Among participants with Alzheimer's disease (AD) we estimated the minimal clinically important difference (MCID) in apathy symptom severity on three scales.

Retrospective anchor- and distribution-based analyses of change in apathy symptom scores.

Apathy in Dementia Methylphenidate Trial (ADMET) and ADMET 2 randomized controlled trials conducted at three and ten clinics specialized in dementia care in United States and Canada, respectively.

Two hundred and sixty participants (60 ADMET, 200 ADMET 2) with clinically significant apathy in Alzheimer’s disease.

The Clinical Global Impression of Change in Apathy scale was used as the anchor measure and the MCID on the Neuropsychiatric Inventory – Apathy (NPI-A), Dementia Apathy Interview and Rating (DAIR), and Apathy Evaluation Scale-Informant (AES-I) were estimated with linear mixed models across all study visits. The estimated thresholds were evaluated with performance metrics.

Among the MCID was a decrease of four points (95% CI: −4.0 to −4.8) on the NPI-A, 0.56 points (95% CI: −0.47 to −0.65) on the DAIR, and three points on the AES-I (95% CI: −0.9 to −5.4). Distribution-based analyses were largely consistent with the anchor-based analyses. The MCID across the three measures showed ∼60% accuracy. Sensitivity analyses found that MMSE scores and apathy severity at baseline influenced the estimated MCID.

MCIDs for apathy on three scales will help evaluate treatment efficacy at the individual level. However, the modest correspondence between MCID and clinical impression of change suggests the need to consider other scales.

There is a high prevalence of sleep disturbances among people living with dementia (PLWD) in nursing homes. Reliable and valid measurements are needed to assess these disturbances. The aim of this systematic review was to identify, analyze and synthesize studies of sleep-related measurements to assess sleep disturbances in PLWD.

The databases PubMed, CINAHL, and PsycINFO were systematically searched in 2019; the search was updated in March 2024. The inclusion criteria were as follows: participants with dementia or probable dementia in any care setting; and studies that reported at least one of the following aspects: (I) theoretical and conceptual frameworks, (II) user or patient involvement by type of users in measurement development, (III) feasibility and practicability of measurements, and (IV) results of psychometric analyses. The quality of the included studies was evaluated using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) criteria and the quality appraisal tool for studies of diagnostic reliability (QAREL) tool.

A total of 5169 studies were identified; ultimately, 15 studies describing three self-administered measurements, three proxy-administered measurements and two technological measurements were included. No sleep-related measurement showed acceptable psychometric properties in any of the COSMIN domains.

No measurement without adaptation can be recommended for PLWD in nursing homes. If existing measurements are used in clinical practice, the self-perspective of PLWD should be taken into account. If this is no longer fully possible, proxy-rating perspectives in combination could be an option. Future research on sleep-related measurements should be strictly based on international consensus-based psychometric quality criteria.

In older patients with mental and physical multimorbidity (MPM), personality assessment is highly complex. Our aim was to examine personality traits in this population using the Hetero-Anamnestic Personality questionnaire (HAP), and to compare the premorbid perspective of patients’ relatives (HAP) with the present-time perspective of nursing staff (HAP-t).

Cross-sectional.

Dutch gerontopsychiatric nursing home (GP-NH) units.

Totally, 142 GP-NH residents with MPM (excluding dementia).

NH norm data of the HAP were used to identify clinically relevant premorbid traits. Linear mixed models estimated the differences between HAP and HAP-t trait scores (0–10). Agreement was quantified by intraclass correlation coefficients (ICCs). All HAP-HAP-t analyses were corrected for response tendency (RT) scores (−10–10).

78.4% of the patients had at least one premorbid maladaptive trait, and 62.2% had two or more. Most prevalent were: “disorderly” (30.3%), “unpredictable/impulsive” (29.1%) and “vulnerable” (27.3%) behavior. The RT of relatives appeared significantly more positive than that of nursing staff (+1.8, 95% CI 0.6–2.9, p = 0.002). After RT correction, the traits “vulnerable”, “perfectionist” and “unpredictable/impulsive” behavior scored higher on the HAP than HAP-t (respectively +1.2, 95% CI 0.6–1.7, p < 0.001; +2.1, 95% CI 1.3–2.8, p < 0.001; +0.6, 95% CI 0.1–1.1, p = 0.013), while “rigid” behavior scored lower (−0.7, 95% CI −1.3 to −0.03, p = 0.042). Adjusted ICCs ranged from 0.15 to 0.58.

