Abstract
INTRODUCTION
Sleep disturbance is common in persons living with dementia (PLWD) and may contribute to day‐to‐day variability in cognitive functioning, with implications for daily activities and caregiving. However, traditional cognitive assessments may not capture these short‐term fluctuations. Ecological momentary cognitive testing (EMCT) enables repeated, real‐world assessment of cognitive performance. This study examined day‐to‐day associations between sleep and cognitive performance in PLWD–caregiver dyads.
METHODS
Baseline data from PLWD–caregiver dyads enrolled in a behavioral sleep trial were analyzed. PLWD completed smartphone‐based EMCT tasks assessing processing speed and response inhibition three times daily for 7 days, along with the Mini‐Mental State Examination (MMSE). Caregivers reported daily sleep outcomes for themselves and PLWD. Pearson correlations and mixed‐effects models adjusted for PLWD age, gender, education, and phone type.
RESULTS
EMCT performance correlated significantly with MMSE scores (r = 0.51–0.58, all p < 0.01). Higher EMCT scores were associated with shorter time in bed and greater percentage of good sleep in both PLWD and caregivers. Mixed‐effects models revealed significant sleep‐by‐session interactions: the association between PLWD sleep and processing speed was stronger in the evening than the morning, while longer caregiver time in bed was more strongly associated with PLWD cognitive performance in the morning.
DISCUSSION
EMCT captured within‐person, time‐varying associations between dyad sleep and cognitive performance in PLWD, highlighting its utility for identifying daily fluctuations relevant to functioning and caregiving. These findings support digital, dyadic approaches to monitoring sleep and cognitive function in dementia care.
Keywords: aging, cognitive function, digital assessment, dementia, EMA, sleep
Highlights
Ecological momentary cognitive testing captured day‐to‐day sleep–cognition dynamics.
Dyad sleep was associated with daily cognitive performance.
Sleep–cognition associations varied by time of day.
EMCT performance correlated with global cognitive status and detected within person sleep‐related variation.
Findings support dyadic, real‐world approaches to monitoring cognition in dementia care.
1. BACKGROUND
Poor sleep affects up to 40% of persons living with dementia (PLWD). 1 This high prevalence may reflect neurodegeneration in brain regions critical for sleep regulation, including the suprachiasmatic nucleus 2 and cortical regions within the default mode network. 3 , 4 Sleep disturbances may be further exacerbated by environmental and behavioral factors such as urinary incontinence and nighttime agitation, 5 and low daytime light exposure, 6 all of which disrupt circadian rhythms and further impair sleep. Poor sleep in PLWD is also associated with adverse sleep outcomes in caregivers 7 and insufficient caregiver sleep is linked to increased risk of chronic medical conditions. 8 Caregiver sleep quality may in turn influence the quality of care and cognitive support provided to PLWD, 9 motivating a dyadic approach to examining sleep–cognitions. Extended time in bed (TIB) is common among PLWD 10 and has been associated with increased dementia risk in the general population. 11 Together, these findings highlight the importance of monitoring and optimizing sleep in both PLWD and caregivers.
Cognitive function and sleep are dynamically linked. This relationship is bidirectional. Sleep disturbance accelerates neurodegeneration, while dementia‐related neurodegeneration further disrupts sleep regulation. 12 While sleep disturbance has been associated with cognitive impairment, 13 less is known about how nightly sleep variation relates to next‐day cognitive performance in PLWD. These short‐term fluctuations in sleep and next‐day cognitive performance may have meaningful implications for daily functioning and caregiving burden. Because standard cognitive assessments are typically administered infrequently, the within‐person sleep–cognition relationship in PLWD has been difficult to study. Ecological momentary cognitive testing (EMCT) enables repeated, real‐world assessment and may be uniquely suited to capture these fluctuations. 14 Yet, to our knowledge it has not been tested in this population.
This study examined the validity and utility of EMCT in PLWD and investigated day‐to‐day associations between sleep and cognitive performance within PLWD–caregiver dyads. Detecting sleep‐related cognitive fluctuations may help identify high‐risk periods for functional impairment, guide the timing of interventions, and improve dyadic care strategies.
