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Efficacy of exercise for functional outcomes in older persons with dementiaSanders, Lianne

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https://doi.org/10.33612/diss.102146202https://research.rug.nl/en/publications/efficacy-of-exercise-for-functional-outcomes-in-older-persons-with-dementia(68bdc914-594e-4c47-882b-3769f9662a1e).htmlhttps://doi.org/10.33612/diss.102146202

Efficacy of exercise for functional outcomes in older persons

with dementia

The randomized controlled trial described in this thesis was conducted at health care centers

of ZINN, Dignis, Meriant, TSN Thuiszorg and NNCZ.

Financial support for the research described in this thesis, and printing of this thesis, was

provided by the Deltaplan Dementia (ZonMW: Memorabel, project number 733050303), the

University of Groningen, the University Medical Center Groningen, Alzheimer Nederland,

the Hersenstichting, and the Research Institute School of Health Research (SHARE).

Cover design: Lianne Sanders, background graphics ©EdNal@Adobe StockLayout: Bastiaan Sanders and Lianne Sanders using LATEXPrint: Gildeprint, EnschedeISBN (printed version): 978-94-034-2067-7ISBN (electronic version): 978-94-034-2066-0

PhD training was facilitated by the Research Institute School of Health Research (SHARE).

Paranymphs: Menno Veldman and Laura Cuijpers

©Lianne Sanders, 2019.

All rights reserved. No part of this book may be reproduced or transmitted in any form or by

any means, electronic or mechanical, including photocopying, recording, or any information

storage or retrieval systems, without the prior written permission from the author.

Efficacy of exercise for functional outcomes in older persons with

dementia

Proefschrift

ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen

op gezag van de rector magnificus prof. dr. C. Wijmenga

en volgens besluit van het College voor Promoties.

De openbare verdediging zal plaatsvinden op

maandag 9 december 2019 om 12:45 uur

door

Lianne Maria Jantien Sanders

geboren op 2 november 1990 te Apeldoorn

Promotores Prof. dr. T. Hortobagyi

Prof. dr. E.J.A. Scherder

Prof. dr. E.A. van der Zee

Copromotor Dr. M.J.G. van Heuvelen

Beoordelingscommissie Prof. dr. G.J.J.M. Kempen

Prof. dr. W.H. Brouwer

Prof. dr. B.C. van Munster

Table of Contents

Foreword 7Chapter 1 General introduction 13Chapter 2 Relationship between drug burden and physical and 27

cognitive function in a sample of nursing home patients

with dementia

Chapter 3 Dose-response relationship between exercise and 49cognitive function in older adults with and without

cognitive impairment: a systematic review and

meta-analysis

Chapter 4 Low- and high-intensity physical exercise has 87small effects on physical but not on cognitive function

in older persons with dementia: a randomized

controlled trial

Chapter 5 Psychometric properties of a Flanker task in a sample 125of patients with dementia: a pilot study

Chapter 6 Summary and general discussion 143Appendices

Abstract 163

Nederlandse samenvatting 164

Dankwoord 168

About the author 171

Research Institute SHARE 175

��

This research is performed within the Deltaplan Dementia as part of the program Train the

Sedentary Brain: Move Smart and Reduce the Risk of Dementia (ZonMW:Memorabel, project

number 733050303) executed by a consortium in which researchers from the University

of Groningen, Vrije Universiteit Amsterdam and Radboudumc participate. The Deltaplan

Dementia is a Dutch multidisciplinary collaborative initiative with the aim of improving

dementia health care in the Netherlands and abroad. The focus of the Deltaplan Dementia lies

on three core areas. These are 1) scientific research (the Memorabel program), 2) improving

dementia care practice, and 3) social innovation to stimulate a dementia-friendly society.

More information on the Deltaplan Dementia can be found on https://deltaplandementie.nl/nl.

Within the Memorabel research program, the Train the Sedentary Brain (TTSB) program

aims to investigate how specific exercise protocols can counter the negative effects of physical

inactivity on the progression of dementia. An overview of the studies within TTSB is given in

Figure 1. Together, these studies generate insight into the deleterious effects of physical inac-

tivity on cognitive function, the interaction between physical inactivity and Apolipoprotein ε4

(ApoE4, themost important biological risk factor for dementia), and the efficacy of physical ac-

tivity protocols for physical and cognitive function. These insights are obtained using preclin-

ical mouse models and clinical protocols. More information on the TTSB program including

study reports can be obtained from https://www.zonmw.nl/nl/over-zonmw/ehealth-en-ict-in-

de-zorg/programmas/project-detail/memorabel/train-the-sedentary-brain-move-smart-to-reduce-

the-risk-of-dementia/.

9

Figu

re1.

Interrelationships

betweenthestu

dies.

10

��

Aging and dementia

The world population is growing older. Between 2010 and 2015, the global life expectancy

has risen by 5.5 years, mainly due to improvements in child survival rates in developing

regions. Today, the global average life expectancy is ∼74 years for women and ∼70 years formen. In developed regions, the life expectancy for women and men approaches or exceeds 80

years [1].

In 2010 it was estimated that there were 524 million old adults (≥65 years). This numberis expected to triple by 2050 [2]. Considering that old age is the most important risk factor

for dementia, it is expected that by 2050 the number of older persons with dementia (PwD)

will triple to 150 million worldwide [3]. Dementia is a syndrome that is characterized by

progressive neurodegeneration that surpasses normal age-related decline. PwD progressively

lose cognitive and physical abilities [3,4] and become increasingly dependent upon formal

and informal care. Dementia may have various causes, and it must be noted that at least

50% of PwD show signs of multiple dementia pathologies [5]. Alzheimer’s Disease (AD)

accounts for 60-80% of all dementia cases [6]. AD is characterized by deposits of Amyloid β

(Aβ) plaques and neurofibrillary tangles. Vascular Dementia (VaD), i.e., dementia resulting

from extensive damage to brain blood vessels appears in ∼40% of PwD [6]. Dementia withLewy Bodies (LBD), characterized by neuronal abnormalities in Alpha-synuclein protein, is

recognized as the third most common cause for dementia, accounting for another >10% of

dementia cases [6].

