MULTIDOMAIN INTERVENTION FOR THE REVERSAL OF COGNITIVE FRAILTY USING A PERSONALIZED APPROACH (AGELESS TRIAL): RECRUITMENT AND BASELINE CHARACTERISTICS OF PARTICIPANTS

 

A.M. Ibrahim¹, D.K.A. Singh¹, A.F.M. Ludin¹, P. Subramaniam¹, C. Ai -Vyrn², N. Ibrahim¹, H. Haron1¹, A.M. Safien¹, N.M. Khalid¹, P. Ponvel¹, N.H.M. Fadzil¹, J.M. Hanipah¹, F. Mangialasche^3,4,5, M. Kivipelto^3,4,6,7, S. Shahar¹

 

1. Centre for Healthy Ageing and Wellness (HCARE), Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia; 2. Geriatric Division, Faculty of Medicine, University Malaya Medical Centre, Kuala Lumpur, Malaysia; 3. Division of Clinical Geriatrics, Alzheimer Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden; 4. Karolinska University Hospital, Theme Inflammation and Aging, Medical Unit Aging, Stockholm, Sweden; 5. FINGERS Brain Health Institute, Stockholm, Sweden; 6. University of Eastern Finland, Institute of Public Health and Clinical Nutrition, Kuopio, Finland; 7. Ageing Research, School of Public Health, Imperial College London, London, UK

Corresponding Author: Suzana Shahar, Centre for Healthy Ageing and Wellness (HCARE), Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia, suzana.shahar@ukm.edu.my

J Prev Alz Dis 2024;
Published online June 13, 2024, http://dx.doi.org/10.14283/jpad.2024.111

 

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Abstract

BACKGROUND: Reversal of cognitive frailty through a multidomain intervention is desirable to prevent dementia. AGELESS Trial was conducted to determine the effectiveness of a comprehensive, multidomain intervention on older adults with cognitive frailty in Malaysia. However, conducting a clinical trial, particularly during and after Covid-19, posed unique challenges.
OBJECTIVE: We aimed to investigate the recruitment process and baseline characteristics of the AGELESS Trial participants to better understand an at-risk population and those who agree to participate in an intervention.
DESIGN/SETTING: 24-month, randomized controlled trial.
PARTICIPANTS: Community-dwelling older adults with independent mobility, aged ≥ 60 years, with a mini mental state examination score of 19-25, a clinical dementia rating of 0.5 ≥ 1 Fried’s physical frailty criteria, and < 22 Beck depression inventory.
INTERVENTION: Participants were randomized 1:1 to a structured multidomain intervention consisting of vascular management, diet, exercise, cognitive and psychosocial stimulation, or to the arm, including routine care and general health consultation.
MEASUREMENT: We analyzed the group differences between (1) cognitive frailty and non- cognitive frailty screened subjects, (2) recruited and non-recruited participants, (3) baseline characteristics of participants by arm, (4) adherence to AGELESS intervention at 12 months, and (5) preliminary findings on the effectiveness of the intervention at 12 months.
RESULTS: A total of 957 older adults from two locations, i.e., urban (n = 764) and rural (n = 193) areas, were screened, of whom 38.9% had cognitive frailty and were eligible to participate. Those with cognitive frailty had fewer years of education (B = -0.08; 95%CI = 0.88-0.97; p = 0.002), and lower functioning cognition (B = -0.24; 95%CI = 0.74-0.84; p < 0.001). Among those from urban areas, only 33.1% (n = 106) agreed to participate, particularly those with multimorbidity (B = 0.86; 95%CI = 1.31-4.30; p = 0.01), higher physical activity (B = -1.02; 95%CI = 0.19-0.69; p = 0.002), slower walking speed (B = 1.26; 95%CI = 1.62-7.61; p = 0.001), and higher systolic blood pressure (B = 0.02; 95%CI = 1.00-1.03; p = 0.03). At baseline, participants’ mean age was 68.1±5.6, years of education was 8.3±3.9, body mass index was 27.5±5.3 kg/m2, and mini mental state examination score was 22.7±4.0. Generally, there were no significant differences between the intervention and control groups for the main outcomes, except those in the intervention group had higher body mass index, mid-upper-arm circumference, and waist circumference (p < 0.05 for all parameters). Overall intervention adherence at 12 months was 52.8%, ranging from 52.8%-90.6% for each of the modules. Preliminary analysis of the effectiveness of the intervention at 12 months was positive on most of the cognitive domains, some of the nutrient intake and food groups, physical function, and vascular outcomes (p < 0.05 for all parameters).
CONCLUSION: Despite the challenges posed by the pandemic, screening, recruitment, and 12-month intervention delivery were achieved in a Malaysian multidomain preventive randomized controlled trial in older adults at risk of dementia, with a satisfactory adherence rate and cognitive benefits at 12 months.

