Prolongation of life span. Age -- Physiological effect. Includes index. Bibliography: pages San Francisco : W. Creation Date. xii, pages : illustrations ; 24 cm. ISBN : X. ISBN : Other info. Display source record. Virtual Browse. Library Catalogue Catalogue home My library account Connect with SFU Library Ask a librarian Feedback.

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Viewing your signed in accounts Click the account icon in the top right to: View your signed in personal account and access account management features. View the institutional accounts that are providing access. Today there are reams of studies to back up these ancient suppositions. One of the subjects in his syllabus is the burgeoning field of exercise oncology.

For example, breast cancer patients who walk briskly for three hours a week after receiving standard treatment have been reported to enjoy 32 percent better outcomes, a success rate few drugs can match, Hicks says.

Other lecture topics include exercise and diabetes, heart disease and brain health. At the beginning of the quarter, Hicks polled the class on how they spend their downtime and created a word cloud to display the results. The No.

Lawrence M. Energy Vitality. Life, Long. Life extension. Life span prolongation. Long life. Prolongation of life span. Age -- Physiological effect. Includes index.

Bibliography: pages San Francisco : W. Creation Date. xii, pages : illustrations ; 24 cm. ISBN : X. ISBN : Other info. Display source record. Virtual Browse.

Library Catalogue Catalogue home My library account Connect with SFU Library Ask a librarian Feedback. Admissions Programs Learning Research Community About. With regard to the psychological domain, control beliefs, depressive symptoms, and cognitive impairment were found to be associated with frailty.

In the physical domain, associations have been found between BMI, number of chronic diseases, polypharmacy, hypertension, sensory problems, pain, and frailty. And last, in the lifestyle domain, physical activity, smoking, alcohol use, and sleep have been found to be associated with frailty [ 2, 9 ].

However, most of these studies included risk factors from 1 or 2 domains of functioning only and built association models, not prediction models. Therefore, it is not yet clear how these factors interact and which set of factors actually predict frailty.

To our knowledge, only 1 study tried to develop and validate a prediction model including multi-domain risk factors [ 10 ]. Ng et al.

However, in their final model, only older age, having no education, heart failure, obstructive respiratory disorders, stroke, depressive symptoms, hearing impairment, visual impairment, chronic airflow obstruction, chronic kidney failure, low hemoglobin, high nutritional risk, and inflammation markers predicted frailty [ 10 ].

However, these results cannot necessarily be generalized to other countries and other age-groups. No such studies have been conducted in Europe or in an older population.

Research on vitality in older adults is scarce. When examining frailty as a continuum, the absence of deficits in multiple domains can be considered as vitality. A study examining transitions in vitality status in Italian older adults aged 65 years and over found educational level, age, sex, BMI, and depressive symptoms to be associated with vitality status [ 8 ].

More research is needed to identify a set of predictors of vitality in older adults. The frailty index FI is an often-used instrument to measure frailty, and it is based on the accumulation of deficits [ 7 ].

And while it has mostly been used to measure gradations in frailty, the FI is a sensitive measure and it encompasses the entire continuum, from fit to frail [ 12 ]. In the current study, we will, therefore, use the FI as a measure of frailty as well as vitality.

Older people with frailty are at increased risk of adverse outcomes such as hospitalization, disability, and mortality [ 2, 4, 13 ]. Previous studies have shown that the risk of certain adverse outcomes due to frailty, for example, hospitalization [ 13 ] and falls [ 14 ], was highest among older adults aged 75 years and over, an age-group that will grow rapidly in the coming years [ 1 ].

Therefore, special attention should be paid to this specific age-group. Knowledge of the predictors of frailty is essential for early identification of at-risk individuals. Furthermore, identifying a set of predictors of frailty and vitality may help the development of strategies to prevent or delay the onset of frailty while identifying predictors of vitality may help to promote vitality among older adults.

Therefore, the aim of this study was to identify predictors of frailty and vitality among older adults aged 75 years and over.

We used data from the Longitudinal Aging Study Amsterdam LASA. LASA is an ongoing, prospective cohort study in the Netherlands on the determinants, trajectories, and consequences of physical, cognitive, emotional, and social functioning in older adults aged 55 years or older.

