The Seattle Longitudinal Study Came To What Conclusion?

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The Seattle Longitudinal Study Came To What Conclusion
The Seattle Longitudinal Study came to what conclusion? Most of people’s mental abilities improve during adulthood. Who proposed the existence of fluid and crystallized intelligences?
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What did the Seattle Longitudinal Study conclude?

The Seattle Longitudinal Study concluded that middle age is a time of: peak performance for verbal ability.
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What do longitudinal studies on intelligence in adulthood report?

Longitudinal research usually shows that intelligent in most abilities increase through out early and middle adulthood. Many adults show intellectual improvement over most of adulthood, with not decline, even by age 60.
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What is the four components that make up expert cognition?

Expert Systems in Cognitive Science – F. Schmalhofer, in International Encyclopedia of the Social & Behavioral Sciences, 2001 Expert Systems, also known as Knowledge-based Systems, Intelligent Agent Systems, or more generally as Knowledge Systems, are computer programs that exhibit a similar high level of intelligent performance as human experts.

An expert system generally consists of four components: a knowledge base, the search or inference system, a knowledge acquisition system, and the user interface or communication system. Knowledge systems solve difficult problems of the real woorld by performing inference processes on explicitly stated knowledge.

The early rule-based systems of the 1970s, the subsequent model-based approaches of the late 1980s, and the newest knowledge systems with common sense, evolutionary knowledge growth and multiagency define three different generations of expert systems.

Together these systems test one of the main hypothesis of the cognitive revolution of the sciences, namely that by virtue of being a physical symbol system, knowledge systems have the necessary and sufficient means for general intelligent action. There are several successful applications of knowledge systems in industry, business, medicine and science, as for example knowledge management systems and various components of e-commerce systems.

Read full chapter URL: https://www.sciencedirect.com/science/article/pii/B0080430767016156
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What do many developmentalists believe is essential for successful aging?

What do many developmentalists believe is essential for successful aging? Selective optimization with compensation : Is a theory that proposes that we seek to compensate for losses by getting better at that which we already do well.
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What did the Seattle Longitudinal Study reveal about intellectual change into later life?

Our studies have shown that there is no uniform pattern of age- related changes across all intellectual abilities. Our data do lend some support to the notion that abilities that are primarily genetically determined tend to decline earlier than abilities that are primarily acquired through schooling or experience.
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How does the Seattle Longitudinal Study work?

The Seattle Longitudinal Study (SLS) begun in 1956, is considered to be one of the most extensive psychological research stu​dies of how adults develop and change through adulthood. The SLS is unique as a cohort-sequential longitudinal study, examining cognitive and psychosocial change in multiple birth cohorts over​ the same chronological age span.
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What was the purpose of the longitudinal study?

Advantages –

Longitudinal studies allow researchers to follow their subjects in real time. This means you can better establish the real sequence of events, allowing you insight into cause-and-effect relationships. Example A cross-sectional study on the impact of police on crime might find that more police are associated with greater crime and wrongly conclude that police cause crime when it is the other way around.

However, a longitudinal study would be able to observe the rise or fall in crime some time after increasing the number of police in an area. Longitudinal studies also allow repeated observations of the same individual over time. This means any changes in the outcome variable cannot be attributed to differences between individuals.

Example You decide to study how a particular weight-training program affects athletic performance. If you choose a longitudinal study, the impact of natural talent on performance should be eliminated, since that would not change over the study period.

  • Prospective longitudinal studies eliminate the risk of recall bias, or the inability to correctly recall past events.
  • Example You are studying the effect of low-carb diets on weight loss.
  • If you asked your subjects to remember how many carbs or how much they weighed at any point in time in the past, they might have difficulty doing so.

In a longitudinal study, you can keep track of these variables in real time.

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    What can longitudinal studies show us?

    Learning Hub | Strengths of longitudinal data Longitudinal studies have a number of particular advantages in terms of the quantity or quality of the data that they collect: Detail over the life course, The value of longitudinal studies increases as each sweep builds on what is already known about the study participants,

    This means that on many topics, longitudinal studies typically contain far more detailed information than could be collected through a one-off survey. For example, many studies collect a detailed array of information about study participants ‘ education, work histories and health conditions. Establishing the order in which events occur.

    Longitudinal data collection allows researchers to build up a more accurate and reliably ordered account of the key events and experiences in study participants ‘ lives. Understanding the order in which events occur is important in assessing causation.

    1. Reducing recall bias.
    2. Longitudinal studies help reduce the impact of recall error or bias, which occurs when people forget or misremember events when asked about them later.
    3. In longitudinal studies, participants provide information about their current circumstances, or are asked to remember events over only a short period of time (that is, since the time of the last sweep ).

    Many of the advantages of longitudinal studies relate to the analytic questions their data can help address. For example, longitudinal data help with: Exploring patterns of change and the dynamics of individual behaviour. Longitudinal data allows researchers to explore dynamic rather than static concepts.

    1. This is important for understanding how people move from one situation to another (for example, through work, poverty, parenthood, ill health and so on).
    2. The link between earlier life circumstances and later outcomes.
    3. By building up detailed information over time, longitudinal studies are able to paint a rich and accurate picture of participants’ lives.

