Hugo Parent-Roberge, R. Maréchal, Adeline Fontvieille, I. Dionne, T. Fulop, M. Pavic, E. Riesco
Background: Kinesiophobia, the fear that movement and physical activity could worsen side effects such as fatigue and pain, is a barrier to exercise in cancer patients. Physical inactivity and deconditioning can lead to functional decline, higher mortality risk and lower quality of life, in older adults, and even more in oncogeriatrics because of a lower physical activity level during cancer treatments. The case: We present the case of an older breast cancer patient recruited in a controlled exercise trial and randomized to the control arm of the study (a 12-week supervised static stretching program). She expressed fear that physical activity might exacerbate some of her cancer-related symptoms during baseline physical capacity assessment (Senior Fitness Test, handgrip strength and maximal lower body strength). After completing the 12-week supervised static stretching program, she exhibited similar and/or larger improvements in many of the physical capacity tests than the mixed exercise intervention group, despite being in the control arm. Conclusions: These observations and physical capacity results have led us to emit the hypothesis that (1) this participant’s baseline physical capacity assessment might have been biased by kinesiophobia and (2) the supervision by exercise physiologist might have mitigated this fear over time. Hence, based on this case, we suggest that kinesiophobia should be measured in future exercise trials and clinical interventions targeting older cancer patients.
{"title":"Breaking Barriers: Could Exercise Supervision Attenuate Kinesiophobia in an Older Cancer Patient?","authors":"Hugo Parent-Roberge, R. Maréchal, Adeline Fontvieille, I. Dionne, T. Fulop, M. Pavic, E. Riesco","doi":"10.20900/AGMR20190011","DOIUrl":"https://doi.org/10.20900/AGMR20190011","url":null,"abstract":"Background: Kinesiophobia, the fear that movement and physical activity could worsen side effects such as fatigue and pain, is a barrier to exercise in cancer patients. Physical inactivity and deconditioning can lead to functional decline, higher mortality risk and lower quality of life, in older adults, and even more in oncogeriatrics because of a lower physical activity level during cancer treatments. \u0000The case: We present the case of an older breast cancer patient recruited in a controlled exercise trial and randomized to the control arm of the study (a 12-week supervised static stretching program). She expressed fear that physical activity might exacerbate some of her cancer-related symptoms during baseline physical capacity assessment (Senior Fitness Test, handgrip strength and maximal lower body strength). After completing the 12-week supervised static stretching program, she exhibited similar and/or larger improvements in many of the physical capacity tests than the mixed exercise intervention group, despite being in the control arm. \u0000Conclusions: These observations and physical capacity results have led us to emit the hypothesis that (1) this participant’s baseline physical capacity assessment might have been biased by kinesiophobia and (2) the supervision by exercise physiologist might have mitigated this fear over time. Hence, based on this case, we suggest that kinesiophobia should be measured in future exercise trials and clinical interventions targeting older cancer patients.","PeriodicalId":72094,"journal":{"name":"Advances in geriatric medicine and research","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81282062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Cheong, R. Choo, N. H. L. Ha, Ivana Chan, A. Wong, L. Wee, P. Yap
Background: Frailty and cognitive impairment are closely related in sharing several possible underlying pathophysiological mechanisms. There is a lack of clarity in published literature on whether cerebral infarcts or Alzheimer’s pathology accounts for the frailty phenotype in patients with mild Alzheimer’s disease (AD). Therefore, we investigated the structural neuroimaging predictors of frailty in mild AD patients to better elucidate the underlying pathophysiology. Methods: We recruited subjects who satisfied inclusion criteria from a clinical database of patients attending a tertiary hospital memory clinic between 2012 and 2017. AD patients with Clinical Dementia Rating (CDR) 0.5 and 1.0 who had undergone MRI brain were included. Frailty Index (FI-CGA) was utilised for frailty assessment, and visual MRI rating scales were used by blinded raters to quantify the brain lesions. Results: A total of 342 mild AD patients with a mean age of 75.79 ± 7.18 were studied. A multivariate linear regression model adjusted for demographics, cognitive scores and functional status revealed only deep white matter hyperintensities (DWMH) but none other brain lesions to be significantly and positively correlated with FI-CGA (β = 0.178, SE = 0.047, p ≤ 0.001) This model which comprised age, CDR sum of boxes, basic activities of living and DWMH, accounted for 47.5% of the FI-CGA variance in the study population. Conclusions: The study has revealed DWMH to be independently associated with frailty in mild AD patients. With the current understanding of the aetiology of DWMH, control of vascular risk factors is vital to preventing and ameliorating frailty in patients with mild AD.