Key words: AGELESS Trial, cognitive frailty, intervention, lifestyle, multi-domain, prevention, RCT.

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Introduction

The Asian region population is currently ageing at an unprecedented pace and is expected to account for 62.3% of the world’s older population by 2050 (1). In Malaysia, the population of older adults reached 3.6 million in 2022 and will be 6.3 million (20%) in 2040 (1, 2). Older adults often experience systemic decline that increases risk of various health conditions and comorbidities. Although age-related changes are inevitable, it is possible to modify pathological changes through interventions as a dementia risk reduction initiative (3, 4).
Dementia, primarily in the form of Alzheimer’s disease (60%-80%), affected 123000 Malaysian older adults in 2015 (4). This number is projected to double in 2030 (5). The early signs of cognitive impairment (CI) may manifest as forgetfulness in remembering information such as appointment dates and often losing things while still being able to perform activities of daily living independently. However, as the condition progresses, it can lead to reduced physical mobility, and the older adult will ultimately be bedbound at a later stage. Considering the social, economic, and psychological burdens faced by individuals with dementia and their caregivers, prevention via risk reduction strategies becomes of utmost importance (6, 7). The concept of reversible cognitive frailty (CF), encompassing both physical frailty (PF), and mild cognitive impairment (MCI), has emerged as a promising approach to the prevention of dementia (7).
Cognitive frailty (CF), characterized by the presence of both PF and MCI (8), is believed to be a prodromal stage of dementia. It has been found that CF confers a higher risk of developing dementia compared to CI or physical impairment alone (9). There is a potential to reverse CF completely or to its lower subtypes (10). Particularly, our previous study indicated that some risk factors of CF included depression, poor cognition, functional immobility, low vitamin D intake, and PF, which are potentially modifiable (11). It has been estimated that addressing the risk factors for dementia could prevent one-third of dementia cases in the next 25 years (12).
Given that dementia has a multifactorial etiopathogenesis, the need for multidomain interventions in dementia prevention or the reversal of predementia stages, such as CF, is important (13, 14). Evidence supports the benefits of non-pharmacological interventions in reducing the incidence of MCI or dementia as well as improving activities of daily living and global functioning cognition (FC), and cognition in older adults at risk of dementia (15–18). However, there is a lack of studies investigating the effectiveness of a multidomain intervention in multi-ethnic older adult populations with CF in low-to-middle-income Asian countries such as Malaysia.
Therefore, the multidomain intervention for reversal of CF using a personalized approach (AGELESS Trial) was examined to determine its effectiveness in reversing CF among multi-ethnic older adults in Malaysia (19). This study is part of the World-Wide FINGERS global network of multidomain trials for dementia risk reduction and prevention (https://fbhi.se/world-wide-fingers-network/), which builds upon the positive results of the Finnish geriatric intervention study to prevent CI and disability (FINGER), and is at the forefront of international collaborations for dementia prevention (18, 20). The present report aims to provide insight into the recruitment and baseline characteristics of the AGELESS trial participants. Furthermore, challenges in the recruitment and the implementation of the trial, which was conducted during and after Covid-19, and updates on adherence to the trial at 12 months are also discussed. Finally, preliminary findings on the effectiveness of intervention at 12 months are reported. Understanding these aspects is essential in further personalizing the multidomain preventive intervention model, and interpreting the findings of the trial. The effectiveness of the 24-month intervention will be reported once the study is completed.

 

Method

The AGELESS Trial protocol has been registered with ISRCTN (ClinicalTrials.gov identifier: ISRCTN75429638), with the method being described in an earlier study (19). This is a multicenter, community-based, randomized controlled trial (RCT) among older adults. The multidomain intervention includes nutrition, psychosocial support, metabolic and vascular risk factor management, exercise, and cognitive training. We benchmarked, upgraded, and added new modules to the AGELESS RCT, based on the WE-RISETM multidomain intervention trial (21). The AGELESS study was approved by the Ethics Committee of Universiti Kebangsaan Malaysia. All participants gave informed consent in accordance with the Declaration of Helsinki Guidelines.

