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Adherence and intensity in multimodal lifestyle-based interventions for cognitive decline prevention: state-of-the-art and future directions

Alzheimer's Research & Therapy volume 17, Article number: 61 (2025) Cite this article

Abstract

Preventing dementia and Alzheimer’s disease (AD) is a global priority. Multimodal interventions targeting several risk factors and disease mechanisms simultaneously are currently being tested worldwide under the World-Wide FINGERS (WW-FINGERS) network of clinical trials. Adherence to these interventions is crucial for their success, yet there is significant heterogeneity in adherence reporting across studies, hindering the understanding of adherence barriers and facilitators. This article is a narrative review of available evidence from multimodal dementia prevention trials. A literature search was conducted using medical databases (MEDLINE via PubMed and SCOPUS) to select relevant studies: nonpharmacological multimodal interventions (i.e., combining three or more intervention domains), targeting individuals without dementia, and using changes in cognitive performance and/or incident mild cognitive impairment or dementia as primary outcomes. Based on the findings, we propose future adherence reporting to encompass both participation (average attendance to each intervention component) and lifestyle change using dementia risk scores (e.g., the LIBRA index). Moreover, we provide an estimation of the expected intensity of multimodal interventions, defined as the ratio of the expected dose (i.e., the overall amount of the intervention offered specified in the trial protocol) to duration (in months). Adjusting the expected dose by average adherence enables estimation of the observed dose and intensity, which could be informative for identifying optimal dosage thresholds that maximize cognitive benefits across different populations. Finally, this article provides an overview of the determinants of adherence to multimodal interventions, emphasizing the need for improved adherence reporting to inform the design and implementation of precision prevention interventions.

Background

Alzheimer’s disease (AD), the most prevalent cause of dementia, develops over a long preclinical period and its progression is associated with modifiable lifestyle factors [1, 2]. This offers a window of opportunity for testing early preventive measures. In the last decade, there has been a shift toward multimodal lifestyle-based interventions for dementia prevention (e.g., combining diet, physical activity, cognitive training, vascular risk monitoring, or social interaction), due to the multifactorial nature of this condition [3]. In contrast to interventions targeting one risk factor alone, multimodal interventions target multiple risk factors simultaneously and are expected to generate additive or synergistic preventive effects. However, there is still limited evidence on the effectiveness of multimodal interventions for the prevention of cognitive decline [4]. Additionally, uncertainties persist regarding barriers and facilitators of adherence and response to multimodal interventions, modes of intervention delivery, and the intervention intensity (dose, duration, and adherence) required to influence cognitive performance [5]. Addressing these questions is crucial for advancing and optimizing multimodal interventions for dementia prevention, both at the individual (i.e., personalized prevention), and population levels (i.e., precision prevention) [6, 7].

The effectiveness of a next generation of precision prevention interventions for cognitive decline will rely on how effectively preventive programs are provided to populations of interest, their ability to adhere to such interventions, and the ability of healthcare providers to monitor adherence and adjust the intervention as needed. Adherence is recognized as the strongest predictor of intervention success [8]. However, there is a paucity of studies addressing the determinants of adherence to multimodal interventions [9,10,11,12,13,14]. This gap in evidence is, in part, attributed to the absence of a gold standard definition of adherence to these complex multimodal interventions. Although a single definition may not universally apply to all studies, the development of consensus-based recommendations for measuring and reporting adherence to multimodal interventions could enhance consistency across studies and thus establish global standards for conducting comparative effectiveness research. This is particularly important in the framework of international collaborative networks conducting multimodal intervention trials aimed at preventing cognitive decline, e.g., the World-Wide FINGERS (WW-FINGERS) network [15]. Harmonizing measures of adherence to multimodal interventions will facilitate pooled analyses, which are crucial for providing robust evidence about adherence profiles and progressing in AD prevention research.

The aim of this narrative review is to provide an overview of the available evidence on adherence and efficacy from multimodal dementia prevention trials.

