An Actigraphy-Based Validation Study of the Sleep Disorder Inventory in the Nursing Home

Intervention for sleep problems in nursing home residents with dementia: a cluster-randomized study

OBJECTIVE

To reduce sleep problems in people living with dementia using a multi-component intervention.

DESIGN

Cluster-randomized controlled study with two parallel groups and a follow-up of 16 weeks.

SETTING

Using external concealed randomization, 24 nursing homes (NH) were allocated either to the intervention group (IG, 12 clusters, 126 participants) or the control group (12 clusters, 116 participants).

PARTICIPANTS

Participants were eligible if they had dementia or severe cognitive impairment, at least two sleep problems, and residence of at least two weeks in a NH.

INTERVENTION

The 16-week intervention consists of six components: (1) assessment of sleep-promoting activities and environmental factors in NHs, (2) implementation of two "sleep nurses," (3) basic education, (4) advanced education for staff, (5) workshops to develop sleep-promoting concepts, and (6) written information and education materials. The control group (CG) received standard care.

MEASUREMENTS

Primary outcome was ≥ two sleep problems after 16 weeks assessed with the Sleep Disorders Inventory (SDI).

RESULTS

Twenty-two clusters (IG = 10, CG = 12) with 191 participants completed the study. At baseline, 90% of people living with dementia in the IG and 93% in the CG had at least two sleep problems. After 16 weeks, rates were 59.3% (IG) vs 83.8% (CG), respectively, a difference of -24.5% (95% CI, -46.3% - -2.7%; cluster-adjusted odds ratio 0.281; 95% CI 0.087-0.909). Secondary outcomes showed a significant difference only for SDI scores after eight and 16 weeks.

CONCLUSIONS

The MoNoPol-Sleep intervention reduced sleep problems of people living with dementia in NH compared to standard care.

Machine learning-based predictive models for the occurrence of behavioral and psychological symptoms of dementia: model development and validation

The behavioral and psychological symptoms of dementia (BPSD) are challenging aspects of dementia care. This study used machine learning models to predict the occurrence of BPSD among community-dwelling older adults with dementia. We included 187 older adults with dementia for model training and 35 older adults with dementia for external validation. Demographic and health data and premorbid personality traits were examined at the baseline, and actigraphy was utilized to monitor sleep and activity levels. A symptom diary tracked caregiver-perceived symptom triggers and the daily occurrence of 12 BPSD classified into seven subsyndromes. Several prediction models were also employed, including logistic regression, random forest, gradient boosting machine, and support vector machine. The random forest models revealed the highest area under the receiver operating characteristic curve (AUC) values for hyperactivity, euphoria/elation, and appetite and eating disorders; the gradient boosting machine models for psychotic and affective symptoms; and the support vector machine model showed the highest AUC. The gradient boosting machine model achieved the best performance in terms of average AUC scores across the seven subsyndromes. Caregiver-perceived triggers demonstrated higher feature importance values across the seven subsyndromes than other features. Our findings demonstrate the possibility of predicting BPSD using a machine learning approach.

How do care home staff use non-pharmacological strategies to manage sleep disturbances in residents with dementia: The SIESTA qualitative study

Background

Sleep disturbances affect 38% of care home residents living with dementia. They are often treated with medication, but non-pharmacological interventions may be safer and effective yet more difficult to implement. In the SIESTA study (Sleep problems In dEmentia: interviews with care home STAff) we explored care home staffs’ experience of managing sleep disturbances in their residents living with dementia.

Methods

We conducted one-to-one semi-structured interviews in four UK care homes, and purposively recruited a maximum variation sample of 18 nurses and care assistants, who were each interviewed once. We used a topic guide and audio-recorded the interviews. Two researchers independently analysed themes from transcribed interviews.

Results

Staff used a range of techniques that often worked in improving or preventing residents’ sleep disturbance. During the daytime, staff encouraged residents to eat well, and be physically active and stimulated to limit daytime sleep. In the evening, staff settled residents into dark, quiet, comfortable bedrooms often after a snack. When residents woke at night, they gave them caffeinated tea or food, considered possible pain and discomfort, and reassured residents they were safe. If residents remained unsettled, staff would engage them in activities. They used telecare to monitor night-time risk. Staff found minimising daytime napping difficult, described insufficient staffing at night to attend to reorient and guide awake residents and said residents frequently did not know it was night-time.

Conclusions

Some common techniques, such as caffeinated drinks, may be counterproductive. Future non-pharmacological interventions should consider practical difficulties staff face in managing sleep disturbances, including struggling to limit daytime napping, identifying residents’ night-time needs, day-night disorientation, and insufficient night-time staffing.

Care home residents with dementia: Prevalence, incidence, and associations with sleep disturbance in an English cohort study

INTRODUCTION

People living with dementia in care homes often have sleep disturbances, but little is known about incidence and importance.