    In the case of birth cohort studies this has allowed researchers to explore how circumstances earlier in life can influence later outcomes. For example, some of the most well-known findings from the cohort studies describe the long-lasting reach of socio-economic disadvantage in childhood.

    Longitudinal data also allow us to assess the time-related characteristics of particular events or circumstances (that is, their duration, frequency or timing). For example, does the impact of ill health change depending on when in their life someone becomes ill, how long they remain ill, and how often they experience illnesses? Providing insights into causal mechanisms and processes.

    Many surveys provide evidence about the association between particular circumstances and outcomes. For example, a cross-sectional study might find that the unemployed have poorer health than those in work (so, in other words, there is an association between health and employment status).

      Longitudinal data allow events to be ordered correctly in time (in our example, this would mean we could establish whether a period of unemployment definitely came before or after an episode of ill health). Longitudinal data also tend to be much richer in detail than cross-sectional studies, which allows analysts to take a wide array of background characteristics or control variables into account. This reduces the risk of ‘ unobserved heterogeneity ‘ or ‘ confounding ‘, Sometimes longitudinal data can be used to exploit ‘natural experiments’. In these cases, analysts take advantage of discontinuities over time or serendipitous events to explore their impact. For example, researchers have exploited the fact that some local authorities in England still have grammar schools, and have used this to examine their impact on children. There are a range of sophisticated statistical techniques that make use of the repeated observations built up over time in longitudinal studies and allow us to test whether relationships are likely to be causal or the result of other differences.

    Distinguishing between age and cohort effects, Longitudinal studies can help researchers to distinguish between changes that happen as people get older, known as ‘ age effects ‘, and generational differences that reflect the historical, economic and social context within which different cohorts grew up, known as ‘cohort’ or ‘generational’ effects.

    For example, cross-sectional data might show a clear relationship between age and political affiliation (with older age groups being more likely to vote for the Conservative party). Longitudinal data would allow analysts to investigate whether the older generations in the UK are more likely than younger ones to support the Conservative party (a cohort effect), or whether people all people become more likely to vote Conservative as they get older (an age effect).

    Age and cohort/generational effects also need to be distinguished from ‘period’ effects; these refer to forces that influence everyone – for example, key events in history that affect everyone irrespective of their age or the generation they were born into.
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    What is the aim of this longitudinal study?

    The main aim of longitudinal studies is to analyse change over time. Childhood is about change; research on children is about development and socialisation processes. Therefore it seems necessary to use research designs that are able to describe individual changes within and beyond single life spans.
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    How does intelligence change with age?

    Does an individual’s IQ change with age? – An individual’s IQ does not change with age. In other words: if you did an IQ test now and then another one in 10 years’ time, your IQ score will probably be very similar. This is because IQ is always measured relative to other people your age.

    IQs are always calculated relative to a person’s age, whether that age is 10, 15, 25, 50, 72, or 88. So 25-year-olds are compared to other 25-year-olds in terms of the number of items they answer correctly on any given task, just as 50-year-olds are compared to other 50-year-olds,” explains Alan Kaufman, an expert in intelligence testing from Yale University in the US.

    “For every age group, the average or mean IQ is set at 100. We can’t directly compare the mean IQs across the adult age range because – by definition – every group will average 100.” Meiran Nachshon, an expert in psychology from Ben-Gurion University in Israel, agrees, saying: “IQ indicates the relative positioning of an individual relative to the average.
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    What are the five cognitive factors in intelligence?