Main text

Methodology

An English-language literature search was conducted using medical databases (MEDLINE via PubMed and SCOPUS, through November 29th, 2024) and keywords such as “multidomain”, “intervention”, “dementia”, “prevention” and “cognitive decline”. Additional studies were identified through the reference lists of selected publications and the researchers’ expertise on WW-FINGERS studies. The search strategy, screening process, and data selection adhered to PRISMA guidelines [16]. The following criteria were used to select relevant studies, including both randomized controlled trials (RCTs) and protocols: nonpharmacological multimodal interventions (defined as combining three or more intervention domains) with a duration of at least 6 months, a target population including individuals without dementia at baseline, and cognitive performance and/or incident mild cognitive impairment (MCI) or dementia as primary or secondary outcomes. This review was not registered in the International Prospective Register of Systematic Reviews (PROSPERO).

During the screening process, two independent reviewers (NS-D and AA-G) assessed eligibility based on titles and abstracts. Data extraction for the narrative review was conducted by one researcher (NS-D), capturing details on study design, multimodal intervention characteristics (e.g., dose, duration, adherence), and primary outcome measures. Quality assessment of the studies included was not conducted.

The database search identified 417 unique articles, with an additional 13 articles retrieved from other sources (Supplementary Fig. 1). After screening, 45 articles were selected for full-text review of clinical trials. Of these, 24 completed clinical trials met the inclusion criteria for analysis, and were distributed geographically as follows: 12 from Asia, 10 from Europe, and 2 from America. Among the excluded articles, 25 protocols of ongoing clinical trials were identified and included in the data synthesis of adherence reporting and assessment of the expected intensity of the multimodal intervention. These protocols were distributed geographically as follows: 8 from Europe, 7 from Asia, 5 from America, 4 from Australia, and 1 from Africa.

Adherence definition in multimodal studies

Adherence is defined as the degree to which the person’s behavior corresponds with the agreed-upon recommendations from a healthcare provider [8]. It differs from compliance, which is the extent to which a patient’s behavior matches the prescriber’s advice, emphasizing obedience rather than actively choosing to make lifestyle changes. Adherence to multimodal interventions should encompass participation in intervention activities and lifestyle change, as both aspects impact cognitive change and are not directly interrelated [10]. Nonetheless, most studies thus far have focused solely on participation in intervention activities as a measure of adherence [9, 11,12,13,14]. Accordingly, good adherence has often been defined as completing at least 66% of prescribed interventions [9], a benchmark often used in behavioral interventions [17]. However, an arbitrary cutoff such as a simple percentage of 66% might not be informative of high adherence, as it depends on the overall amount of the intervention offered.

Adherence reporting in multimodal studies

As shown in Table 1, there was significant heterogeneity in the reporting of adherence across the completed multimodal intervention studies. Adherence is commonly reported by intervention domain; however, certain studies, such as the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) [9] and the GOIZ-ZAINDU [18], have also assessed simultaneous adherence to all assigned components, albeit with the use of cut-offs. This diversity in adherence reporting makes cross-trial comparisons of adherence to multimodal interventions difficult. This challenge could be improved by reporting average participation (mean (SD), in percentage units) to each intervention component.

Table 1 Reporting of adherence to multimodal interventions for preventing cognitive decline
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On the other hand, ensuring consistency in reporting lifestyle changes across multimodal interventions can be challenging due to the considerable heterogeneity of the assessment tools used to measure lifestyle changes. A solution to this harmonization challenge could be the use of dementia risk scores such as The Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) risk score or the Lifestyle for Brain Health (LIBRA) index [19]. These scores can be calculated uniformly across studies irrespective of the various measurement instruments used to evaluate lifestyle or cardiovascular risk factors [20, 21]. Moreover, they have been suggested as surrogate outcomes for lifestyle-based multimodal prevention trials because they may register changes in dementia risk before detectable cognitive changes [22, 23]. However, risk scores often attribute points using categorical scoring systems, which might reduce responsiveness, as large changes in individual risk factors may not be registered if these changes do not cross the categorical cutoff points [22]. To improve their performance, it has been proposed to use continuous scoring systems (crude and weighted z-score versions), taking into account all changes in risk factors, not only those crossing specific cutoff values [22]. Using this approach, the LIBRA index demonstrated greater responsiveness to change, than did the CAIDE dementia risk score, as it includes a larger number of modifiable risk factors and a broader range of scores [22]. However, risk scores often put very limited weight on the lifestyle changes most frequently targeted in multimodal interventions (e.g., CAIDE has physical activity only, and LIBRA has physical activity, cognitive activity and diet) and contain many factors that cannot be changed (the effect of multimodal interventions on cardiovascular risk factors is modest, particularly when medications are not a part of the program).