背景:虚弱和认知障碍在共享几个可能的潜在病理生理机制方面密切相关。关于脑梗死或阿尔茨海默病病理是导致轻度阿尔茨海默病(AD)患者虚弱表型的原因，已发表的文献尚不明确。因此，我们研究了轻度AD患者虚弱的结构神经影像学预测因素，以更好地阐明潜在的病理生理。方法:我们从2012年至2017年在三级医院记忆门诊就诊的患者临床数据库中招募符合纳入标准的受试者。纳入临床痴呆评分(CDR)为0.5和1.0并行脑MRI的AD患者。虚弱指数(FI-CGA)用于虚弱评估，视觉MRI评分量表由盲法评分者量化脑病变。结果:共纳入轻度AD患者342例，平均年龄75.79±7.18岁。经人口统计学、认知评分和功能状态调整后的多元线性回归模型显示，深度白质高信号(DWMH)与FI-CGA呈正相关(β = 0.178, SE = 0.047, p≤0.001)，其他脑损伤与FI-CGA均无显著正相关(β = 0.178, SE = 0.047, p≤0.001)。该模型包括年龄、CDR盒数、基本生活活动和DWMH，占研究人群FI-CGA方差的47.5%。结论:该研究显示DWMH与轻度AD患者的虚弱独立相关。根据目前对DWMH病因的了解，控制血管危险因素对于预防和改善轻度AD患者的虚弱至关重要。
{"title":"Deep but Not Periventricular White Matter Disease Is a Marker for Frailty in Older Patients with Early Alzheimer’s Disease","authors":"C. Cheong, R. Choo, N. H. L. Ha, Ivana Chan, A. Wong, L. Wee, P. Yap","doi":"10.20900/agmr20190009","DOIUrl":"https://doi.org/10.20900/agmr20190009","url":null,"abstract":"Background: Frailty and cognitive impairment are closely related in sharing several possible underlying pathophysiological mechanisms. There is a lack of clarity in published literature on whether cerebral infarcts or Alzheimer’s pathology accounts for the frailty phenotype in patients with mild Alzheimer’s disease (AD). Therefore, we investigated the structural neuroimaging predictors of frailty in mild AD patients to better elucidate the underlying pathophysiology. \u0000Methods: We recruited subjects who satisfied inclusion criteria from a clinical database of patients attending a tertiary hospital memory clinic between 2012 and 2017. AD patients with Clinical Dementia Rating (CDR) 0.5 and 1.0 who had undergone MRI brain were included. Frailty Index \u0000(FI-CGA) was utilised for frailty assessment, and visual MRI rating scales were used by blinded raters to quantify the brain lesions. \u0000Results: A total of 342 mild AD patients with a mean age of 75.79 ± 7.18 were studied. A multivariate linear regression model adjusted for demographics, cognitive scores and functional status revealed only deep white matter hyperintensities (DWMH) but none other brain lesions to be significantly and positively correlated with FI-CGA (β = 0.178, SE = 0.047, p ≤ 0.001) This model which comprised age, CDR sum of boxes, basic activities of living and DWMH, accounted for 47.5% of the FI-CGA variance in the study population. \u0000Conclusions: The study has revealed DWMH to be independently associated with frailty in mild AD patients. With the current understanding of the aetiology of DWMH, control of vascular risk factors is vital to preventing and ameliorating frailty in patients with mild AD.","PeriodicalId":72094,"journal":{"name":"Advances in geriatric medicine and research","volume":"121 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88765123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Studies of the natural progression and temporal co-occurrence of physical frailty and cognitive impairment are needed to validate the construct of cognitive frailty, a state of mild cognitive impairment caused by physical frailty. Method: We analysed data from Singapore Longitudinal Ageing Studies (SLAS-1 and SLAS-2) participants (N = 2554), free of functional disability, dementia, neurodegenerative diseases, and stroke, who were categorized at baseline as robust and cognitive normal (N = 1252), physically frail alone (N = 913), cognitively impaired alone (N = 197), and concurrently frail and cognitively impaired (N = 232) with average 5-years of follow up. Physical frailty was defined as pre-frailty/frailty (Fried criteria scores 1–5) and cognitive impairment MMSE scores <27 (age and education adjusted). Results: Among cognitively normal and robust participants, the occurrence of pre-frailty/frailty alone was 80.4%, cognitive impairment alone was 0.6%, and co-occurring pre-frailty/frailty and cognitive impairment (cognitive frailty) was 3.8%. Among cognitively normal and pre-frail/frail participants, the occurrence of cognitive frailty (5.9%) was significantly higher (OR = 1.53, 95% CI 1.02–2.28, adjusted for sex and age). Among cognitively normal and robust individuals, baseline number of comorbid medical comorbidities (OR = 1.37 (95% CI: 1.08–1.74) significantly predicted cognitive frailty. From following up a hypothetical cohort of 1000 robust and cognitively normal individuals, 88 of 91 outcome cases of co-occurring frailty and cognitive impairment were preceded by frailty alone (N = 48), or concurrent frailty and cognitive impairment (N = 40); only 3 cases were preceded by cognitive impairment alone (not cognitive frailty).Conclusions: The validity of cognitive frailty as a construct of mild cognitive impairment due to physical frailty is supported.