    Results – Descriptive statistics for all study variables are shown in Table 1, Bivariate correlations between the traits and the cognitive tasks are shown in Additional file 1 : Tables S1, S2, and S3. Although there were some minor violations of the assumptions of linear regression, the bivariate correlations were similar across the Pearson correlations (parametric) and Spearman correlations (nonparametric) that suggest the associations found in the regression analyses are not simply due to violations of the assumptions of linear regression. Tables 2, 3, and 4 show the association between personality and performance across the five domains of cognitive function. Note that for all analyses, the pattern of associations and significance was identical when outliers were removed from the analysis. Several patterns emerged from the analyses that were consistent with our hypotheses. First, Neuroticism was associated with worse performance on every cognitive task: Individuals higher in Neuroticism performed worse on measures of episodic memory, speed-attention-executive, visuospatial ability, fluency, and numeric reasoning. Second, although not apparent across all individual tasks, higher Openness and higher Conscientiousness were both associated generally with better performance. Third, the association between Extraversion and cognition was limited to better performance in the speed-attention-executive and fluency domains; Extraversion was unrelated to episodic memory, visuospatial ability, and numeric reasoning. Fourth, although unexpected, Agreeableness was associated with better performance in four out of the five domains (all domains except numeric reasoning). Fifth, the personality traits tended to have more associations with the speed-attention-executive and fluency tasks than the other cognitive functions. Within the memory domain, the traits had more associations with the word list learning and recall tasks than with the story memory tasks. Finally, as could be expected, the associations were slightly stronger with the scores combined across tasks, and the effect sizes were generally small. For episodic memory, the adjusted R 2 was,278 for the covariates only model and change in adjusted R 2 ranged from,004 (Agreeableness and Conscientiousness) to,009 (Neuroticism) for personality. For speed-attention-executive, the adjusted R 2 was,391 for the covariates only model and change in adjusted R 2 ranged from,005 (Extraversion) to,015 (Conscientiousness) for personality. For visuospatial ability, the adjusted R 2 was,313 for the covariates only model and change in adjusted R 2 ranged from,000 (Extraversion) to,011 (Openness) for personality. For fluency, the adjusted R 2 was,205 for the covariates only model and change in adjusted R 2 ranged from,003 (Neuroticism, Extraversion, and Conscientiousness) to,011 (Openness) for personality. For numeric reasoning, the adjusted R 2 was,292 for the covariates only model and change in adjusted R 2 ranged from,000 (Extraversion) to,005 (Neuroticism) for personality. See Additional file 1 : Tables S4, S5, and S6 for full reporting of the adjusted R 2 for each analysis. Table 1 Descriptive Statistics for All Study Variables Table 2 Associations Between Personality Traits and Episodic Memory Table 3 Associations Between Personality Traits and Speed-Attention-Executive Table 4 Associations Between Personality Traits and Visuospatial Ability, Fluency, and Numeric Reasoning There was little evidence that the associations between personality and the five cognitive domains were moderated by sociodemographic factors or mental status. There was a stronger association between Agreeableness and episodic memory at relatively lower than higher levels of education (β interaction  = −.04, p  < .01), whereas higher education amplified the effect of Openness on fluency (β interaction  = .04, p  < .01). There was also a stronger association between Neuroticism and visuospatial abilities at relatively older than younger ages (β interaction  = −.05, p  < .01). Finally, although apparent across race, the association between Conscientiousness and visuospatial abilities was stronger among African American participants than white participants (β interaction  = .06, p  < .01). None of the associations was moderated by sex, ethnicity, or global cognitive function. Further, the associations were similar when participants with cognitive impairment as indicated by the MMSE ( n  = 277) were excluded from the analysis. Finally, the threshold analyses generally paralleled the linear regressions (Table 5 ). Specifically, higher Neuroticism was associated with a 20% (fluency) to 40% (speed-attention-executive) increased risk of poor performance, whereas lower Conscientiousness was associated with a 19% (fluency) to 39% (speed-attention-executive) increased risk. Extraversion was likewise associated with 16% (visuospatial ability) to 20% (speed-attention-executive and fluency) greater likelihood of better performance, Openness was associated with a 19% (episodic memory) to 35% (visuospatial ability) greater likelihood of better performance, and Agreeableness was associated with a 19% (visuospatial ability) to 33% (speed-attention-executive) greater likelihood of better performance. Of note, with the exception of Openness, the strongest associations in the threshold analyses were for the speed-attention-executive domain. Table 5 Associations Between Personality Traits and Risk of Performance One Standard Deviation Below the Mean View complete answer

    What are the 3 main components of the human cognition system?

    As we discussed at the start of this unit, a model is a representation of something that we can easily work with. In the previous section we looked at models of user requirements – essentially models of users’ context. In contrast, this section considers models of users’ cognitive abilities.

    1. That is, rather than looking at the context in which users work we will consider models of how people behave.
    2. These cognitive models are important because they provide a way of understanding people’s cognitive abilities which can be used to inform design and, as we shall see in Unit 9, to evaluate systems.

    In this section we look at two cognitive models: GOMS and ICS. GOMS uses an explicit model of cognitive processes developed from studies of users. GOMS itself stands for Goals, Operators, Methods, and Selection rules. This core set of concepts describes the tasks that users perform, and how they achieve them.

    GOMS is more than just a way to describe tasks though, it provides a family of modelling techniques which can be used to generate predictions of the time to complete tasks given descriptions of the tasks and the user interfaces to be used. These predictions are based on a simple model of cognition called the Model Human Processor (MHP) and have an absolute accuracy of between 10% and 20%.

    The basic assumption of GOMS is evident in its name – that users understand their goals, GOMS considers the actions to meet such goals. Having made this assumption GOMS can give us feedback on the coverage and consistency of the user interface. By coverage we mean whether the user interface contains the necessary functionality to support the tasks it was designed for.

    • Consistency refers to whether similar tasks are performed similarly with the user interface.
    • Moreover, GOMS can give an indication of whether frequent goals can be achieved quickly using the given interface.
    • The central part of GOMS (Card, Moran, and Newell, 1983; see Chapter 6 of Dix 1998 for a summary) is the Model Human Processor (MHP).

    This is a simplified view of the psychological processes involved in human cognition which can be used to make approximate predictions of user behaviour. Card et al. based this model on empirical evidence i.e. psychological studies of users. Even though it is based on psychological evidence and theory the motivation for the MHP is that is should be usable by non-psychologists e.g.

    • set of processors – systems which process information and may make decisions
    • set of memories – areas in which information is stored in various forms by processors
    • set of principles of operation – these guide the operation of the processors and are crucial to a realistic account of human behaviour

    The MHP itself is divided into 3 interacting subsystems: the perceptual system, the motor system, and the cognitive system. Each of these subsystems has their own memories and processes and works on different kinds of information as illustrated in the following diagram.