It is estimated that 1/3 of dementia cases are attributable to a combination of nine modifi-

able risk factors: low education in early life; hypertension, obesity and hearing loss in midlife;

and smoking, depression, physical inactivity, social isolation and diabetes in later life [7]. The

most important biological risk factor for dementia (AD in particular) is the Apolipoprotein ε4

(ApoE4) allele. ApoE4 is directly linked to AD through impaired lipoprotein metabolism [8],

which is associated with immunoreactive accumulations of Aβ and tau pathology and sub-

sequent declines in neurocognitive health [9,10]. Compared with ApoE2 and/or E3 carriers,

ApoE4 heterozygotes are three times more likely to develop AD and ApoE4 homozygotes are

eight times more likely to develop AD [9].

The economic burden of dementia is high. Costs for prevention, diagnosis, symptom

treatment, informal care and environmental adaptations for PwD increasingly accumulate

with time. In 2018, the global cost of dementia exceeded $1 trillion [3]. Considering the

high societal and economic burden of dementia it is of great importance that treatments are

15

developed. Pharmacological treatments are still insufficient in producing clinically relevant

effects [11] and frequently cause side effects such as gastrointestinal issues and vertigo

[12]. Non-pharmacological treatments for dementia may pose fewer side-effects and include

exercise, music therapy, cognitive stimulation, social stimulation and sensory enrichment.

Amongst these, exercise may be the preferred choice as it is consistently associated with

better physical and mental health outcomes in older populations [13] conform the philosophy

’Exercise is medicine’.

Physical activity in old adults with and without dementia

The American College of Sports Medicine (ACSM) defines physical activity (PA) as ‘any

bodilymovement produced by skeletal muscles that results in energy expenditure above resting

levels’ [14]. Exercise is defined as ‘PA that is planned, structured, and repetitive and that has

as a final or intermediate objective the improvement or maintenance of physical fitness’. In

other words, exercise is PA that is specifically intended to improve fitness.

The WHO [13] advises all old adults to perform ≥150 minutes of moderate-intensityaerobic PA and/or ≥75 minutes of vigorous-intensity aerobic PA, in bouts of ≥10 minutes,every week. Whole-body muscle strengthening exercises should be done at least two days

per week. Especially old adults with mobility impairments are advised to perform balance

exercises on ≥3 days of the week. For old adults with poor physical health, even low amountsof PA are beneficial. However, a review of PA levels in old adults from six continents

reported that only 20-60% of old adults met the recommended amounts of PA [15]. PwD

are even less active as compared to their healthy peers. Van Alphen and colleagues [16]

showed that community-dwelling PwD are ∼22% less active than cognitively healthy peers.Institutionalized PwD even spend ∼72% of their day sitting. Low levels of PA are associatedwith poor health, increased risk ofmortality and hospitalization and increased risk of cognitive

impairment [17]. Increasing PA may ameliorate poor health in PwD.

Exercise and functional outcomes in old adults with and without dementia

In healthy old adults, aerobic and strength exercise are associated with improvements in

physical function such as endurance, mobility, gait speed, muscle strength and balance [18-

21]. Multimodal exercise, i.e., a combination of aerobic, strength, and coordination training

has the highest efficacy for physical function. Likewise, the beneficial effects of exercise on the

16

aforementioned physical functions are apparent in PwD with multimodal exercise having the

highest efficacy (see [22] for a review). Furthermore, multimodal exercise may be preferable

over aerobic-only exercise for Activities of Daily Life (ADL) in PwD [23].

Exercise-induced improvements in physical function may facilitate cognitive function

through increases in brain plasticity and activation [24-26]. In 1999, Arthur Kramer and

colleagues were among the first to show a positive effect of exercise on executive processes

in a sample of 124 healthy but sedentary old adults [27]. Since then there has been a growing

body of evidence that aerobic, anaerobic and multimodal exercise is related to better cognitive

function in healthy old adults, with multimodal exercise having the highest efficacy for

cognitive function (see [28] for a meta-analysis). In PwD, the effects of exercise on cognition

are inconclusive [29-33]. A combination of alternating aerobic and strength exercise appears

to be the most beneficial for physical and cognitive function [23,30] perhaps because 1)

aerobic exercise is facilitated through strength increases especially in the lower limbs and 2)

the neuromotor stimulus is higher in combined exercise compared with aerobic- only training

due to compensatory mechanisms. However, it is unknown whether such a combination is

also effective in PwD in earlier stages of dementia. Furthermore, it is unknown whether this

combination of alternating aerobic and strength training is effective when performed for a

longer period of time. Last, it is uncertain whether the beneficial effects of exercise in PwD

last after detraining.

Several underlying neurobiological mechanisms may play a role in the neuroprotective

effects of exercise. In animals and healthy old adults, exercise is related to dose-dependent in-

creases in growth factors (insulin-like growth factor type I (IGF1), brain derived neurotrophic

factor (BDNF), vascular endothelial growth factor (VEGF)), increases in neurotransmitters

(noradrenalin, serotonin, dopamine) and decreases in inflammatory markers (homocysteine

[34,35]). These neurobiological changes are related to changes in brain structure and con-

nectivity [36-39] in areas important for healthy cognitive function e.g. frontal and temporal

areas. Whether this is also true for PwD is yet to be determined by future studies. Figure 1

models the relationships between exercise and physical and cognitive function in PwD. This

model is adapted from Bossers [40].

17

Figure 1. Relationships between exercise, physical function and cognition in PwD. Continu-ous lines represent evidence-based relationships whereas dotted lines represent hypothesized

relationships.