{"title":"Frailty and Cognition Transitions and the Development of Cognitive Frailty among Community-Living Older Adults in the Singapore Longitudinal Ageing Studies","authors":"T. Ng, M. Nyunt, Q. Gao, X. Gwee, K. Yap","doi":"10.20900/AGMR20190007","DOIUrl":"https://doi.org/10.20900/AGMR20190007","url":null,"abstract":"Background: Studies of the natural progression and temporal co-occurrence of physical frailty and cognitive impairment are needed to validate the construct of cognitive frailty, a state of mild cognitive impairment caused by physical frailty. Method: We analysed data from Singapore Longitudinal Ageing Studies (SLAS-1 and SLAS-2) participants (N = 2554), free of functional disability, dementia, neurodegenerative diseases, and stroke, who were categorized at baseline as robust and cognitive normal (N = 1252), physically frail alone (N = 913), cognitively impaired alone (N = 197), and concurrently frail and cognitively impaired (N = 232) with average 5-years of follow up. Physical frailty was defined as pre-frailty/frailty (Fried criteria scores 1–5) and cognitive impairment MMSE scores <27 (age and education adjusted). Results: Among cognitively normal and robust participants, the occurrence of pre-frailty/frailty alone was 80.4%, cognitive impairment alone was 0.6%, and co-occurring pre-frailty/frailty and cognitive impairment (cognitive frailty) was 3.8%. Among cognitively normal and pre-frail/frail participants, the occurrence of cognitive frailty (5.9%) was significantly higher (OR = 1.53, 95% CI 1.02–2.28, adjusted for sex and age). Among cognitively normal and robust individuals, baseline number of comorbid medical comorbidities (OR = 1.37 (95% CI: 1.08–1.74) significantly predicted cognitive frailty. From following up a hypothetical cohort of 1000 robust and cognitively normal individuals, 88 of 91 outcome cases of co-occurring frailty and cognitive impairment were preceded by frailty alone (N = 48), or concurrent frailty and cognitive impairment (N = 40); only 3 cases were preceded by cognitive impairment alone (not cognitive frailty).Conclusions: The validity of cognitive frailty as a construct of mild cognitive impairment due to physical frailty is supported.","PeriodicalId":72094,"journal":{"name":"Advances in geriatric medicine and research","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79125035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Whittaker, E. Asamane, J. Aunger, D. Bondarev, A. Cabbia, P. Doody, N. Gensous, Barbara Iadarola, K. Ramsey, Belina Rodrigues, Muhammad Rizwan Tahir, S. Yeung
Background: The ageing of the population is a global challenge and the period of life spent in good health, although increasing, is not keeping pace with lifespan. Consequently, understanding the important factors that contribute to healthy ageing and validating interventions and influencing policy to promote healthy ageing are vital research priorities. Method: The PANINI project is a collaboration of 20 partners across Europe examining the influence of physical activity and nutrition in ageing. Methods utilised encompass the biological to the social, from genetics to the influence of social context. For example, epigenetic, immunological, and psychological assessments, and nutritional and sports science-based interventions have been used among older adults, as well as mathematical modelling and epidemiology. The projects are multi-disciplinary and examine health outcomes in ageing from a range of perspectives. Results: The results discussed here are those emerging thus far in PANINI from 11 distinct programmes of research within PANINI as well as projects cross-cutting the network. New approaches, and the latest results are discussed. Conclusions: The PANINI project has been addressing the impact of physical activity and nutrition on healthy ageing from diverse but interlinked perspectives. It emphasises the importance of using standardized measures and the advantages of combining data to compare biomarkers and interventions across different settings and typologies of older adults. As the projects conclude, the current results and final data will form part of a shared dataset, which will be made open access for other researchers into ageing processes.