METHODS

We interviewed 1483 participants in 97 care homes and report prevalence, 1-year incidence, and baseline associations of clinically significant sleep disturbance in people with dementia.

RESULTS

Baseline prevalence of clinically significant sleep disturbance was 13.7% (200/1460); 31.3% (457/1462) had them at least once over 16 months. One-year incidence was 25.2%. At baseline, residents with sleep disturbance had lower quality of life (mean difference -4.84; 95% confidence interval [CI] -6.53 to -3.16) and were more frequently prescribed sleep medications (odds ratio 1.75; CI 1.17 to 2.61) than other residents.

DISCUSSION

Approximately one-third of care home residents with dementia have or develop sleep disturbances over 1 year. These are associated with lower quality of life and prescription of sedatives, which may have negative outcomes; therefore, it is important to develop effective treatments.

Factors Associated With Behavioral and Psychological Symptoms of Dementia: Prospective Observational Study Using Actigraphy

BACKGROUND

Although disclosing the predictors of different behavioral and psychological symptoms of dementia (BPSD) is the first step in developing person-centered interventions, current understanding is limited, as it considers BPSD as a homogenous construct. This fails to account for their heterogeneity and hinders development of interventions that address the underlying causes of the target BPSD subsyndromes. Moreover, understanding the influence of proximal factors-circadian rhythm-related factors (ie, sleep and activity levels) and physical and psychosocial unmet needs states-on BPSD subsyndromes is limited, due to the challenges of obtaining objective and/or continuous time-varying measures.

OBJECTIVE

The aim of this study was to explore factors associated with BPSD subsyndromes among community-dwelling older adults with dementia, considering sets of background and proximal factors (ie, actigraphy-measured sleep and physical activity levels and diary-based caregiver-perceived symptom triggers), guided by the need-driven dementia-compromised behavior model.

METHODS

A prospective observational study design was employed. Study participants included 145 older adults with dementia living at home. The mean age at baseline was 81.2 (SD 6.01) years and the sample consisted of 86 (59.3%) women. BPSD were measured with a BPSD diary kept by caregivers and were categorized into seven subsyndromes. Independent variables consisted of background characteristics and proximal factors (ie, sleep and physical activity levels measured using actigraphy and caregiver-reported contributing factors assessed using a BPSD diary). Generalized linear mixed models (GLMMs) were used to examine the factors that predicted the occurrence of BPSD subsyndromes. We compared the models based on the Akaike information criterion, the Bayesian information criterion, and likelihood ratio testing.

RESULTS

Compared to the GLMMs with only background factors, the addition of actigraphy and diary-based data improved model fit for every BPSD subsyndrome. The number of hours of nighttime sleep was a predictor of the next day's sleep and nighttime behaviors (odds ratio [OR] 0.9, 95% CI 0.8-1.0; P=.005), and the amount of energy expenditure was a predictor for euphoria or elation (OR 0.02, 95% CI 0.0-0.5; P=.02). All subsyndromes, except for euphoria or elation, were significantly associated with hunger or thirst and urination or bowel movements, and all BPSD subsyndromes showed an association with environmental change. Age, marital status, premorbid personality, and taking sedatives were predictors of specific BPSD subsyndromes.

CONCLUSIONS

BPSD are clinically heterogeneous, and their occurrence can be predicted by different contributing factors. Our results for various BPSD suggest a critical window for timely intervention and care planning. Findings from this study will help devise symptom-targeted and individualized interventions to prevent and manage BPSD and facilitate personalized dementia care.

Ambient bright light treatment improved proxy-rated sleep but not sleep measured by actigraphy in nursing home patients with dementia: a placebo-controlled randomised trial

BACKGROUND

Up to 70% of nursing home patients with dementia suffer from sleep problems. Light is the main zeitgeber to the circadian system and thus has a fundamental impact on sleep-wake behaviour. Low indoor light levels in nursing homes have been reported, and in combination with age-related reductions in light sensitivity, insufficient light exposure is likely to contribute to sleep problems in this population. Increasing daytime light exposure using bright light treatment (BLT) may represent a feasible non-pharmacological treatment for sleep problems in nursing home patients with dementia.

METHODS

The present study reports on sleep outcomes, which are the primary outcomes of the DEM.LIGHT trial (Therapy Light Rooms for Nursing Home Patients with Dementia- Designing Diurnal Conditions for Improved Sleep, Mood and Behavioural Problems), a 24-week cluster-randomised placebo-controlled trial including 8 nursing home units and 69 resident patients. The intervention comprised ambient light of 1000 lx and 6000 K from 10:00 to 15:00, with gradually increasing and decreasing light levels prior to and following this interval, using ceiling mounted light-fixtures and light emitting diode technology. The placebo condition had continuous standard light levels (150-300 lx, ~ 3000 K). Sleep was assessed at baseline and follow-up at week 8, 16, and 24, using the proxy-rated Sleep Disorder Inventory (SDI) and actigraphy (Actiwatch II, Philips Respironics). Mixed linear models were used to evaluate intervention effects, adjusting for relevant covariates such as age, gender, number of drugs, severity of dementia, eye disease, and estimated light exposure.