Moderators and confounders of exercise effects on physical and cognitive function inPwD

As previously outlined, the evidence for the efficacy of exercise for functional outcomes in

PwD is inconclusive. Furthermore, there is insufficient evidence to determine the variables

that may act as moderators and confounders of exercise effects on physical and cognitive

function in PwD. Such data are needed to optimize exercise programs for PwD and implement

18

exercise efficiently in daily health care. Previous reviews and meta-analyses in healthy old

adults have identified several potential moderators and confounders. The effects of exercise

on executive processes may be more pronounced in older women vs. men [41] and in older vs.

younger seniors [42]. In addition, the effects of exercise on cognitive function in old adults

may bemoderated by genes and dietary factors [43]. In PwD, the effects of exercise on physical

and cognitive function appear to be independent of sex, age and dementia subtype, although

it is uncertain whether the effects of exercise are moderated by disease severity [22,32,33,44].

There is little or inconclusive evidence on several other potential moderators and confounders

of exercise effects in PwD. Use of medications with anticholinergic and/or sedative properties

(‘inappropriate medications’) as measured by the Drug Burden Index (DBI) is associated

with lower physical and cognitive function in healthy old adults [45-48]. Whether this is true

also for PwD is unknown. Data on the associations between DBI and physical and cognitive

function in PwD is needed to determine whether the effects of exercise on physical and

cognitive function are potentially confounded by anticholinergic and sedative drug burden.

In addition to drug burden, exercise type and dose-parameters (program duration, session

duration, frequency and intensity) may moderate the magnitude of exercise effects on physical

and cognitive function in PwD. However, the dose-response relationships between exercise

with physical and cognitive function in PwD are poorly understood. Especially exercise

intensity may be an important moderator of exercise effects because higher exercise intensity

is associated with better physical fitness (i.e., VO2max) and health outcomes [49-52] that may

fuel exercise effects on physical and cognitive function. Studies in which exercise intensities

are compared among randomized PwD are needed to investigate exercise intensity as potential

moderator in PwD. No such studies have been performed thus far. Last, preliminary evidence

has identified ApoE4-carriership to be a potential moderator of exercise effects on functional

outcomes in old adults with and without dementia [43,53]. However, it is uncertain whether

ApoE4 carriers as compared to non-carriers show greater or fewer benefits from exercise

on physical and cognitive function [53-56]. Furthermore, there is a scarcity of randomized

studies that investigate the strength and direction of this hypothesized moderation in PwD

specifically.

The effects of exercise on physical and cognitive function in PwD and potential moderators

and confounders can only be adequately assessed with performance-based tests that are

feasible, valid and reliable in PwD. The reliability of six motor tests for endurance, gait speed,

balance, strength and functional mobility was previously found to be good to excellent in PwD,

19

although the reliability was lower for lower-functioning individuals [57]. Unfortunately, apart

from global cognitive batteries, there is limited data on the psychometric properties of many

cognitive tests in PwD. Furthermore, cognitive tests that are frequently used in healthy old

adults may not be feasible, reliable and valid in PwD. This may be especially true for the

STROOP task. The STROOP task measures selective attention and inhibitory control [58].

The STROOP taskmay be difficult for PwDbecause PwDmay experience color confusion [59]

and difficulties with verbal communication [60]. Furthermore, PwD may find the STROOP

task instructions difficult to comprehend. The Flanker task could be a suitable non-verbal

alternative for the STROOP task in PwD. The Flanker task requires the ability to inhibit

nonrelevant competing responses to a nonverbal target stimulus [61]. The Flanker task may

be a suitable alternative to the STROOP task in PwD because the Flanker task does not rely

upon verbal responses and the use of colors. However, the psychometric properties of the

Flanker task in PwD are yet to be evaluated.

Objectives and outline of this thesis

Themain objective of this thesis is to examine the efficacy of alternating aerobic and lower-limb

strength exercise vs. control activities for physical and cognitive function in a sample of older

persons with mild-to-moderate all-cause dementia. The secondary objective of this thesis is

to examine potential moderators and confounders of exercise effects in PwD. In this thesis

we will focus on anticholinergic and sedative drug burden, exercise type, dose-parameters

(program duration, session duration, frequency and intensity) and ApoE4-carriership. The

last objective of this thesis is to examine the suitability of a Flanker task as a measure of

inhibitory control in PwD.

Chapter 2 describes the results of a cross-sectional analysis into the associations betweenanticholinergic and sedative drug burden with physical and cognition function in 140 nursing

home PwD. Chapter 3 describes the results of a systematic review and meta-analysis into theeffects of exercise on cognitive function in old adults with and without cognitive impairments.

We investigated cognitive status (healthy vs. cognitively impaired), exercise type (aerobic vs.

anaerobic vs. multimodal vs. psychomotor) and dose-parameters (program duration, session

duration, frequency and intensity) as potential moderators of exercise effects on cognition.

Chapter 4 describes the feasibility and results of a 24-week randomized controlled trial (RCT)in 69 PwD visiting daycare or residing in residential care facilities. The primary outcomes

were physical and cognitive functions measured with performance-based tests. We used a

20

two-arm exercise vs. control design, with the exercises being performed at a low (weeks

1-12) vs. high (weeks 13-24) intensity to investigate exercise intensity as potential moderator

of exercise effects. ApoE4-status was determined post-intervention as potential moderator

of exercise effects. Chapter 5 describes the results of a pilot study into the psychometricproperties (feasibility, validity and tests-retest reliability) of a computerized Flanker task

in 22 PwD. Finally, Chapter 6 summarizes and discusses the main findings of this thesis.Additionally, Chapter 6 offers suggestions for the implementation of exercise in dementia

health care practice.

21

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25

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Abstract

Purpose

The Drug Burden Index (DBI) is a tool to quantify the anticholinergic and sedative load

of drugs. Establishing functional correlates of the DBI could optimize drug prescribing in

patients with dementia. In this cross-sectional study, we determined the relationship between

DBI and cognitive and physical function in a sample of patients with dementia.

Methods

Using performance-based tests, we measured physical and cognitive function in 140 nursing

home patients aged over 70 with all-cause dementia. We also determined anticholinergic

(AChDBI) and sedative (SDBI) drug burden separately and in combination as total drug

burden (TDB).

