Background:Sleep-dependent memory consolidation (SDMC), the process by which sleep supports the transfer of memories into long-term storage, declines with age but remains underexplored in older adults with subjective cognitive decline and mild cognitive impairment. Traditional SDMC assessments are typically conducted in lab settings, with limited evidence for feasibility to do these assessments at home for this clinical population. Objective:To address this, we co-designed the Sleep Memories app, which assesses overnight memory for a previously validated 32-item word-pairs task. This pilot study first explored the feasibility and acceptability of the app, including willingness to participate. Second, we explored various demographic, clinical, and subjective sleep factors associated with task completion and SDMC performance in a memory clinic sample. Methods:Within an 8-month period, we invited 141 older adults aged 50 years and above (mean 71.27, SD 7.51 y) from the Healthy Brain Ageing clinic, a specialist brain health and memory clinic in Sydney, to pilot the Sleep Memories app. Of these, 76 (mean 70.19, SD 7.75) agreed to participate. All participants underwent a full neuropsychological test battery, medical assessment, and mood assessment. Results:Sixty-eight participants completed at least 1 test of the word-pairs task. The word-pairs task completion rate for all trials was over 50%. There was 57% (39/68) completion of both evening and morning delayed recall tests. Lower willingness to participate was associated with lower global cognitive scores, poorer sleep behavior, and clinical factors. Higher task completion was associated with greater education and greater anxiety levels. User feedback indicated that the app was well-accepted and liked, although some participants reported minor technical difficulties. Conclusions:These findings support the feasibility of mobile app-based SDMC assessment in older adults at risk of cognitive decline and underscore the importance of considering individual characteristics (eg, subjective sleep factors, clinical characteristics, and education) when designing digital SDMC tools.

Background:Sleep-dependent memory (SDM) is the phenomenon where newly obtained memory traces are consolidated from short-term memory stores to long-term memory, underpinning memory for daily life. Administering SDM tasks presents considerable challenges, particularly for older adults with memory concerns, due to the need for sleep laboratories and research staff being present to administer the task. In response, we have developed a prototype mobile app aimed at automating the data collection process. Objective:This study investigates the perspectives of older adults, with subjective or objective cognitive impairment, regarding barriers and facilitators to using a new mobile app for at-home assessment of SDM. Methods:In total, 11 participants aged 50 years and older were recruited from the Healthy Brain Ageing memory clinic, a specialized research memory clinic that focuses on the assessment and early intervention of cognitive decline. Two focus groups were conducted and thematically analyzed using NVivo (version 13; Lumivero). Results:On average, participants were aged 68.5 (SD 5.1) years, and 4/11 were male. Eight participants had subjective cognitive impairment, and 3 participants had mild cognitive (objective) impairment. Two main themes emerged from the focus groups, shedding light on participants' use of mobile phones and the challenges and facilitators associated with transitioning from traditional laboratory-based assessments to home assessments. These challenges include maintaining accurate data, engaging with humans versus robots, and ensuring accessibility and task compliance. Additionally, potential solutions to these challenges were identified. Conclusions:Our findings underscore the importance of app flexibility in accommodating diverse user needs and preferences as well as in overcoming barriers. While some individuals required high-level assistance, others expressed the ability to navigate the app independently or with minimal support. In conclusion, older adults provided valuable insights into the app modifications, user needs, and accessibility requirements enabling home-based SDM assessment.

While sleep disturbances are prevalent in older people and are linked with poor health and cognitive outcomes, screening for the range of sleep disturbances is inefficient and therefore not ideal nor routine in memory and cognition clinic settings. We aimed to develop and validate a new brief self-report questionnaire for easy use within memory and cognition clinics. The design for this study was cross-sectional. Older adults (aged ≥50 in Sydney, Australia) were recruited from a memory and cognition research clinic. Participants (N = 497, mean age 67.7 years, range 50-86, 65.0% female) completed a comprehensive medical, neuropsychological, and mental health assessment, alongside self-report instruments, including existing sleep questionnaires and a new 10-item sleep questionnaire, the CogSleep Screener. We examined the factor structure, convergent validity, internal consistency, and discriminant validity of this novel questionnaire. Using exploratory factor analysis, a 3-factor solution was generated highlighting the factors of Insomnia, Rapid Eye Movement (REM) Symptoms and Daytime Sleepiness. Each factor was significantly correlated with currently used sleep questionnaires for each subdomain (all Spearman rho >0.3, all p < 0.001), suggesting good convergent validity. Internal consistency was also good (Revelle's ω = .74). Receiver operating characteristic curves showed good discriminative ability between participants with and without sleep disturbances (all area under curve >0.7, all p < 0.01). The CogSleep Screener has good psychometric properties in older to elderly adults attending a memory and cognition clinic. The instrument has the potential to be used in memory clinics and other clinical settings to provide quick and accurate screening of sleep disturbances. [Correction added on April 2025, after first publication: The number of participants has been updated and associated statistics have been updated].