2. METHODS
2.1. Study design, participants, and setting
This cross‐sectional study used baseline data from 111 participants enrolled in a clinical trial (NCT05452031) testing a behavioral dyadic sleep intervention for PLWD and their family caregivers in California, USA. PLWD were eligible if they: (a) had a documented dementia diagnosis; (b) were community dwelling; (c) reported at least one sleep problem occurring at least three times per week on the Neuropsychiatric Inventory Nighttime Behavior Scale 15 ; (d) were ambulatory; and (e) had an eligible family caregiver. Exclusion criteria included severe untreated sleep disorders, life expectancy < 6 months, or being bedbound. Caregivers were eligible if they: (a) lived with the PLWD; (b) were ≥ 18 years old; (c) provided assistance with at least one activities of daily living (ADLs) 16 or instrumental ADLs (IADLs) 17 for ≥ 6 months; (d) had poor sleep quality (Pittsburgh Sleep Quality Index score 18 > 5); (e) had intact cognition (Montreal Cognitive Assessment score 19 ≥ 23); and (f) spoke English. Written informed consent was obtained from caregivers and PLWD or legally authorized representatives.
2.2. Procedures
PLWD completed EMCT via web links sent to caregivers’ smartphones three times daily for 7 days, using the NeuroUX platform (Quick Tap 1 [QT1], Quick Tap 2 [QT2], or Matching Pairs [MP]). PLWD also completed the Mini‐Mental State Examination (MMSE) 20 . Caregivers completed daily Ecological Momentary Assessment (EMA) sleep diaries for themselves and PLWD.
2.3. Measures
All EMCT measures assessed processing speed; QT2 additionally assessed response inhibition. In QT1, participants tapped a target image as quickly as possible. In QT2, they tapped a target image while inhibiting responses to non‐target images. In MP, participants selected matching shapes/colors. Details are provided elsewhere, 21 and example screenshots are provided in Figure S1.
Data were cleaned per NeuroUX protocols. 22 Exclusions were made at the participant‐level (assisted testing [n = 4 participants] QT trials with zero accuracy, MP scores of zero only when accompanied by zero scores on both QT1 and QT2, and extreme values [± 3 standard deviations {SD}]) and trial‐level (computer‐based sessions [n = 45 sessions]). Weighted QT scores incorporating reaction time (capped at 2000 ms) and accuracy were calculated. 23 In total, 138/906 QT1, 149/926 QT2, and 99/935 MP trial‐level and participant‐level trials were excluded. Aggregate EMCT scores (mean, median, maximum, and variability) were computed across sessions. Higher scores indicate better performance; variability reflects intraindividual fluctuation.
Sleep was assessed using four daily diary questions completed by caregivers for themselves and PLWD over 7 days. The questions included: (a) bedtime (time lights were turned off and attempting to sleep), (b) two dichotomous disturbance items assessing whether it took more than 30 minutes to fall asleep and whether more than 30 minutes were spent awake during the night), and (c) get‐up time (time out of bed in the morning). “Good sleep” was defined as responding “no” to both disturbance items. Percentage of nights with good sleep was calculated. 24 TIB was computed as the difference between bedtime and get‐up time. Both average and daily sleep values were derived. Covariates included PLWD age, gender, education, and phone type used for EMCT (iOS/Android).
2.4. Data analysis
Descriptive statistics were computed. Pearson correlations examined associations between aggregated EMCT outcomes and percentage of nights with good sleep and average TIB. Descriptive and bivariate analyses included 75 dyads with valid aggregate EMCT data. Excluded dyads did not differ on key demographic or sleep variables. Mixed‐effect regression models tested interactions between prior‐night sleep and assessment sessions (morning, midday, and evening) predicting PLWD cognitive outcomes, adjusted for covariates, with final analytic samples of 1052 to 1117 observations across 91 dyads. Sensitivity analyses were conducted with MMSE scored added as a covariate. Significance was set at p < 0.05. Analyses were conducted in SAS 9.4.
RESEARCH IN CONTEXT
Systematic review: Across the studies identified in this review, single‐time‐point or clinic‐based cognitive assessments were the predominant approach, with only a small proportion of studies incorporating repeated or real‐world cognitive sampling methods such as ecological momentary cognitive testing (EMCT) among older adults. No studies used EMCT in samples focused on persons living with dementia (PLWD), leaving a critical gap in understanding real‐world cognitive variability in this population.
Interpretation: Using high‐frequency EMCT, this study found that dyad sleep was associated with daily cognitive performance in PLWD. Cognitive performance varies across the day, and sleep–cognition association differed by assessment timing. EMCT has the potential to capture within‐person variability in cognitive performance in real‐world settings.