    1. Perceptual system – input from the eyes and ears are stored symbolically in visual and auditory image stores which go on to be processed in working memory by the cognitive system, and possibly stored in long term memory for later access.E.g. the sound of a car horn is coded as a loud warning.
    2. Cognitive system – processes information from working and long term memory to make decisions about how to respond.E.g. processing the fact that there is a loud warning noise, recalling that evasive action should be taken in such situations, and deciding to run to the pavement.
    3. Motor system – executes responses to decisions made by the cognitive system.E.g. running to the pavement.

    Each component of these systems has parameters which are used in the generation of behavioural predictions. Each memory system is defined in terms of its storage capacity (how many pieces of information can it hold at one time), and the decay time of an item (how long is it before an item is forgotten).

    Similarly, each processor is defined in terms of processor cycle times – how long it takes to process one piece of information. Card et al. (1985; see Johnson, 1992, for a summary) defined approximate values for these parameters based on psychological research which are listed below. Note that these values assume skilled (errorless) performance and only predict time for responses to stimuli – not for self initiated actions.

    Table 7.1. Memory parameters

    Memory type Storage capacity Decay time
    Auditory 4 letters 1500 msec
    Visual 17 letters 200 msec
    Working memory 7 chunks of information 7 sec
    Long term memory Unlimited Never

    Table 7.2. Processor parameters

    Processor type Processing cycle time
    Processor type 100 msec
    (eye movement) 230 msec
    Cognitive 70 msec
    Motor 70 msec

    So, we have described the MHP, but we need to return to the core of GOMS – the goals, operators etc. – before we can see how these parameters are used to make predictions. Goals are something that the user wants to achieve e.g. go to airport, delete a file, or create a directory.

    • They have a hierarchical structure – that is they are composed of many sub-goals which need to be achieved to meet the larger goal.
    • These are similar to the goals identified in Task Analyses (see Unit 8).
    • Operators are elementary (can not be decomposed into smaller operations) perceptual, motor or cognitive acts which are necessary to change user’s mental state or environment.

    As such they are the lowest level of a GOMS analysis. Using GOMS a user’s behaviour can be recorded as a sequence of operators as operators can’t occur concurrently. They are a similar level of description as actions in task analysis (see Unit 8). For example, to move a file to a different folder the user might perform the following operations:

    • Move cursor to item
    • Hold mouse button down
    • Locate destination icon
    • Let go of mouse button

    From operators we build up methods which are sequences of steps that accomplish a goal (and so are like tasks in a task analysis (see Unit 8)). As with goals these methods can include other (sub) goals. A fundamental assumption in GOMS is that methods are learned and routine (so no problem solving involved), and that there is only one way a user stores knowledge of a task.

    • Goal – move file to a different folder
    • Method – move file
    • Operators – Move cursor to item, Hold mouse button down, Locate destination icon, Let go of mouse button

    If there is more than one method to accomplish a goal, the Selection rules tell you which method to use. Again, as with methods, they assume error-free performance (so the user does not selected the wrong method by accident). They are written as IF THEN statements as below: IF THEN accomplish For example: IF THEN ELSE IF THEN ELSE For the example in Activity 3 construct a GOMS model of a customer withdrawing money from a cash machine.

    • A Discussion on this activity can be found at the end of the chapter,
    • The lowest level of GOMS analysis is called the Keystroke Level Model (KLM).
    • This produces quantitative predictions of the time it would take a skilled operator to complete a task.
    • Again, it assumes error-free performance by the operator.

    Execution of a task is described in terms of

    • 5 physical-motor operators:
      1. Tk : (k)eying – how long it takes to press a key (including using modifiers such as the shift key)
      2. Tp : (p)ointing – how long it takes to move the mouse (or other such input device) to a target
      3. Th : (h)oming – how long it takes to change between input devices e.g. changing between mouse and keyboard
      4. Td : (d)rawing – how long it takes to draw a line using an input such as a mouse
      5. Tb : click (b)utton – how long it takes to click the mouse button
    • Tm : (m)ental operator – how long it takes to perform the mental processing for the task
    • Tr : system (r )esponse operator – how long the system takes to respond

    Therefore, execution time for a task is described in terms of the sum of the operators used. For example, suppose we had typed the sentence the quick fox jumps over the lazy dog. Now we want to insert brown just after quick, using a word processor, and assuming that the current point is at the end of the sentence, we need to perform the following steps:

    1. move hand to mouse
    2. position mouse just after quick
    3. move hand to keyboard
    4. formulate word to insert – brown
    5. type brown
    6. reposition insertion point at end of sentence

    In terms of the KLM the following operators are needed for the above steps:

    1. H (mouse)
    2. P, B
    3. H (keyboard)
    4. M
    5. K (b) K (r) K (o) K (w) K (n)
    6. H (mouse), M, P, B

    So, in total the execution time for this simple task is 3Th + 2Tp + 2Tb + 2Tm + 5Tk (assuming there is no significant response time for the system). Card et al. derived values for the time to complete these operators from empirical studies. These are listed below (for an expert typist), and give a total execution time of 1.2 + 2.2 + 0.4 + 2.7 + 0.6 = 7.1s in this case.