Recruitment

The recruitment phase of this study was initially planned for July 2020, but due to Covid-19, it was postponed to April 2021 and ended in March 2022. During the pandemic, movement control orders (MCOs) were announced, and the research activities were conducted as a hybrid, both online and face-to-face, following the standard operation procedure to prevent the spread of Covid-19. We had to adapt the recruitment strategies by leveraging on online channels, social media, and community outreach to engage potential participants. The pandemic necessitated a rapid shift to virtual platforms for research participation, enabling broader access to diverse participant pools and facilitating remote data collection. We have carried out a feasibility study using a virtual platform for the screening phase. However, we found that it introduced a selection bias, as not all individuals have equal access to technology, leading to potential disparities in sample representation. Thus, recruitment pamphlets were disseminated through social media and communication (e.g., phone calls, emails) with local authorities of various places, mainly communities (residential areas and low-cost housing areas), religious centers, and senior citizen centers such as older adult activity centers (PAWE) around Klang Valley in the center of Malaysia (Kuala Lumpur and Selangor) and a rural area of Seremban (further South of Klang Valley) (Table 1). Each time the MCO was lifted or loosened, efforts were made to meet face-to-face with the authorities to get permission and arrange dates for screening. Potential candidates aged ≥ 60 years and with independent mobility (with or without walking aids) were invited for screening by representatives of the centers via verbal and visual (posters) communication.

 

Screening Measures

Screening was conducted at 57 centers from April 2021 till March 2022. As mentioned earlier, this period coincided with the MCOs during Covid-19. Screening parameters included cognitive measures using the mini mental state examination (MMSE) (22, 23), the clinical dementia rating (CDR) (8), psychological aspects, i.e. depression using the Beck depression inventory (BDI) (24, 25), PF using Fried’s Frailty criteria (26), and physical activity (PA) using the physical activity scale for the elderly (PASE) (27). All measures were collected via interviews with the study participants. Research assistants were trained on assessment prior to data collection. In order to be eligible for this study, the participants had to score 19-25 on the MMSE, 0.5 on the CDR, have ≥ 1 Fried’s PF criteria, and < 22 on the BDI (22, 24, 26). The exclusion criteria included major depression, dementia, other major psychiatric disorders, severe CI, malignant diseases, or other conditions that may hinder safe participation in the study as judged by the researcher, such as poor balance/high risk of falls, or being unable to comprehend instructions, or coincident participation in any intervention trial.

Randomization and Blinding

A double-blind design was adopted to the largest extent possible: arm allocation was not actively disclosed to participants, who were asked not to discuss the intervention during the outcome measurement sessions. Also, possibilities for between-group interactions were restricted, and outcome assessors were blinded to allocation and were not involved in intervention activities. The participants were randomly allocated to intervention and control arms using Research Randomizer software by a trial manager (28). Block randomization based on sex was applied. Only participants from the urban area were selected for the intervention due to logistic reasons that allowed the intervention programme to be effectively conducted, also considering the concurrent travel restrictions and MCOs related to the pandemic. The urban area was nearer to the university (approximately 11 km), which facilitated the implementation of the interventions. The specific outcome, i.e., radio imaging (functional magnetic resonance imaging, fMRI), required the participants to travel to the university hospital for the assessment. Rural participants have been selected for a multidomain telehealth intervention, which is in the process of development (19).

