Assessment of fall-associated risk factors in the Muslim community-dwelling older adults of Peshawar, Khyber Pakhtunkhwa, Pakistan

Exploring factors contributing to falls in home-dwelling older adults: A cross-sectional study in Northeastern Thailand

Falls are the most common accidents among older adults in home settings. Older adults experience falls due to several risk factors. In 2005, Thailand became an aging society, with projections indicating that by 2021, older adults would represent the majority of the population, and by 2035, approximately 30 percent of the population would be older adults. Approximately 3 million fall episodes transpire among older adults each year in Thailand, leading to almost 60,000 hospitalizations. The aim of this study was to examine the factors associated with falls among older adults in northeastern Thailand, hypothesizing that characteristics such as cognitive capacity, visual acuity, hearing acuity, balance ability, and mobility are associated with fall risk in this population. A cross-sectional analytical study involved 264 older adults aged 60 years or older using a questionnaire and a battery of tests that assessed the participants' cognitive capacity, eyesight proficiency, hearing ability, balancing ability, and mobility. The variables that exhibited a statistically significant association (p < 0.05) were employed in a binary logistic regression analysis. The results revealed that falls among home-living older adults were significantly associated with sex, family size, congenital issues, and mobility. Older adults who were female and had a large family, congenital disorders, or mobility impairments were at a higher risk of falls, which emphasizes the need for personalized prevention strategies. It is recommended to adopt a proactive healthcare strategy to prevent falls and ensure safe living conditions. Interventions aimed at improving balance, mobility, and mental health, as well as encouraging an active lifestyle, may reduce the risk of falls among older adults living in the community. The findings may aid private and government agencies in developing effective fall prevention programs for older adults living at home.

Video game-based application for fall risk assessment: a proof-of-concept cohort study

Background

Fall(s) are a significant cause of morbidity and mortality especially amongst elderly with polyneuropathy and cognitive decline. Conventional fall risk assessment tools are prone to low predictive values and do not address specific vulnerabilities. This study seeks to advance the development of an innovative, engaging fall prediction tool for a high-risk cohort diagnosed with diabetes.

Methods

In this proof-of-concept cohort study, between July 01, 2020, and May 31, 2022, 152 participants with diabetes performed clinical examinations to estimate individual risks of fall (timed "up and go" (TUG) test, dynamic gait index (DGI), Berg-Balance-Scale (BBS)) and participated in a video game-based fall risk assessment with sensor-equipped insoles as steering units. The participants engaged in four distinct video games, each designed to address capabilities pertinent to prevent fall(s): skillfulness, reaction time, sensation, endurance, balance, and muscle strength. Data were collected during both, seated and standing gaming sessions. By data analyses using binary machine learning models a classification of participants was achieved and compared with actual fall events reported for the past 24 months.

Findings

Overall 22 out of 152 participants (14.5%) underwent at least one episode of fall during the past 24 months. Adjusted risk classification accuracies of TUG, DGI, and BBS reached 58.7%, 58.3%, and 47.5%, respectively. Data analyses from gaming sessions in seated and standing positions yielded two models with six predictors from the four games with accuracies of 82.8% and 88.6% (area under the receiver-operating-characteristic curve 0.84 (95% confidence interval (CI): 0.77-0.91) and 0.91 (95% CI: 0.85-0.97), respectively). Key capabilities that were distinctly different between the groups related to endurance (0.6 ± 0.1 vs. 0.5 ± 0.2; p = 0.03) and balance (0.7 ± 0.2 vs. 0.6 ± 0.2; p = 0.05). The AI-driven analysis allowed to extract a list of game features that showed highly significant predictive values, e.g., reaction times in specific task, deviation from ideal steering routes in parcours and pressure-related parameters.

Interpretation

Thus, video game-based assessment of fall risk surpasses traditional clinical assessment tools and scores (e.g., TUG, DGI, and BBS) and may open a novel resource for patient evaluation in the future. Further research with larger, heterogeneous cohorts is needed to validate these findings and especially predict future fall risk probabilities in clinical as well as outpatient settings.

Funding

This project was funded by the Ministry of Science, Economics, and Digitalization of the State of Saxony-Anhalt and the European Fund for Regional Development under the Autonomy in Old Age Program (Funding No: ZS/2016/05/78615, ZS/2018/12/95325) and Healthy Cognition and Nerve function (HeyCoNer, ZS/2023/12/183088).

A novel score for predicting falls in community-dwelling older people: a derivation and validation study

BACKGROUND

Early detection of patients at risk of falling is crucial. This study was designed to develop and internally validate a novel risk score to classify patients at risk of falls.

METHODS

A total of 334 older people from a fall clinic in a medical center were selected. Least absolute shrinkage and selection operator (LASSO) regression was used to minimize the potential concatenation of variables measured from the same patient and the overfitting of variables. A logistic regression model for 1-year fall prediction was developed for the entire dataset using newly identified relevant variables. Model performance was evaluated using the bootstrap method, which included measures of overall predictive performance, discrimination, and calibration. To streamline the assessment process, a scoring system for predicting 1-year fall risk was created.

RESULTS

We developed a new model for predicting 1-year falls, which included the FRQ-Q1, FRQ-Q3, and single-leg standing time (left foot). After internal validation, the model showed good discrimination (C statistic, 0.803 [95% CI 0.749-0.857]) and overall accuracy (Brier score, 0.146). Compared to another model that used the total FRQ score instead, the new model showed better continuous net reclassification improvement (NRI) [0.468 (0.314-0.622), P < 0.01], categorical NRI [0.507 (0.291-0.724), P < 0.01; cutoff: 0.200-0.800], and integrated discrimination [0.205 (0.147-0.262), P < 0.01]. The variables in the new model were subsequently incorporated into a risk score. The discriminatory ability of the scoring system was similar (C statistic, 0.809; 95% CI, 0.756-0.861; optimism-corrected C statistic, 0.808) to that of the logistic regression model at internal bootstrap validation.

CONCLUSIONS

This study resulted in the development and internal verification of a scoring system to classify 334 patients at risk for falls. The newly developed score demonstrated greater accuracy in predicting falls in elderly people than did the Timed Up and Go test and the 30-Second Chair Sit-Stand test. Additionally, the scale demonstrated superior clinical validity for identifying fall risk.

Effectiveness of blended happy age education program (B-HAEP) on active aging promotion among pre-disable community dwelling older adults in Pakistan

BACKGROUND

Addressing aging related functional declines in older adults has become a top priority for countries around the world. The aim of this study was to investigate the effectiveness of a blended happy age education program in promoting active aging among community-based pre-disable older adults.

METHODS

We conducted a two-arm trial study in Khyber Pakhtunkhwa, Pakistan. Participants were randomly assigned into two groups using a computer-generated random sequence of 150 participants.

RESULTS

Blended Happy Age Education Program (BHAEP) reduced time for 3 m walk (Estimated mean 19.5 ± 3.4 to 13.7 ± 1.3, effect size ηp² = 0.98, (P < 0.001). The current level of happiness improved in B-HAEP group from 4.7 ± 1.05 scores to 7.8 ± 1.6, P < 0.001, effect size (ηp² = 0.540). Healthy lifestyle significantly improved (P < 0.001, ηp² = 0.4).

CONCLUSIONS

B-HAEP can be an effective intervention strategy to promote active aging in older adults with risk for immobility.