    Operators Time (s)
    Tk 0.12
    Tp 1.10
    Th 0.40
    Td 1.06
    Tb 0.20
    Tm 1.35

    Carrying on with the example in Activity 4, imagine that customers could withdraw money using their personal computer. In this case data entry would be via the keyboard, and selection of options would be done using the mouse. Using KLM, work out the execution time for the activity of withdrawing £10 assuming that both keyboard and mouse are to be used, that the PIN is 1234, that it takes the system 10s to return the card and cash, and that £10 is one of the predetermined amounts listed.

    A Discussion on this activity can be found at the end of the chapter, GOMS provides a way of making predictions about the time an expert user would take to complete a task using a given user interface. Furthermore, as GOMS modeling makes user tasks and goals explicit these descriptions could be usefully re-employed in the development of an on-line help system.

    These descriptions can additionally be used to suggest questions users will ask and the answers in terms of actions needed to complete tasks and meet goals. However, as mentioned several times before, the tasks must be must be goal-directed, that is the user must have a specific aim in mind.

    1. Some activities are more goal-directed than others, but it could be argued that even creative activities contain goal-directed tasks.
    2. Furthermore, GOMS assumes that tasks involve routine cognitive skill as opposed to problem solving, and that no errors occur, which is hardly realistic.
    3. GOMS is a cognitive modelling approach.

    What does it model, and how? Answer at the end of the chapter,
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    What 3 things define successful aging?

    Introduction – In recent years, the concept of successful ageing has induced much debate ( 1 – 3 ), and various definitions of the concept have been introduced in various studies ( 4 ). According to the classic concept of Rowe and Kahn, successful ageing is defined as high physical, psychological, and social functioning in old age without major diseases ( 5, 6 ). The dimensions of successful ageing. Modified from Fernandez-Ballesteros 2019, ( 7 ). The main focus in the concept of successful ageing is how to expand healthy and functional years in the life span ( 8, 9 ). The phenomenon of successful ageing can be viewed from a population or an individual perspective ( 7 ).

    • At the population level, definition includes determinants of health and participation for the purpose of promoting policies, whereas at the individual level it is defined by outcomes of health, physical, and cognitive function, and life involvement ( 7 ).
    • Because, successful ageing is a multidimensional concept encompassing domains of physical, functional, social, and psychological health, all of these dimensions should be taken into account, both with objective and subjective conditions, when studying the phenomenon ( 4, 8, 10, 11 ).

    Kim and Park ( 12 ) conducted a meta-analysis of the correlates of successful ageing and they identified that four domains describing successful ageing were; avoiding disease and disability, having high cognitive, mental and physical function, being actively engage in life, and being psychologically well adapted in later life.

    1. Similarly, in the model of “Aging well” by Fernandez-Ballesteros et al.
    2. 13, 14 ), successful ageing is defined by the domains of health and activities of daily living (ADL), physical and cognitive functioning, social participation and engagement, and also positive affect and control, when the definition by Baltes et colleagues ( 15, 16 ) is also considered.

    Kok et al. ( 18 ) found in their study that many older adults were ageing relatively successfully, but there was a variation between indicators of characters of successful ageing, and the combinations of successful indicators varied also between individuals.

    Most definitions of successful ageing include also outcomes which can be described as the operational definitions of the concept ( 7 ). The operational definitions are generally based on objective measurements of health and functionality and do not necessarily take into account individual’s perceptions of their own health and wellbeing which would give more comprehensive view of ageing ( 4 ).

    Kleinedam and colleagues ( 19 ) have suggested that well-constructed operationalisation of successful ageing includes measurements of physiological health, well-being and social engagement, with subjective and objective aspects. The aim of this brief review is to describe and discuss about conceptual and operational definitions of successful ageing with the multidimensional approach.
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    What are the three pillars of successful aging?

    A New Strategic Approach to Successful Aging and Healthy Aging Successful aging is not a new concept, although its definition remains controversial, because of its multi-dimensional nature. To the biomedical scientist, successful aging is defined by the absence of disease, physical, and cognitive disability,

    1. This is distinct from usual aging, which is associated with age-related decline in physical and cognitive function.
    2. To the social scientist, successful aging emphasizes life satisfaction and personal well-being, usually achieved through socialization,
    3. Healthy aging is a related concept in that it captures the essence of physical and cognitive functional preservation, but without the requirement of disease avoidance,

    Healthy aging is therefore a more inclusive concept, one that more accurately describes more individuals as the population ages. The World Health Organization (WHO) defines healthy aging as the process of developing and maintaining the functional ability that enables well-being in older age,

    Under the WHO conceptual construct, there are four essential requirements for healthy aging: (i) a change in the way we think about aging and older people, (ii) creation of age-friendly environments, (iii) alignment of health systems to the needs of older people, and (iv) development of systems for long-term care,

    These four requirements have clinical, research, and policy implications, therefore lending themselves to form the four pillars of a new strategic approach to healthy aging. To change the way we think about aging, a fundamental pre-requisite is to revisit the theoretical constructs that we use to study aging.