OBJECTIVE:The study explored the experiences of Australian aged care providers in supporting clients on a home care package to die at home.METHODS:Semistructured interviews were conducted with 13 aged care managers responsible for delivering services under the Home Care Package Program. Interviews were analysed thematically.RESULTS:Four themes emerged that illuminated managers' experiences: struggling to meet a preference to die at home; lack of opportunities to build workforce capacity in end-of-life care; challenges in negotiating fragmented funding arrangements between health and aged care providers; and mixed success in collaborating across sectors.CONCLUSIONS:Aged care providers want to support older Australians who prefer to stay at home at the end of life. However, most clients are admitted to a residential facility when their care needs exceed a home care budget long before a specialist palliative care team will intervene. Budgets for health and aged care providers must be sufficient and flexible to support timely access to end-of-life care, to reward collaboration across sectors and to invest in building palliative care skills in the nursing and personal care workforce.

Prior work has shown that sleep is critical for memory through a process called sleep-dependent memory (SDM). However, SDM appears to diminish with age, and may be further compromised in those with mild cognitive impairment (MCI) and dementia. Typically, studies examining SDM have been small, often restricted by the need for administration in the sleep laboratory. Consequently, understanding of SDM decrements in MCI has been limited, hindering targeted developments of treatments for SDM. We aimed to develop an app-based ‘chatbot’ for measuring SDM, allowing data to be obtained at scale and in response to treatments without the need for sleep laboratory attendance. An initial prototype of the chatbot was developed for Android and iOS utilising a 32-item word-pair task demonstrated to be suitable for people with MCI. The task delivery included pre-sleep learning and memory and post-sleep recall. To reduce content-specific practice effects, an alternative word-pair list was included for repeat testing. Co-design focus group methodologies were used to ascertain usability and acceptability for older people with MCI who attended a memory clinic. Participants (N = 11) attended one of two 90-minute focus groups in which semi-structured questions probed technology use and gathered feedback for app improvement. Participant feedback validated that core useability and content features of the chatbot were appropriately developed for the specific use of older adults at risk of dementia. Participants were comfortable with the chatbot obtaining demographics and habitual sleep and wake times and providing notifications for completing the task pre- and post-sleep. No negative feedback was received on the layout of the instructions for the test or delivery of the word-pairs task. Suggested improvements included ability to defer testing and customise notifications, and provision of performance feedback or rewards for compliance. This information was used to further develop the chatbot. The SDM chatbot app will now undergo further clinical and psychometric testing to determine how performance changes in relation to, rest-activity rhythms, clinical features, and sleep and dementia biomarkers.

The ability to recognise emotion from faces or voices appears to decline with advancing age. However, some studies have shown that emotion recognition of auditory-visual (AV) expressions is largely unaffected by age, i.e., older adults get a larger benefit from AV presentation than younger adults resulting in similar AV recognition levels. An issue with these studies is that they used well-recognised emotional expressions that are unlikely to generalise to real-life settings. To examine if an AV emotion recognition benefit generalizes across well and less well recognised stimuli, we conducted an emotion recognition study using expressions that had clear or unclear emotion information for both modalities, or clear visual, but unclear auditory information. Older (n = 30) and younger (n = 30) participants were tested on stimuli of anger, happiness, sadness, surprise, and disgust (expressed in spoken sentences) in auditory-only (AO), visual-only (VO), or AV format. Participants were required to respond by choosing one of 5 emotion options. Younger adults were more accurate in recognising emotions than older adults except for clear VO expressions. Younger adults showed an AV benefit even when unimodal recognition was poor. No such AV benefit was found for older adults; indeed, AV was worse than VO recognition when AO recognition was poor. Analyses of confusion responses indicated that older adults generated more confusion responses that were common between AO and VO conditions, than younger adults. We propose that older adults' poorer AV performance may be due to a combination of weak auditory emotion recognition and response uncertainty that resulted in a higher cognitive load.