Results

Nearly one half of patients (48%) used at least one DBI-contributing drug. In 33% of the

patients, drug burden was moderate (0

Introduction

With the world population progressively growing older, the number of older adults with

dementia increases. Globally, 47.5 billion people suffer from dementia, resulting in a health

care expenditure of $600 billion [1]. Cognitive and physical abilities of patients with dementia

decline steadily, compromising daily function and independence and requiring approximately

one half of patients to move to a nursing home (NH) and receive assistance [2]. The prevalence

of comorbidities among patients with dementia is high [3]. Depending on the study, 43%

to 92% of dementia patients are exposed to polypharmacy, i.e., the concurrent use of five or

more medications from different drug categories [4-7], which can result in serious Adverse

Drug Reactions (ADRs) such as cognitive impairment, functional decline, and an increase

in risk for falls and fractures [8,9]. To minimize suboptimal drug use by older adults, the

Beers criteria categorize inappropriate drugs that patients should use with caution or avoid

[10]. Even though anticholinergic and sedative psychotropic drugs have especially high risks

to cause adverse effects, it is estimated that 23% to 47% of dementia patients take at least one

anticholinergic or sedative drug [11-13].

Several tools exist to quantify anticholinergic drug burden in older adults, but the agree-

ment between tools is limited [14]. The Drug Burden Index (DBI) has been identified

as an appropriate tool to quantify anticholinergic and sedative drug burden in older adults

[15,16,17]. In community-dwelling older adults, a higher DBI correlates with low physical

function [18-21], high fall rate [22], difficulty in performing activities of daily living (ADL)

[20,21], mortality, and hospitalization [23]. However, the evidence is mixed regarding the

relationship between DBI and cognitive function [15,24,25]. In dementia patients, a higher

DBI correlates with low self-reported health-related quality of life [7] and a high risk of

hospitalization and mortality [23]. As far as we know, the relationship between DBI and

physical and cognitive function has not yet been examined in patients with dementia. To

minimize suboptimal drug prescribing for patients with dementia, it is necessary to establish

the functional associations of the DBI in this patient group.

The purpose of the present cross-sectional studywas to determine the relationship between

DBI and cognitive and physical function in patients with all-cause dementia. We quantified

the relationship between anticholinergic (AChDBI), sedative (SDBI), and total drug burden

(TDB=AChDBI+SDBI) and physical and cognitive function. We hypothesized that AChDBI

and SDBI individually and in combination in the form of TDB, are inversely associated with

29

physical and cognitive function.

Methods

Design

This study combined data fromaDutch cross-sectional (NederlandsTrial Register (NTR)1230,

74 participants, data collected between 2004-2007) study and baseline data from a related

Dutch intervention study (NTR2269, 66 participants, collected between 2010-2014). Each

participant or a legal representative signed an informed consent approved by the University

Medical Ethical Committee. The studies were conducted in accordance with the principles

of the Declaration of Helsinki (64th amendment).

Sample and procedures

The current sample consisted of 140 NH residents over age 70 with a dementia diagnosis.

The two studies differed minimally in terms of inclusion and exclusion criteria, resulting in

the following overall inclusion criteria for the total sample: age >70, a physician-diagnosed

dementia reported in the medical chart, the ability to walk short distances without a walking

aid, and an MMSE score ≥10 and ≤24 (indicating mild-to-moderate dementia). Multipledisease-related exclusion criteria were used for safety reasons (see NTR files 1230 and 2269,

Appendix 1, online supplementary material). Appendix 1 also describes the recruitment

procedures. Trained research assistants recorded data on socio-demographic factors (age,

gender, level of education) and cognitive and physical abilities. To minimize test burden, the

assessor performed the cognitive and physical assessments in two separate sessions within

seven days. The researchers extracted data on medical conditions and medication use for all

participants from the nursing homes’ medical files. Medication data comprised a record of

all medications taken by the participant at the time of assessment and the subsequent dose,

frequency and the method of administration. We excluded medication ‘as needed’, topical

ointments, lubricating eye drops and over-the-counter medications from the current analyses.

Measurements

Medication assessment

To code the drugs, we used theAnatomical Therapeutic Chemical (ATC) classification system,

30

as recommended by the World Health Organization [26]. We quantified the total anticholiner-

gic and sedative load with the DBI and calculated total Drug Burden (TDB) as follows [15]:

(1) TDB=AChDBI+SDBI

where AChDBI and SDBI represent, respectively, total anticholinergic and sedative load.

We determined sedative and/or anticholinergic load for each drug and summed up as follows:

(2) AChDBI or SDBI = Σ Di / (δi + Di),

where Di represents the daily dose taken by the participant and δ represents the recom-

mended minimum daily dose of the drugs with respectively an anticholinergic or sedative

load. The recommended minimum daily dose was specified for each drug (i) based on the

lowestminimumoral dose that is prescribed for any commonmedical indication in older adults.

Classification of anticholinergic and sedative drugs

To determine if a drug had sedative and/or anticholinergic effects, the Expertise Centre Phar-

macotherapy in Older persons (Ephor) [27] was consulted. Figures 1 and 2 show the decision

tree for the classification process. A drug was classified as anticholinergic when the three

most common anticholinergic side effects obstipation/constipation, xerostomia and urinary

retention were described in Ephor or in at least two of the remaining sources: the Sum-

mary of Product Characteristics (SmPC [28], the Pharmacotherapeutic Compass [29] and the

Medicines Information Centre of the Royal Dutch Pharmacists Association [30]. If all three

anticholinergic side effects were mentioned in one of the remaining sources and at least one

side effect was mentioned in the two other remaining sources, the drug was also classified as

anticholinergic. A drug was classified as sedative when either sedation, drowsiness, somno-

lence or impaired coordination and reaction time were listed as side effects in Ephor or at least

two of the remaining sources. If a drug was known to have both anticholinergic and sedative

effects, it was classified as anticholinergic [15].

Polypharmacy

Polypharmacy and excessive polypharmacy were defined as the concurrent use of 5–9 and >9

drugs, respectively [4].

31

Non-DBI-contributing drugs

Other than inclusion in polypharmacy measures, we excluded all non-DBI-contributing drugs

from the current analyses.