Future directions: Longitudinal studies with larger sample sizes are needed to determine whether high‐frequency cognitive assessments improve sensitivity to sleep‐related cognitive changes, inform optimal timing of cognitive evaluations, and improve dyadic care in dementia research.
3. RESULTS
3.1. Cognitive and sleep characteristics
Characteristics of PLWD and caregivers with aggregate EMCT data (n = 75 dyads), including cognitive status (MMSE) and functional status (ADLs/IADLs), are presented in Table 1. Distributions of each EMCT measures are shown in Figure S2. Mean PLWD age was 78.8 years (SD 9.4), and mean MMSE score was 16.5 (SD 8.6). PLWD averaged 9.9 hours in bed and 52.4% of nights were classified as good sleep.
TABLE 1.
Participant characteristics (n = 75 dyads).
| Characteristics | Caregiver | PLWD |
|---|---|---|
| Age, mean (SD) | 64.38 (12.30) | 78.82 (9.43) |
| Gender, n (%) | ||
| Female | 60 (81.1) | 38 (51.4) |
| Male | 14 (18.9) | 36 (48.6) |
| Race, n (%) | ||
| White | 46 (62.2) | 50 (67.6) |
| Asian | 13 (17.6) | 10 (13.5) |
| Black | 4 (5.4) | 5 (6.8) |
| Other or more than one | 8 (10.8) | 4 (5.4) |
| Unknown | 3 (4.1) | 5 (6.8) |
| Ethnicity, n (%) | ||
| Not Hispanic | 61 (82.4) | 60 (81.1) |
| Hispanic | 13 (17.6) | 14 (18.9) |
| Education, n (%) | ||
| Less than high school graduate | 1 (1.4) | 9 (12.2) |
| High school diploma/equivalent | 3 (4.1) | 7 (9.5) |
| Some college or vocational school | 17 (23.0) | 24 (32.4) |
| College graduate | 23 (31.1) | 14 (18.9) |
| Graduate degree/professional school | 30 (40.5) | 20 (27.0) |
| ADLs score (0–6), mean (SD) | — | 4.22 (1.89) |
| IADLs score (0–8), mean (SD) | — | 1.78 (1.77) |
| EMCT measures, mean (SD) | ||
| QT1‐mean | — | 4.58 (1.40) |
| QT1‐median | — | 4.59 (1.47) |
| QT1‐standard deviation | — | 1.03 (0.45) |
| QT1‐maximum | — | 6.36 (1.42) |
| QT2‐mean | — | 5.63 (1.27) |
| QT2‐median | — | 5.74 (1.30) |
| QT2‐standard deviation | — | 0.97 (0.34) |
| QT2‐maximum | — | 7.11 (0.95) |
| MP‐mean | — | 116.85 (59.47) |
| MP‐median | — | 115.50 (60.88) |
| MP‐standard deviation | — | 33.27 (17.24) |
| MP‐maximum | — | 177.10 (81.60) |
| Phone type, n (%) | ||
| iOS | — | 57 (76.0) |
| Android | — | 18 (24.0) |
| MMSE, mean (SD) | — | 16.46 (8.62) |
| Sleep measures, mean (SD) | ||
| % days with good sleep | 47.36 (34.25) | 52.38 (36.40) |
| Hours in bed | 8.16 (1.31) | 9.92 (1.62) |
Note: Percentages may not sum exactly to 100 because of rounding. EMCT measures are mean (SD) across PLWDs’ aggregated within‐subject statistic (mean, median, maximum, and SD). Higher ADL and IADL scores indicate better daily functioning. n = 74 for demographics, n = 72 for QT2 & MP, and n = 68 for MMSE.
Abbreviations: ADLs, activities of daily living; EMCT, ecological momentary cognitive testing; IADL, instrumental ADLs; MMSE, Mini‐Mental State Examination; MP, Matching Pair; PLWD, persons living with dementia; QT1, Quick Tap 1; QT2, Quick Tap 2; SD, standard deviation.
3.2. Associations between EMCT and global cognitive function
EMCT performance was significantly associated with global cognitive status. Higher QT1 mean, QT2 mean, and MP mean scores were associated with higher MMSE scores (r = 0.57, 0.58, 0.51 respectively, all p < 0.01).
3.3. Associations between cognitive measures and PLWD sleep
In unadjusted analyses, higher QT1 mean scores were associated with shorter PLWD TIB (r = −0.22, p = 0.054). Higher QT2 maximum scores were significantly associated with shorter TIB (r = −0.30, p = 0.012). MMSE scores were also associated with shorter TIB (r = −0.37, p = 0.002).