    1. Historically, the focus has been on single-organ diseases.
    2. The emergence of geroscience is one way of transforming our thinking about older people.
    3. Geroscience refers to an inter-disciplinary approach to understanding the genetic, molecular, and cellular mechanisms that make aging a risk factor and driver of chronic health conditions in older people,

    A number of the world-leading aging research institutions have already adopted this innovative approach, in contrast to the traditional single-organ or disease-specific approach. Translational research studies in geroscience have begun to reveal new insights in a number of areas, including genomics (such as the relationship between the Apolipoprotein E allelic composition and longevity), proteomics (such as the role of cytokines in cerebrovascular disease and Alzheimer’s disease), metabolomics (such as the presence of abnormal metabolites in diabetes mellitus and certain cancers), and microbiomics (such as the impact of losing microbiome diversity with increased bacilli and proteobacteria in older people).

    • Geroscience is a new and different strategic approach towards advancing our understanding about healthy aging.
    • Innovative health research and educational platforms should be developed to embrace and support geroscience as an emerging field.
    • The environment is an important determinant of health, and the creation of an age-friendly environment has attracted significant attention in recent years, both in the scientific community and society at large.

    The WHO characterizes these environments as accessible, equitable, inclusive, safe and secure, and supportive, There are macro and micro examples of age-friendly environments. At the macro level, global cities are classified as age-friendly if they perform well under a list of pre-specified core indicators,

    The indicators are in turn organized within a program logic model, with inputs, outputs, outcomes, and impact. At the micro level, personal living spaces can become age-friendly with deliberate considerations to implement the same WHO characteristics as described earlier. An important enabler is technology.

    Innovative examples include the Internet of Things, wearable devices, tablet devices that are community-based and connected to electronic health records, social robots, various forms of decision support, and machine learning, to name a few. Recent efforts have also explored the combination of age-friendly environments and effective healthcare delivery.

    The patient medical home (PMH) is a good example that integrates primary care and community needs, The attributes of the PMH are evidence-based to provide care that is person-centred care, timely, comprehensive, continuous, coordinated, team-based, and subject to quality improvement. Overall, the creation of age-friendly environments provides an essential foundation for healthy aging.

    A natural corollary is the need to assemble inter-disciplinary research and implementation teams that bring together expertise from medicine, engineering, architecture, business, and sociology. The appropriate alignment of health systems to the needs of older people plays a pivotal role in supporting healthy aging.

    1. Many jurisdictions around the world have come a long way in transforming health systems to be more age-friendly, as evidenced in the significant advances and improvements in hospital medicine for older people,
    2. However, transformative changes of this nature are often opportunistic, inconsistent, and not widespread,

    Moreover, gaps in healthcare delivery continue to exist, especially with respect to primary care delivery and at transition points, such as acute hospital care and long-term care, Moving forward, age-friendly health systems should focus on healthy aging and preventive geriatrics.

    The programmatic emphasis should be on primary care geriatrics. While innovation may be disruptive in nature, future health systems should augment connectivity (actual and virtual) between community care, acute care, and long-term care. A system-based approach of quality improvement can be helpful, The process of health systems realignment will take time.

    Proactive and meaningful engagement of older people throughout the process is crucial for success. In addition, there is an opportunity to better integrate health service delivery and research, preferably starting at the point of entry into health systems.

    Last but not least, the development of systems for long-term care is important for healthy aging. Innovative models of long-term care are needed to support aging in place, which refers to the ability to live in a person’s own community safely, independently, and comfortably, regardless of age, income, or ability level,

    This is the aspiration and desire of older people who are aging healthily. Long-term care system improvements should therefore address how customized services can be deployed in the community when individual health needs change, often as a result of physical and/or cognitive functional decline.

    Effective interventions are likely multi-component in nature and require inter-professional delivery, although it might be difficult to delineate the exact benefit conferred by each individual intervention component, Data science can be a helpful enabler. Health informatics (“big data”) can incorporate longitudinal personal health data from population-based electronic health records to predict important health outcomes.

    The abundance of population-based physiologic data would also allow deep learning or machine learning to take place, so that each older person can be risk-stratified in terms of the person’s own level of frailty and risks of developing various adverse events, such as the common geriatric issues of delirium and falls.

    1. This article highlights the four pillars of a new strategic approach to healthy aging.
    2. To ensure its successful implementation, stakeholder education is the key,
    3. This includes education for health professionals, researchers, policy decision makers, and most importantly, the general public.
    4. A recent study suggests that the perception of aging in the community can influence the effect of healthy aging programs,

    Education can play an important role in empowering people and altering perception. Healthy aging is important for all of us and it touches every one of us. Act before it is too late. This research received no external funding. The author declares no conflict of interest.1.

    Rowe J.W., Kahn R.L. Successful Aging. Pantheon Books; New York, NY, USA: 1998.2. Havighurst R.J. Successful aging. In: Williams R.H., Tibbits C., Donahue W., editors. Process of Aging. Atheron Press; New York, NY, USA: 1963. pp.299–320.3. McLaughlin S.J., Jette A.M., Connell C.M. An examination of healthy aging across a conceptual continuum prevalence estimates, demographic patters, and validity.J.