Intensity of multimodal interventions

A measure of the intensity of the intervention that combines the dose delivered (i.e., total number of sessions) and the length of the intervention may be more useful for comparing adherence to different multimodal interventions. Among large multimodal intervention studies, the FINGER is the only one that demonstrated benefits on cognition [24]. The Multimodal Alzheimer Preventive Trial (MAPT) [9] and the Prevention of Dementia by Intensive Vascular Care (PreDIVA) [14], for example, reported no intervention effect on their primary cognitive outcomes. In addition to differences in the target population (at-risk individuals in the FINGER, frail individuals with subjective cognitive impairment in the MAPT, and the general population in the PreDIVA), these three studies differed substantially in terms of intervention intensity and delivery mode (structured intervention programs in the FINGER and MAPT, and mainly self-guided intervention in the PreDIVA). The expected intensity of the FINGER study, calculated as the ratio between the prescribed dose (number of prespecified sessions) and the length (months), was 10.6 points. In contrast, the MAPT had an intensity of 1.2 points, and the PreDIVA had an even lower intensity of 0.3 points (Table 2).

Table 2 Intensity of multimodal intervention studies
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As shown in Fig. 1A, structured multimodal intervention programs with expected intensities greater than 10 points are more likely to succeed in terms of improving cognitive performance or meeting primary cognitive outcomes. In turn, mainly self-guided or remote interventions should probably need greater intensities to impact cognition (Fig. 1B), because participants might be less likely to adhere, as evidenced by the AgeWell.de or HATICE results [25, 26]. On the other hand, ongoing studies are mainly framed within the WW-FINGERS network and thus follow the FINGER model of structured multimodal intervention, with expected intensities over 10 points (Fig. 1C). Specifically, the expected intensities ranged from 32 points in the US POINTER [27, 28], 31.8 points in the AU-ARROW [29], 30.3 points in the LatAm-FINGERs [30], 22.2 points in the LETHE trial [31], 20.4 points in the PENSA Study [32], 15.0 points in the Africa-FINGERS [33], 14.1 points in the CITA GO-ON [34], 12.9 points in the J-MINT and MET-FINGER trials [35, 36], 10.5 points in the SINGER [37], 9.2 points in the FINOMAIN [38], and 4.7 points in the FINGER-NL [39, 40]. Other ongoing multimodal intervention studies that are not members of the WW-FINGERS network usually have lower intensity scores ranging from 0.3 to 6.5 points, except for the MINE trial, which has an expected intensity of 29.3 points [41]. However, it is important to note that the intensity of the intervention is closely affected by the adherence and the content/quality of the intervention (although difficult to quantify). For instance, the observed intensity in the PENSA study after adjusting the expected dose by the average adherence to each intervention component was 14.5 points instead of 20.4 points (30% lower than expected by the design). Similarly, the observed intensity of the MAPT study was 33% lower than that expected by the design [9, 42], and this number was 36% in the GOIZ-ZAINDU study [18], and 55% in the HATICE study [26, 43]. However, the reporting of adherence or participation in intervention activities using cutoffs (e.g., percentage of participants with at least 66% adherence) prevents the assessment of the observed intensity, which requires adjusting the expected intensity by the average adherence to each intervention component. Moreover, information on the duration of cognitive or physical training sessions could allow a more accurate estimation of the intensity, as it could be equivalent to performing, for example, a 60-minute cognitive training session once a week and a 30-minute cognitive training session twice a week. Another aspect that could influence the estimation of the intensity is that the score proposed gives the same weight to all intervention components, although typically cognitive training or physical activity interventions have higher doses (e.g., weekly sessions) than nutrition or cardiovascular risk monitoring interventions (e.g., (bi)monthly visits).

