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Intelligence Trajectories in Adolescents and Adults with Down Syndrome: Cognitively Stimulating Leisure Activities Mitigate Health and ADL Problems

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Title: Intelligence Trajectories in Adolescents and Adults with Down Syndrome: Cognitively Stimulating Leisure Activities Mitigate Health and ADL Problems
Language: English
Authors: Lifshit, Hefziba Batya (ORCID 0000-0002-4185-3945), Bustan, Noa, Shnitzer-Meirovich, Shlomit
Source: Journal of Applied Research in Intellectual Disabilities. Mar 2021 34(2):491-506.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 16
Publication Date: 2021
Document Type: Journal Articles
Reports - Research
Descriptors: Down Syndrome, Adolescents, Adults, Health Behavior, Life Style, Leisure Time, Intelligence, Age Differences, Cognitive Development, Intelligence Tests, Health, Correlation
Assessment and Survey Identifiers: Block Design Test, Raven Progressive Matrices, Wechsler Adult Intelligence Scale
DOI: 10.1111/jar.12813
ISSN: 1360-2322
Abstract: Goals: This study examined: (a) crystallized/fluid intelligence trajectories of adolescents and adults with Down syndrome; and (b) the contribution of endogenous (health, activities of daily living--ADL) and exogenous (cognitively stimulating leisure activities) factors on adults' intelligence with age. Method: Four cohorts (N = 80) with Down syndrome participated: adolescents (ages 16-21) and adults (ages 30-45, 46-60 and 61+). All completed Vocabulary and Similarities (crystallized) and Block Design and Raven (fluid) intelligence tests (WAIS-IIIHEB, Wechsler, 2001). Results: The 30-45 cohort significantly outperformed the 16-21 cohort. Except for Vocabulary, which remained stable, onset of decline was at 40-50. Age-related declining health and ADL correlated with participants' lower fluid intelligence, but cognitive leisure activities mitigated this influence. Conclusions: Intelligence development into adulthood supported the continuous trajectory and compensation age theory, rather than accelerated or stable trajectories. Not only endogenous factors but also exogenous factors determined intelligence levels in adults with Down Syndrome, supporting cognitive activity theory.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1284961
Database: ERIC
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  Value: <anid>AN0148517631;e0301mar.21;2021Feb08.02:36;v2.2.500</anid> <title id="AN0148517631-1">Intelligence trajectories in adolescents and adults with down syndrome: Cognitively stimulating leisure activities mitigate health and ADL problems </title> <p>Goals: This study examined: (a) crystallized/fluid intelligence trajectories of adolescents and adults with Down syndrome; and (b) the contribution of endogenous (health, activities of daily living—ADL) and exogenous (cognitively stimulating leisure activities) factors on adults' intelligence with age. Method: Four cohorts (N = 80) with Down syndrome participated: adolescents (ages 16–21) and adults (ages 30–45, 46–60 and 61+). All completed Vocabulary and Similarities (crystallized) and Block Design and Raven (fluid) intelligence tests (WAIS‐IIIHEB, Wechsler, 2001). Results: The 30–45 cohort significantly outperformed the 16–21 cohort. Except for Vocabulary, which remained stable, onset of decline was at 40–50. Age‐related declining health and ADL correlated with participants' lower fluid intelligence, but cognitive leisure activities mitigated this influence. Conclusions: Intelligence development into adulthood supported the continuous trajectory and compensation age theory, rather than accelerated or stable trajectories. Not only endogenous factors but also exogenous factors determined intelligence levels in adults with Down syndrome, supporting cognitive activity theory.</p> <p>Keywords: adolescents and adults with Down syndrome; cognitively stimulating leisure activities; crystallized and fluid intelligence; trajectories</p> <hd id="AN0148517631-2">INTRODUCTION</hd> <p>Down syndrome is the most common genetic cause (Bayen, Possin, Chen, Cleret de Langavant, & Yaffe, 2018; Silverman, 2007) of intellectual disability (ID), although individuals with this aetiology can vary considerably in their disability levels and IQ scores (Vicari, Bellucci, & Giovanni, 2006). In the early 20th century, persons with Down syndrome were documented with high mortality rates and cognitive decline leading to dementia even in their 30s and 40s (Wisniewski, Wisniewski, & Wen, 1985). Over the last century, improvements in medical care, nutrition and public health policy led to these individuals' increased life expectancy, even up to their 70s and 80s (Bayen et al., 2018; Heller, Scott, & Janicki, 2018). Such individuals may now experience good health and successful ageing (Krinsky‐McHale et al., 2008; Wiseman et al., 2015), without cognitive deterioration (Chicoine & McGuire, 1997). However, research methodologies and findings have been mixed regarding the developmental trajectories of intelligence over the lifespan in persons with Down syndrome as well as about the impact of health, daily adaptive functioning and lifestyle on these trajectories.</p> <p>The current study sought to deepen investigation into these intelligence trajectories as well as their possible associations with personal and environmental factors. A body of research is available on the efficacy of mediated, directed interventions aiming to improve specific cognitive functions or to offset age‐related decline in adults with ID, including those with Down syndrome (e.g. see Heller, Miller, Hsieh, & Stern, 2000; Lifshitz, Weiss, Tzuriel, & Tzemach, 2010; and review in Lifshitz, 2020). In contrast, less research is available on unmediated cognitively stimulating leisure activities as they may contribute to crystallized and fluid intelligence in adults with Down syndrome.</p> <p>Specifically, this study aimed: (a) to trace the development of crystallized and fluid intelligence from adolescence into adulthood, and through different stages of adulthood, in order to scrutinize patterns of growth, stability and decline in intelligence measures among individuals with Down syndrome; and (b) to look at the possible role played by cognitively stimulating life experiences (i.e. leisure activities) in mitigating the potentially detrimental effects of age‐related changes in health and adaptive functioning on intelligence in this population.</p> <hd id="AN0148517631-3">Crystallized and fluid abilities in down syndrome</hd> <p>Down syndrome is characterized by an uneven cognitive profile, with greater difficulties in verbal skills including verbal memory, alongside more preserved visuo‐spatial abilities (Vicari et al., 2006) with great variability between individuals with down syndrome themselves. Persons with this aetiology of ID exhibit delays in language development and difficulties in expressive and receptive vocabulary, syntax, morphology and pragmatic language; however, they demonstrate relative strengths in comprehension, non‐verbal learning and memory, and spatial abilities (Godfrey & Raitano, 2018; Grieco, Pulsifer, Seligsohn, Skotko, & Schwartz, 2015).</p> <p>Crystallized and fluid intelligence markers were redefined by McGrew (2009) based on the Horn–Cattell model (Cattell, 1943, 1987; Horn & Cattell, 1967). Verbal IQ, interpreted as a good measure of crystallized intelligence (Kaufman, 2001), is defined as "a person's acquired knowledge of the language, information and concepts of a specific culture" (Cattell, 1943, p. 5). Thus, crystallized intelligence is considered a "maintained" ability, which in the general population increases into one's 60s and 70s (Góngora, Vega‐Hernández, Jahanshahi, Valdés‐Sosa, & Bringas‐Vega., 2020; Kaufman, 2001; McGrew, 2009; Rabbitt, 2016; Schaie, Willis, & Pennak, 2005) and then declines. For example, the Vocabulary and the Similarities subscales of the Wechsler Abbreviated Intelligence Scale (WAIS) IQ test (Wechsler, 2001) may serve as indices of crystallized intelligence.</p> <p>Performance IQ, interpreted as a good measure of fluid intelligence (Kaufman, 2001), is defined as "the use of deliberate and controlled mental operations to solve novel problems that cannot be performed automatically" (Cattell, 1943, p. 5). Thus, fluid intelligence is considered a "vulnerable" ability, which in the general population peaks in one's early 20s and then declines (Kaufman, 2001; McGrew, 2009). It is associated with frontal executive functions (Kaufman, 2001), working memory and analogy/metaphor understanding. For example, the Block Design subscale of the WAIS IQ test (Wechsler, 2001) and the Raven Standard Progressive Matrices (Raven, Raven, & Court, 1998) may serve as indices of fluid intelligence.</p> <hd id="AN0148517631-4">Models of growth, stability and decline in intelligence over the lifespan</hd> <p>In the general population, the trajectory of intelligence (the full IQ including both crystallized and fluid intelligence) reveals steady development from childhood and into early adulthood, reaching its peak level in the 20s, when a plateau of stability ensues that lasts until the onset of decline at ages 50–60 (Horn & Cattell, 1967; Kaufman, 2001). For populations with ID, including Down syndrome, scholars have proposed three possible intelligence trajectories compared to typical populations: accelerated, stable or continuous (Fisher & Zeaman, 1970; Lifshitz, 2020; Lifshitz‐Vahav, 2015).</p> <p>Fisher and Zeaman's (1970) <emph>accelerated trajectory</emph> model predicted a slow, restricted development of intelligence into the 20s, stability thereafter and then accelerated (steep) decline already starting in the mid‐30s or 40s. For example, reviewing studies from the 1970s among adults with Down syndrome, during a period of shorter life expectancy for these individuals (~30–50 years), Carr (2005) reported a 17%–29% decline in crystallized intelligence from age 20 to 49, whereas fluid intelligence measures declined by half an IQ point per year.</p> <p>The <emph>stable trajectory</emph> model predicted that intelligence of individuals with Down syndrome will reach its peak in their 20s, with a longer plateau of stability than in the accelerated trajectory, until the onset of decline at ages 50–60 (Fisher & Zeaman, 1970). Supporting the stable model, for example, Facon (2008) found that the increase in crystallized intelligence noted from the 20s to the 50s in adults with non‐specific ID was similar to that of adults in the general population. However, in both groups, fluid intelligence decreased from the 20s.