RESULTS

Sleep as measured by the SDI was significantly improved in the intervention group compared to the control group from baseline to week 16 (B = - 0.06, 95% CI -0.11 - -0.01, p < .05) and from baseline to week 24 (B = - 0.05, 95% CI -0.10 - -0.01, p < .05). There was no effect according to the SDI at week 8 and no significant effects in terms of actigraphically measured sleep.

CONCLUSIONS

Proxy-rated sleep improved among nursing home patients with dementia following 16 and 24 weeks of BLT. These improvements were not corroborated by actigraphy recordings.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03357328 . Registered 29 November 2017 - Retrospectively registered.

Sleep and its Association With Pain and Depression in Nursing Home Patients With Advanced Dementia - a Cross-Sectional Study

Objective: Previous research suggests a positive association between pain, depression and sleep. In this study, we investigate how sleep correlates with varying levels of pain and depression in nursing home (NH) patients with dementia.

Materials and methods: Cross-sectional study (n = 141) with sleep-related data, derived from two multicenter studies conducted in Norway. We included NH patients with dementia according to the Mini-Mental State Examination (MMSE ≤ 20) from the COSMOS trial (n = 46) and the DEP.PAIN.DEM trial (n = 95) whose sleep was objectively measured with actigraphy. In the COSMOS trial, NH patients were included if they were ≥65 years of age and with life expectancy >6 months. In the DEP.PAIN.DEM trial, patients were included if they were ≥60 years and if they had depression according to the Cornell Scale for Depression in Dementia (CSDD ≥ 8). In both studies, pain was assessed with the Mobilization-Observation-Behavior-Intensity-Dementia-2 Pain Scale (MOBID-2), and depression with CSDD. Sleep parameters were total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL), wake after sleep onset (WASO), early morning awakening (EMA), daytime total sleep time (DTS) and time in bed (TiB). We registered use of sedatives, analgesics, opioids and antidepressants from patient health records and adjusted for these medications in the analyses.

Results: Mean age was 86.2 years and 76.3% were female. Hierarchical regressions showed that pain was associated with higher TST and SE (p < 0.05), less WASO (p < 0.01) and more DTS (p < 0.01). More severe dementia was associated with more WASO (p < 0.05) and TiB (p < 0.01). More severe depression was associated with less TST (p < 0.05), less DTS (p < 0.01) and less TiB (p < 0.01). Use of sedative medications was associated with less TiB (p < 0.05).

Conclusion: When sleep was measured with actigraphy, NH patients with dementia and pain slept more than patients without pain, in terms of higher total sleep time. Furthermore, their sleep efficiency was higher, indicating that the patients had more sleep within the time they spent in bed. Patients with more severe dementia spent more time awake during the time spent in bed. Furthermore, people with more severe depression slept less at daytime and had less total sleep time Controlling for concomitant medication use did not affect the obtained results.

The minimum clinically important difference on the sleep disorders inventory for people with dementia

OBJECTIVES

Sleep disturbances in dementia causes distress to people with dementia and their family carers and are associated with care home admission. The Sleep Disorders Inventory (SDI) is a validated questionnaire of sleep disturbances in dementia often used to measure treatment effectiveness, but the minimum clinically important difference (MCID) is unknown.

METHODS

We triangulated three investigative methods to determine the MCID of the SDI. Using data on SDI from a randomised controlled trial (RCT) with 62 participants in an intervention for sleep disorders in dementia, we (1) calculated distribution-based values where MCID = 0.33 of a SD (SD) (2) an anchor based approach using quality of life (measured using DEMQOL-Proxy) as an anchor. We also employed a Delphi consensus process asking 12 clinicians, sleep researchers and family carers to rate which changes on vignettes were equivalent to a MCID.

RESULTS

We found that 0.33 SD in the SDI = 4.86. Reduction in SDI total score was not significantly correlated with improvement in DEMQOL-Proxy (Pearson's correlation = -0.01; P = 0.96) score. The Delphi consensus required two rounds to reach a consensus and concluded that changes equivalent to three points on the SDI equated to the MCID.

CONCLUSIONS

Taking into account both the distribution-based values and the Delphi process we used a whole number at the midpoint and judged the minimum clinically important difference MCID to be equal to four points. We note the clinicians and carers opinions from the Delphi process determined the MCID to be lower at three points.