Association Between Fatigue and Falls Risk Among the Elderly Aged Over 75 Years in China: The Chain Mediating Role of Falls Efficacy and Lower Limb Function

He, Yudi; Zhang, Huaguo; Song, Mi Ryeong; Wu, Hongyi; Pi, Hongying

doi:10.3389/fpubh.2022.850533

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…13 Additionally, fatigue can cause patients with sepsis to be at risk of falls 29 because it can impact lower limb function. 30 In our study, the incidence of a fall was not a major symptom experienced nor the primary cause for older adults with sepsis to visit the ED. However, previous studies found that falls were the most frequent cause of ED admission.…”

Section: Symptoms and Signs Of Sepsis Presentationmentioning

confidence: 54%

Perceptions of Relatives about Symptoms and Signs in Older Adults with Sepsis: A Cross-Sectional Study

Singjan,

Sutti,

Supreeda Monkong

2023

PRIJNR

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Sepsis is a common health condition in older adults that may present with typical or atypical symptoms and signs. The perception of warning symptoms and signs by relatives is important in their decision to seek hospital medical treatment. This cross-sectional study was conducted to explore the symptoms and signs of sepsis in older adults as perceived by their relatives, the awareness of symptoms and signs of sepsis, and the duration from the symptom onset to hospital arrival at a tertiary care hospital in Bangkok, Thailand. Seventy-seven dyads of older adults with sepsis and their relatives were recruited. Data were collected using the Personal Information of Older Adults and Relatives Questionnaire and the Symptoms and Signs of Sepsis Questionnaire. Descriptive statistics and Fisher’s exact test were used to analyze the data. Results revealed that both typical and atypical symptoms and signs occurred in older adults with sepsis. The top five typical presentations were drowsiness/deep sleep, rapid breathing, fever, chilling, and difficulty breathing. The top five atypical presentations were malaise or fatigue, loss of appetite, signs of dehydration, comorbidity exacerbations, and decreased activities of daily living. Atypical presentations were the most common presentations that relatives were unsure of and did not recognize as sepsis. The majority of older adults with sepsis (72.7%) arrived at the emergency department within 24 hours of the onset of symptoms or signs. There was no significant difference between the number of participants arriving at the emergency department within ≥24 hours, classified by typical and atypical presentations. These findings point to the important role of relatives to be proactive in the management of sepsis in older adults, starting at home. Nurses should teach relatives about early detection and timely seeking of medical care in hospitals. A system of telehealth consultations through phone or video calls should be established to facilitate healthcare-seeking decisions prior to clinical deterioration.

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Section: Symptoms and Signs Of Sepsis Presentationmentioning

confidence: 54%

Perceptions of Relatives about Symptoms and Signs in Older Adults with Sepsis: A Cross-Sectional Study

Singjan,

Sutti,

Supreeda Monkong

2023

PRIJNR

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“…Being able to identify and monitor patterns of activities could inform self-management strategies. Fatigue also has a strong correlation with the risk of falls particularly with older individuals [10]. IoT-enabled smart healthcare can be used for the detection of falling events [11].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Sensing for Fatigue Detection Using Wearables and RF

Taylor,

Hill,

Adam

et al. 2024

IEEE Sensors J.

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In the field of smart healthcare, wearable and sensing devices are connected to the Internet of Things (IoT) to assess patients within their own homes. Fatigue is a multidimensional experience that can be characterised by exhaustion and reduced physical performance. Monitoring people within their homes to detect slower pace could be a promising method to objectively measure aspects of human fatigue. This paper makes use of both a wearable bracelet and Radio Frequency (RF) sensing to detect simulated fatigue from human activity monitoring. Different activities are collected at a normal pace to represent no fatigue and then repeated at a slower pace to represent fatigue. Artificial intelligence (AI) is used to detect if there is fatigue present or not regardless of activity taking place as well as identifying which activity took place and if fatigue is present in said activity. When using the data from both the bracelet and RF sensing, Random Forest and ResNet algorithms achieved 100 % in detecting fatigue as opposed to non-fatigue using algorithms. When using only the bracelet, only the Random Forest algorithm was able to achieve 100 % accuracy. Using only RF data, 94.80 % accuracy was achieved with a Convolutional Neural Network (CNN). When detecting individual activities with fatigue and no fatigue, the Random Forest algorithm achieved an accuracy score of 97.40 % using both the bracelet and RF sensing and with only the bracelet data. CNN was again the best algorithm for RF sensing only with an accuracy score of 89.84 %.

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“…Ultimately, endeavors aiming to empower older people to prevent and manage falls need a clear understanding of falls efficacy. Some articles featured in the Frontiers have demonstrated these efforts, such as presenting the mediating role of falls efficacy between fatigue and falls risk (He et al, 2022 ) and the role of a falls risk-reduction program on falls efficacy (Cho et al, 2014 ). Given the advances in research on falls efficacy, have we adequately understood this self-efficacy concept and have the most appropriate measure applied for the construct of interest?…”

Section: Introductionmentioning

confidence: 99%