Our study shows high percentages of premorbid maladaptive personality traits, which calls for attention on personality assessment in MPM NH residents. Results also indicate that the HAP and HAP-t questionnaires should not be used interchangeably for this patient group in clinical practice.

We aimed to examine associations between neuropsychiatric symptoms (NPS) and white matter hyperintensities (WMH) status in older adults without dementia under the hypothesis that WMH increased the odds of having NPS.

Longitudinal analysis of data acquired from the National Alzheimer’s Coordinating Center Uniform Data Set.

Data were derived from 46 National Institute on Aging – funded Alzheimer’s Disease Research Centers.

NACC participants aged ≥50 years with available data on WMH severity with a diagnosis of mild cognitive impairment (MCI) or who were cognitively unimpaired (CU) were studied. Among 4617 CU participants, 376 had moderate and 54 extensive WMH. Among 3170 participants with MCI, 471 had moderate and 88 had extensive WMH.

Using Cardiovascular Health Study (CHS) scores, WMH were coded as no to mild (CHS score: 0–4), moderate (score: 5–6) or extensive (score: 7–8). NPS were quantified on the Neuropsychiatric Inventory Questionnaire. Binary logistic regression models estimated the odds of reporting each of 12 NPS by WMH status separately for individuals with MCI or who were CU.

Compared to CU individuals with no to mild WMH, the odds of having elation [9.87, (2.63–37.10)], disinhibition [4.42, (1.28–15.32)], agitation [3.51, (1.29–9.54)] or anxiety [2.74, (1.28–5.88)] were higher for the extensive WMH group, whereas the odds of having disinhibition were higher for the moderate WMH group [1.94, (1.05–3.61)]. In the MCI group, he odds of NPS did not vary by WMH status.

Extensive WMH were associated with higher odds of NPS in CU older adults but not in those with MCI.

Few studies have examined the impact of late-life depression trajectories on specific domains of cognitive function. This study aims to delineate how different depressive symptom trajectories specifically affect cognitive function in older adults.

Prospective longitudinal cohort study

Australia and the United States of America

In total, 11,035 community-dwelling older adults with a mean age of 75 years

Depressive trajectories were modelled from depressive symptoms according to annual Centre for Epidemiological Studies Depression Scale 10 (CES-D-10) surveys. Four trajectories of depressive symptoms were identified: low (“nondepressed”), consistently mild (“subthreshold depression”), consistently moderate (“persistent depression”), and initially low but increasing (“emerging depression”). Global cognition (Modified Mini-Mental State Examination [3MS]), verbal fluency (Controlled Oral Word Association Test [COWAT]), processing speed (Symbol Digit Modalities Test [SDMT]), episodic memory (Hopkins Verbal Learning Test – Revised [HVLT-R]), and a composite z-score were assessed over a subsequent median 2 years.

Subthreshold depression predicted impaired performance on the SDMT (Cohen’s d −0.04) and composite score (−0.03); emerging depression predicted impaired performance on the SDMT (−0.13), HVLT-R (−0.09), 3 MS (−0.08) and composite score (−0.09); and persistent depression predicted impaired performance on the SDMT (−0.08), 3 MS (−0.11), and composite score (−0.09).

Depressive symptoms are associated with later impaired processing speed. These effects are small. Diverse depression trajectories have different impacts on cognitive function.

We aimed to psychometrically evaluate and validate a Japanese version of the Social Functioning in Dementia scale (SF-DEM-J) and investigate changes in social function in people with dementia during the coronavirus disease-19 (COVID-19) pandemic.

We interviewed people with mild cognitive impairment (MCI) and mild dementia and their caregivers during June 2020–March 2021 to validate patient- and caregiver-rated SF-DEM-J and compared their scores at baseline (April 2020 to May 2020) and at 6–8 months (January 2021 to March 2021) during a time of tighter COVID-19 restrictions.

The neuropsychology clinic in the Department of Psychiatry at Osaka University Hospital and outpatient clinic in the Department of Psychiatry and Neurology at Daini Osaka Police Hospital, Japan.

103 dyads of patients and caregivers.

SF-DEM-J, Mini-Mental State Examination, Neuropsychiatric Inventory, UCLA Loneliness Scale, and Apathy Evaluation Scale.

The scale’s interrater reliability was excellent and test–retest reliability was substantial. Content validity was confirmed for the caregiver-rated SF-DEM-J, and convergent validity was moderate. Caregiver-rated SF-DEM-J was associated with apathy, irritability, loneliness, and cognitive impairment. The total score of caregiver-rated SF-DEM-J and the score of Section 2, “communication with others,” significantly improved at 6–8 months of follow-up.