In higher-level cognitive tasks, older compared to younger adults show a bias towards positive emotion information and away from negative information (a positivity effect). It is unclear whether this effect occurs in early perceptual processing. This issue is important for determining if the positivity effect is due to automatic rather than controlled processing. We tested this with older and younger adults on a positive/negative face emotion valence classification task using masked priming. Positive (happy) and negative (angry) face targets were preceded by masked repetition or valence primes with neutral face baselines. In Experiment 1, 30 younger and 30 older adults were tested with 50 ms primes. Younger adults showed repetition priming for both positive and negative targets. Older adults showed repetition priming for positive but not negative targets. Neither group showed valence priming. In Experiment 2, 30 older and 29 younger adults were tested with longer duration primes. Younger adults showed repetition priming for both positive and negative emotions, and no valence priming. Older adults only showed repetition and valence priming for positive targets. We proposed older adults' lack of angry face priming was due to an early attention orienting strategy favouring happy expressions at the expense of angry ones.

Changes in social behavior are recognized as potential symptoms of behavioral-variant frontotemporal dementia (bvFTD) and semantic dementia (SD), yet objective ways to assess these behaviors in natural social situations are lacking. This study takes a truly social (or second-person) approach and examines changes in real-world social behavior in different dementia syndromes, by analyzing non-scripted social interactions in bvFTD patients (n = 20) and SD patients (n = 20), compared to patients with Alzheimer's disease (AD) (n = 20). Video recordings of 10-min conversations between patients and behavioral neurologists were analyzed for the presence of socially engaging (e.g., nodding, smiling, gesturing) and disengaging behavior (e.g., avoiding eye contact, self-grooming, interrupting). Results demonstrated disease-specific profiles, with bvFTD patients showing less nodding and more looking away than AD, and SD patients showing more gesturing than AD. A principal components analysis revealed the presence of four unobserved components, showing atypical disengaging patterns of behavior. Whole-brain voxel-based morphometry analyses revealed distinct neurobiological bases for each of these components, with the brain regions identified previously associated with behavior selection, abstract mentalization and processing of multi-sensory and socially-relevant information, in mediating socially engaging and disengaging behavior. This study demonstrates the utility of systematic behavioral observation of social interactions in the differential diagnosis of dementia.

Talkers produce different types of spoken prosody by varying acoustic cues (e.g., F0, duration, and amplitude), also making complementary head and face movements (visual prosody). Perceivers can categorise auditory and visual prosodic expressions at high levels of accuracy. Research using eye-tracking trained participants to recognise the visual prosody of two-word sentences and found that the upper face is more critical for determining prosody than the lower face. However, recent studies using longer sentences have shown that untrained perceivers can match lower and upper faces across modalities. Given these, we aimed to extend the eye-tracking research by examining the gaze patterns of untrained participants when judging prosody with longer utterances. Twelve participants were presented questions, narrowly focussed, or broad focussed (neutral) utterances for a 3 alternative forced-choice identification task while eye gaze was recorded. Identification accuracy was high (81-97%) and did not differ among expression types. Participants gazed at eye regions longer and more often than mouth regions for all expressions. They gazed less at the mouth region for questions than for broad and narrow focussed statements. These results are consistent with the early research indicating the importance of the upper face for determining visual prosody.

Talkers can express different meanings or emotions without changing what is said by changing how it is said (by using both auditory and/or visual speech cues). Typically, cue strength differs between the auditory and visual channels: linguistic prosody (expression) is clearest in audition; emotional prosody is clearest visually. We investigated how well perceivers can match auditory and visual linguistic and emotional prosodic signals. Previous research showed that perceivers can match linguistic visual and auditory prosody reasonably well. The current study extended this by also testing how well auditory and visual spoken emotion expressions could be matched. Participants were presented a pair of sentences (consisting of the same segmental content) spoken by the same talker and were required to decide whether the pair had the same prosody. Twenty sentences were tested with two types of prosody (emotional vs. linguistic), two talkers, and four matching conditions: auditory-auditory (AA); visual-visual (VV); auditory-visual (AV); and visual-auditory (VA). Linguistic prosody was accurately matched in all conditions. Matching emotional expressions was excellent for VV, poorer for VA, and near chance for AA and AV presentations. These differences are discussed in terms of the relationship between types of auditory and visual cues and task effects.

The study examined the recognition of emotional speech as a function of the clarity of expression, the modality of presentation, and participants’ age (Mage = 19.8 vs. 73.9). Based on the results of a previous study, expression clarity was varied by selecting AuditoryVisual (AV) recordings of one actor who had well recognised expressions of anger, happiness, sadness, surprise, disgust, and neutral and one actor who did not. The young (n = 24) and older (n = 19) participants were presented these stimuli in Auditory-Only (AO), Visual-Only (VO), or AV format and made a forcedchoice judgement on each. Older adults performed worse than younger ones for all presentation modalities except clear VO expressions. Importantly, whereas younger adults showed an AV benefit (AV > VO), older adults did not (showing a presentation mode by clarity interaction). The importance of varying signal clarity when investigating age effects was discussed.