Measurements are conducted approximately every 3 years and include a main face-to-face computer-assisted interview, a face-to-face computer-assisted medical interview in which clinical measurements are performed and additional questions are asked, and a self-administered questionnaire.

A telephone interview is offered to those respondents who refused to participate in a full or an abbreviated face-to-face interview. The telephone interview takes approximately 15 min and includes a selection of key indicators of functioning.

Sampling, response, and procedures are described in detail elsewhere [ 15 ]. For the current study, data from — T1 and — T2 were used.

At T1, 1, respondents participated, aged Those excluded due to lack of follow-up were more often frail at baseline, lower educated, and older than those with a follow-up measurement. We ended up with a sample of respondents. Our outcome measures were frailty and vitality at T2.

They were measured with the LASA-FI [ 16 ]. The LASA-FI includes 32 health deficits, including self-reported chronic conditions 11 items , functional limitations 6 items , self-rated health 2 items , mental health 6 items , physical performance measured by gait speed 1 item , self-reported memory complaints 5 items , and physical activity 1 item.

An overview of all included items and cutoff values can be found elsewhere [ 16 ]. All deficits were scored 0 or 1, where 0 indicates the absence of the deficit and 1 the presence of a deficit.

The sum of the health deficit scores was divided by the total number of health deficits measured. This resulted in a score between 0 no deficits present and 1 all deficits present.

The distribution of the LASA-FI can be found in Figure 1. The mean number of deficits in the vital group using this cutoff was 3. We used candidate predictors from the following domains: the sociodemographic, lifestyle, social, psychological, and physical domains.

An FI does not specify which combination of deficits must be present for someone to be considered frail. Rather, it is the accumulation of deficits that matters.

The FI is largely indifferent to its underlying items [ 19 ], thus including predictors that are also part of the index is not problematic. To assess the income of the household, respondents were asked what their monthly household income was, choosing from 24 categories, with the lowest category being EUR — and the highest category EUR 5, or more.

To ensure comparability of income between persons with and without a partner in the household, income was multiplied by 0. Income was categorized into tertiles. Respondents were asked about their smoking and drinking habits.

Smoking was categorized as nonsmoker, former smoker, or current smoker. Respondents were also asked how many days per week they drink alcohol and how many consumptions they drink each time. Physical activity was measured with the LASA Physical Activity Questionnaire LAPAQ [ 20 ]. Respondents were asked about the frequency and duration of various activities during the previous 2 weeks.

Activities include walking outside, doing light and heavy household work, cycling, gardening, and a maximum of 2 sports.

The WHO recommends older adults to engage in moderately intensive physical activities for at least min throughout the week [ 21 ]. We categorized respondents as physically inactive if they did not meet this recommendation and as physically active if they did.

Respondents were asked about their sleep problems in the self-administered questionnaire. They were asked whether they had problems with falling asleep, waking up in the night, or waking up too early in the morning, with 4 response categories ranging from 1 almost never to 4 almost always.

A sum score was calculated ranging from 3 no problems to 12 many problems. Loneliness was assessed using the De Jong-Gierveld Loneliness Scale, which ranges from 0 to 11 [ 22 ]. Respondents were considered to be lonely when they had a score of 3 or higher.

Respondents were asked to name the persons they were in frequent contact with and who were also important to them. By doing so, the size of the social network was identified 0— Respondents were also asked how much instrumental and emotional support they received from the 9 most frequently contacted persons from their social network.

Response possibilities were 1 never, 2 seldom, 3 sometimes, and 4 often. A sum score 0—36 was calculated, with higher scores indicating more support.

We included 3 types of informal and formal social participation: participating in leisure activities and membership and volunteering in community organizations.

Respondents indicated how often 1 almost never, 2 a few times a year, 3 every month, 4 a few times a month, 5 every week, 6 a few times a week, and 7 every day they engaged in 7 leisure activities e.