The SF-DEM-J is acceptable as a measure of social function in MCI and mild dementia. Our results show that the social functioning of people with dementia, especially communicating with others, improved during the COVID-19 pandemic, probably as a result of adaptation to the restrictive life.

Eating problems frequently occur in people with dementia with Lewy bodies (DLB), but few studies have investigated the clinical background of this phenomenon. This study examined the relationship between eating problems and various symptoms of DLB and the relation between the treatment needs for DLB people with eating problems and the understanding of their eating problems by caregivers and physicians.

This was a subanalysis of a cross-sectional, questionnaire-based survey study. Two hundred sixty-one subjects with DLB were divided into subjects with or without eating problems. Logistic or linear regression analysis was used to investigate the factors influencing eating problems. The treatment needs of DLB people for their eating problems and the understanding of these needs by caregivers and physicians were calculated as participant–caregiver and participant–physician kappa coefficient.

Of the 261 participants, 27% suffered from eating problems. The presence of eating problems in participants with DLB was related to depression (p = 0.01, OR : 2.19, 95% CI: 1.23–3.91) and apathy (p = 0.01, OR 2.15, 95% CI: 1.20–3.87), while the worsening of eating problems was related to dysphagia (β = 0.24, p = 0.03), apathy (β = 0.23, p = 0.05), and nighttime behavior (β = 0.24, p = 0.04). The participant–physician kappa coefficient for physician understanding of constipation, weight loss, dysphagia, weight gain, and increase in appetite was significantly lower than the corresponding participant–caregiver kappa coefficient (p-value of five symptoms < 0.01).

Physicians need to pay more attention to eating problems and their neuropsychiatric background in the long-term support and management of DLB subjects.

Cognition in MCI has responded poorly to pharmacological interventions, leading to use of computerized training. Combining computerized cognitive training (CCT) and functional skills training software (FUNSAT) produced improvements in 6 functional skills in MCI, with effect sizes >0.75. However, 4% of HC and 35% of MCI participants failed to master all 6 tasks. We address early identification of characteristics that identify participants who do not graduate, to improve later interventions.

NC participants (n = 72) received FUNSAT and MCI (n = 92) participants received FUNSAT alone or combined FUNSAT and CCT on a fully remote basis. Participants trained twice a week for up to 12 weeks. Participants “graduated” each task when they made one or fewer errors on all 3–6 subtasks per task. Tasks were no longer trained after graduation.

Between-group comparisons of graduation status on baseline completion time and errors found that failure to graduate was associated with more baseline errors on all tasks but no longer completion times. A discriminant analysis found that errors on the first task (Ticket purchase) uniquely separated the groups, F = 41.40, p < .001, correctly classifying 94% of graduators. An ROC analysis found an AUC of .83. MOCA scores did not increase classification accuracy.

More baseline errors, but not completion times, predicted failure to master all FUNSAT tasks. Accuracy of identification of eventual mastery was exceptional. Detection of risk to fail to master training tasks is possible in the first 15 minutes of the baseline assessment. This information can guide future enhancements of computerized training.

Late-life depression (LLD) is common and frequently co-occurs with neurodegenerative diseases of aging. Little is known about how heterogeneity within LLD relates to factors typically associated with neurodegeneration. Varying levels of anxiety are one source of heterogeneity in LLD. We examined associations between anxiety symptom severity and factors associated with neurodegeneration, including regional brain volumes, amyloid beta (Aβ) deposition, white matter disease, cognitive dysfunction, and functional ability in LLD.

Older adults with major depression (N = 121, Ages 65–91) were evaluated for anxiety severity and the following: brain volume (orbitofrontal cortex [OFC], insula), cortical Aβ standardized uptake value ratio (SUVR), white matter hyperintensity (WMH) volume, global cognition, and functional ability. Separate linear regression analyses adjusting for age, sex, and concurrent depression severity were conducted to examine associations between anxiety and each of these factors. A global regression analysis was then conducted to examine the relative associations of these variables with anxiety severity.

Greater anxiety severity was associated with lower OFC volume (β = −68.25, t = −2.18, p = .031) and greater cognitive dysfunction (β = 0.23, t = 2.46, p = .016). Anxiety severity was not associated with insula volume, Aβ SUVR, WMH, or functional ability. When examining the relative associations of cognitive functioning and OFC volume with anxiety in a global model, cognitive dysfunction (β = 0.24, t = 2.62, p = .010), but not OFC volume, remained significantly associated with anxiety.