</p> <p>The <emph>continuous trajectory</emph> model predicted that instead of becoming stable in the 20s, intelligence in adults with ID will continue to grow for a longer period, until the late 40s, demonstrating stability only in the 50s and then declining only around the 60s (Fisher & Zeaman, 1970). Supporting the continuous model, major research evidence as early as the 1950s on individuals with ID suggested that intelligence is not static but rather is capable of rising in the adult years and that this improvement is associated with changes in environment such as comparing less adverse to very adverse environments (Clarke & Clarke, 1954). For example, in a longitudinal study, Devenny et al. (1996) found that adults with Down syndrome (aged 27–63) continued improving their verbal long‐term memory scores (a fluid intelligence test) until their 50s and only declined thereafter. Cherry, Njardvik, and Dawson (2000) reported better long‐term memory performance by adults with ID (ages 34–54 years) compared to typically developing adults with the same mental age, supporting a compensatory effect with age in adults with ID up to their 50s.</p> <hd id="AN0148517631-5">Comparing Intelligence in Adolescence versus Adulthood for Down Syndrome</hd> <p>Tracing developmental trajectories of intelligence between childhood and early adulthood has been of interest in the professional literature since the 1940s and 1950s (Clarke & Clarke, 1954; Clarke, Clarke, & Reiman, 1958; Fisher & Zeaman, 1970; Thorndike, 1940) regarding populations with or without ID. Prior cross‐sectional studies, without any direct cognitive intervention, have demonstrated higher intelligence functioning and working memory among adults with ID compared to adolescents with ID who were matched by their standard IQ scores (Chen, Lifshitz, & Vakil, 2017; Lifshitz, Kilberg, & Shnitzer‐Meirovich, 2019), in line with the aforementioned continuous growth trajectory. This advantage found for adults over IQ‐matched adolescents with ID implicates possible cognitive growth processes occurring as a result of maturity and cumulative life experiences between the adolescent and adult time periods, namely, the "compensation age theory" (Lifshitz, 2020; Lifshitz‐Vahav, 2015). Accordingly, as they grow and mature into early adulthood, individuals with ID appear to continue attaining skills that were previously absent from their behavioural repertoire (Facon, 2008; Lifshitz, 2020; Lifshitz‐Vahav, 2015).</p> <p>Indeed, examining adolescents versus adults with non‐specific ID (IQ = 50–70), Chen et al. (2017) found a continuous cognitive growth trajectory from adolescence (ages 16–21) to adulthood (ages 25–40) on two measures of crystallized intelligence (raw scores on the WAIS Vocabulary and Similarities tests, Wechsler, 2001) and likewise on two standard measures of fluid intelligence (raw scores on the Raven matrices, Raven et al., 1998, and on the WAIS Block Design test, Wechsler, 2001). However, Chen et al. focused only on adolescents and adults with non‐specific ID aetiologies. Examining individuals with non‐specific ID and with Down syndrome, Lifshitz et al. (2019) reported a similar pattern of growth from adolescence (16–21) to adulthood (30–45) on phonological and verbal working memory tasks that resembled crystallized intelligence. In addition, parental survey data have highlighted large growth in cognitive functioning (e.g. reading and even some basic arithmetic skills) among some youngsters with Down syndrome into their 20s and 30s (de Graaf, Levine, Goldstein, & Skotko, 2019); however, that survey did not examine intelligence.</p> <hd id="AN0148517631-6">Tracing intelligence trajectories across adulthood in down syndrome</hd> <p>Following investigation of possible growth in intelligence measures from adolescence into adulthood, this study's next aim was to empirically examine the pattern of possible continued growth and then stability and then decline in intelligence across the stages of adulthood in individuals with Down syndrome. In line with the proposed continuous cognitive growth trajectory (Fisher & Zeaman, 1970) and compensation age theory (Lifshitz, 2020; Lifshitz‐Vahav, 2015), the intelligence scores of adults with Down syndrome may be expected to grow during the adulthood period and even at advanced ages. Genetic and brain studies suggest that the notion of a compensation mechanism with increasing age may be of particular relevance for adults with Down syndrome (Head, Lott, Patterson, Doran, & Haier, 2007). Genes that are overexpressed in Down syndrome (APP, DSCAM, MNB/DYRK1A, RCAN1) may lead to cognitive deficits at younger ages, but paradoxically, with ageing, they produce proteins critical for neuron and synapse growth, development and maintenance, and may participate in molecular cascades supporting neuronal compensation (Ghezzo et al., 2014).</p> <p>However, the rates and ages at onset of cognitive decline in adults with Down syndrome are controversial. Some research has shown that although not all adults with Down syndrome show behavioural evidence of Alzheimer's, all of those who are 40 years of age or older show neuropathological features consistent with the disease, including early formation of senile plaques and neurofibrillary tangles (Hithersay et al., 2019). This association was attributed to triplication of the gene for the beta‐amyloid precursor protein (β‐APP), which is located on the proximal part of the long arm of chromosome 21 (Goldgaber, Lerman, McBride, Saffiotti, & Gajdusek, 1987; Wiseman et al., 2015). Hawkins, Eklund, James, and Foose (2003) and Heller et al. (2018) also pointed to the 50s as the age of onset of decline among individuals with Down syndrome. In a longitudinal study, Carr and Collins (2018) found that 5 out of 27 individuals seen at the age of 50 (18%) exhibited dementia. Strydom, Chan, King, Hassiotis, and Livingston (2013) reported that incidence rates were high for those age 60+ (54.6/1000 person‐years) and highest for ages 70–74 (97.8/1000 person‐years).</p> <p>In contrast, in a 6‐year longitudinal study on 91 adults with Down syndrome aged 31–63, only 4.4% showed mental status changes leading to an Alzheimer's prognosis (Devenny et al., 1996). In a recent study by Fortea et al. (2020), prodromal Alzheimer's disease was manifested at a median age of 50.2 years, and Alzheimer's disease dementia was manifested at a median of 53.7 years. However, this study included only persons with mild or moderate disability who had clinical signs of Alzheimer's (apolipoprotein E allele carrier status in plasma or in PET). In the same study, there were persons with ID who did not show signs of dementia until their 60s.</p> <p>Sinai et al. (2018) demonstrated that dementia's prevalence in persons with Down syndrome doubled every 5 years up to age 60, reaching 32.1% of individuals in the 55–59 age bracket, but decreased after age 60. Strydom, Dickinson, Shende, Pratico, and Walker (2009) estimated 13.1% prevalence of dementia from age 60+ and 18.3% at age 65+, whereas Bayen et al. (2018) reported a 49% prevalence after age 65, and McCarron, McCallion, Reilly, and Mulryan (2014) reported 90% prevalence by age 65. These mixed rates and onsets for dementia in persons with Down syndrome stem from different sample sizes, assessment tools and cultural environments (Silverman, Zigman, Krinsky‐Mchale, Ryan, & Schupf, 2013; Sinai et al., 2018), calling for further empirical investigation.</p> <hd id="AN0148517631-7">The Impact of Endogenous Factors on Intelligence in Adults with Down Syndrome</hd> <p>Our next study aim was to examine ageing‐related endogenous factors such as gradual declines in physical health and adaptive behaviours across adulthood (World Health Organization—WHO, 2016) as they may impact the cognitive ability of adults with Down syndrome. Adults with Down syndrome experience the same age‐associated health problems as elderly adults without ID, but there is disagreement as to age at onset of decline (Heller, 2017; Heller et al., 2018; Jahoda et al., 2015). For example, the WHO (2018) determined the formal onset of "old age" in persons with ID as age 50, whereas Bayen et al. (2018) pinpointed 58 years of age.</p> <p>Ageing‐related health problems typically include declines in vision, hearing and mobility (Evenhuis, 1997; Haveman et al., 2009; Lifshitz, Merrick, & Morad, 2008). Other health issues may include hypertension, heart disease, diabetes, gastrointestinal diseases, bowel and bladder incontinence, skeletal disorders, immune system problems and cancer (Alcedo, Fontanil, Solís, Pedrosa, & Aguado, 2017; Haveman et al., 2009; Lifshitz et al., 2008).</p> <p>Adaptive behaviour is a collection of learned conceptual, social and practical skills that people perform in everyday life (American Psychiatric Association, 2013). Adaptive behaviour deficits are at the core of ID definitions and classifications. Deficits in adaptive functioning result in failure to meet developmental and sociocultural standards for personal and social independence. According to the AAIDD (2002; Luckasson et al., 2002) and the <emph>DSM‐5</emph> (American Psychiatric Association, 2013), adaptive behaviour consists of three major skill domains: communication‐ conceptual skills (4 components), social participation skills (9 components) and independent living practical skills (10 components). The independent living domain is further divided into two parts: activities of daily living (ADL; 4 skills) and instrumental activities (6 skills that include leisure time skills and volunteer and paid work) across multiple environments, such as home, school, work and recreation.</p> <p>In adults with Down syndrome, declines in adaptive behaviour have been reported as associated with dementia of the Alzheimer's type (Devenny et al., 1996; Devenny, Hill, Patxot, Silverman, & Wisniewski, 1992; Silverman, Zigman, Kim, Krinsky‐McHale, & Wisniewski, 1998; Zigman et al., 2004). There is disagreement as to the age at onset of such decline; early researchers pointed to age 30 (White, 1969), while others reported age 40 or 50 (Carr & Collins, 2018; Hithersay, Hamburg, & Knight, 2017; Lifshitz et al., 2008; Strydom et al., 2009). Fortea et al. (2020) indicated that the preclinical biomarkers of Alzheimer's disease appear among persons with Down syndrome along the two decades prior to the appearance of clinical signs (at around ages 30–40). This suggests that declines in ADL may occur slowly from the 30s among persons with ID. On the other hand, Gunzburg (1968) claimed that the maturity and life experiences of adult and elderly persons with ID could help them acquire previously lacking basic skills that had been absent from their behaviour repertoire. McGreevey (2019) and de Graaf et al. (2019) also asserted that functional skills (e.g. eating, preparing meals, reading, writing, preparing meals, working and living skills) can be achieved and enhanced well into adulthood. Our study examined the impact of decline in health and adaptive behaviour on crystallized and fluid intelligence during adulthood.