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Abstract Background Social isolation and low levels of physical activity are strong drivers for frailty, which is linked to poor health outcomes and transition to long-term care. Frailty is multifactorial, and thus an integrated approach is needed to maintain older adults’ health and well-being. Intergenerational programs represent a novel multifactorial approach to target frailty, social isolation and physical decline but these have not yet been rigorously tested in Australia. Here, we present the results of our pilot study which aimed to test the feasibility of a 10-week intergenerational program between older adults and preschool children. Methods A non-randomised wait-listed controlled trial was conducted. Participants were allocated to either the intervention or wait-list control group. The intervention group received 10 weekly 2-h intergenerational sessions led by trained child educators; the control group continued with their usual routine and received their intergenerational program after the 10-week control period. All participants were assessed at baseline and 10 weeks. The primary outcome was the feasibility and acceptability of the program including measures of recruitment eligibility, adherence and effective data collection across the multiple domains important for frailty, including functional mobility and balance, grip strength, cognitive function, mood, social engagement, quality of life and concerns about falling. Results Nineteen adults were included, with nine in the intervention and ten in the control group. A total of 42% of older adults screened were eligible, 75% of participants were present at each intervention session and the overall attrition rate was 21% (n = 4). The reasons for participant absence were primarily health-related. Missing data was minimal for the majority of assessments but more apparent for the cognitive testing where completion rates ranged from 53 to 79% for baseline tests and 73 to 100% for those who received follow-up testing. Conclusions The high program compliance and low attrition show that a 10-week intergenerational program embedded in the local community, designed for community-living older adults and preschool children, is feasible and acceptable to older adults. Our next trial will test the efficacy of intergenerational programs in this setting.

BackgroundMeasuring function with passive in-home sensors has the advantages of real-world, objective, continuous, and unobtrusive measurement. However, previous studies have focused on 1-person homes only, which limits their generalizability. ObjectiveThis study aimed to compare the life space activity patterns of participants living alone with those of participants living as a couple and to compare people with mild cognitive impairment (MCI) with cognitively normal participants in both 1- and 2-person homes. MethodsPassive infrared motion sensors and door contact sensors were installed in 1- and 2-person homes with cognitively normal residents or residents with MCI. A home was classified as an MCI home if at least 1 person in the home had MCI. Time out of home (TOOH), independent life space activity (ILSA), and use of the living room, kitchen, bathroom, and bedroom were calculated. Data were analyzed using the following methods: (1) daily averages over 4 weeks, (2) hourly averages (time of day) over 4 weeks, or (3) longitudinal day-to-day changes. ResultsIn total, 129 homes with people living alone (n=27, 20.9%, MCI and n=102, 79.1%, no-MCI homes) and 52 homes with people living as a couple (n=24, 46.2%, MCI and n=28, 53.8%, no-MCI homes) were included with a mean follow-up of 719 (SD 308) days. Using all 3 analysis methods, we found that 2-person homes showed a shorter TOOH, a longer ILSA, and shorter living room and kitchen use. In MCI homes, ILSA was higher in 2-person homes but lower in 1-person homes. The effects of MCI status on other outcomes were only found when using the hourly averages or longitudinal day-to-day changes over time, and they depended on the household type (alone vs residing as a couple). ConclusionsThis study shows that in-home behavior is different when a participant is living alone compared to when they are living as a couple, meaning that the household type should be considered when studying in-home behavior. The effects of MCI status can be detected with in-home sensors, even in 2-person homes, but data should be analyzed on an hour-to-hour basis or longitudinally.

Objectives To adapt the content and functionalities of Brain Health PRO, a web-based multidomain program designed to increase dementia literacy, to the context and needs of users, providers and community organisations across Québec, Canada.Design Five consecutive qualitative co-creation focus group sessions 30–90 min in duration each, exploring potential barriers and facilitators to usability, accessibility, comprehensibility, participant recruitment and retention.Setting Virtual meetings.Participants A 15-member team based in Québec and Ontario, Canada, consisting of 9 researchers (including a graduate student and the project coordinator), representing occupational therapy, sensory rehabilitation, neuropsychology, psychology, health science and research methods, 3 informal caregivers of older adults living with cognitive decline and 3 members of the Federation of Quebec Alzheimer Societies.Data analysis Session recordings were summarised through both qualitative description and thematic analysis.Results The synthesised recommendations included adjustments around diversity, the complexity and presentation styles of the materials, suggestions on refining the web interface and the measurement approaches; it influenced aspects of participant recruitment, retention efforts and engagement with the content of Brain Health PRO.Conclusions Co-creation in dementia prevention research is important because it involves collaboration between researchers, community support and service providers, and persons with lived experience as care providers, in the design and implementation of clinical studies. This approach helps to ensure that the content and presentation of educational material is relevant and meaningful to the target population and those involved in its delivery, and it leads to a greater understanding of their needs and perspectives.