Respondents were considered to engage in leisure activities if they engaged in at least 1 leisure activity at least every month except for shopping, which had to be at least once a week. Furthermore, respondents indicated whether they were members of 12 types of community organizations, ranging from a church, trade unions, and sports organizations to choirs.

Answer categories range from 1 strongly disagree to 5 strongly agree. Sum scores ranged from 7 to 35, with higher scores indicating a higher sense of mastery. Self-efficacy was measured with the General Self-Efficacy Scale GSES , constructed by Sherer et al.

In LASA, an abbreviated version was used, consisting 12 items [ 25 ]. These items cover 3 different aspects: willingness to initiate behavior, persistence when facing adversity, and effort to complete behavior.

Respondents could answer each question on a 5-point scale ranging from 1 strongly disagree to 5 strongly agree. Sum scores ranged from 12 to 60, with higher scores reflecting more self-efficacy.

Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale CES-D [ 26 ]. The CES-D is a item self-report scale ranging from 0 to 60, with higher scores reflecting more depressive symptoms.

Anxiety was measured with the Hospital Anxiety and Depression Scale-Anxiety HADS-A [ 27 ]. The HADS-A is a 7-item self-report questionnaire.

The scale ranges from 0 to 21, with higher scores indicating higher levels of anxiety. Cognitive functioning was measured using the Mini-Mental State Examination MMSE [ 28 ]. The MMSE consists of 23 items and scores range from 0 to 30, with higher scores reflecting better cognitive functioning.

Grip strength was measured using a JAMAR J1 Hydraulic Hand Dynamometer. Respondents were instructed to perform 2 maximum grip strength trials with each hand.

Blood pressure was measured twice, with significant time between the measurements, using an automatic Omron device Omron HEM F. All measurements were performed at the upper left arm.

When this was not possible, the right arm was used. Respondents were not allowed to smoke, eat, or be physically active during the last hour before the measurement.

Height and weight were also measured during the visit. Respondents were asked about their medication use. To measure hearing problems, respondents were asked whether they could follow a conversation in a group of 3 or 4 persons with and without a hearing aid, and whether they could follow a conversation with 1 person with and without a hearing aid.

Response categories were 1 yes, without difficulty, 2 yes, but with some difficulty, 3 yes, but with much difficulty, and 4 no, I cannot. Respondents were categorized as having hearing problems if they had at least some difficulty with more than one of these items.

Response categories and categorization were the same as for hearing problems. The number of chronic diseases was measured by self-reports of the following 7 chronic diseases: chronic nonspecific lung disease, cardiovascular diseases, peripheral artery diseases, diabetes mellitus, stroke, arthritis, and malignancies.

Five items measuring pain were included in the self-administered questionnaire: I am in pain when I am standing, I find it painful to change position, I am in pain when I am sitting, I am in pain when I walk, and I am in constant pain [ 29 ].

Response categories were 1 no and 2 yes. Sum scores were calculated ranging from 5 no symptoms of pain to 10 5 symptoms of pain. We used the multivariate imputation by chained equations package in R statistical software [ 30 ] to multiply impute missing values in the predictor variables.

Missingness at random was assumed. We used logistic regression analyses to first examine univariable associations between the predictors and the outcomes.

Because prediction models generally perform better in the sample used to develop the model than in an external sample, shrinkage factors can be used to correct for this optimism [ 33 ]. If the calibration slope of a model is lower than 1, this reflects overfitting and it can be interpreted as reflecting the need for shrinkage of the coefficients.

First, bootstrap samples were drawn from each imputed dataset before results were combined [ 34 ]. The AUC was corrected for optimism in each imputed dataset, and a pooled estimate was presented.

All analyses were performed in R version 3. In reporting our prediction models, we followed the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis TRIPOD statement [ 36 ].

Table 1 shows the characteristics of the sample. In our sample, When we stratified by age-group, we found a prevalence of In Table 2 , the estimates of the univariable regression analyses can be found, as well as the optimism adjusted estimates of the final models.

The final model predicting vitality included: age, sex, alcohol use, received emotional support, depressive symptoms, hearing problems, number of chronic diseases, and SRH.