After covariate adjustment (PLWD age, gender, education levels, and phone type), greater MP variability was associated with a higher percentage of good sleep among PLWD (r = 0.28, p = 0.030). MMSE remained significantly associated with shorter TIB (r = −0.38, p = 0.005).
3.4. Associations between cognitive measures and caregiver sleep
In unadjusted analyses, higher QT2 mean, median, and maximum scores were associated with greater caregiver percentage of good sleep (r = 0.29, p = 0.015; r = 0.26, p = 0.028; r = 0.35, p = 0.003) and shorter caregiver TIB (r = −0.25, p = 0.036; r = −0.25, p = 0.037; r = −0.34, p = 0.003). Higher MP mean and median scores were associated with shorter caregiver TIB (r = −0.25, p = 0.037–0.033), and higher MP median scores showed a trend toward association with caregiver good sleep (r = 0.23, p = 0.052).
After adjustment, higher QT1 mean scores were associated with shorter caregiver TIB (r = −0.25, p = 0.048). QT2 maximum scores remained significantly associated with greater caregiver good sleep (r = 0.27, p = 0.032) and shorter caregiver TIB (r = −0.33, p = 0.009).
3.5. Mixed‐effects models
Adjusted mixed‐effects models showed a significant main effect of session on QT1 performance with higher QT1 scores observed in the morning (b = 0.78, p < 0.001) and midday (b = 0.67, p < 0.001) compared with the evening (Table 2). A significant sleep‐by‐session interaction was observed for PLWD sleep and QT1 performance. Specifically, the association between PLWD prior‐night good sleep and QT1 performance differed by time of day, with a significantly weaker association in the morning compared to the evening (b = −0.53, p = 0.049), and no significant difference between midday and evening.
TABLE 2.
Mixed effects models with significant associations between EMCT and sleep measures.
| Model | b (SE) | 95% CI | p‐value |
|---|---|---|---|
| Quick tap 1 (dependent variable) | |||
| Intercept | 7.48 (3.18) | 1.17, 13.80 | 0.021 |
| Sleep (PLWD good sleep) | 3.66 (3.65) | −0.35, 1.08 | 0.316 |
| Session (reference: evening) | |||
| Morning | 0.78 (0.19) | 0.41, 1.15 | <0.001 |
| Midday | 0.67 (0.20) | 0.28, 1.06 | <0.001 |
| Sleep x Session (reference: evening) | |||
| Sleep x Morning | −0.53 (0.27) | −1.05, −0.003 | 0.049 |
| Sleep x Midday | 0.12 (0.28) | −0.04, 0.67 | 0.677 |
| Matching pair (dependent variable) | |||
| Intercept | 148.94 (65.54) | 18.66, 279.22 | 0.026 |
| Sleep (caregiver nighttime hours in bed) | −0.79 (2.02) | −5.01, 3.17 | 0.694 |
| Session (reference: evening) | |||
| Morning | −36.49 (12.34) | −60.70, −12.28 | 0.003 |
| Midday | −11.22 (13.03) | −36.80, 14.36 | 0.389 |
| Sleep × Session (reference: evening) | |||
| Sleep × Morning | 4.02 (1.47) | 1.14, 6.91 | 0.006 |
| Sleep × Midday | 1.09 (1.55) | −1.94, 4.13 | 0.480 |
Note: Models included: main effects of sleep, session, and day; interaction terms sleep‐by‐session and sleep‐by‐day; and covariates (PLWD age, gender, education, and type of phone used for EMCT). Results adjusted for PLWD age, gender, education levels, and phone type.
Abbreviations: CI, confidence interval; EMCT, ecological momentary cognitive testing; PLWD, persons living with dementia; SE, standard error.
In the model examining caregiver TIB and PLWD MP performance, a significant caregiver TIB‐by‐session interaction was also observed. The association between caregiver TIB and MP performance differed between morning and evening sessions, with a stronger positive association in the morning relative to the evening (b = 4.02, p = 0.006), while no significant difference was observed between midday and evening. No significant sleep‐by‐session interactions were observed for QT2 outcomes.
3.6. Sensitivity analysis results
Sensitivity analyses adding MMSE score as a covariate yielded largely consistent results. Key findings from both bivariate and mixed effects models were replicated, and overall findings were robust to the inclusion of MMSE as a covariate. Full results of adjusted analyses are shown in Tables S1–S4.