</p> <hd id="AN0148517631-8">The impact of exogenous factors on intelligence in adults with down syndrome</hd> <p>In modern gerontology for the general population, <emph>cognitive activity theory</emph> postulates that participation in cognitive activities during midlife has short‐term effects on current cognitive functioning as well as long‐term effects reducing the risk of cognitive decline leading to Alzheimer's disease (Marquinea, Segawa, Wilson, Bennett, & Barnes, 2012; Rosenberg, Mangialasche, Ngandu, Solomon, & Kivipelto, 2020; Wilson & Bennett, 2003). Although the mechanisms underlying these associations are not well understood, cognitively stimulating activity has been posited to contribute to "cognitive reserve" (Stern, 2012). The viewpoint is on the rise that "the adult brain is adaptive at any age and has lifelong capacity for change" (Mahncke et al., 2006, p. 12,524). Citing the "use it or lose it" phrase, Zhu, Bao, Swaab, and Neurosci (2019) proposed that by stimulating the brain, repair mechanisms are stimulated and "cognitive reserve" is increased, thereby reducing the risk of dementia and decreasing rates of cognitive decline. Zhu et al. pinpointed stimulating experiences such as bilingualism/multilingualism, education, occupation, musical experience, physical exercise and leisure activities. Even listening to the radio or watching TV demands some information processing to understand broadcasters' messages; associate them with past, present and future events; and draw conclusions. Moreover, music or auditory stimulation might affect the listener's attention, auditory discrimination, working memory or executive function (Gooding, Abner, Jicha, Kryscio, & Schmitt, 2014; Kim & Kim, 2014; Phillips, 2017). Painting might involve visual discrimination; understanding of colour, shape and genre; and spatial orientation.</p> <p>In a longitudinal study, healthy older adults (age 65+) who reported very frequent cognitively stimulating activities maintained their earlier cognitive testing scores (e.g. memory, word fluency and digit recitation) exhibiting half the risk of developing Alzheimer's disease compared to those with less frequent participation in cognitively stimulating activities (Marquinea et al., 2012; Wilson & Bennett, 2003). In a meta‐analysis of 19 studies, Yates, Ziser, Spector, and Orrell (2016) identified the same trends. Lifshitz‐Vahav, Shnitzer, and Mashal (2015) found that participation in cognitively stimulating activities such as reading, watching TV, using technological devices and playing checkers made a significant contribution to the explained variance in most cognitive tests among individuals with Down syndrome. The current study expanded investigation of participation in cognitively stimulating activities to examine their impact on intelligence tests.</p> <hd id="AN0148517631-9">The current study</hd> <p>The research objectives and hypotheses for individuals with Down syndrome were threefold: (a) to examine the trajectory of crystallized and fluid intelligence from adolescence (ages 16–21) to adulthood (ages 30–45). Based on compensation age theory (Lifshitz‐Vahav, 2015), the present authors anticipated a continuous trajectory: higher scores at ages 30–45 compared to 16–21 on both crystallized and fluid intelligence tests; (b) to investigate the trajectory of crystallized and fluid intelligence within adulthood (30–45, 46–60 and 61+). Based on mixed prior results regarding cognitive change in adults with Down syndrome from middle adulthood (age 46+), the present authors anticipated stability between ages 30 and 60 and decline only from 61+; and (c) to examine change in health status and ADL functioning with age and the contribution of endogenous factors (chronological age, IQ, health status and ADL functioning) and exogenous factors (participation in cognitively stimulating leisure activities) on the crystallized and fluid intelligence of the adult age cohorts (ages 30–45, 46–60 and 61+). Based on cognitive activity theory (Wilson & Bennett, 2003), the present authors anticipated that participating in cognitively stimulating leisure activities would be associated with higher raw scores for crystallized and fluid intelligence.</p> <hd id="AN0148517631-10">METHOD</hd> <p></p> <hd id="AN0148517631-11">Participants</hd> <p>The sample (<emph>N</emph> = 80) included four age cohorts of individuals with Down syndrome, comprising 20 participants per cohort, who were matched for age‐standardized IQ scores: "adolescents" (ages 16–21 years), "younger adults" (ages 30–45 years), "middle‐age adults" (ages 46–60 years) and "older adults" (ages 61+). The oldest participant was 69. Table 1 presents descriptive data for the four cohorts' ages and standardized IQ scores. Mean IQ for the sample was 64.35 (<emph>SD</emph> = 4.98), with no significant differences between the age cohorts, <emph>F</emph>(<reflink idref="bib3" id="ref1">3</reflink>,<reflink idref="bib76" id="ref2">76</reflink>) = 1.41, <emph>p</emph> =.25, η<emph><subs>p</subs></emph><sups>2</sups> = 0.05. Participants included 42 females (52.5%) and 38 males (47.5%), with no significant sex differences between age cohorts, <emph>χ</emph><sups>2</sups>(<reflink idref="bib3" id="ref3">3</reflink>) = 1.00, <emph>p</emph> =.80.</p> <p>1 TableMeans, standard deviations and significance values for age, prior tested IQ and current intelligence test raw scores by age (N = 80)</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Measure</th><th align="left">Age cohort (<italic>n</italic> = 20 each)</th><th align="left"><italic>F</italic></th><th align="left"><italic>p</italic></th><th align="left">η<italic><sub>p</sub><sup>2</sup></italic></th><th align="left"><italic>Scheffe</italic></th></tr><tr><th align="left"><p>Adolescents</p><p>(16–21)</p></th><th align="left"><p>Younger</p><p>adults (30–45)</p></th><th align="left">Middle‐aged adults (46–60)</th><th align="left"><p>Older</p><p>adults (61+)</p></th></tr><tr><th align="left"><italic>M</italic></th><th align="left"><italic>SD</italic></th><th align="left"><italic>M</italic></th><th align="left"><italic>SD</italic></th><th align="left"><italic>M</italic></th><th align="left"><italic>SD</italic></th><th align="left"><italic>M</italic></th><th align="left"><italic>SD</italic></th></tr></thead><tbody><tr><td align="left">Age</td><td align="char" char=".">18.45</td><td align="char" char=".">2.11</td><td align="char" char=".">36.65</td><td align="char" char=".">5.92</td><td align="char" char=".">50.20</td><td align="char" char=".">3.36</td><td align="char" char=".">63.85</td><td align="char" char=".">3.05</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /><td align="left" /></tr><tr><td align="left">Prior standardized IQ test</td><td align="char" char=".">65.05</td><td align="char" char=".">4.08</td><td align="char" char=".">65.75</td><td align="char" char=".">3.60</td><td align="char" char=".">63.80</td><td align="char" char=".">5.30</td><td align="char" char=".">62.80</td><td align="char" char=".">6.31</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /><td align="left" /></tr><tr><td align="left">Vocabulary<xref ref-type="fn" rid="tfn2" /></td><td align="char" char=".">11.65</td><td align="char" char=".">4.27</td><td align="char" char=".">13.10</td><td align="char" char=".">5.07</td><td align="char" char=".">10.50</td><td align="char" char=".">3.89</td><td align="char" char=".">10.15</td><td align="char" char=".">4.84</td><td align="char" char=".">1.72</td><td align="char" char=".">.17</td><td align="char" char=".">0.06</td><td align="left">ns</td></tr><tr><td align="left">Similarities<xref ref-type="fn" rid="tfn2" /></td><td align="char" char=".">12.10</td><td align="char" char=".">1.68</td><td align="char" char=".">14.25</td><td align="char" char=".">1.52</td><td align="char" char=".">12.05</td><td align="char" char=".">2.23</td><td align="char" char=".">11.00</td><td align="char" char=".">3.04</td><td align="char" char=".">7.68<xref ref-type="fn" rid="tfn4" /></td><td align="char" char=".">.00</td><td align="char" char=".">0.23</td><td align="left">Y > A,M,O</td></tr><tr><td align="left">Block Design<xref ref-type="fn" rid="tfn3" /></td><td align="char" char=".">18.75</td><td align="char" char=".">6.26</td><td align="char" char=".">24.55</td><td align="char" char=".">4.08</td><td align="char" char=".">18.55</td><td align="char" char=".">7.60</td><td align="char" char=".">12.40</td><td align="char" char=".">5.31</td><td align="char" char=".">13.89<xref ref-type="fn" rid="tfn4" /></td><td align="char" char=".">.01</td><td align="char" char=".">0.35</td><td align="left">Y > A,MM > O</td></tr><tr><td align="left">Raven<xref ref-type="fn" rid="tfn3" /></td><td align="char" char=".">28.40</td><td align="char" char=".">5.44</td><td align="char" char=".">34.60</td><td align="char" char=".">8.44</td><td align="char" char=".">26.75</td><td align="char" char=".">5.20</td><td align="char" char=".">18.20</td><td align="char" char=".">7.01</td><td align="char" char=".">20.66<xref ref-type="fn" rid="tfn4" /></td><td align="char" char=".">.00</td><td align="char" char=".">0.45</td><td align="left">Y > A,MM > O</td></tr></tbody></table> </ephtml> </p> <ulist> <item>2 a Crystallized test.</item> <item>3 b Fluid test.</item> <item>4 *** <emph>p</emph> <.001.</item> </ulist> <hd id="AN0148517631-12">Assessment measures</hd> <p>the present authors used raw scores on all intelligence tests because standard scores account for chronological age, and the present authors wanted to examine possibly changing abilities along the lifespan.</p> <hd id="AN0148517631-13">Crystallized intelligence battery</hd> <p>Raw scores from two Wechsler subtests (WAIS‐III<sups>HEB</sups>, Wechsler, 2001) measured crystallized intelligence. <emph>Vocabulary</emph> assessed expressive word knowledge, verbal concept formation and fund of knowledge (33 items; scores: 0–66). <emph>Similarities</emph> assessed conceptualization of dissimilar objects (19 items; scores: 0–33).