Comorbidities

We quantified comorbidity based on the Functional Comorbidity Index (FCI-18) [31] (Ap-

pendix 3, online supplementary material). The FCI comprises 18 medical conditions that

negatively impact physical function. The presence or absence of each condition is listed. A

higher score represents a greater number of comorbidities.

Functional outcomes

Motor function

To characterize motor function, we used several performance-based tests that are frequently

used for patients with dementia [32]. Functional mobility was quantified with the Six Meter

Walk Test (m/s, [33]), Timed Up&Go (seconds [34] and 30-seconds Sit to Stand (number of

correct attempts, [35]). Balance was measured with the Frailty and Injuries: Cooperative

Studies of Intervention Techniques subtest 4 (FICSIT-4 [36]) and Figure of Eight (seconds

[37]). Grip strength (kg) was assessed using a Jamar © hand dynamometer.

Cognitive function

We employed frequently-used neuropsychological tests [32] to quantify cognitive function,

including: global cognitive function (Mini Mental State Examination [38]), verbal memory

(Eight Word Task immediate recall and recognition [39]); verbal working memory (Digit

Span Forward and Backward [40]); visual memory (Visual Memory Span Forward and Back-

ward [40]; Rivermead Behavioural Memory Test (RBMT) Faces and Pictures [41]), abstract

reasoning (Groninger Intelligence Test (GIT) incomplete figures [42]) and basic information

processing speed (STROOP word card [43], adapted 45 second version). We determined the

number of correct responses for all tests as outcome measure. For all tasks, higher scores

indicate a better performance.

32

Figure 1. Classification process of drug as anticholinergic.

Figure 2. Classification process of drug as sedative.

33

Statistical analyses

We used SPSS Statistics 23.0 (IBM, Armonk, NY) to compute means and standard deviations

(SDs) for motor and cognitive outcomes and to analyze the data. Scores on the SixMeterWalk

Test, Timed Up&Go and Figure of Eight were positively skewed and thus, log10-transformed.

We imputed missing data for cognitive (5.8% missing) and physical variables (6.0% missing)

using the Maximum Likelihood - Expectation Maximization algorithm [43]. The scores on

the cognitive and motor tests, as well as socio-demographic factors and comorbidities were

used as predictors for missing data completions.

We set DBI as a categorical ordinal variable (DBI=0, 0

of drugs did not correlate with TDB, AChDBI or SDBI. Dementia severity as measured by

MMSE score did not correlate with TDB, AChDBI or SDBI (respectively r=-0.003; r=-0.002;

r=-0.002). Age inversely correlated with TDB (r = -0.200, p=0.018). Table 1 summarizes

patient characteristics in the TDB subgroups. Appendix 2 (online supplementary material)

lists the DBI-contributing drugs used in the sample. The most commonly used anticholinergic

drug was the antidepressant Citalopram (n=16). Oxazepam, a benzodiazepine, was the most

commonly used sedative drug (n=12).

Relationship between DBI and physical function

The model not controlled for confounders revealed no group differences in measures of

motor performance in the TDB subgroups ((F(12,264)=1.159, p=0.313, Wilk’s ∧=0.902, par-tial η2=0.050). The same applies to physical performance in AChDBI ((F(12,264)=0.538,

p=0.889, Wilk’s ∧=0.953, partial η2=0.024) and SDBI (F(12,264)=1.382, p=0.174, Wilk’s∧=0.885, partial η2=0.059) subgroups. After controlling the models for age, gender, edu-cation and use of walking aid, there were no differences in physical outcomes between the

subgroups TDB (F(12,220)=1.063, p=0.393, Wilk’s ∧=0.893, partial η2=0.055), AChDBI(F(12,220)=0.766, p=0.685, Wilk’s ∧=0.921, partial η2=0.040), or SDBI (F(12,220)=1.121,p=0.344, Wilk’s ∧=0.888, partial η2=0.058). When FCI score is taken into account as addi-tional covariate, there were no group differences for TDB (F(12,206)=1.114, p=0.350, Wilk’s

∧=0.882, partial η2=0.061), AChDBI (F(12,206)=0.726, p=0.725, Wilk’s ∧=0.920, partialη2=0.041) and SDBI (F(12,206)=1.303, p=0.219, Wilk’s ∧=0.864, partial η2=0.071).

Relationship between DBI and cognitive function

In the multivariate model not controlled for potential confounders, cognitive outcomes

were not different for the TDB classes (F(22,254)=1.191, p=0.256, Wilk’s ∧=0.822, par-tial η2=0.093). We found equivocal results when the AChDBI (F(22,254)=0.976, p=0.495,

Wilk’s ∧=0.850, partial η2=0.078) or the SDBI (F(22,254)=1.005, p=0.459, Wilk’s ∧=0.846,partial η2=0.080) were taken into account separately. After controlling for age, gender and

education, there were no differences in cognitive outcomes between the subgroups TDB

(F(22,220)=1.303, p=0.171, Wilk’s ∧=0.783, partial η2=0.115), AChDBI (F(22,220)=1.074,p=0.377, Wilk’s ∧=0.815, partial η2=0.097), and SDBI (F(22,220)=1.170, p=0.277, Wilk’s∧=0.801, partial η2=0.105). With FCI as an additional covariate, therewere no differences

35

Table 1. Patient characteristics in the total sample (N = 140).

Characteristics Valuea TDB = 0 0

in cognitive outcomes between the TDB (F(22,206)=1.352, p=0.142, Wilk’s ∧=0.764, partialη2=0.126), AChDBI (F(22,206)=1.052, p=0.403, Wilk’s ∧=0.808, partial η2=0.101) andSDBI subgroups (F(22,206)=1.250, p=0.210, Wilk’s ∧=0.778, partial η2=0.118).

Discussion

To our best knowledge, this is the first cross-sectional study to examine functional correlates

of the DBI in a population of patients with mild-to-moderate all-cause dementia. We used a

wide range of reliable and valid measurements to assess cognitive and physical function. We

found no multivariate relationships between DBI and cognitive and physical functions.