Baseline Measurements

Various measurements were taken to provide comprehensive profile description and report changes with the intervention programme. Primary outcome measures for PF comprised Fried’s frailty criteria, a 2-min step test, grip strength test, chair sit-to-stand test, chair sit-and-reach test, timed up-and-go test, back scratch test, exercise self-efficacy scale (ESES), and maximum oxygen uptake (VO2 max), while FC was measured using the modified neuropsychological test battery (mNTB), MMSE, and CDR based on participant interviews. Secondary outcomes included (1) vascular measures by collecting 30 ml blood at baseline and follow-up for biochemical analysis of metabolic profile, nutriomes, albumin, amino acid and fatty acid profile, vitamin D receptor, vitamin B12, folic acid, vitamin E, and homocysteine; (2) body composition measurement by recording weight, height, arm span, mid-upper arm circumference, waist circumference, hip circumference, calf circumference, fat mass, and muscle mass; (3) psychosocial and functional measures by recording medical outcome social support (MOSS), BRIEF-cope, De Jong Gierveld loneliness scale, resilience scale, and general self-efficacy scale (GSES), University of Rhode Island change assessment (URICA) psychotherapy scores; (4) dietary measures by completing a diet history questionnaire (DHQ); and (5) brain activation using fMRI (19). Abiding by the standards of distancing practice during Covid-19, the number of participants was limited according to available space. Assessors wore personal protective equipment during assessment sessions.

Intervention

The 24-month-long AGELESS modules (iAGELESS modules) comprised nutritional counselling, psychosocial intervention, PA, cognitive training, and metabolic and vascular risk factor management. Conversely, the control arm continued their usual care with the physician and received group counselling sessions and health talks based on standard public health practice.

Determinants of Adherence

Baseline characteristics of the study participants were assessed as potential determinants of adherence during the first 12 months of the trial. They were selected based on a literature review of factors associated with adherence to multidomain interventions (29) and known dementia risk factors (30). A set of variables were assessed as follows: sociodemographic (age, sex, and YoE), FC (MMSE, fluency test, Rey auditory verbal learning test (RAVLT), digit span, verbal paired, visual paired), depressive symptom (geriatric depression scale), physical performance scale (2-min step test, hand grip strength, 30-sec sit-to-stand, chair sit-and-reach test, timed up-and-go test, back scratch test), psychosocial (URICA psychotherapy version, De Jong Gierveld loneliness scale, MOSS, GSES, Connor Davidson and BRIEF-cope) and cardiovascular risk factors (history of high blood pressure, diabetes, high cholesterol, BMI, and smoking status).

Statistical Analysis

Screened participants were classified into subjects with and without CF. Differences between those with and without CF, and among subjects who agreed and disagreed to be enrolled in the trial, as well as characteristics of those randomized to each of the two trial arms, were analyzed using an Independent t-test, analysis of variance (ANOVA), and Chi-square (χ2), where appropriate. Factors that influenced willingness to enroll in the trial, and adherence over the 12-month intervention period were determined using logistic regression. The preliminary findings on the effectiveness of the intervention at 12 months were analyzed using repeated measure ANOVA. Data were analyzed using IBM® Statistical Package for the Social Sciences (SPSS®) version 21. The level of significance was 5% in all analyses.

 

Results

A total of 957 older adults were screened for eligibility criteria. Most of the participants (n = 764, 79.8%) were from the urban area, and a total of 106 (33.1%) participants had CF, met the eligibility criteria, and agreed to enroll in the trial (Figure 1). Parameters for sociodemographic, medical history, anthropometry, FC, and cardiovascular risk were compared for all screened subjects, between non-CF and CF (Table 2a). Compared to screened subjects without CF, older adults with CF were significantly older (68.4±6.5 vs. 66.5±5.1 years, p < 0.001); comprised of mainly Malays (78.5%), followed by Indians (14.0%), and Chinese (6.7%) (p < 0.05); had fewer YoE (7.9±4.0 vs. 10.4±3.8 years, p<0.001); weighed less (64.4±12.6 vs. 67.3±12.2 kg, p < 0.001); were shorter (154.7±8.3 vs. 156.6±9.2 cm, p < 0.001); had lower skeletal muscle indices (6.4±1.1 vs. 6.8±1.11, p < 0.001); less fat-free mass (40.0±7.62 vs. 42.2±8.2 kg, p < 0.001); and less skeletal muscle mass (21.5±4.58 vs. 22.9±5.01 kg, p < 0.001). Total cardiovascular risk score, which included age, sex, hypertension, hypercholesterolemia, diabetes mellitus, overweight, and smoking was significantly higher in the CF group (3.7±1.4 vs. 3.4±1.3, p < 0.001). However, only YoE (B = -0.08, 95%CI = 0.88-0.97, p = 0.002) and FC (B = -0.24, 95%CI = 0.74-0.84, p < 0.001) were found to be associated to CF in the multivariate analysis (Table 2b).

