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Objective: We explored which factors are associated with subjective age (SA), i.e. feeling younger, the same as, or older than one's chronological age, and whether these factors differ between men and women and between two age sub-groups., Design: Cross-sectional study using qualitative and quantitative data for 1457 individuals (mean age= 67.2 years)., Main Outcome Measures: Participants reported how old they feel they are and provided comments in relation to their SA judgments., Results: By using content analysis participants' comments were assigned to 13 categories, grouped into three higher-order categories ( antecedents of age-related thoughts, mental processes, and issues when measuring subjective age ). SA may result from the interaction between factors that increase or decrease age-related thoughts and mental processes that individuals use to interpret age-related changes. Chi-squared tests show that individuals reporting an older SA are more likely to experience significant negative changes and to engage in negative age-related thoughts than individuals reporting an age-congruent SA or a younger SA. Women experience a more negative SA and more age-salient events than men., Conclusion: Individuals reporting an older SA may benefit from interventions promoting adaptation to negative age-related changes. There is the need to eradicate negative societal views of older women.

Background: A questionnaire assessing awareness of positive and negative age-related changes (AARC gains and losses) was developed in the US and Germany. We validated the short form of the measure (AARC-10 SF) and the cognitive functioning subscale from the 50-item version of the AARC (AARC-50) questionnaire in the UK population aged 50 and over., Methods: Data from 9410 participants (Mean (SD) age = 65.9 (7.1)) in the PROTECT cohort were used to explore and confirm the psychometric properties of the AARC measures including: validity of the factor structure; reliability; measurement invariance across men and women, individuals with and without a university degree, and in middle age, early old age, and advanced old age; and convergent validity with measures of self-perception of aging and mental, physical, and cognitive health. We explored the relationship between demographic variables (age, sex, marital status, employment, and university education) and AARC., Results: We confirmed the two-factor structure (gains and losses) of the AARC-10 SF and the AARC-50 cognitive functioning subscale. Both scales showed good reliability and good convergent validity for AARC losses, but weak convergent validity for AARC gains. For both scales metric invariance was held for the two subgroups defined by education level and age. For the AARC-50 subscale, but not for the AARC-10 SF, strong invariance was also held for the two subgroups defined by sex. Age, sex, marital status, employment, and university education predicted AARC gains and losses., Conclusions: The AARC-10 SF and AARC-50 cognitive functioning subscale identify UK individuals who perceive age-related changes in their mental, physical, and cognitive health.

Background and Objectives: This systematic review aimed to synthesize and quantify the associations of awareness of age-related change (AARC) with emotional well-being, physical well-being, and cognitive functioning., Research Design and Methods: We conducted a systematic review with a correlational random effects meta-analysis. We included quantitative studies, published from January 1, 2009 to October 3, 2018, exploring associations between AARC and one or more of the following outcomes: emotional well-being, physical well-being, and cognitive functioning. We assessed heterogeneity (I2) and publication bias., Results: We included 12 studies in the review, 9 exploring the association between AARC and emotional well-being and 11 exploring the association between AARC and physical well-being. No study explored the association between AARC and cognitive functioning. Six articles were included in the meta-analysis. We found a moderate association between a higher level of AARC gains and better emotional well-being (r = .33; 95% CI 0.18, 0.47; p <.001; I2 = 76.01) and between a higher level of AARC losses and poorer emotional (r = -.31; 95% CI -0.38, -0.24; p < .001; I2 = 0.00) and physical well-being (r = -.38; 95% CI -0.51, -0.24; p < .001; I2 = 83.48). We found a negligible association between AARC gains and physical well-being (r = .08; 95% CI 0.02, 0.14; p < .122; I2 = 0.00). Studies were of medium to high methodological quality., Discussion and Implications: There is some indication that AARC gains and losses can play a role in emotional well-being and that AARC losses are associated with physical well-being. However, the number of included studies is limited and there was some indication of heterogeneity., Prospero Registration: CRD42018111472., (© The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America.)

Background: Up to 40% of incident dementia is considered attributable to behavioral and lifestyle factors. Given the current lack of medical treatments and the projected increase in dementia prevalence, a focus on prevention through risk reduction is needed. Objective: We aim to increase dementia risk knowledge and promote changes in dementia risk behaviors at individual and population levels. Methods: The Island Study Linking Aging and Neurodegenerative Disease (ISLAND) is a long-term prospective, web-based cohort study with nested interventions that will be conducted over a 10-year period. Target participants (n=10,000) reside in Tasmania and are aged 50 years or over. Survey data on knowledge, attitudes, and behaviors related to modifiable dementia risk factors will be collected annually. After each survey wave, participants will be provided with a personalized dementia risk profile containing guidelines for reducing risk across 9 behavioral and lifestyle domains and with opportunities to engage in educational and behavioral interventions targeting risk reduction. Survey data will be modeled longitudinally with intervention engagement indices, cognitive function indices, and blood-based biomarkers, to measure change in risk over time. Results: In the initial 12 months (October 2019 to October 2020), 6410 participants have provided baseline data. The study is ongoing. Conclusions: Recruitment targets are feasible and efforts are ongoing to achieve a representative sample. Findings will inform future public health dementia risk reduction initiatives by showing whether, when, and how dementia risk can be lowered through educational and behavioral interventions, delivered in an uncontrolled real-world context. [ABSTRACT FROM AUTHOR]