Relationship between drug burden and physical function

In the present study, TDB did not correlate with physical function. We hypothesized that

TDB would negatively correlate with physical function because anticholinergic and sedative

drugs target central nervous system (CNS) functions that affect physical function, such as

the gastrointestinal system and neuromuscular processes [21]. A lack of association between

TDB and physical function in our study contrasts with data in cognitively healthy populations

[18-20], possibly for two dementia-related potential reasons.

First, drug prescribing might be more optimal for patients with dementia compared with

healthy older populations. For dementia patients, a larger emphasis may be placed upon

tolerability and quality of life instead of treatment of symptoms and quantity of life, resulting

in better-tailored drug prescribing [45]. Indeed, in our sample, less than half of patients

used DBI drugs, which is a lower rate compared with the rate in a sample of healthier

older adults [46]. Such a careful approach may become even more pronounced with older

age, a hypothesis perhaps reflected by a negative relationship between age and TDB in our

study. Not only the presence, but also the severity of dementia may be related to more

appropriate prescribing. However, dementia severity (as measured with MMSE) and DBI

were unrelated in our study, confirming a lack of co-variation between the odds of being

prescribed inappropriate medication and dementia severity [6]. Thus, presence rather than

severity of dementia may be a better indicator of lower risk of suboptimal prescribing.

A second reason why our results showed no relationship between physical function and

drug burden could be that numerous dementia-related factors that affect physical health might

confound the relationship between TDB and physical function in patients with dementia. De-

37

mentia progression [47], age, poor health [48], sedentariness [49], adverse life events, and a

decline in general well-being [50] all unfavorably affect physical function. The presence and

manifestation of such factors may greatly vary in patients with dementia and thus increase

variability within the sample. In combination, these factors might minimize the influence of

DBI drugs on physical and cognitive function, nullifying a potential relation. However, this

hypothesis of confounding factors is weakened by the results of a previous randomized con-

trolled trial (RCT) that aimed to reduce anticholinergic exposure through a 12-week reduction

intervention in institutionalized patients with dementia. The authors found that physical func-

tion as measured with the Barthel Index did not improve after a decrease in anticholinergic

exposure after 12 weeks [51]. Considering that the randomized controlled nature of the study

should minimize the influence of confounding variables, we cannot definitively conclude that

confounding factors underlie the lack of relationship between drug burden and physical func-

tion.

Our findings qualitatively agree with data in hospitalized patients with multimorbidity,

43.5% of whom suffered from dementia. In these patients, the use of three or more psy-

chotropic drugs was related to lower hand-grip strength in both hands, but not lower extremity

muscle strength [52]. However, further comparisons of our sample with other dementia pop-

ulations are difficult because the relationship between drug burden (DBI or other measures)

and physical function in patients with dementia is understudied. Future research should im-

prove our understanding of the relationship between physical function and drug burden in this

patient group. Within such research, it is important to consider the care setting as being a

determinant for more appropriate prescribing in patients with dementia. Our sample included

patients with dementia in nursing homes, who may be at a lower risk for suboptimal prescrib-

ing compared with community-dwelling populations with or without cognitive impairment

[23,46]. Indeed, the use of DBI drugs declines by approximately 5% after NH admission

[53,54], although there is a paucity of data concerning DBI drug usage in specifically Dutch

NHs. There may be four reasons why DBI drug prescribing may be more optimal after NH

admission: 1. NH physicians may be less inclined to prescribe DBI drugs because they are

specialized in the medical aspects of older patients compared with primary care physicians

[54], 2. NH staff can quickly recognize and address the adverse effects of DBI-contributing

drugs; 3. Behavioral disturbances are generally less medicalized in a NH vs. home setting

because behavior is interpreted in a broader, more accepting context and approached as such

[55], and 4. NH physicians in particular might recognize the diminished benefit of drugs in

38

light of patients’ low functional level [56,57]. However, the present study could not examine

in more detail the impact of care setting on drug burden, as we studied only a NH population.

In addition to TDB, we hypothesized that higher AChDBI and SDBI separately correlated

with lower physical function. Separate associations of AChDBI vs. SDBI with physical

function could arise from pharmacodynamic differences between these two drug classes:

whereas anticholinergic drugs mainly target the cholinergic system involved in excitatory

processes, sedative drugs generally influence inhibitory mechanisms by targeting levels of

gamma-aminobutyric acid (GABA), although several DBI drugs target both systems (e.g.,

citalopram). In older women anticholinergic compared with sedative drug burden (not mea-

sured with DBI) more strongly correlated with impaired balance, mobility, gait, chair stands

and grip strength [58]. Sedative burden was associated with impaired mobility and grip

strength only. In contrast, Gnjidic et al. [21] reported that SDBI predicted poorer perfor-

mance on measures of gait, balance and grip strength in older men, whereas AChDBI was

associated with weaker grip strength only. The difference between these studies might be

explained by differences in sedative drug usage [58] or gender differences. However, con-

trasting with these studies, neither AChDBI nor SDBI correlated with physical function in our

sample. The effects of anticholinergic and sedative drugs may be less discernable in dementia

patients vs. community-dwelling populations because amyloid β deposition in Alzheimer’s

Disease (AD) disrupts the excitatory/inhibitory balance system [59]. Consequently, DBI

drugs that target the anticholinergic system, may disrupt the GABA system as well (and vice

versa). Thus, functional correlates of AChDBI/SDBI, if any, may be indiscernible in dementia

patients.

Relationship between drug burden and cognitive function

Contrary to our hypothesis, we found no association between TDB and cognitive function.

Associations between higher drug burden and lower cognitive function in other populations

can be explained by the detrimental effects of anticholinergic and sedative drugs on CNS

processes involving vision, attention, sedation and psychomotor speed [21]. The finding

that higher anticholinergic burden was not related to lower cognitive function is similar to

the results of a study in 224 community-dwelling patients with AD, where anticholinergic

load was quantified with the Anticholinergic Burden scale [60], and in line with a study

in patients with multimorbidity (43.5% dementia) showing that users of anticholinergic or

sedative drugs did not have lower cognitive function compared with non-users [52]. The

39

aforementioned explanations for a lack of association between TDB and physical function

may be equally applicable to the lack of association between TDB and cognitive function.