</p> <hd id="AN0148517631-14">Fluid intelligence battery</hd> <p>Raw scores from two tests measured fluid intelligence. <emph>Block Design</emph> (WAIS‐III<sups>HEB</sups>, Wechsler, 2001) assessed analysis and synthesis of abstract visual stimuli, visual perception and organization, simultaneous processing and visual–motor coordination (14 items; scores: 0–68). The <emph>Raven</emph> Standard Progressive Matrices (Raven et al., 1998) assessed ability to form comparisons, deduce relationships and correlates, and reason by analogy (60 items; scores: 0–60).</p> <hd id="AN0148517631-15">Health/morbidity</hd> <p>The Greater Rochester Area Health Status Survey (short version; Janicki & Davidson, 1999; Israeli adaptation: Lifshitz et al., 2008) assessed participants' number of health problems in the last five years. Staff familiar with participants for a 5‐year minimum rated: heart, digestive/intestinal, thyroid and urinary problems (e.g. incontinence); blood pressure; anaemia; diabetes; menopause; weight loss/gain; diseases (cancer); and increased emotional/psychiatric problems as expressed by medication prescription (scores: 1–11).</p> <hd id="AN0148517631-16">ADL skills (adaptive behavioural functioning)</hd> <p>Familiar staff assessed participants' autonomy in four domains—eating, dressing, continence and washing—on a 4‐point Likert scale: <emph>independent</emph> (a), <emph>needs some help</emph> (b), <emph>needs physical help</emph> (c) and <emph>not independent</emph> (d). Higher scores indicated lower ADL functioning (Scores: 4–16).</p> <hd id="AN0148517631-17">Cognitive stimulation from leisure activities</hd> <p>The 20‐item Participation in Cognitively Stimulating Activities Questionnaire (Lifshitz‐Vahav et al., 2015) derived from the satisfaction from leisure/recreational activities indices in the Later Life Planning Inventory (Heller et al., 2000; Lifshitz, 2002) and from Wilson and Bennett's (2003) leisure questionnaire. Ten "high‐stimulation" activities were more cognitively stimulating: playing card games, radio listening and TV watching, talking on phone with friends/family (to socialize), playing checkers or chess, reading, using laptop/tablet technologies (i.e. for games, internet searches, texting, social media and constructing websites), attending academic courses, acting in drama classes, drawing and painting, and photography. Ten "low‐stimulation" activities were less cognitively stimulating: listening to music, dancing, using graphics editing software, making jewellery, arranging flowers, gardening, doing sports exercises, singing with karaoke, cooking/baking and pet care.</p> <p>Leisure activities' frequency and cognitive load were both assessed to measure each participant's total weekly cognitive stimulation load, per recommendations in prior research (Lifshitz, 2020; Lifsitz‐Vahav et al., 2015). First, the reviewer asked participants whether and how frequently they had engaged in each of 20 activities during the past week using Wilson & Bennett's (2003) 5‐point scale ranging from <emph>every day</emph> (<reflink idref="bib5" id="ref4">5</reflink>) to <emph>once weekly</emph> (<reflink idref="bib1" id="ref5">1</reflink>). Then, to give cognitively demanding pursuits appropriate weight in assessment, each activity's frequency was multiplied by its cognitive load, using Lifshitz‐Vahav et al.'s (2015) taxonomy (e.g. a load of 1 for music listening and a load of 4 for drama club). Thus, for each of the 20 activities, the total weekly cognitive stimulation load for each participant was calculated by multiplying each activity's self‐reported frequency by its cognitive load (see Table 2). Separate scores were calculated for each of the two 10‐item activity participation subscales—low and high cognitive stimulation.</p> <p>2 TableTaxonomy of cognitively stimulating leisure activities by descending cognitive load (Lifshitz, 2020)</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Leisure activity</th><th align="left">Load</th><th align="left">Cognitive components</th></tr></thead><tbody><tr><td align="left">Academic courses</td><td align="char" char=".">5</td><td align="left"><list list-type="Bullet"><list-item><p>Oral and reading comprehension</p></list-item><list-item><p>New version of Bloom's cognitive taxonomy adapted for populations with ID <xref ref-type="fn" rid="tfn6" /></p></list-item><list-item><p>Identification of titles, new words and keywords</p></list-item><list-item><p>Distinguishing principal from secondary information</p></list-item><list-item><p>Asking/answering questions</p></list-item><list-item><p>Planning/organization</p></list-item><list-item><p>Recall/application of prior knowledge</p></list-item><list-item><p>Working/long‐term memory</p></list-item><list-item><p>Cognitive processes (comparison, categorization, classification, class inclusion, seriation and quantitative/spatial relations)</p></list-item><list-item><p>Drawing conclusions</p></list-item><list-item><p>Metacognitive processes</p></list-item></list></td></tr><tr><td align="left">Technology devices (tablet, computer and smartphone)</td><td align="char" char=".">4</td><td align="left">Reading and writingBloom's adapted taxonomy<xref ref-type="fn" rid="tfn6" />Internet searching skillsPlanning and organization</td></tr><tr><td align="left">Drama</td><td align="char" char=".">4</td><td align="left">Remembering and reciting textImitatingGetting into character</td></tr><tr><td align="left">Reading</td><td align="char" char=".">3</td><td align="left">Reading skillsBloom's adapted taxonomy<xref ref-type="fn" rid="tfn6" /></td></tr><tr><td align="left">Playing chess and checkers</td><td align="char" char=".">3</td><td align="left">PlanningCalculatingUnderstanding game's specific rules</td></tr><tr><td align="left">Drawing, painting and using graphic art editing software</td><td align="char" char=".">3</td><td align="left">Perceptual concepts (colour, composition, shape, texture and shading)Utilizing different materials/media/tools (oil, water, canvas, paper, brushes, pencils, markers, pastels, charcoals, graphic pen and computer touchscreen/mouse)Artistic styles and genres (classical, native, Roman, impressionist, realism, expressionism, surrealism, symbolism, figurative and abstract)Learning artists' names</td></tr><tr><td align="left">Photography</td><td align="char" char=".">3</td><td align="left">Planning a shotOrganizing equipment/subjectsCamera/lens typesPhotography techniquesLight, shade, sharpness, angles and estimates</td></tr><tr><td align="left">Karaoke singing</td><td align="char" char=".">3</td><td align="left">Differentiating music styles and genresUnderstanding songs' ease/difficultyUnderstanding lyricsKeeping rhythm with melodyCollaborating (duets)</td></tr><tr><td align="left">Making jewellery</td><td align="char" char=".">3</td><td align="left">Learning concepts (colour, size and sequence)Matching jewellery to body partsUsing materials (mineral, organic, metallic, stones, wax, wire, string and clasps) and tools (pliers, rulers, wire cutters, carving or casting tools)</td></tr><tr><td align="left">Aerobic exercise and dancing</td><td align="char" char=".">2</td><td align="left">Understanding instructionsConcepts of time, space and rhythmIdentifying dancing styles</td></tr><tr><td align="left">Arranging flowers</td><td align="char" char=".">2</td><td align="left">Basic concepts (colour, size, shape, comparison, classification and generalization)Math concepts (quantity, numbers and addition)Science concepts (fragrances, varieties, habitat and geographical origin)Arrangement concepts (colour, composition, cutting, size, amount, design and event themes)</td></tr><tr><td align="left">Gardening</td><td align="char" char=".">2</td><td align="left">Basic concepts (colour, size, shape, comparison, classification and generalization)Math concepts (quantity, numbers and addition)Science concepts (odours, seasons, growth stages, habitat, genus, soil components and sunlight)Using work tools (hoe, shovel and sprinkler)</td></tr><tr><td align="left">Cooking and baking</td><td align="char" char=".">2</td><td align="left">Using materials/tools (peelers, knives, scrapers, baking pans and pots)Measuring (amounts, weights, timing and temperature)Understanding major food groups, food composition and sequence in a recipe</td></tr><tr><td align="left">Animal care</td><td align="char" char=".">2</td><td align="left">Learning animals' preferences and patterns (nocturnal/diurnal, lone or joint lifestyle, and mating); foods (herbivore/carnivore/omnivore); classifications (domestic/wild; land/sea/sky; and animal families like cats and birds)Safety in handling animals</td></tr><tr><td align="left">Tabletop games (cards and puzzles)</td><td align="char" char=".">2</td><td align="left">Basic concepts (colour, size, shape, comparison, classification and generalization)Math concepts (quantity, numbers, addition, subtraction and planning)</td></tr><tr><td align="left">Listening to radio and watching television</td><td align="char" char=".">2</td><td align="left">Bloom's adapted taxonomy<xref ref-type="fn" rid="tfn6" /></td></tr><tr><td align="left">Listening to music</td><td align="char" char=".">1</td><td align="left">Differentiating music styles, genres and geographical origins (classical, blues, jazz, hip hop, rock, electronic, easy, showtunes, African, Japanese and British)Identifying musical instruments and instrument families</td></tr><tr><td align="left">Talking on the telephone</td><td align="char" char=".">1</td><td align="left">Socializing, using language and memory</td></tr></tbody></table> </ephtml> </p> <p>1 Note</p> <ulist> <item>5 Scoring of 1 for an activity rated as including 0–4 of the 20 possible cognitive components; scoring of 2 for 5–8 components; scoring of 3 for 9–12 components; scoring of 4 for 13–16 components; scoring of 5 for 17–20 components.</item> <item>6 a New version of Bloom's cognitive taxonomy: remembering, understanding, applying, analysing, synthesizing and evaluating (Anderson & Krathwohl, 2001, adapted by Lifshitz‐Vahav, 2015).</item> </ulist> <p>For example, for the high‐stimulation subscale, if a person watched TV twice (load of 2 × 2 times = 4.0), listened to the radio every day (load of 2 × 7 times = 14.0) and read once (5 × 1 = 5.0), then her total weekly high‐stimulation cognitive load would be 23.0. If she participated in academic courses once a week but exhibited all 11 of the courses' cognitive mediation activities (see Table 2), then her total weekly high‐stimulation cognitive load would be 11.0 (load of 11 × 1 time).