    Gerontol. A Biol. Sci. Med. Sci.2012; 67 :783–789. doi: 10.1093/gerona/glr234.4. What Is Healthy Ageing. ; Available online: 5. Ageing and Health. ; Available online: 6. Bowling A., Dieppe P. What is successful ageing and who should define it? BMJ.2005; 331 :1548–1551.

    1. Doi: 10.1136/bmj.331.7531.1548.7.
    2. Healthy Aging in Canada: A New Vision, a Vital Investment.
    3. Available online: 8.
    4. Geroscience: The Intersection of Basic Aging Biology, Chronic Disease, and Health.
    5. Available online: 9.
    6. Zierer J., Menni C., Kastenmüller G., Spector T.D.
    7. Integration of ‘omics’ data in aging research: From biomarkers to systems biology.

    Ageing Cell.2015; 14 :933–944. doi: 10.1111/acel.12386.10. Age-Friendly Environments. ; Available online: 11. World Health Organization, Measuring the Age-Friendliness of Cities: A Guide to Using Core Indicators. WHO Press; Geneva, Switzerland: 2015. Available online: 12.

    Patient Medical Homes. ; Available online: 13. Wong R.Y.M. Older people presenting to acute care hospitals. In: Michel J.-P., Beattie B.L., Martin F.C., Walston J.D., editors. Oxford Textbook of Geriatric Medicine. Oxford University Press; Oxford, UK: 2018. pp.247–254.14. Wong R.Y. Strategies to promote broad-based implementation of Acute Care for Elders (ACE) units.

    Geriatrics.2018; 3 :58. doi: 10.3390/geriatrics3030058.15. Wong R.Y. Improving health care transitions for older adults through the lens of quality improvement.J. Am. Med. Dir. Assoc.2013; 14 :637–638. doi: 10.1016/j.jamda.2013.05.014.16. Wong R.Y. Clinical burden, quality of care, organizational context: Different lenses to optimize care for older people.J.

    Am. Med. Dir. Assoc.2015; 16 :444–445. doi: 10.1016/j.jamda.2015.03.001.17. Health Places Terminology. ; Available online: 18. Wong R.Y. Attributing the benefits of individual components in a multi-component intervention: A familiar challenge in comprehensive geriatric care.J. Am. Med. Dir. Assoc.2014; 15 :381–382.

    doi: 10.1016/j.jamda.2014.03.018.19. Madden K., Wong R. The health of Geriatrics in Canada—More than meets the eye. Can. Geriatr.J.2013; 16 :1–2. doi: 10.5770/cgj.16.75.20. Mendoza-Núñez V.M., Sarmiento-Salmorán E., Marín-Cortés R., Martínez-Maldonado M.L., Ruiz-Ramos M.
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    Which theory of aging is most correct?

    The most widely accepted overall theory of aging is the evolutionary senescence theory of aging.
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    What did the Seattle Longitudinal Study reveal about the decline of intelligence in older adults?

    The Seattle Longitudinal Study of Adult Intelligence The Seattle Longitudinal Study of Adult Intelligence has followed a group of more than 5000 people for well over four decades. The program began in 1956 and participants have been tested across a whole gamut of mental and physical abilities at seven year intervals since that date. The study has found:

    • no uniform pattern of age-related change across all intellectual abilities
    • some support for the idea that abilities that are primarily genetically determined tend to decline earlier than abilities that are primarily acquired through schooling or experience (although there may be gender differences here)
      • although abilities that are primarily genetic may decline earlier, abilities acquired through training decline more steeply after late 70s the change in perceptual speed begins in young adulthood and declines in a linear fashion (that is, the rate of decline is constant)
      • the rate and magnitude changes in intelligence seen in those entering old age showed greater decline in the 1 st 3 cycles (till 1970); at the same time, younger members are scoring lower on tests at the same age.
      • a decline in psychometric abilities is not reliably observed before 60, but is reliably observed by 74. However, even by 81, fewer than half showed reliable decrements over the past seven years.
      • the size of this decline however is significantly reduced when age changes in perceptual speed are taken into account.
      • substantial cohort / generational differences have been observed. Later-born groups have attained successively higher scores at the same ages for inductive reasoning, verbal meaning, and spatial orientation; however, they’ve scored successively lower in number skill and word fluency (number skill peaked with the 1924 cohort). These changes presumably reflect educational changes.
      • substantial similarity between parents and their adult children and between siblings has been found for virtually all mental abilities and measures of flexibility (the exceptions are the attitude measure of social responsibility, and a measure of perceptual speed). The magnitude of similarity varied for different abilities, and was closer between parent & child than between siblings.
      • the following variables may reduce the risk of cognitive decline in old age:
        • absence of chronic diseases
        • a complex and intellectually stimulating environment
        • a flexible personality style at mid-life
        • high intellectual status of spouse
        • maintenance of high levels of perceptual processing speed
      • cognitive training studies suggested that the observed decline in many community-dwelling older people is probably a function of disuse and is often reversible. Some 2/3 of participants in a cognitive training program showed significant improvement, and 40% of those who had declined significantly were indeed returned to their earlier (pre-decline) level of cognitive functioning. These training gains were retained over seven years.