 

SMI: Skeletal muscle index, BFM: Body fat mass, FFM: Fat free mass, SMM: Skeletal muscle mass, PBF: Percentage body fat, MMSE-Mini mental state examination, *p-value<0.05

SMI- skeletal muscle index, FFM-fat free mass, SMM- skeletal muscle mass, MMSE-Mini mental state examination, *p-value<0.05

 

Among the 372 persons who were eligible for the trial, almost one-third (n = 106) agreed to be enrolled. Compared to those eligible who did not agree to be enrolled, those who agreed to participate were more likely to be more PA (70.6 vs. 57.1%, p = 0.02), had slower walking speeds (22.6 vs. 10.7%, p = 0.007), had hypercholesterolemia based on self-reported data (60 vs. 48.5%, p = 0.05), had multimorbidity (60 vs. 47.5%, p < 0.05), weighed more (66.9±13.3 vs. 63.4±12.1 kg, p = 0.02), had higher skeletal muscle indices (6.6±1.1 vs. 6.3±1.1, p = 0.046), and higher systolic blood pressure (140.8±19.2 vs. 135.8±20.4, p = 0.04) (Table 3a). Further analysis using backward logistic regression indicated that participation in the trial was associated with higher likelihood of having multimorbidity (B = 0.86, 95%CI = 1.31-4.30, p = 0.01), more PA (B = -1.02, 95%CI = 0.19-0.69, p = 0.002), slower walking speed (B = 1.26, 95%CI = 1.62-7.61, p = 0.001), and higher systolic blood pressure (B = 0.02, 95%CI = 1.00-1.03, p = 0.03) (Table 3b).

WHR- waist hip ratio, SMI- skeletal muscle index, BFM- body fat mass, FFM-fat free mass, SMM- skeletal muscle mass, MMSE-Mini mental state examination, *p-value<0.05

SMI-skeletal muscle index, Less PA-less physical activity, *p-value<0.05

 

Random allocation of 106 participants yielded equal sex and age ranges in both the intervention and control groups. Baseline profiles in various aspects including sociodemographic, anthropometry, vascular, psychosocial, physical performance, and FC were compared between the intervention and control groups (Table 4). All parameters were homogeneous, except that the intervention group had a higher BMI (28.6±5.7 vs. 26.4±4.6 kg/m2, p = 0.04), higher mid-upper arm circumference (29.4±4.2 vs. 27.7±3.3 cm, p = 0.03), and higher waist circumference (97.6±13.9 vs. 91.9 ±12.6 cm, p = 0.03).

BMI- body mass index, MMSE-Mini mental state examination, *p-value<0.05; , RAVLT- Rey auditory verbal learning test,*p-value<0.05

 

Adherence to the multidomain intervention was measured by attendance at the sessions of the five modules during the first year. The cut-off value was 50%, as in FINGERS Trial (29). As seen in Table 5, 52.8% (n=28) participants attended each of the iAgeless modules > 50% while 47.2% (n=25) participants attended < 50%. The vascular intervention module had the highest attendance (90.6%, n=48), followed by psychosocial (60.4%, n=32), nutrition (58.4%, n=31), and exercise and cognition (52.8%, n=28). The attendance decreased as the number of the sessions of the modules increased. The sociodemographic data, cognition, depressive symptoms, cardiovascular risk factors, physical fitness test, or psychosocial factor variables did not correlate with the trial’s adherence rate. Only fluency correlated with adherence to the overall module (B=0.81, 95%CI=0.66-0.99, p 0.04) (Table 6).

*OR and CI obtained using enter and stepwise methods analysis

 