That is, drug prescribing may be more optimal for patients with dementia compared with

healthy older populations because of a higher emphasis on tolerability and quality of life

instead of treatment of symptoms and quantity of life. Alternatively, the effects of drug use

on cognitive function may be harder to detect in patients with dementia patients due to the

large variety of dementia-related influencing factors.

Similar to TDB, we found no evidence in support of our hypothesis that higher AChDBI

and SDBI are separately associated with lower cognitive function. In older women, higher

anticholinergic burden (not measured with DBI) correlated more strongly with lower global

cognitive function than sedative burden [58]. No evidence was reported for different cognitive

domains. In addition, a higher AChDBI was associated with lower memory performance and

lower performance on the Trail Making Test B in cognitively healthy older adults [25]. No

associations between SDBI and cognitive domains were reported. The lack of discernible

associations of anticholinergic vs. sedative drugs on cognitive function in our study may

result from the simultaneous dysregulation of the excitatory/inhibitory systems by either

anticholinergic or sedative drugs in patients with dementia, as described previously in this

paper.

Study limitations

Several limitations warrant caution in interpreting our results. First, the sample size of the

current study is small compared to other studies [18] and the studied DBI-subgroups were

of unequal sizes. This might have negatively affected statistical power. Second, our sample

consisted of patients with mild to moderate dementia, with the mean MMSE score indicating

moderate dementia. Therefore, the results are not directly generalizable to a more severe

dementia population. Also, due to the cross-sectional design of our study, we cannot exclude

the issue of confounding by indication. We are thus unable to make claims about causal

effects of DBI drugs on functional outcomes in dementia. A complicating factor is that the

efficacy of DBI drugs in the later stages of dementia is not yet adequately assessed. To gain

optimal understanding of the positive and negative effects of DBI drugs in dementia patients,

future experimental studies on the efficacy of DBI drugs with varying degrees of dementia

are needed. Additionally, differences between how previous studies and we classified DBI

is one source of inconsistency [18]. The source of this inconsistency is a lack of consensus

40

as to which drugs to classify as anticholinergic, sedative, or both. In particular, the current

DBI may be an underestimation of the true anticholinergic and sedative burden, as drugs

with both anticholinergic and sedative effects are classified as anticholinergic-only. Thus,

our indices, compared with other studies, could yield a different drug burden value compared

with other methods. To minimize such differences, we used a detailed classification process

that included several reliable sources and we also strictly adhered to the original method [15]

in estimating DBI. Also, the DBI does not account for medications-as-needed, which could

have influenced the study results if participants took such medications before the assessments.

Furthermore, patients with dementia in NHs may be inherently different from community-

dwelling patients. Associations of DBI with functional outcomes are therefore not directly

generalizable to a community-dwelling dementia population. Further research could focus

on the associations of DBI with functional outcomes in different samples of community-

dwelling versus institutionalized patients with dementia. Longitudinal research could be done

by following dementia patients through the process of institutionalization, while tracking

medication records and functional outcomes. In addition, the mean number of comorbidities

in our sample (M=2) was lower compared with another sample of NH patients with dementia

(M=4 on a summary scale (not FCI), [61]). The difference between our study and the study

by Sloane et al. [61] could result from the exclusion of people with multiple health-related

conditions in our sample, which could have resulted in a healthier-than-average group of

participants. Contrarily, the FCI does not comprise all medical conditions, and the lower

comorbidity scores in our sample may have resulted from the exclusion of several medical

conditions on the FCI, such as cancer or thyroid disease. Altogether, caution is advised when

generalizing our results to other NH patients with dementia. Moreover, the DBI does not

include a weighting factor for the relative anticholinergic activity of each DBI-drug. Not all

drugs have equally strong anticholinergic effects [62]. The use of specifically high potency

anticholinergics has been linked to an increased risk of all-cause dementia in older adults

[63]. Duran et al. [64] provide a differentiation in anticholinergic potency of drugs with

anticholinergic properties (Appendix 2, online supplementary material). Appendix 2 shows

that our sample predominantly used drugs with low anticholinergic potency, which is not

reflected in the DBI. This may at least partly account for the absence of a relationship between

DBI and functional outcomes, and warrants a careful generalization of our results to other

NH populations with dementia. Lastly, Dutch NHs might be inherently different in terms of

drug prescribing practices compared with NHs in other countries [65]. Therefore, we urge

41

caution in generalization of these results to other NHs.

Clinical implications

A lack of association between DBI and functional outcomes raises questions about the DBI

as a clinical assessment tool of drug burden in patients with dementia. DBI is considered

as a valid and useful tool to evaluate drug burden in many populations. However, it does

not account for the many drug-drug interactions between DBI drugs. Also, DBI does not

consider possible adverse effects of other non-DBI contributing drugs. The identification

of inappropriate prescribing in patients with dementia is particularly challenging because

evidence-based guidelines are lacking and health care practitioners are unsure about the best

prescribing choices [66]. As a result, DBI might over- or underestimate true drug burden. To

prevent underestimation of drug burden, perceived medication effects could be assessed by

inquiring patients and caregivers.

Conclusion

In contrast with previous studies in healthy older adults, DBI did not correlate with cognitive

and physical function in a sample of institutionalized patients with dementia. The lower

use of DBI contributing drugs in our sample compared with a community-dwelling healthy

population, might indicate that drug prescribing is more optimal for patients with dementia

compared with cognitively healthy older adults. Further experimental research into the

efficacy of DBI drugs for patients with dementia of different severities and etiologies, and

in different care settings, is needed to clarify the relationship between DBI and functional

outcomes in patients with dementia. To achieve or maintain optimal disease management for

patients with dementia, prudence is urged when prescribing anticholinergic or sedative drugs

for the treatment of neuropsychiatric complaints.

