. We conducted content and thematic analysis of open-ended survey responses to identify five key reasons for still-altered routines 3 years after the pandemic onset: (1) COVID-19 risk and exposure, (2) altered access, (3) broader life circumstances, (4) emotional health, and (5) physical health. DiscussionThese findings highlight the enduring impacts of the pandemic on older adults’ routines and underscore the importance of integrating public health strategies that prioritize routine stability to enhance mental, physical, and social health. To support older adults’ wellbeing during and beyond public health emergencies, we recommend strengthening community-based programs, improving access to health and social services, and designing adaptable interventions that help individuals rebuild and maintain meaningful daily routines. 

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