</p> <hd id="AN0148517631-18">Procedure</hd> <p>Adolescent participants were recruited from two special education schools for students with ID under Israeli Ministry of Education supervision (Special Education Department). All adult participants were recruited from residential and vocational centres under Israeli Ministry of Welfare supervision (Division of Intellectual Disability). Authorizations were obtained from the University Ethics Committee, the Ministries of Welfare and Education and parents/guardians of participants with Down syndrome. Based on the normalization principle (Wolfensberger, 2002), like typically developing research participants, the current participants signed adapted informed consent after receiving explanations about the study's aims and procedures and then chose payment or a gift for participating in the study.</p> <p>The current test battery was administered individually by the first author over two sessions, in schools or employment settings. The three WAIS‐III<sups>HEB</sups> subtests were administered in one session (approx. one hour). One day later, the present authors first administered the Raven Matrices (lasting approx. 45 min), and then, after a 20‐min break, the reviewer asked participants whether and how frequently they had engaged in each of 20 activities during the week (approx. 20 min).</p> <hd id="AN0148517631-19">RESULTS</hd> <p>Due to each age cohort's small sample size, Shapiro–Wilk test was conducted to examine whether each cohort's dependent variables had normal distribution. Results indicated that some variables did not (<emph>p</emph>>.05). The present authors conducted analysis of variance (ANOVA) and multivariate (MANOVA) analyses instead of non‐parametric analyses. Considering our similar findings from non‐parametric and parametric tests, only parametric findings are presented. Figure 1 shows the mean raw scores for all four intelligence tests for the four age cohorts: 16–21, 30–45, 46–60 and 61+.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/E03/01mar21/jar12813-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="jar12813-fig-0001.jpg" title="1 Mean raw scores of study measures by age cohort" /> </p> <p></p> <hd id="AN0148517631-21">Trajectories of crystallized and fluid intelligence from adolescence through adulthood</hd> <p>To examine differences between the four age cohorts (16–21, 30–45, 46–60 and 61+), one‐way MANOVAs were conducted separately for crystallized (Vocabulary, Similarities) and fluid (Block Design, Raven) intelligence, followed by one‐way ANOVAs and post hoc Scheffe analysis conducted for each test separately (see Table 1). For crystallized intelligence, a significant main effect emerged, <emph>F</emph>(<reflink idref="bib6" id="ref6">6</reflink>, 150) = 3.68, <emph>p</emph> <.01, η<emph><subs>p</subs></emph><sups>2</sups> = 0.13. Post hoc tests showed that the Similarities scores of younger adults (30–45) were significantly higher compared to adolescents (16–21), <emph>p</emph> <.05; middle‐agers (46–60), <emph>p</emph> <.05; and older adults (61+), <emph>p</emph> <.001. Younger adults' Vocabulary scores were also higher than in the other three groups, but these results did not reach statistical significance.</p> <p>For fluid intelligence, a significant main effect also emerged, <emph>F</emph>(<reflink idref="bib6" id="ref7">6</reflink>, 150) = 10.69, <emph>p</emph> <.001, η<emph><subs>p</subs></emph><sups>2</sups> = 0.30. Post hoc tests showed that both Block Design and Raven scores were significantly higher in younger adulthood (30–45), compared to adolescence, <emph>p</emph> <.05, and middle age, <emph>p</emph> <.01. Moreover, Block Design and Raven scores were significantly lower in older adults (61+) than in middle‐agers (46–60), <emph>p</emph> <.01.</p> <hd id="AN0148517631-22">Trajectories of crystallized and fluid intelligence from 30s to 40s to 50s</hd> <p>Overall, these findings suggested that declines in Similarities, Block Design and Raven tests began somewhere between 46 and 60 years. To further pinpoint the onset of this decline, the present authors re‐divided participants aged 30–59 into three age cohorts: the 30s (<emph>n</emph> = 13; 38% males; 30–39 years; mean age = 32.92), the 40s (<emph>n</emph> = 18; 55% males; 40–49 years; mean age = 46.00) and the 50s (<emph>n</emph> = 9; 33% males; 50–59 years; mean age = 53.44), with no significant differences between cohorts for sex, <emph>χ</emph><sups>2</sups>(<reflink idref="bib2" id="ref8">2</reflink>) = 1.53, <emph>p</emph> =.46, or for IQ level, <emph>F</emph>(<reflink idref="bib2" id="ref9">2</reflink>, 37) = 1.06, <emph>p</emph> =.36, η<emph><subs>p</subs></emph><sups>2</sups> = 0.05. To examine differences between these three adult cohorts (30s, 40s and 50s), one‐way MANOVAs were conducted separately for the crystallized and fluid intelligence tests, followed by one‐way ANOVAs and post hoc Scheffe analysis conducted for each test separately (see Table 3).</p> <p>3 TableMeans, standard deviations and F values for the three adult groups' intelligence tests</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Intelligence</th><th align="left">Test</th><th align="left">Adult age group</th><th align="left"><italic>F</italic></th><th align="left"><italic>p</italic></th><th align="left">η<italic><sub>p</sub><sup>2</sup></italic></th><th align="left">Scheffe</th></tr><tr><th align="left"><p>30–39</p><p>(<italic>N</italic> = 13)</p></th><th align="left"><p>40–49</p><p>(<italic>N</italic> = 18)</p></th><th align="left"><p>50–59</p><p>(<italic>N</italic> = 9)</p></th></tr><tr><th align="left"><italic>M</italic></th><th align="left"><italic>SD</italic></th><th align="left"><italic>M</italic></th><th align="left"><italic>SD</italic></th><th align="left"><italic>M</italic></th><th align="left"><italic>SD</italic></th></tr></thead><tbody><tr><td align="left">Crystallized</td><td align="left">Vocabulary</td><td align="char" char=".">13.15</td><td align="char" char=".">4.74</td><td align="char" char=".">12.50</td><td align="char" char=".">5.22</td><td align="char" char=".">10.44</td><td align="char" char=".">2.92</td><td align="char" char=".">0.97</td><td align="char" char=".">0.39</td><td align="char" char=".">0.05</td><td align="left">—</td></tr><tr><td align="left">Similarities</td><td align="char" char=".">13.77</td><td align="char" char=".">1.17</td><td align="char" char=".">13.50</td><td align="char" char=".">2.31</td><td align="char" char=".">11.56</td><td align="char" char=".">2.51</td><td align="char" char=".">3.54<xref ref-type="fn" rid="tfn7" /></td><td align="char" char=".">0.04</td><td align="char" char=".">0.16</td><td align="left">30s > 50s, 40s = 30s</td></tr><tr><td align="left">Fluid</td><td align="left">Block Design</td><td align="char" char=".">24.62</td><td align="char" char=".">4.09</td><td align="char" char=".">19.94</td><td align="char" char=".">8.00</td><td align="char" char=".">20.33</td><td align="char" char=".">6.14</td><td align="char" char=".">2.11</td><td align="char" char=".">0.13</td><td align="char" char=".">0.10</td><td align="left">—</td></tr><tr><td align="left">Raven</td><td align="char" char=".">33.00</td><td align="char" char=".">7.46</td><td align="char" char=".">32.00</td><td align="char" char=".">8.74</td><td align="char" char=".">24.67</td><td align="char" char=".">3.39</td><td align="char" char=".">3.84<xref ref-type="fn" rid="tfn7" /></td><td align="char" char=".">0.03</td><td align="char" char=".">0.17</td><td align="left">30s > 50s,40s = 30s</td></tr></tbody></table> </ephtml> </p> <p>7 * <emph>p</emph> <.05.</p> <p>For crystallized intelligence, the main effect of age was not significant, <emph>F</emph>(<reflink idref="bib4" id="ref10">4</reflink>, 72) = 1.87, <emph>p</emph> =.12, η<emph><subs>p</subs></emph><sups>2</sups> = 0.09. One‐way ANOVAs for each test separately showed no significant differences on Vocabulary between the three adult cohorts or on Similarities between the 30s and 40s cohorts (<emph>p</emph> =.94), indicating stability. However, the 50s cohort scored significantly lower on Similarities than the 30s cohort (<emph>p</emph> <.05), indicating decline around age 50.</p> <p>For fluid intelligence, a significant main effect emerged, <emph>F</emph>(<reflink idref="bib4" id="ref11">4</reflink>, 72) = 2.87, <emph>p</emph> <.05, η<emph><subs>p</subs></emph><sups>2</sups> = 0.14. One‐way ANOVA showed lower Block Design scores in the 40s cohort than the 30s cohort, but this did not reach significance, perhaps because of the large standard deviations (<emph>p</emph> =.16). In the Raven, scores for the 30s and 40s cohorts did not differ significantly (<emph>p</emph> =.93), indicating stability, but the 50s cohort scored significantly lower than the 30s cohort (<emph>p</emph> <.05), indicating decline around age 50.</p> <hd id="AN0148517631-23">Contribution of age, health, ADL skills and cognitive leisure activities to crystallized and...</hd> <p>Before conducting regression analyses to investigate the endogenous and exogenous variables' contribution to intelligence scores, preliminary analyses examined these variables' correlations with intelligence as well as age differences. These analyses related only to the three adult cohorts: younger (30–45), middle‐aged (46–60) and older (61+).</p> <hd id="AN0148517631-24">Correlations of study measures with intelligence tests</hd> <p>Pearson correlations indicated that poorer health significantly correlated only with lower fluid intelligence scores (<emph>p</emph> =.01): Raven, <emph>r =−</emph>0.26; Block Design,<emph>r</emph> = −0.44. Likewise, lower independence in ADL skills significantly correlated only with lower fluid intelligence scores (<emph>p</emph> =.01): Raven,<emph>r</emph> = −0.52; Block Design,<emph>r</emph> = −0.55. However, the weekly cognitive load on the high‐stimulation leisure activity subscale correlated significantly with all four intelligence measures (<emph>p</emph> =.001): Vocabulary, <emph>r</emph> = 0.70; Similarities, <emph>r</emph> = 0.48; Block Design, <emph>r</emph> = 0.35; and Raven, <emph>r</emph> = 0.56. Likewise, the weekly cognitive load on the low‐stimulation leisure activity subscale also correlated significantly with all four intelligence measures (<emph>p</emph> =.001): Vocabulary, <emph>r</emph> = 0.44; Similarities, <emph>r</emph> = 0.40; Block Design, <emph>r</emph> = 0.35; and Raven, <emph>r</emph> = 0.38.</p> <hd id="AN0148517631-25">Age differences in health status</hd> <p>An ANOVA indicated significantly more health problems in the older adults (<emph>M</emph> = 3.15; <emph>SD</emph> = 1.23) compared to middle‐agers (<emph>M</emph> = 2.35, <emph>SD</emph> = 1.81) and to younger adults (<emph>M</emph> = 1.90, <emph>SD</emph> = 1.25), <emph>p</emph> <.05. The latter two cohorts did not differ significantly, <emph>p</emph> =.62.