Fig. 1
figure 1

Intensity of multimodal intervention (MI) studies aimed at preventing cognitive decline, including (A) completed structured intervention programs (A, B) completed predominantly self-guided intervention programs, and (C) ongoing intervention programs categorized by WW-FINGERS network membership. Details of each study are provided in Table 2

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While there is a strong rationale for delivering intensive, high-dose multimodal interventions to promote cognitive improvement or delay the onset of cognitive decline, it is equally crucial to address the challenge of achieving a sustained pattern of lifestyle modification. Striking a balance between intervention intensity, feasibility, cost-effectiveness, and long-term engagement is essential for the success and real-world applicability of these interventions. One potential approach involves a gradual increase in the intervention dose during the first 12–18 months, maintaining this heightened dose until the 2-year mark (in alignment with evidence of efficacy observed in the FINGER study), and thereafter gradually reducing the intervention dose so that the participants are likely to maintain the activities on their own after the intervention period is over. Another approach is to offer part of the program in a semistructured manner, for example, by using a hybrid intervention design. This approach is currently being tested in FINGER-NL, where part of the intervention program for all lifestyle domains is offered through a digital intervention platform. For example, online exercise instruction videos are made available with options for adapting the intensity of the work-out.

Determinants of adherence

Understanding why individuals engage (or do not engage) in a particular behavior is vital in the context of behavior modification [44]. Moreover, the identification of determinants of adherence is linked to the mechanisms of an intervention as described in, for example, the Medical Research Council guidance regarding complex interventions [45]. Evidence suggests that baseline social and health conditions matter for adherence and efficacy, and interventions that consider psychosocial factors for engaging in a healthy lifestyle (e.g., motivation, environmental adjustment) may achieve better results [46].

To identify determinants of global adherence to multimodal interventions, we conducted a systematic search on PubMed (see details in the introduction section) and employed snowball methods, involving the pursuit of references within references and electronic citation tracking. Table 3 provides a summary of the evidence on the determinants of global adherence in multimodal interventions.

Table 3 Overview of determinants of global adherence to multimodal interventions identified in previous studies
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In the recent years, numerous theories, frameworks, and models have emerged within the field of implementation science; however, their application in aging research has been limited [47]. An example of this is the Health Belief Model (HBM), which serves as a framework to explain and predict adherence to health and medical care recommendations. Its main premise lies in the notion that identifying beliefs and motivations related to health behaviors can inform the development of interventions aimed at increasing desirable health behaviors. This model defines key factors that explain health behaviors, including health knowledge, perceived susceptibility, perceived severity, perceived benefits of action, perceived barriers to action, cues to action, and self-efficacy. Notably, the HBM has been identified as the best-suited model for the development and evaluation of dementia prevention interventions [48]. In the context of the HBM, some studies have investigated the barriers and facilitators to participating in or implementing lifestyle interventions for dementia prevention using qualitative methods, targeting participants [49, 50], healthcare professionals [51] or the general public [52]. Experience with cognitive disorders (through family history or indirectly), motivated attitudes toward prevention and willingness to participate in a prevention trial were found to be facilitators, while beliefs that dementia is part of normal aging and not preventable were found to be barriers to participation. However, barriers to and facilitators of dementia prevention may differ, for example, between different socioeconomic groups, cultures, and genders [53]. Barriers and facilitators of overall participation in a trial may also differ from factors associated with adherence, and they can also differ between the intervention domains.

While HBM factors provide insight into the determinants of adherence, it is unlikely that a single intervention strategy can universally enhance adherence among all participants. The success of lifestyle interventions may depend upon tailoring interventions to the individual characteristics of participants. This is especially relevant in the context of multimodal interventions, which can be burdensome and not universally accepted. Participants may require different focuses on different domains, so tailoring is needed within the intervention. It has been proposed that some people (for example those who are frail or have a lower cognitive reserve) may benefit from higher dose of intervention, while for other people a lower dose might be sufficient [42]. Other factors related to the design of multimodal interventions such as the type, intensity and delivery method, or context (e.g., population, setting, community) may also influence adherence [9]. The adaptation of evidence-based programs to particular settings or populations is thus essential for maximizing their effectiveness [54]. Addressing this evidence-to-practice gap can be facilitated by incorporating implementation science approaches such as Intervention Mapping [55, 56]. These methodologies emphasize the evaluation of context and integrate social determinants of health in the development of interventions. Moreover, they involve program users (implementers, adopters, and maintainers) in the evaluation process [56]. The use of Intervention Mapping or similar methodologies in the design of dementia prevention studies can enhance their relevance in diverse populations. For instance, the LatAM-FINGERS and the AFRICA-FINGERS initiatives adopted the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework to assess a project’s effectiveness, feasibility, and sustainability [30, 33]. This approach has the potential to narrow the gap in dementia prevention research between low and middle-income countries and high-income countries [30, 33].