{"title":"Physical Activity and Nutrition INfluences in Ageing: Current Findings from the PANINI Project","authors":"A. Whittaker, E. Asamane, J. Aunger, D. Bondarev, A. Cabbia, P. Doody, N. Gensous, Barbara Iadarola, K. Ramsey, Belina Rodrigues, Muhammad Rizwan Tahir, S. Yeung","doi":"10.20900/AGMR20190005","DOIUrl":"https://doi.org/10.20900/AGMR20190005","url":null,"abstract":"Background: The ageing of the population is a global challenge and the period of life spent in good health, although increasing, is not keeping pace with lifespan. Consequently, understanding the important factors that contribute to healthy ageing and validating interventions and influencing policy to promote healthy ageing are vital research priorities. \u0000Method: The PANINI project is a collaboration of 20 partners across Europe examining the influence of physical activity and nutrition in ageing. Methods utilised encompass the biological to the social, from genetics to the influence of social context. For example, epigenetic, immunological, and psychological assessments, and nutritional and sports science-based interventions have been used among older adults, as well as mathematical modelling and epidemiology. The projects are multi-disciplinary and examine health outcomes in ageing from a range of perspectives. \u0000Results: The results discussed here are those emerging thus far in PANINI from 11 distinct programmes of research within PANINI as well as projects cross-cutting the network. New approaches, and the latest results are discussed. \u0000Conclusions: The PANINI project has been addressing the impact of physical activity and nutrition on healthy ageing from diverse but interlinked perspectives. It emphasises the importance of using standardized measures and the advantages of combining data to compare biomarkers and interventions across different settings and typologies of older adults. As the projects conclude, the current results and final data will form part of a shared dataset, which will be made open access for other researchers into ageing processes.","PeriodicalId":72094,"journal":{"name":"Advances in geriatric medicine and research","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78139584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Brown, John B. Young, E. Teale, G. Santorelli, A. Clegg
Background: Pain prevalence is higher in older people with frailty compared to fit older people. However, little is known about pain impact on the lives of older people with frailty. Objectives: To investigate pain impact in community dwelling older people (≥75 years) using data from the Community Ageing Research 75+ (CARE75+) cohort study (UKCRN 18043). Methods: Participants were assessed as not frail, pre-frail or frail (phenotype model of frailty). Pain impact was measured using the Geriatric Pain Measure Short-Form (GPM-12), an instrument incorporating 10 items on how pain impacts on ambulation, social engagement, ability to accomplish tasks and sleep, along with current pain intensity and average pain intensity (last 7 days). Intrusive pain was calculated from an item in the Short-Form 36 questionnaire. Differences in the GPM-12 scores between frailty categories were compared using Kruskal-Wallis H tests. Logistic regression models were used to investigate the association between frailty and intrusive pain. Results: 887 participants: not frail 139; pre-frail 471; and frail 268. Total GPM-12 median (IQR): not-frail 5.0 (0.0, 12.5); pre-frail 10.0 (0.0, 27.5); and frail 40.0 (10.0, 65.0) (p ≤ 0.0001). Current pain: not frail 0.0 (0.0, 1.0); pre-frail 0 (0.0, 3.0); and frail 3.0 (0.0, 5.0) (p ≤ 0.0001). Average pain: not-frail 0.0 (0.0, 2.0); pre-frail 1 (0.0, 4.0); frail 4.0 (2.0, 6.8) (p ≤ 0.0001). There was a strong association between being frail and intrusive pain (adjusted for sex, ethniciaty, mood and high comorbid burden): OR 3.53 (95% CI 2.47, 5.04). Conclusions: This research has identified an important new finding that pain in older people with frailty appears to be of sufficient severity to impact negatively on multiple aspects of day-to-day life