    , K. Warner 1998. The Seattle Longitudinal Studies of adult intelligence. In M. Powell Lawton & Timothy A. Salthouse (eds) Essential papers on the psychology of aging. NY: NY Univ Pr. Pp263-271.

    : The Seattle Longitudinal Study of Adult Intelligence
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    What were the results of the Seattle Longitudinal Study quizlet?

    The Seattle Longitudinal Study found that most differences from cross-sectional research could be attributed to cohort membership (cohort effects, years of education, experience doing work involving cognitive vs. physical activity).
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    What is one of limitations of longitudinal research studies?

    Disadvantages of longitudinal studies –

    1. Research time The main disadvantage of longitudinal surveys is that long-term research is more likely to give unpredictable results. For example, if the same person is not found to update the study, the research cannot be carried out. It may also take several years before the data begins to produce observable patterns or relationships that can be monitored.
    2. An unpredictability factor is always present It must be taken into account that the initial sample can be lost over time. Because longitudinal studies involve the same subjects over a long period of time, what happens to them outside of data collection times can influence the data that is collected in the future. Some people may decide to stop participating in the research. Others may not be in the correct demographics for research. If these factors are not included in the initial research design, they could affect the findings that are generated.
    3. Large samples are needed for the investigation to be meaningful To develop relationships or patterns, a large amount of data must be collected and extracted to generate results.
    4. Higher costs Without a doubt, the longitudinal survey is more complex and expensive. Being a long-term form of research, the costs of the study will span years or decades, compared to other forms of research that can be completed in a smaller fraction of the time.

    The advantages and disadvantages of longitudinal studies show us that there is enormous value in the ability to find long-term patterns and relationships, so it is important to plan and take the necessary steps to avoid potential bias.
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    What is the main importance of longitudinal studies in the study of personality?

    What is the main importance of longitudinal studies in the study of personality? – its focus on a search for meaning in life. – using logic to determine life choices.
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    What did the Seattle Longitudinal Study reveal about the decline of intelligence in older adults?

    The Seattle Longitudinal Study of Adult Intelligence The Seattle Longitudinal Study of Adult Intelligence has followed a group of more than 5000 people for well over four decades. The program began in 1956 and participants have been tested across a whole gamut of mental and physical abilities at seven year intervals since that date. The study has found:

    • no uniform pattern of age-related change across all intellectual abilities
    • some support for the idea that abilities that are primarily genetically determined tend to decline earlier than abilities that are primarily acquired through schooling or experience (although there may be gender differences here)
      • although abilities that are primarily genetic may decline earlier, abilities acquired through training decline more steeply after late 70s the change in perceptual speed begins in young adulthood and declines in a linear fashion (that is, the rate of decline is constant)
      • the rate and magnitude changes in intelligence seen in those entering old age showed greater decline in the 1 st 3 cycles (till 1970); at the same time, younger members are scoring lower on tests at the same age.
      • a decline in psychometric abilities is not reliably observed before 60, but is reliably observed by 74. However, even by 81, fewer than half showed reliable decrements over the past seven years.
      • the size of this decline however is significantly reduced when age changes in perceptual speed are taken into account.
      • substantial cohort / generational differences have been observed. Later-born groups have attained successively higher scores at the same ages for inductive reasoning, verbal meaning, and spatial orientation; however, they’ve scored successively lower in number skill and word fluency (number skill peaked with the 1924 cohort). These changes presumably reflect educational changes.
      • substantial similarity between parents and their adult children and between siblings has been found for virtually all mental abilities and measures of flexibility (the exceptions are the attitude measure of social responsibility, and a measure of perceptual speed). The magnitude of similarity varied for different abilities, and was closer between parent & child than between siblings.
      • the following variables may reduce the risk of cognitive decline in old age:
        • absence of chronic diseases
        • a complex and intellectually stimulating environment
        • a flexible personality style at mid-life
        • high intellectual status of spouse
        • maintenance of high levels of perceptual processing speed
      • cognitive training studies suggested that the observed decline in many community-dwelling older people is probably a function of disuse and is often reversible. Some 2/3 of participants in a cognitive training program showed significant improvement, and 40% of those who had declined significantly were indeed returned to their earlier (pre-decline) level of cognitive functioning. These training gains were retained over seven years.

    , K. Warner 1998. The Seattle Longitudinal Studies of adult intelligence. In M. Powell Lawton & Timothy A. Salthouse (eds) Essential papers on the psychology of aging. NY: NY Univ Pr. Pp263-271.

    : The Seattle Longitudinal Study of Adult Intelligence
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    What were the results of the New York longitudinal study?

    The New York Longitudinal Study found that nine traits influenced temperament. Each trait had a range of intensity, and the strength of the traits combined to create the child’s unique temperament.
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    What did the Seattle Longitudinal Study discover regarding peoples intelligence as they age quizlet?

    What did the Seattle Longitudinal Study discover regarding people’s intelligence as they age? It remains the same until around 67, when there is a slight drop, and then a deeper drop after 80.
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    What did researchers in the bridge experiment conclude?

    Researchers in the bridge experiment concluded that: People can misattribute what caused their emotions. What Is Psychology?
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