Preliminary findings on effectiveness of the intervention at 12 months indicated significant changes in most of the cognition variables; followed by some of the nutrient intake and food group variables, physical function, vascular, and anthropometry variables. The cognition variable comprised mNTB (group: p<0.05, η2=0.09; interaction: p<0.05, η2=0.05), RAVLT delay (group: p<0.05, η2=0.15; interaction: p<0.05, η2=0.20), digit span (group: p<0.05, η2=0.07; interaction: p<0.001, η2=0.25), verbal paired associates (group: p<0.001, η2=0.19; time: p<0.05, η2=0.12; interaction: p<0.001, η2=0.18), visual paired associates (group: p<0.001, η2=0.15; interaction: p<0.05, η2=0.07), visual paired associates delayed (group: p<0.05, η2=0.08); and brain activation-0 back correct response (%) (interaction: p<0.05, η2=0.1), Stroop color and word test (SCWT) control correct response (interaction: p<0.05, η2=0.11), SCWT congruent right dorsolateral prefrontal cortex (DLPFC) activation (group: p<0.05, η2=0.22; interaction: p<0.05, η2=0.15).
Improvements were also observed in nutrient intake variable, which comprised niacin intake among women (interaction: p<0.05, η2=0.06) and calcium intake (interaction: p<0.05, η2=0.08). The intervention group’s consumption of specific food groups was also higher than that of the control group; namely, vegetables (time: p<0.001, η2=0.16; group: p<0.001, η2=0.56; interaction: p<0.001, η2=0.38), fruits (time: p<0.001, η2=0.10; group: p<0.001, η2=0.82; interaction: p<0.001, η2=0.18), fish (time: p=0.01, η2=0.04; group: p=0.001, η2=0.09; interaction: p<0.001, η2=0.12), legumes (time: p<0.001, η2=0.14; group: p<0.001, η2=0.33; interaction: p<0.001, η2=0.22), and milk and milk products (time: p<0.05, η2=0.03; interaction: p<0.05, η2=0.03).
With respect to the physical function variable chair sit-and-reach (group: p < 0.05, η2 = 0.08; interaction: p < 0.001, η2 = 0.13) and timed up and go (interaction: p < 0.05, η2 = 0.06), only some of the vascular and anthropometry outcomes improved in the intervention group as compared to the control. This included the homeostatic model assessment of insulin resistance (HOMA-IR) (group: p < 0.001, η2 = 0.13; interaction: p < 0.001, η2 = 0.18); and anthropometry-weight (only men) (interaction: p < 0.05, η2 = 0.22).

 