4. DISCUSSION
This study examined associations between dyad sleep and ecological momentary cognitive performance in PLWD using smartphone‐based assessments. EMCT measures demonstrated meaningful associations with sleep outcomes in both members of the dyad, and these associations were largely robust to adjustment for MMSE scores and other covariates. The strong correlations between EMCT performance and MMSE scores support the construct validity of this approach and suggest that smartphone‐based momentary tasks capture meaningful variance in global cognitive function, even across a wide range of dementia severity. These findings extend prior validation work on EMCT in cognitively intact and mildly impaired older adults to a population with more significant cognitive decline. 14 , 25
The relationship between sleep and EMCT‐based cognitive performance varied across the day. Better prior‐night sleep among PLWD was more strongly associated with cognitive performance in the evening than in the morning. Morning performance may be relatively preserved regardless of prior sleep quality due to residual alertness following the sleep period, whereas evening performance may be more sensitive to sleep‐related variations as cognitive resources become depleted across the day. This finding has practical implications for assessment timing; evening sessions may be more sensitive to sleep‐related cognitive fluctuation than morning sessions alone.
Associations between caregiver sleep and PLWD cognitive performance differed in the opposite direction, with a stronger positive association between caregiver TIB and PLWD morning MP performance relative to the evening. This may reflect the role of caregiver behavior in supporting PLWD cognitive performance: better‐rested caregivers may provide more attentive cognitive scaffolding during morning tasks, when structured caregiver–PLWD interaction is most common. These findings highlight the importance of considering both assessment timing and dyadic context when examining sleep–cognition relationships in PLWD and suggest that supporting caregiver sleep may have downstream benefits for PLWD functioning.
This study has several limitations. The cross‐sectional design and 7‐day observation window limit conclusions about temporal or bidirectional relationships between sleep and cognition. The sample was drawn from a behavioral sleep intervention trial, which may limit generalizability. Sleep was assessed via caregiver‐reported diary rather than actigraphy, and data on sleep medication use were not collected, which could influence both sleep patterns and cognitive performance. The modest sample size and the number of statistical tests conducted increase the risk of Type 1 error. Therefore, findings should be interpreted as preliminary and hypothesis generating, and replication in larger, more diverse samples using confirmatory analytic approaches is needed.
Overall, this study provides preliminary evidence that EMCT is feasible in PLWD and sensitive to sleep‐related cognitive variation that standard infrequent assessments are unlikely to capture. The moderation of sleep–cognition associations by time of day, and the contrasting pattern observed for caregiver versus PLWD sleep, highlight the value of ecologically sensitive, repeated‐measures approaches for cognitive monitoring in this population. Future studies with longer observation periods, objective sleep measurement, and larger samples are needed to characterize the temporal dynamics of sleep and cognition in PLWD and to evaluate the clinical utility of EMCT for guiding intervention timing and caregiver support.
CONFLICT OF INTEREST STATEMENT
Raeanne C. Moore is a co‐founder of KeyWise AI and a co‐founder and receives consulting fees from NeuroUX. The terms of these arrangements have been reviewed and approved by UC San Diego in accordance with its conflict‐of‐interest policies. Laura M. Campbell is a consultant for NeuroUX Inc. Jennifer L. Martin received research funding from MediBio, Inc. The other authors declare no conflicts of interest. Author disclosures are available in the Supporting Information.
CONSENT STATEMENT
All participants provided informed consent. For persons living with dementia who lacked capacity to consent, informed consent was obtained from their legally authorized representative.
Supporting information
Supporting Information: trc270279‐sup‐0001‐ICMJE.pdf
Supporting Information: trc270279‐sup‐0002‐SuppMat.docx
ACKNOWLEDGMENTS
Part of the results were submitted at the 2026 Alzheimer's Association International Conference. This study was supported by the National Institute on Aging (R01AG076756, Yeonsu Song). Jennifer L. Martin was supported by the National Heart Lung and Blood Institute (K24HL143055).
DATA AVAILABILITY STATEMENT
Data collection for the parent study is ongoing. The final dataset will be made available upon completion of the study in a relevant public data repository.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supporting Information: trc270279‐sup‐0001‐ICMJE.pdf
Supporting Information: trc270279‐sup‐0002‐SuppMat.docx
Data Availability Statement
Data collection for the parent study is ongoing. The final dataset will be made available upon completion of the study in a relevant public data repository.