The calibration slope of this model was 0. The optimism adjusted AUC was 0. We found that age, depressive symptoms, number of chronic diseases, and SRH were also predictors in the final model predicting frailty. In addition, cognitive functioning, polypharmacy, and pain remained in the final prediction model.

The calibration slope for this model was 0. The frailty model also had a good discriminative value, with an optimism adjusted AUC of 0. To ensure baseline frailty and vitality status did not affect our models, we conducted sensitivity analyses in which we adjusted the prediction models for baseline frailty and vitality status.

These analyses yielded similar prediction models. The number of older adults aged 75 years and over will strongly increase in the coming decades. This particular age-group is not only more at risk of frailty but also seems to be at higher risk of adverse outcomes due to frailty [ 13, 14 ].

Identifying older adults at risk of frailty or vitality could guide health and social care professionals in appropriate use of health and social care resources and implementation of person-oriented preventive strategies. It has been suggested that early intervention initiatives would be best situated within a setting where older people feel comfortable, for example, in their own homes, at their GP practice, or at facilities they visit regularly [ 37 ].

Therefore, it is important to identify a set of predictors that can be easily measured in such a setting. The prevalence of frailty at follow-up in our sample was Frailty prevalence rates vary greatly between studies, depending on the countries where the studies were conducted, the age of the sample, and measurements used [ 38 ].

Studies using the FI, which is a multidimensional frailty measure, generally report higher prevalence compared to studies using the physical frailty phenotype Fried criteria , another widely used frailty construct. The prevalence found in our study is consistent with another Dutch study estimating the prevalence of frailty between The prevalence of frailty increases substantially with age, explaining the rather high prevalence of frailty in our sample with a mean age of The prevalence of vitality was This means that almost one-fifth of older adults aged 75 years and over did not experience a decrease in physiological reserve in multiple domains of functioning.

This finding emphasizes the heterogeneity in older adults and the need to not only focus on frailty but to consider the entire continuum from fit to frail. Almost all of our 33 candidate predictors were univariably associated with frailty and vitality.

After backward stepwise selection, prediction models for both frailty and vitality included age, depressive symptoms, number of chronic diseases, and SRH. We also found differences between the models for vitality and frailty. Male sex, moderate alcohol use, more emotional support received, and no hearing problems were all predictors of being vital after 3 years.

Lower cognitive functioning, polypharmacy, and having more pain were predictors of frailty after 3 years. These findings are supported by earlier research examining factors associated with frailty [ 2, 9, 10 ] and vitality [ 8 ]. To our knowledge, we are the first to develop and validate prediction models for frailty and vitality in European older adults using such a broad set of predictors.

Our study has some limitations. First, we were not able to externally validate the prediction models. We did internally validate the models using bootstrapping techniques, but because bootstrap samples are derived from the same dataset, this only partially solves the problem of optimism.

Therefore, external validation is recommended in future research. Second, the low percentage of vital older adults in our sample led to a low events-per-variable EPV. While an EPV of 10 is generally recommended, a recent study concluded that the evidence for this criterion is weak [ 40 ], suggesting that a violation of this EPV recommendation may not necessarily lead to biased results.

As our prediction models were stable, we expect that exceeding the recommended EPV did not influence the performance of our models. Third, we operationalized vitality as having less than 5 out of 32 deficits, using the LASA-FI.

Thus, we only considered the absence of deficits rather than the presence of positive traits. While the use of an FI to identify both frail and vital older adults has practical advantages, further research is needed to assess whether vitality operationalized in this way is indeed associated with a higher risk of positive outcomes and a lower risk of adverse outcomes.

While for frailty the FI methodology considers each item in the FI as equal, it is possible that for vitality the absence of some deficits may be more important than others. For example, to remain vital, the absence of mobility limitations may be more important than the absence of a disease that can be easily controlled with medication.

Therefore, in future studies, other cutoffs as well as the option of weighting items should be examined when using the FI to measure vitality. Finally, we included predictors that were also included in the FI: chronic diseases, several items of the CES-D depressive symptoms , physical activity, memory complaints, and several items of the MMSE cognitive functioning.