Discussion

To the best of our knowledge, this is the first multidomain intervention testing lifestyle modifications aiming to reverse CF among a population of multi-ethnic older adults in a low- and middle-income country (LMIC) such as Malaysia, with a longer duration of intervention. More so, with the aim of risk reduction and prevention of dementia, this study is crucial in preparing the country towards an ageing society. Targeting CF seems to be appropriate as it is associated with a higher risk of developing dementia (9). In the present study, the CF participants had fewer YoE and, as expected, a lower FC compared to those without CF. YoE and FC are among the main predictors of dementia (31). This study explored the profile of older adults with CF with a wide range of parameters.
In several studies, the importance of the single intervention components, such as nutrition, exercise, vascular health, cognitive training or the psychosocial aspect, has been investigated (32, 33). To date, none of these five components has been tested in people with CF as an integrated multidomain preventive approach, as provided in the AGELESS intervention trial (30, 34). Nevertheless, we observed a rather high rate of non-participation of 66.6%, as compared to another trial, such as AgeWell.de (12.5%), which was also conducted during Covid-19 (35). It is important to note that AgeWell.de recruited their sample through general practitioners (GPs) around Germany, which resulted in higher success in recruitment than the present study, which used voluntary community-based recruitment. Our study findings that older adults with health conditions such as multimorbidity were more likely to participate in the present AGELESS trial is similar to AgeWell.de that reported that those with high blood pressure were also keen to be involved in such a trial. Although having multimorbidity and higher systolic blood pressure, those who participated in the AGELESS trial had a better PA than non-participants. Apparently, those already engaged in lifestyle changes are more willing to participate in lifestyle modification-based interventions (36). There have been no local studies similar to AGELESS. However, to compare participation among Malaysian older adults in research, the previous longitudinal TUA study among Malaysian older adults observed 34.5% defaulters (37). Meanwhile, in the Malaysia elders longitudinal research (MELoR) study, 53.1% met the inclusion criteria but did not agree to participate in the study (38). Hence, it is challenging to recruit our older adults, possibly also due to family commitments with spouses and grandchildren.
The level of YoE was significantly different between individuals with CF and without CF, being lower in those with CF. This result can also be related to the MMSE score, which, as expected, was lower in the CF participants than in the non-CF participants. This is similar to previous findings among people with CF (37), and consistent with other studies investigating individuals at risk of dementia, including MCI, subjective cognitive decline, and pre-MCI with subjective cognitive decline (39). Education is an early life potentially modifiable risk factor associated with late-life dementia (31, 40). The likelihood of being classified as having very mild dementia as opposed to healthy declines with every ten extra YoE (41). However, in our study, participants´ YoE differed only between two and three years. Probably, there is a need to adjust the score of the CF categorization tool (e.g., CDR) to suit a population with fewer YoE, such as those from LMICs, e.g., by adding the score for the individual with and without formal YoE.
The mean age of the participants in the present study, 68.1 ± 5.6 years, was comparable to FINGERS (29) and the AgeWell.de (35) study of 69.4 ± 4.7 and 69.0 ± 4.9 years, respectively. However, the French multi-domain Alzheimer’s preventive trial (MAPT) participants were older (75.3±4.3 years). The mean YoE of the AGELESS trial participants (8.3±3.9 years) is almost similar to that of AgeWell.de (35) and MAPT (29), where most of the participants had a higher YoE. However, in the FINGERS trial, most participants had fewer YoE (29).
Pertaining to exercise intervention, in one trial that was conducted for 24 weeks among older adults at risk of dementia in Australia, an adherence rate of 78.2% was achieved (42). The adherence in the multimodal preventive trial for Alzheimer’s disease (MIND-ADmini), a six-month feasibility study in people with prodromal Alzheimer’s disease, is yet to be reported (43). On the other hand, our local WE RISE study with 24 weeks of intervention achieved excellent adherence, ranging between 96.6% and 99.4% (21). This result could be due to a shorter duration of weekly commitment whereby the first 12 weeks involved center-based intervention and the following 12 weeks involved home-based intervention.
In another study, testing the Dejian mind-body intervention (memory intervention) versus a Chinese-based lifestyle intervention (Chinese martial arts and medical principle) for 10 weeks, both intervention groups yielded positive outcomes on cognitive status. However, the study did not report the adherence rate (44). The simultaneous adherence rate to at least 50% of all intervention components in the AGELESS trial was 52.8% at 12 months, and this is comparable with other multidomain interventions, including FINGERS, (38.9% over two years) (29) and MAPT (53.5% over three years) (29), which differed in duration but also for intervention intensity. In AGELESS trial, the adherence rate was highest for the vascular domain (91%) and lowest for exercise and cognitive training (53%). These patterns of adherence are similar to the FINGERS trial, whereby the highest adherence rate was for vascular and nutritional (individual), followed by nutritional (group), gym training and cognitive training (45). Limited studies based on lifestyle modifications among older adults have been done in our local setting, for comparison. However, adherence was also highlighted as an issue in a study on multifactorial intervention to reduce falls conducted among 268 Malaysian older adults (46).
The FINGER study reported a slightly higher rate of adherence in the exercise component (60%). The exercise intervention in the FINGERS trial was conducted at the gym, and this could have gained participants’ interest. In comparison, the exercise component in the iAGELESS module was conducted at respective community centers using simple exercise equipment such as sandbags. This was mainly because accessibility to gyms was low due to logistic and financial constraints. Moreover, the AGELESS trial participants had difficulties committing to twice-a-week exercise sessions at the community centers. Thus, they were given a home-based exercise package to ensure better compliance.