Among multiple factors typically associated with neurodegeneration, cognitive dysfunction stands out as a key factor associated with anxiety severity in LLD which has implications for cognitive and psychiatric interventions.

Geriatric depression (GD) is associated with cognitive impairment and brain atrophy. Tai-Chi-Chih (TCC) is a promising adjunct treatment to antidepressants. We previously found beneficial effects of TCC on resting state connectivity in GD. We now tested the effect of TCC on gray matter volume (GMV) change and the association between baseline GMV and clinical outcome.

Forty-nine participants with GD (>=60 y) underwent antidepressant treatment (38 women).

Participants completed 3 months of TCC (N = 26) or health and wellness education control (HEW; N = 23).

Depression and anxiety symptoms and MRI scans were acquired at baseline and 3-month follow-up. General linear models (GLMs) tested group-by-time interactions on clinical scores. Freesurfer 6.0 was used to process T1-weighted images and to perform voxel-wise whole-brain GLMs of group on symmetrized percent GMV change, and on the baseline GMV and symptom change association, controlling for baseline symptom severity. Age and sex served as covariates in all models.

There were no group differences in baseline demographics or clinical scores, symptom change from baseline to follow-up, or treatment-related GMV change. However, whole-brain analysis revealed that lower baseline GMV in several clusters in the TCC, but not the HEW group, was associated with larger improvements in anxiety. This was similar for right precuneus GMV and depressive symptoms.

While we observed no effect on GMV due to the interventions, baseline regional GMV predicted symptom improvements with TCC but not HEW. Longer trials are needed to investigate the long-term effects of TCC on clinical symptoms and neuroplasticity.

The association between sleep quality and cognition is widely established, but the role of aging in this relationship is largely unknown.

To examine how age impacts the sleep–cognition relationship and determine whether there are sensitive ranges when the relationship between sleep and cognition is modified. This investigation could help identify individuals at risk for sleep-related cognitive impairment.

Sample included 711 individuals (ages 36.00–89.83, 59.66 ± 14.91, 55.7 % female) from the Human Connectome Project-Aging (HCP-A).

The association between sleep quality (Pittsburgh Sleep Quality Index, PSQI) and cognition (Crystallized Cognition Composite and Fluid Cognition Composite from the NIH Toolbox, the Trail Making Test, TMT, and the Rey Auditory Verbal Learning Test, RAVLT) was measured using linear regression models, with sex, race, use of sleep medication, hypertension, and years of education as covariates. The interaction between sleep and age on cognition was tested using the moderation analysis, with age as both continuous linear and nonlinear (quadratic) terms.

There was a significant interaction term between the PSQI and nonlinear age term (age2) on TMT-B (p = 0.02) and NIH Toolbox crystallized cognition (p = 0.02), indicating that poor sleep quality was associated with worse performance on these measures (sensitive age ranges 50–75 years for TMT-B and 66–70 years for crystallized cognition).

The sleep–cognition relationship may be modified by age. Individuals in the middle age to early older adulthood age band may be most vulnerable to sleep-related cognitive impairment.

The aim of this systematic review is to examine the cognitive impact of psychotropic medications including benzodiazepines, antidepressants, mood stabilizers, antipsychotics, or a combination of these drugs on older adults.

Systematic review.

We searched Medline, PsycINFO, and Embase through the Ovid platform, CINAHL through EBSCO, and Web of Science.

Randomized control trials (RCTs) and cohort studies that used a validated scale to measure cognition with a follow-up period of at least six months were included.

The primary outcome of interest was cognitive change associated with psychotropic medication use.

A total of 7551 articles were identified from the primary electronic literature search across the five databases after eliminating duplicates. Based on full-text analysis, 27 articles (two RCTs, 25 cohorts) met the inclusion criteria. Of these, nine each examined the impact of benzodiazepines and antidepressants, five examined psychotropic combinations, three on antipsychotic drugs, and one on the effects of mood stabilizers.

This is the first systematic review to examine the cognitive impact of multiple psychotropic drug classes in older adults over an extended follow-up period (six months or more) using robust sample sizes, drug-free control groups, and validated cognitive instruments. We found evidence to indicate cognitive decline with the cumulative use of benzodiazepines and the use of antidepressants, especially those with anticholinergic properties among older adults without cognitive impairment at baseline. Further, the use of antipsychotics and psychotropic combinations is also associated with cognitive decline in older adults.