Acknowledgements

The cross-sectional study (NTR1230) was funded by Stichting Fontis Vitaal and the VU

University Amsterdam. The intervention study (NTR2269) was funded by Fonds NutsOhra

and the UniversityMedical Centre Groningen. The current study is supported by the Deltaplan

Dementie (ZonMW: Memorabel) and the University Medical Centre Groningen.

42

Conflict of interest

The authors declare no conflicts of interest.

43

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Abstract

This systematic review and meta-analysis examined the dose-response relationship between

exercise and cognitive function in older adults with and without cognitive impairments.

We included single-modality randomized controlled aerobic, anaerobic, multicomponent or

psychomotor exercise trials that quantified training frequency, session and program duration

and specified intensity quantitatively or qualitatively. We defined total exercise duration in

minutes as the product of program duration, session duration, and frequency. For each study,

we grouped test-specific Hedges’ d (n=163) and Cohen’s d (n=23) effect sizes in the domains

Global cognition, Executive function and Memory. We used multilevel mixed-effects models

to investigate dose-related predictors of exercise effects.In healthy older adults (n=23 studies),

there was a small positive effect of exercise on executive function (d=0.27) and memory

(d=0.24), but dose-parameters did not predict the magnitude of effect sizes. In older adults

with cognitive impairments (n=13 studies), exercise had a moderate positive effect on global

cognition (d=0.37). For older adults with cognitive impairments, we found evidence for

exercise programs with a short session duration and high frequency to predict higher effect

sizes (d=0.43-0.50). In healthy older adults, dose-parameters did not predict the magnitude of

exercise effects on cognition. For older adults with cognitive impairments, exercise programs

with shorter session duration and higher frequency may generate the best cognitive results.

Studies are needed in which different exercise doses are directly compared among randomized

subjects or conditions.

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Introduction

The number of dementia patients may triple to 135M globally by 2050 [1]. Dementia is char-

acterized by a progressive decline in neurocognitive function. Pharmacological treatments

may moderate symptoms but can cause adverse effects [2]. Exercise might be an effective and

safe alternative to drugs to slow cognitive decline. Exercise may improve certain cognitive

functions in old age by inducing the release of brain-derived neurotrophic factor (BDNF

[3,4]) and insulin-like growth factor-1 (IGF-1 [5,6]), thereby potentially facilitating structural

and connectivity changes in the hippocampus, temporal lobe, frontal areas and corpus callo-

sum [7-11], structures that are activated during tasks requiring executive function, attention,

processing speed and memory.

Exercise type and dose-parameters may determine the magnitude of effects on cognition

and how long these effects persist after an intervention [12,13]. Dose-parameters include

program duration (number of weeks), session duration (length of each session in minutes

including warm-up and cool-down), frequency (session rate per week) and intensity. Exercise

intensity refers to the amount of effort or energy that is required to perform a physical activity

[14] and is often expressed as percentage of maximal oxygen update (VO2max) required

during a physical activity [15].

High compared with low exercise dose-parameters tend to predict better physical fitness

outcomes in older adults. Meta-analyses revealed that longer program duration [16-18]

and higher intensity [16,18] were associated with gains in muscle strength and VO2max of

older adults. Program duration also correlated with gains in endurance, lower extremity

muscle strength, balance and levels of Activities of Daily Living (ADL) in older subjects

with dementia [19]. Exercise intensity was related to improvements in fitness- and health-

related parameters such as VO2max and mortality in healthy middle-aged and older adults

[20-25]. Exercise-induced improvements in physical fitness may facilitate brain plasticity and

secondarily improvements in cognitive function through increases in brain activation. Indeed,

higher cardiorespiratory fitness [26,27] and exercise-induced adaptions in blood lactate [28]

were previously associated with higher brain activation in anterior and motor areas [26,27],

fronto-cingulo-parietal networks [28] and better executive performance [26,27]. Considering

that exercise dose-parameters are related to increases in fitness, and fitness increases may in

turn be related to cognitive function by facilitating brain plasticity, exercise dose-parameters

may be related to increases in cognitive functions. Indeed, in healthy young and older adults,

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high dose-parameters of acute exercise were related to gains in executive function such as

processing speed and inhibitory control [29-32]. Exercise-induced cognitive benefits of acute

exercise may accumulate to greater and lasting cognitive improvements with chronic exercise

in a dose-specific way.

The relationship between exercise dose-parameters and cognitive functions in chronic

exercise studies is still not fully understood. A meta-analysis suggested that exercising

for 45-60 minutes per session, at least at moderate intensity, and at the highest feasible

frequency can improve global cognition, attention, executive function and (working) memory

in healthy adults over 50 [33], but the authors did not examine total dose. A meta-analysis of

18 randomized controlled trials (RCTs) [34] showed that weekly exercise duration (≤150or >150 minutes) was not related to changes in cognitive function in older adults with

cognitive impairments, specifically Alzheimer’s disease (AD) and non-AD dementia, but

other dose-parameters were not investigated. There is thus a need to systematically review

whether improvements in cognitive function scale with exercise dose-parameters separately

and as total dose and if dosing effects vary with cognitive status. The aim of the present

review was to examine the relationship between exercise dose-parameters (program and

session duration, frequency, intensity) and cognitive function (global cognition, executive

function, memory) in adults with vs. without cognitive impairments. We quantified the dose-

response relationship separately between the responses to aerobic, anaerobic, multimodal, and

psychomotor interventions and changes in global cognition, executive function, and memory

using advanced statistical modeling. We hypothesized that the magnitude of exercise effects

on global cognition, executive function, and memory is related to exercise dose-parameters

separately or combined as total dose. The results of the present study can be used to update

exercise recommendations and implement exercise programs for older adults with and without

cognitive impairments.

Methods

The current protocol is registered with the Open Science Framework

(url: https://osf.io/qe43p/). PRISMA guidelines were followed [35] (S6 Checklist, online

supplementary material).

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Search strategy and selection criteria

We searched databases PubMed, Embase, Psycinfo, Web of Science and the Cochrane Central

Reg

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