</p> <hd id="AN0148517631-26">Age differences in ADL</hd> <p>An ANOVA indicated a significant main effect for age, <emph>F</emph>(<reflink idref="bib2" id="ref12">2</reflink>,<reflink idref="bib57" id="ref13">57</reflink>) = 17.60, <emph>p</emph> <.001, η<emph><subs>p</subs></emph><sups>2</sups> = 0.38. Post hoc Scheffe analysis showed significantly lower ADL scores, <emph>p</emph> <.001, for the 61 + cohort (<emph>M</emph> = 3.05, <emph>SD</emph> = 0.05) than for the younger adults (<emph>M</emph> = 4.00, <emph>SD</emph> = 0.00) or the middle‐agers (<emph>M</emph> = 3.80, <emph>SD</emph> = 0.41). The latter two cohorts did not differ significantly, <emph>p</emph> =.87.</p> <hd id="AN0148517631-27">Age differences in cognitive stimulation from leisure activities</hd> <p>An ANOVA conducted for the weekly cognitive load on the low‐stimulation leisure activity subscale showed significantly lower scores in the 61 + cohort (<emph>M</emph> = 5.55, <emph>SD</emph> = 4.14) than in middle‐agers (<emph>M</emph> = 10.85, <emph>SD</emph> = 6.06) or younger adults (<emph>M</emph> = 11.44, <emph>SD</emph> = 5.79), <emph>F</emph>(<reflink idref="bib2" id="ref14">2</reflink>, 46) = 8.64, <emph>p</emph> <.001, η<emph><subs>p</subs></emph><sups>2 </sups>= 0.27. Likewise, ANOVA conducted for the weekly cognitive load on the high‐stimulation leisure activity subscale showed significantly lower scores in the 61 + cohort (<emph>M</emph> = 6.20, <emph>SD</emph> = 2.04) than in middle‐agers (<emph>M</emph> = 8.50, <emph>SD</emph> = 2.67) or younger adults (<emph>M</emph> = 10.00, <emph>SD</emph> = 2.83), <emph>F</emph>(<reflink idref="bib2" id="ref15">2</reflink>, 46) = 6.38, <emph>p</emph> <.01, η<emph><subs>p</subs></emph><sups>2</sups> = 0.22.</p> <hd id="AN0148517631-28">Regression analyses</hd> <p>Hierarchical and stepwise regression analyses were performed, with age (30–45, 46–60 and 61+), health, ADL functioning and weekly cognitive load on leisure activities as the independent variables, to examine their contribution to crystallized and fluid intelligence. In Step 1, participants' age was entered, then health and ADL scores. The two scores for weekly leisure activities (low/high cognitive stimulation) were then entered in a stepwise manner (see Table 4).</p> <p>4 TableMixed regressions for intelligence tests by age, health, activities of daily living (ADL) and participation in cognitive leisure activities</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Intelligence</th><th align="left">Dependent variables</th><th align="left">Steps</th><th align="left">Independent variables</th><th align="left">B</th><th align="left">SE.B</th><th align="left">β</th><th align="left"><italic>t</italic></th><th align="left"><italic>R</italic><sup>2</sup></th><th align="left">∆<italic>R</italic><sup>2</sup></th></tr></thead><tbody><tr><td align="left">Crystallized</td><td align="left">Vocabulary</td><td align="char" char=".">1</td><td align="left">Age</td><td align="left">−0.12</td><td align="char" char=".">0.07</td><td align="left">−0.25</td><td align="left">−1.79</td><td align="char" char=".">0.064</td><td align="char" char=".">0.064</td></tr><tr><td align="left" /><td align="char" char=".">2</td><td align="left">Age</td><td align="left">0.02</td><td align="char" char=".">0.05</td><td align="left">0.04</td><td align="left">0.32</td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left" /><td align="char" char="." /><td align="left">Cognitive leisure stimulation</td><td align="left">0.61</td><td align="char" char=".">0.10</td><td align="left">0.71</td><td align="left">6.15<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.486<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.422<xref ref-type="fn" rid="tfn10" /></td></tr><tr><td align="left">Similarities</td><td align="char" char=".">1</td><td align="left">Age</td><td align="left">−0.11</td><td align="char" char=".">0.03</td><td align="left">−0.41</td><td align="left">−3.05<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.165<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.165<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="left" /><td align="char" char=".">2</td><td align="left">Age</td><td align="left">−0.07</td><td align="char" char=".">0.04</td><td align="left">−0.25</td><td align="left">−1.86</td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left" /><td align="char" char="." /><td align="left">Cognitive leisure stimulation</td><td align="left">0.18</td><td align="char" char=".">0.07</td><td align="left">0.37</td><td align="left">2.73<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.282<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.116<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="left">Fluid</td><td align="left">Block design</td><td align="char" char=".">1</td><td align="left">Age</td><td align="left">−0.33</td><td align="char" char=".">0.09</td><td align="left">−0.47</td><td align="left">−3.67<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.223<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.223<xref ref-type="fn" rid="tfn10" /></td></tr><tr><td align="left" /><td align="char" char=".">2</td><td align="left">Age</td><td align="left">−0.22</td><td align="char" char=".">0.10</td><td align="left">−0.31</td><td align="left">−2.10<xref ref-type="fn" rid="tfn8" /></td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left" /><td align="char" char="." /><td align="left">ADL functioning</td><td align="left">0.74</td><td align="char" char=".">0.31</td><td align="left">0.33</td><td align="left">2.37<xref ref-type="fn" rid="tfn8" /></td><td align="char" char=".">0.308<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.085<xref ref-type="fn" rid="tfn8" /></td></tr><tr><td align="left">Raven</td><td align="char" char=".">1</td><td align="left">Age</td><td align="left">−0.43</td><td align="char" char=".">0.09</td><td align="left">−0.56</td><td align="left">−4.69<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.319<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.319<xref ref-type="fn" rid="tfn10" /></td></tr><tr><td align="left" /><td align="char" char=".">2</td><td align="left">Age</td><td align="left">−0.31</td><td align="char" char=".">0.09</td><td align="left">−0.40</td><td align="left">−3.37<xref ref-type="fn" rid="tfn9" /></td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left" /><td align="char" char="." /><td align="left">Cognitive leisure stimulation</td><td align="left">0.54</td><td align="char" char=".">0.17</td><td align="left">0.39</td><td align="left">3.26<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.447<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.128<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="left" /><td align="char" char=".">3</td><td align="left">Age</td><td align="left">−0.23</td><td align="char" char=".">0.09</td><td align="left">−0.31</td><td align="left">−2.50<xref ref-type="fn" rid="tfn8" /></td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left" /><td align="char" char="." /><td align="left">Cognitive leisure stimulation</td><td align="left">0.42</td><td align="char" char=".">0.17</td><td align="left">0.30</td><td align="left">2.45<xref ref-type="fn" rid="tfn8" /></td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left" /><td align="char" char="." /><td align="left">ADL functioning</td><td align="left">0.64</td><td align="char" char=".">0.31</td><td align="left">0.26</td><td align="left">2.05<xref ref-type="fn" rid="tfn8" /></td><td align="char" char=".">0.494<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.047<xref ref-type="fn" rid="tfn8" /></td></tr></tbody></table> </ephtml> </p> <ulist> <item>8 * <emph>p</emph> <.05.</item> <item>9 ** <emph>p</emph> <.01.</item> <item>10 *** <emph>p</emph> <.001.</item> </ulist> <p>As seen in Table 4, participants' age contributed significantly to the explained variance for Similarities (11%), for the Raven (32%) and for Block Design (41%), where higher age predicted lower intelligence test scores. Participants' ADL scores contributed significantly to the explained variance for Similarities (19%), Block Design (−32%), and Raven (−42%), beyond participants' age, where lower ADL functioning predicted lower intelligence test scores. Poorer health contributed significantly to lower scores on the Raven test (−54%). The low‐stimulation leisure subscale did not contribute significantly to any intelligence test; however, the high‐stimulation leisure subscale contributed significantly to the explained variance for Vocabulary (62%), Similarities (18%) and the Raven (44%) but not for the other fluid test, Block Design.</p> <hd id="AN0148517631-29">DISCUSSION</hd> <p>Our findings can be divided into two parts regarding the intelligence trajectories of participants with Down syndrome. Regarding the trajectory from adolescence (16–21) into adulthood (30–45), a continuous increase was shown in crystallized and fluid intelligence scores. Regarding the trajectory through the stages of adulthood, Vocabulary remained stable throughout middle age, while decline was noted for Similarities from age 50, for Block Design from age 45 and for Raven (near‐significant) from age 50. Chronological age, health status and ADL functioning were correlated negatively mainly to the fluid tests and contributed negatively to Similarities, Block Design and Raven scores. However, participation in highly cognitively stimulating leisure activities mitigated these negative effects. The research questions are next discussed separately.</p> <hd id="AN0148517631-30">Cognitive trajectories from adolescence to younger adulthood</hd> <p>Our hypothesis of significantly higher intelligence scores in adulthood (ages 30–45) compared to adolescence (ages 16–21) was supported for three of the four tests (Similarities, Block Design and Raven), with non‐significant findings in the same direction for Vocabulary. This growth differed from research outcomes in the 1970s (Carr, 2005), which showed declines in crystallized and fluid intelligence scores from age 20 to 49. These discrepancies may be attributed in part to the less developed medical care and poorer nutrition decades ago, when persons with Down syndrome had shorter life expectancies (Wisniewski et al., 1985). In addition, in that time period, adults did not yet have the same opportunities for early education because the Individuals with Disabilities Education Act (1975), which recommended integrating pupils with special needs into regular classrooms in a less restrictive environment, was still in its infancy. Only in later decades did improvements in public health and inclusion policies such as the Individuals with Disabilities Education Act (1990; United States Department of Education, 2010) impact the educational and occupational opportunities for individuals with ID including Down syndrome (Bayen et al., 2018; Heller, 2017).