{"title":"A Cross-Sectional Study of the Impact of Pain in Older People with Frailty: Findings from the Community Ageing Research 75+ (CARE75+) Study","authors":"L. Brown, John B. Young, E. Teale, G. Santorelli, A. Clegg","doi":"10.20900/AGMR20190002","DOIUrl":"https://doi.org/10.20900/AGMR20190002","url":null,"abstract":"Background: Pain prevalence is higher in older people with frailty compared to fit older people. However, little is known about pain impact on the lives of older people with frailty. Objectives: To investigate pain impact in community dwelling older people (≥75 years) using data from the Community Ageing Research 75+ (CARE75+) cohort study (UKCRN 18043). Methods: Participants were assessed as not frail, pre-frail or frail (phenotype model of frailty). Pain impact was measured using the Geriatric Pain Measure Short-Form (GPM-12), an instrument incorporating 10 items on how pain impacts on ambulation, social engagement, ability to accomplish tasks and sleep, along with current pain intensity and average pain intensity (last 7 days). Intrusive pain was calculated from an item in the Short-Form 36 questionnaire. Differences in the GPM-12 scores between frailty categories were compared using Kruskal-Wallis H tests. Logistic regression models were used to investigate the association between frailty and intrusive pain. Results: 887 participants: not frail 139; pre-frail 471; and frail 268. Total GPM-12 median (IQR): not-frail 5.0 (0.0, 12.5); pre-frail 10.0 (0.0, 27.5); and frail 40.0 (10.0, 65.0) (p ≤ 0.0001). Current pain: not frail 0.0 (0.0, 1.0); pre-frail 0 (0.0, 3.0); and frail 3.0 (0.0, 5.0) (p ≤ 0.0001). Average pain: not-frail 0.0 (0.0, 2.0); pre-frail 1 (0.0, 4.0); frail 4.0 (2.0, 6.8) (p ≤ 0.0001). There was a strong association between being frail and intrusive pain (adjusted for sex, ethniciaty, mood and high comorbid burden): OR 3.53 (95% CI 2.47, 5.04). Conclusions: This research has identified an important new finding that pain in older people with frailty appears to be of sufficient severity to impact negatively on multiple aspects of day-to-day life","PeriodicalId":72094,"journal":{"name":"Advances in geriatric medicine and research","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90952421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Efforts to provide patients with individualized treatments have led to tremendous breakthroughs in healthcare. However, a precision medicine approach alone will not offset the rapid increase in prevalence and burden of chronic non-communicable illnesses that is continuing to pervade the world's aging population. With rapid advances in technology, it is now possible to collect digital metrics to assess, monitor and detect chronic disease indicators, much earlier in the disease course, potentially redefining what was previously considered asymptomatic to pre-symptomatic. Data science and artificial intelligence can drive the discovery of digital biomarkers before the emergence of overt clinical symptoms, thereby transforming the current healthcare approach from one centered on precision medicine to a more comprehensive focus on precision health, and by doing so enable the possibility of preventing disease altogether. Presented herein are the challenges to the current healthcare model and the proposition of first steps for reversing the prevailing intractable trend of rising healthcare costs and poorer health quality.
{"title":"Aging Well: Using Precision to Drive Down Costs and Increase Health Quality.","authors":"Rhoda Au, Marina Ritchie, Spencer Hardy, Ting Fang Alvin Ang, Honghuang Lin","doi":"10.20900/agmr20190003","DOIUrl":"10.20900/agmr20190003","url":null,"abstract":"<p><p>Efforts to provide patients with individualized treatments have led to tremendous breakthroughs in healthcare. However, a precision medicine approach alone will not offset the rapid increase in prevalence and burden of chronic non-communicable illnesses that is continuing to pervade the world's aging population. With rapid advances in technology, it is now possible to collect digital metrics to assess, monitor and detect chronic disease indicators, much earlier in the disease course, potentially redefining what was previously considered asymptomatic to pre-symptomatic. Data science and artificial intelligence can drive the discovery of digital biomarkers before the emergence of overt clinical symptoms, thereby transforming the current healthcare approach from one centered on precision medicine to a more comprehensive focus on precision health, and by doing so enable the possibility of preventing disease altogether. Presented herein are the challenges to the current healthcare model and the proposition of first steps for reversing the prevailing intractable trend of rising healthcare costs and poorer health quality.</p>","PeriodicalId":72094,"journal":{"name":"Advances in geriatric medicine and research","volume":"1 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6656386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41221540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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