Although we could not find any participant characteristic associated with adherence to the iAGELESS module intervention except for the fluency test, other studies reported that self-efficacy and good self-rated mental health were enablers for such interventions. Meanwhile, depression and distance from exercise facilities were found to be barriers (47). In addition, another study based on the MAPT and FINGER trials reported that older age, a history of diabetes, depression, and current smoking were factors that negatively impact adherence, while higher self-reported PA and intermediate YoE were factors linked to better adherence to multidomain interventions (29). Multimorbidity appeared to be associated with participation in the AGELESS intervention, indicating that perceived illnesses could have motivated participation as they were important in treatment outcomes (38).
Although the trial is yet to be completed at 24 months, some of the outcomes showed encouraging positive findings at the 12-month follow-up, particularly cognition, followed by nutrient intake and food groups, physical function, and vascular outcomes. Some studies, including the FINGER trial, have suggested that multidomain interventions can have positive effects on cognition among older adults at risk of dementia (18). Similar to AGELESS trial, the FINGER trial included a combination of cognitive training, PA, dietary counselling, social stimulation, and control of vascular and metabolic risk factors. The FINGER trial demonstrated that a multidomain approach was associated with improvements in cognitive performance and a reduced risk of cognitive decline (18). Improvement in cognitive measures by multidomain intervention among at-risk-of-dementia older adults is supported by a recent review by Castro et al. (2023). As nutritional factors play a crucial role in brain health, increased food intake of vegetables, fruits, and fish may have contributed to the cognitive benefits (49).
This study’s strength is in the random allocation of the intervention and control groups and the use of validated modules for the intervention (50). Furthermore, compared to ongoing studies in this field, the inclusion of a psychosocial domain as part of the intervention is the principal advantage, as this component is lacking in other trials. Nevertheless, it should be acknowledged that the sample size was rather small, therefore limiting the external validity and the generalizability of the findings. The planned sample size was three times higher than the present but was not achieved due to the time limit and restrictions during Covid-19. Nevertheless, the prospective harmonization of AGELESS trial with RCTs within the World-Wide FINGERS network, and particularly with the FINGER and MIND-AD RCTs, enables the possibility of joint data analysis, providing the increased statistical power needed to confirm the efficacy of the multidomain intervention in older adults at risk of dementia (20).
Other researchers also faced similar challenges to our experimental study due to the pandemic. The pandemic impacted the safety and engagement of clinical trial operations, particularly involving a vulnerable group such as older adults (51, 52). Additionally, given the high percentage of older adults’ population in rural (2020:7.3%) than urban (2020:6.6%), inclusion of rural participants would have provided better insights on participation in multidomain lifestyle interventions from this group. It therefore should be considered in future studies (53).
Indeed, the pandemic has made people aware of the need for flexibility in response to lockdowns and social distancing measures to adopt more flexible research designs and participation schedules. For example, we attempted hybrid intervention sessions, but they were not well accepted due to low societal economic status, which restricts access to better devices with sufficient Internet connections. Moreover, low societal economic status is also associated with digital illiteracy (54). However, it could be possible with financial support, technological advancement, health awareness, education, and training. Maintaining face-to-face contact while incorporating local culture in the intervention seemed more suitable for older adults, whilst using suitable technological tools (based on information and communication technology literacy, etc.) will probably aid in improving adherence in future trials.
Multidomain interventions could be challenging to implement, as they involve various factors, including intrinsic factors, such as older adults’ motivation and perception, and also extrinsic factors, such as those involving support from the community, the policymakers, and the government. However, such effort in intervening towards risk reduction and prevention, which has economic and societal benefits in the long term, is in line with the World Health Organization’s guidelines for risk reduction of cognitive decline and dementia (55).
Understanding factors that influence recruitment and participation in multidomain preventive studies is crucial in personalizing the trials to the targeted group and interpreting the data within the context of the study population. Future studies should consider recruiting potential participants at healthcare facilities, planning strategies to improve adherence, and lessening participant hardship. Besides, participants´ willingness to change should also be considered before allocating participants according to the commitments required. Multidomain interventions could also be approached more individually, with participants receiving interventions tailored to their unique risk factors and additional motivating interventions added for those most at risk of non-adherence. In addition, considering the preference of face-to-face sessions, hybrid telehealth interventions should be implemented, particularly for longer-term interventions, to increase adherence towards intervention and possible effectiveness.

 

Acknowledgements:The authors would like to thank all the researchers in the AGELESS Programme and all the staff at the Centre for Healthy Aging and Wellness (HCare), Faculty of Health Sciences, Universiti Kebangsaan Malaysia. This research is a part of the World-Wide FINGERS Network. As such, we would like to thank WW-FINGERS Network and its Global Scientific Coordinating Center for their contributions to this research. We would also like to convey our gratitude to all the participants.

Competing Interest: The authors report no conflicts of interest. The funding agencies did not interfere with either the design of the study or the interpretation of the results.

Funding: This study was supported by the Long-Term Research Grant Scheme (LRGS/1/2019/UMUKM/1/4). The authors MK and FM are also supported by the FORTE Grant 2023-01125 and funding from the Region Stockholm (ALF).
.
Ethics Approval and Participant Consent: This study was approved by the Ethics Committee of Universiti Kebangsaan Malaysia. All the participants provided informed consent in accordance with the Declaration of Helsinki guidelines. We examined their ability to make an informed decision about participating and efforts were made to support them in the decision-making process. Furthermore, participants with deteriorating blood profile during follow-up will be advised and referred to nearby health clinics.

 

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