</p> <p>The current growth pattern coincided with more recent research demonstrating improvement in IQ scores from adolescence to adulthood in individuals with ID, without any direct intervention, while extending the literature because previous research focused on non‐specific ID aetiologies (Chen et al., 2017) or did not distinguish individuals with ID from those with borderline IQ (35–87) levels (Clarke & Clarke, 1954; Clarke et al., 1958). Thus, the current findings lend support to the continuous cognitive growth trajectory (Fisher & Zeaman, 1970), compensation age theory (Lifshitz, 2020; Lifshitz‐Vahav, 2015) and cognitive reserve theory (Stern, 2012), indicating that these individuals continued to develop from adolescence to adulthood as a product of maturity and environmental experiences.</p> <hd id="AN0148517631-31">Cognitive trajectories from younger adulthood to older adulthood</hd> <p>Our hypothesis of stability in intelligence scores in middle age (46–60) compared to younger adulthood (30–45) was supported only for Vocabulary. The other three tests revealed a decline from ages 30–45 to ages 46–60. Considering that prior research pointed to the 50s as the age of onset of decline in individuals with Down syndrome (Devenny et al., 1996; Hawkins et al., 2003; Heller et al., 2018), the present authors re‐divided our adult sample into 30s, 40s and 50s to enrich identification of the precise onset of decline.</p> <p>Regarding crystallized intelligence, both Vocabulary and Similarities scores (WAIS‐III<sups>HEB</sups>) continued to grow in our participants until the mid‐40s (age 45), and thereafter, the two tests revealed different results. Vocabulary remained stable from the 30s until after age 60, coinciding with results for the typically developing population who may continue to develop crystallized intelligence into their 60s or 70s (Kaufman, 2001; Salthouse, 2009; Schaie, Willis, & Caskie, 2004; and see Chen et al., 2017 for a typical Israeli sample). Our findings coincide with those of Carr and Collins (2018), who reported stability from ages 30 to 50 on the British Picture Vocabulary Scale (Dunn, Dunn, Whetton, & Pintillie, 1982) and only a small but non‐significant drop (0.99) from ages 21 to 50 on the preschool version of the Wechsler (1967). According to these authors, receptive and expressive abilities served as protective factors against decline in memory and ADL.</p> <p>A deeper look at the results for Vocabulary indicates that our participants could define an average of 10 out of 33 given words. Once they knew a word, they maintained it even until age 69 (our oldest participant). In the more abstract Similarities test, requiring participants to find the common denominator between two given words, scores significantly declined from the 40s to the 50s but then remained stable after age 60. The regression analysis supported these trends, indicating that chronological age contributed significantly and negatively to the explained variance for Similarities, with higher age predicting lower verbal abstraction scores. Thus, although Similarities is considered a crystallized test, it appeared to resemble a fluid test in its sensitivity to age.</p> <p>Regarding fluid intelligence, the regression indicated that chronological age contributed significantly and negatively to the explained variance for both the Block Design (WAIS‐III<sups>HEB</sups>) and the Raven, with higher age predicting lower scores. For the Raven, which assesses the ability to form comparisons, deduce relationships and reason by analogy (Raven, Court, & Raven, 1986), adults with Down syndrome declined from the 40s to the 50s, substantiating prior research that pointed to the 50s as the age of onset of cognitive decline in the Down syndrome aetiology (Das & Mishra, 1995; Hawkins et al., 2003; Heller et al., 2018). Block Design (WAIS‐III<sups>HEB</sups>, Wechsler, 2001) suggested a possible earlier decline, from the 46s to the 50s. According to Devenny, Krinsky‐McHale, Sersen, and Silverman (2000), early decline in this visual–motor test can be a marker for early signs of Alzheimer's in the Down syndrome aetiology. Thus, whereas fluid intelligence in the general population reaches its peak around age 20 and then declines (Kaufman, 2001; Salthouse, 2009), in our sample with Down syndrome this vulnerable type of intelligence continued to grow from adolescence into younger adulthood but then showed a steep decline around age 50 for the Raven (and a non‐significant tendency to decline around age 46 for Block Design).</p> <p>Overall, these findings on adults' intelligence trajectories support the assertion underlying compensation age theory (Lifshitz‐Vahav, 2015); to wit, crystallized and fluid intelligence and other cognitive skills of individuals with Down syndrome can continue to grow until their middle 40s as a result of maturity and cumulative life experience, which help them to acquire cognitive skills that were previously absent from their behavioural repertoire (Facon, 2008).</p> <p>Another possible explanation is neurologically oriented. Brain imaging has demonstrated that brain maturation, including the frontal lobe and prefrontal cortex, appears to reach its peak in late adolescence in the general population (Cromer, Schembri, Harel, & Maruff, 2015; Crone & Ridderinkhof, 2011; Hudspeth & Pribram, 1990). Yet, in individuals with ID, brain maturation processes appear to manifest differently, occurring more slowly in adolescents and reaching fuller maturation later, in adulthood. This may possibly extend their period of cognitive development beyond middle adulthood. Caution should be exercised regarding this conjecture, and further brain research is sorely needed to validate this assertion.</p> <hd id="AN0148517631-32">Contribution of endogenous and exogenous factors to adults' crystallized and fluid intelligen...</hd> <p>The current findings indicated that adults' health and their ADL skills correlated negatively with fluid intelligence measures in all three adult cohorts (30–45, 46–60 and 61+). Thus, it appears that fluid intelligence in the Down syndrome aetiology is more sensitive to age‐related deterioration in health and ADL functioning (Devenny et al., 2000) than crystallized intelligence. The regression analysis revealed that both health status and ADL functioning contributed significantly to the explained variance of both fluid tests. Participants' more independent ADL functioning correlated with higher intelligence test scores, while more health problems correlated with lower test scores, especially on Block Design.</p> <p>The current findings indicated that participation in highly cognitively stimulating leisure activities like playing checkers, using digital devices or academic courses contributed positively and significantly to both crystallized intelligence tests (Vocabulary and Similarities) and to the Raven fluid intelligence test. This supports the applicability of cognitive activity theory (Rosenberg et al., 2020; Wilson et al., 2010; Wilson & Bennett, 2003) to the population with Down syndrome. This finding where a larger weekly load of high‐stimulation activities predicted higher intelligence scores coincided with Lifshitz‐Vahav et al.'s (2015) outcomes for individuals with ID aged 25–55, with and without Down syndrome, thus expanding the literature to adults with Down syndrome aged 30–69. According to the regression analysis, participation in high‐stimulation leisure activities was shown to mitigate the negative influences of increasing chronological age and of declining health status and ADL functioning on intelligence test scores. These outcomes support compensation age theory (Lifshitz, 2020; Lifshitz‐Vahav, 2015) among adults with Down syndrome, indicating that not only endogenous but also exogenous factors such as cognitively demanding leisure pursuits may determine the performance of adults with Down syndrome on intelligence tests. Block Design was the only test that was not associated with participation in cognitively stimulating activities, which again implies this visual perception and processing test's vulnerability to age‐related decline.</p> <p>To be noted, the current study did not yield any significant contribution of low‐stimulation leisure activities (those with lower cognitive load such as dancing or cooking) to our four measures. Lifshitz‐Vahav et al. (2015) found that such activities did contribute significantly to phonemic fluency (naming categories for listed items, like fruits or transportation) among their participants with non‐specific ID but not among their participants with Down syndrome. This disadvantage for individuals with Down syndrome is not surprising, given their deficits in linguistic skills, with comprehension and semantic abilities exceeding their phonological ability (Grieco et al., 2015). Our current findings coincided with those of Lifshitz‐Vahav et al. (2015) in that mere participation in low‐stimulation leisure activities, which were less cognitively demanding, was not enough to mitigate the influence of age, health problems and ADL decline in adults with Down syndrome.</p> <hd id="AN0148517631-33">Study Limitations and Implications for Future Research</hd> <p>Several limitations of this study should be considered. First, this was a cross‐sectional study, which is characteristic of research on this population considering that longitudinal data are difficult to collect from adults with ID, especially those over 60 (Facon, 2008). Cross‐sectional research may suffer from cohort effects, whereas longitudinal research may suffer from "retest effects." However, Kaufman (2001) found the same trajectories of crystallized and fluid intelligence using cross‐sectional and longitudinal methods in a typically developing population. Our hope is that this cross‐sectional study will serve as a basis for future longitudinal research.