It is a matter of social justice that accessibility to health services is provided with the principle of equity. Populations with low socioeconomic status (SES) are known to be less likely to access health care and, more importantly, may be more likely to have less healthier lifestyles. Moreover, previous literature has shown a clear relationship between the prevalence of dementia and low SES, measured as annual income, educational level, occupation, and even neighborhood income levels [57]. Another important social determinant of health associated with dementia development is gender. Both a higher prevalence and incidence of dementia have been reported in women than in men, with two-thirds of individuals living with dementia and AD being women [58]. Risk factors associated with gender, which are all interrelated, range from lifestyle and psychosocial factors to cognitive reserve and may also affect participation in and adherence to multimodal interventions [59].

Conclusions

Multimodal interventions for dementia prevention are currently in the trial phase, seeking to prove their efficacy across diverse contexts and target populations and in combination with pharmaceuticals or nutraceuticals. A significant challenge lies in determining the minimum required dose and duration of lifestyle intervention to impact cognition, maintain participant adherence and ensure cost-effectiveness. Ideally, the multimodal intervention dosage should be the minimum required to induce the desired lifestyle changes that, in turn, result in meaningful cognitive benefits [42]. However, it is currently unknown how many metabolic equivalents (METs) of physical activity, or minutes of cognitive training, or the degree of adherence to a healthy diet, are necessary to impact cognition. The room for improving cognition through multimodal interventions in older adults is also unclear, as it is generally assumed that cognition cannot improve ad infinitum, but there is a plateau in which a higher dose does not translate into more cognitive benefits [42, 60]. If this information was available, the intervention dose could be adjusted according to each participant’s characteristics. This could entail, for instance, providing fewer physical activity sessions to a participant who already meets physical activity recommendations at baseline; but increasing the number of sessions if this participant decreased the adherence to such recommendations during the follow-up. This information gap has led to standardizing the dose of lifestyle intervention for all participants. Consequently, the “dose” in multimodal interventions is typically established based on evidence of feasibility and efficacy gathered from prior single-domain or multimodal interventions, considering the necessity for sufficient intensity (as general lifestyle recommendations alone may not be enough to influence cognitive change) [61], while also ensuring that participant motivation is sustained in the long-run.

To better understand the bidirectional relationship between the intervention dose, intensity and adherence, we propose harmonizing the reporting of adherence across multimodal lifestyle-based intervention studies for cognitive decline/dementia prevention, including both participation in intervention activities (average participation in each component) and lifestyle change, using dementia risk scores such as the LIBRA index. The intensity of a multimodal intervention could then be estimated by leveraging the dose, length and observed adherence with each intervention component. Although this intensity score may not encompass all potentially relevant factors (e.g., population or contextual characteristics, intervention quality or duration of sessions), it serves as a preliminary attempt to quantitatively describe this aspect of multimodal interventions. Cross-trial comparisons will be easier across studies, e.g., WW-FINGERS network trials, and others, facilitating an understanding of how intervention observed dose and intensity influence efficacy.

The identification of determinants of adherence to multimodal intervention is important for informing the design and implementation of precision prevention interventions. This article provides an overview of the evidence on the determinants of adherence to multimodal interventions, emphasizing the need to gain knowledge on barriers and facilitators of enrollment, participation, and engagement. The incorporation of implementation science approaches such as Intervention Mapping could enhance the representativeness of the target population, especially including low SES groups, potentially benefiting the most from dementia prevention initiatives. As the field progresses, a commitment to standardized reporting and a nuanced understanding of adherence determinants will be pivotal for advancing dementia prevention research toward meaningful outcomes for diverse populations worldwide.

The topic of adherence may also be quite important to the people participating in these interventions. Additional work should be carried out to understand how this information about adherence should be communicated to participants and to complement the understanding of adherence to multimodal interventions from the perspective of the participants. This type of work could complement and enrich our understanding of adherence in the short and long term.