BackgroundDescribing changes in health and behavior that precede and follow a sentinel health event, such as a cancer diagnosis, is challenging because of the lack of longitudinal, objective measurements that are collected frequently enough to capture varying trajectories of change leading up to and following the event. A continuous passive assessment system that continuously monitors older adults’ physical activity, weight, medication-taking behavior, pain, health events, and mood could enable the identification of more specific health and behavior patterns leading up to a cancer diagnosis and whether and how patterns change thereafter. ObjectiveIn this study, we conducted a proof-of-concept retrospective analysis, in which we identified new cancer diagnoses in older adults and compared trajectories of change in health and behaviors before and after cancer diagnosis. MethodsParticipants were 10 older adults (mean age 71.8, SD 4.9 years; 3/10, 30% female) with various self-reported cancer types from a larger prospective cohort study of older adults. A technology-agnostic assessment platform using multiple devices provided continuous data on daily physical activity via wearable sensors (actigraphy); weight via a Wi-Fi–enabled digital scale; daily medication-taking behavior using electronic Bluetooth-enabled pillboxes; and weekly pain, health events, and mood with online, self-report surveys. ResultsLongitudinal linear mixed-effects models revealed significant differences in the pre- and postcancer trajectories of step counts (P

Abstract Introduction Conventional Z‐scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these “adjusted” Z‐scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z‐scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency. Methods In this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance). Results Corrected Z‐scores based on nonlinear shape constrained additive models provide improved adjustment for age, sex, and education, as indicated by higher adjusted‐R2. Discussion Nonlinearly corrected Z‐scores with respect to age, sex, and education with age‐varying residual standard deviation allow for improved detection of non‐normative extreme cognitive scores.

Introduction: Clinically relevant information can go uncaptured in the conventional scoring of a verbal fluency test. We hypothesize that characterizing the temporal aspects of the response through a set of time related measures will be useful in distinguishing those with MCI from cognitively intact controls.Methods: Audio recordings of an animal fluency test administered to 70 demographically matched older adults (mean age 90.4 years), 28 with mild cognitive impairment (MCI) and 42 cognitively intact (CI) were professionally transcribed and fed into an automatic speech recognition (ASR) system to estimate the start time of each recalled word in the response. Next, we semantically cluster participant generated animal names and through a novel set of time-based measures, we characterize the semantic search strategy of subjects in retrieving words from animal name clusters. This set of time-based features along with standard count-based features (e.g., number of correctly retrieved animal names) were then used in a machine learning algorithm trained for distinguishing those with MCI from CI controls.Results: The combination of both count-based and time-based features, automatically derived from the test response, achieved 77% on AUC-ROC of the support vector machine (SVM) classifier, outperforming the model trained only on the raw test score (AUC, 65%), and well above the chance model (AUC, 50%).Conclusion: This approach supports the value of introducing time-based measures to the assessment of verbal fluency in the context of this generative task differentiating subjects with MCI from those with intact cognition.

BACKGROUND:Changes in criteria and differences in populations studied and methodology have produced a wide range of prevalence estimates for mild cognitive impairment (MCI). METHODS:Uniform criteria were applied to harmonized data from 11 studies from USA, Europe, Asia and Australia, and MCI prevalence estimates determined using three separate definitions of cognitive impairment. RESULTS:The published range of MCI prevalence estimates was 5.0%-36.7%. This was reduced with all cognitive impairment definitions: performance in the bottom 6.681% (3.2%-10.8%); Clinical Dementia Rating of 0.5 (1.8%-14.9%); Mini-Mental State Examination score of 24-27 (2.1%-20.7%). Prevalences using the first definition were 5.9% overall, and increased with age (P < .001) but were unaffected by sex or the main races/ethnicities investigated (Whites and Chinese). Not completing high school increased the likelihood of MCI (P ≤ .01). CONCLUSION:Applying uniform criteria to harmonized data greatly reduced the variation in MCI prevalence internationally.