</p> <p>Second, generalization should be regarded with caution due to the relatively small sample size, which usually characterizes research on this syndrome considering its low prevalence in the population. Future research using a larger sample would help validate our findings and rule out significant findings due to chance. Third, the WAIS‐III<sups>HEB</sups> (Wechsler, 2001), used in Israel to determine intelligence for typically developing adults, may not be sufficiently sensitive to individual differences among persons with ID. Future research using a broad range of crystallized and fluid intelligence tests should be carried out. Fourth, our participants had been evaluated with mild‐to‐moderate ID (IQ = 50–70) but were not assessed for other endogenous variables that have previously been linked to cognitive skills in persons with ID, such as emotional intelligence (Adibsereshki, Shaydaei, & Movallali, 2015), personality integration (Clarke & Clarke, 1954; Clarke et al., 1958) or social intelligence (Ganaie & Mudasir, 2015; Thorndike, 1920). Further research should examine how the emotional and social trajectories of individuals with ID and specifically with Down syndrome may be associated with the trajectory of their cognitive development. Moreover, future researchers would do well to examine the cognitive trajectory of individuals with more severe ID and with other aetiologies. Finally, our study supports the short‐term implications of applying cognitive activity theory to the population with Down syndrome. Longitudinal research is needed to examine this theory's claim about reducing the risk of Alzheimer's disease among older adults with Down syndrome.</p> <hd id="AN0148517631-34">Practical recommendations</hd> <p>In line with the current findings' support for compensation age theory (Lifshitz, 2020; Lifshitz‐Vahav, 2015), policymakers and administrators of community residences and vocational centres for adults with Down syndrome should be explicitly introduced to the potential inherent during younger adulthood and even middle adulthood as periods of compensation and growth for crystallized and fluid intelligence. In addition, individuals with Down syndrome should be exposed to mediated learning starting at young ages (Clarke & Faragher, 2014).</p> <p>Furthermore, corresponding with the present outcomes' support for cognitive activity theory, the present authors recommend that leaders and practitioners in the field be apprised of the added value of participation in cognitively stimulating leisure activities for this population. Youth with ID are frequently marginalized from leisure activities organized for young people in the general population (Melbøe & Ytterhus, 2017), and when such activities are implemented across the lifespan, their goal is most often to improve satisfaction, well‐being or quality of life (Bergström, Hochwälder, Kottorp, & Elinder, 2013; Melbøe & Ytterhus, 2017) rather than to stimulate cognition or mitigate age‐related declines in health or adaptive daily functioning.</p> <p>Inasmuch as cognitively high‐stimulation activities may be commonplace in educational, rehabilitational and even vocational settings for individuals with ID, leisure activities can serve as a cost‐effective and relatively easy tool to apply. Individuals with Down syndrome should be encouraged to engage in a greater diversity of activities from younger ages, and staff should be guided to use leisure activities to enhance the cognitive literacy of adults with ID by inserting more cognitively demanding components into the less stimulating leisure activities geared towards enhancing emotional and behavioural functioning.</p> <p>In particular, our finding that the 61+ age cohort showed a significantly lower mean weekly cognitive stimulation score compared to their younger adult counterparts suggests the need for intervention in this age group at risk for dementia. Perhaps their lower participation rate reflects limitations due to health and functioning levels; however, it may also stem from older participants' lower exposure to stimulating activity over their lifespan (Marquinea et al., 2012; Rosenberg et al., 2020). As public attitudes and policies towards persons with ID have developed in recent decades, younger people in the study were likely to have engaged in a greater diversity of activities. Elderly persons with Down syndrome should especially be encouraged to participate in leisure activities that involve greater cognitive stimulation as a means to delay and alleviate age‐related cognitive deterioration as much as possible.</p> <hd id="AN0148517631-35">ACKNOWLEDGMENTS</hd> <p>This work was supported by a grant from the Shalem Fund for the Development of Services for People with Intellectual Disabilities in the Local Councils in Israel.</p> <ref id="AN0148517631-36"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref5" type="bt">1</bibl> <bibtext> Adibsereshki, N., Shaydaei, M., & Movallali, G. (2015). The effectiveness of emotional intelligence training on the adaptive behaviors of students with intellectual disability. 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  Label: Title
  Group: Ti
  Data: Intelligence Trajectories in Adolescents and Adults with Down Syndrome: Cognitively Stimulating Leisure Activities Mitigate Health and ADL Problems
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Lifshit%2C+Hefziba+Batya%22">Lifshit, Hefziba Batya</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4185-3945">0000-0002-4185-3945</externalLink>)<br /><searchLink fieldCode="AR" term="%22Bustan%2C+Noa%22">Bustan, Noa</searchLink><br /><searchLink fieldCode="AR" term="%22Shnitzer-Meirovich%2C+Shlomit%22">Shnitzer-Meirovich, Shlomit</searchLink>
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Applied+Research+in+Intellectual+Disabilities%22"><i>Journal of Applied Research in Intellectual Disabilities</i></searchLink>. Mar 2021 34(2):491-506.
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  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Label: Peer Reviewed
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  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 16
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2021
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Down+Syndrome%22">Down Syndrome</searchLink><br /><searchLink fieldCode="DE" term="%22Adolescents%22">Adolescents</searchLink><br /><searchLink fieldCode="DE" term="%22Adults%22">Adults</searchLink><br /><searchLink fieldCode="DE" term="%22Health+Behavior%22">Health Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Life+Style%22">Life Style</searchLink><br /><searchLink fieldCode="DE" term="%22Leisure+Time%22">Leisure Time</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligence%22">Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Age+Differences%22">Age Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Development%22">Cognitive Development</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligence+Tests%22">Intelligence Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Health%22">Health</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink>
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  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Block+Design+Test%22">Block Design Test</searchLink><br /><searchLink fieldCode="SU" term="%22Raven+Progressive+Matrices%22">Raven Progressive Matrices</searchLink><br /><searchLink fieldCode="SU" term="%22Wechsler+Adult+Intelligence+Scale%22">Wechsler Adult Intelligence Scale</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/jar.12813
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1360-2322
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Goals: This study examined: (a) crystallized/fluid intelligence trajectories of adolescents and adults with Down syndrome; and (b) the contribution of endogenous (health, activities of daily living--ADL) and exogenous (cognitively stimulating leisure activities) factors on adults' intelligence with age. Method: Four cohorts (N = 80) with Down syndrome participated: adolescents (ages 16-21) and adults (ages 30-45, 46-60 and 61+). All completed Vocabulary and Similarities (crystallized) and Block Design and Raven (fluid) intelligence tests (WAIS-IIIHEB, Wechsler, 2001). Results: The 30-45 cohort significantly outperformed the 16-21 cohort. Except for Vocabulary, which remained stable, onset of decline was at 40-50. Age-related declining health and ADL correlated with participants' lower fluid intelligence, but cognitive leisure activities mitigated this influence. Conclusions: Intelligence development into adulthood supported the continuous trajectory and compensation age theory, rather than accelerated or stable trajectories. Not only endogenous factors but also exogenous factors determined intelligence levels in adults with Down Syndrome, supporting cognitive activity theory.
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  Label: Entry Date
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  Data: 2021
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  Label: Accession Number
  Group: ID
  Data: EJ1284961
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1284961
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        Value: 10.1111/jar.12813
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 491
    Subjects:
      – SubjectFull: Down Syndrome
        Type: general
      – SubjectFull: Adolescents
        Type: general
      – SubjectFull: Adults
        Type: general
      – SubjectFull: Health Behavior
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      – SubjectFull: Life Style
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      – SubjectFull: Leisure Time
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      – SubjectFull: Intelligence
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      – SubjectFull: Age Differences
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      – SubjectFull: Cognitive Development
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      – SubjectFull: Intelligence Tests
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      – SubjectFull: Health
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      – SubjectFull: Correlation
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      – SubjectFull: Block Design Test
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      – SubjectFull: Raven Progressive Matrices
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      – SubjectFull: Wechsler Adult Intelligence Scale
        Type: general
    Titles:
      – TitleFull: Intelligence Trajectories in Adolescents and Adults with Down Syndrome: Cognitively Stimulating Leisure Activities Mitigate Health and ADL Problems
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            – TitleFull: Journal of Applied Research in Intellectual Disabilities
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