Data availability

No datasets were generated or analysed during the current study.

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Funding

EU Joint Program—Neurodegenerative Disease Research (JPND) Multi-MeMo grant (National Institute of Health Carlos III, Spain [Grant AC22/00004]; Research Council of Finland, Finland; The Netherlands Organization for Health Research and Development, Netherlands). RTF led the Consolidated Research Group “Clinical Research Group in Pharmacology and Development of Biomarkers and New Drugs”, a Research Group supported by the Generalitat de Catalunya (2021 SGR 00253). This research was also supported by CIBER -Consorcio Centro de Investigación Biomédica en Red- (CB06/03/0028), Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación and Unión Europea – European Regional Development Fund. FINGER-NL was supported by a Crossover grant (MOCIA 17611) from the Dutch Research Council (NWO). The MOCIA program is a public-private partnership (see https://mocia.nl/scientific/). MK received funding from the Alzheimer’s Disease Data Initiative (ADDI), Region Stockholm (ALF, Sweden); Center for Innovative Medicine (CIMED) at Karolinska Institute (Sweden); Stiftelsen Stockholms Sjukhem (Sweden); Swedish research council for health, working life and welfare (FORTE). AS was also supported by the European Research Council grant 804371, Alzheimerfonden (Sweden). Grant AC22/00004 funded by Instituto de Salud Carlos III (ISCIII), and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia (MRR). The funders of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report.

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Authors and Affiliations

  1. Integrative Pharmacology and Systems Neuroscience Research Group, Neuroscience Research Program, Hospital del Mar Research Institute, Dr. Aiguader 88, Barcelona, 08003, Spain

    Natalia Soldevila-Domenech, Amaia Ayala-Garcia, Laura Forcano & Rafael de la Torre

  2. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, Barcelona, 08003, Spain

    Natalia Soldevila-Domenech

  3. Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Yliopistonranta 1C, Kuopio, 70211, Finland

    Mariagnese Barbera, Jenni Lehtisalo & Alina Solomon

  4. The Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, Charing Cross Hospital, St Dunstan’s Road, London, W6 8RP, UK

    Mariagnese Barbera, Miia Kivipelto & Alina Solomon

  5. Population Health Unit, Finnish Institute for Health and Welfare, Mannerheimintie 166, P.O. Box 30, Helsinki, Finland

    Jenni Lehtisalo & Tiia Ngandu

  6. CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Av. Monforte de Lemos 3-5, Madrid, 28029, Spain

    Laura Forcano & Rafael de la Torre

  7. Alzheimer Europe, Sennengerbierg Nidderaanwen, Luxembourg City, 1736, Luxembourg

    Ana Diaz-Ponce

  8. Alzheimer Center, Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, De Boelelaan 1117, Amsterdam, 1081 HV, Netherlands

    Marissa Zwan & Wiesje M. van der Flier

  9. Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Karolinska Vägen 37A, Solna, 171 64, Sweden

    Tiia Ngandu, Miia Kivipelto & Alina Solomon

  10. Medical Unit Aging, Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, D1: 04, 171 76, Sweden

    Miia Kivipelto

  11. Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Yliopistonrinne 3, Kuopio, FI-70211, Finland

    Miia Kivipelto

  12. Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Dr Aiguader 80, Barcelona, 08003, Spain

    Rafael de la Torre

  13. Neurosciences Research Program, Hospital del Mar Research Institute (HMRI), Dr Aiguader 88, Barcelona, 08003, Spain

    Rafael de la Torre

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  1. Natalia Soldevila-Domenech
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Contributions

RdlT, NS-D and AA-G conceptualized this paper and drafted the manuscript for intellectual content. MG, JL, LF, AD-P, MZ, WMvdF, TN, MK and AS critically revised the manuscript for intellectual content. All the authors have read and approved the final manuscript.

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Correspondence to Rafael de la Torre.

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Soldevila-Domenech, N., Ayala-Garcia, A., Barbera, M. et al. Adherence and intensity in multimodal lifestyle-based interventions for cognitive decline prevention: state-of-the-art and future directions. Alz Res Therapy 17, 61 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13195-025-01691-0

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Keywords

Alzheimer's Research & Therapy

ISSN: 1758-9193