Approaches to Personalize Multidomain Lifestyle Interventions for Dementia Prevention
Year: 2024
Type: article
Abstract: Multidomain lifestyle interventions have recently demonstrated effectiveness in dementia risk reduction.[1,2] These interventions target multiple risk factors and typically include physical activity, dietary modifications, vascular management, cognitive training, and social engagement. Studies such as the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability highlight the effectiveness of simultaneous exercise, diet, social engagement, and cognitive training in maintaining daily functioning among older adults at risk of cognitive decline.[3] Comparable results have been reported in the Prevention of Dementia by Intensive Vascular Care Trial, Multidomain Alzheimer Preventive Trial, and other trials.[4,5] Unfortunately, scaling these interventions involves complex challenges, primarily due to the individualized nature of these interventions and the need for substantial resources and coordination. Given the current lack of safe disease-modifying interventions for patients with dementia of various types, emerging research has focused on the prevention and delay of dementia.[6,7] In this article, we discuss two established and two novel approaches to personalize multidomain lifestyle interventions for individuals at risk of cognitive decline. RISK SCORE GENERATION AND COMMUNICATION The generation and communication of risk scores to at-risk individuals is an established personalization approach in illness prevention and health promotion and could be applied to individuals at risk of developing dementia.[8] Risk scores derived from modifiable lifestyle and psychosocial characteristics, and unmodifiable sociodemographic data, genetic information, and biomarker insights have been used to predict cognitive decline and disorder in studies such as the Framingham Heart Study and the Singapore Longitudinal Ageing Study.[9,10] These risk scores have demonstrated promising predictive accuracy, allowing for the identification and counseling of at-risk individuals. While the literature is replete with evidence supporting the clustering of health-promoting behaviors and unhealthy behaviors among adults and adolescents,[11-13] there is emerging evidence supporting the clustering of heterogeneous groupings of health-related behaviors.[14] Recent research have used person-centered analyses such as latent profile analysis (LPA) and latent class analysis (LCA) to distinguish specific groupings of interrelated lifestyle factors that influenced the cognitive outcomes synergistically and to identify predictors for multiplicative groupings of outcomes.[14,15] One study on Australian older adults employed LPA to uncover three distinct lifestyle behavioral profiles, namely (1) highly active, unhealthy drinkers, (2) moderate activity, nonsmokers, and (3) inactive, poor diet, and their associations with cognitive outcomes.[14] In another study, LCA characterized four lifestyle engagement groups, namely (1) social, (2) intellectual, (3) variety, and (4) least active, and their associations with dementia risk.[15] However, the effective communication of dementia risk scores is not without its challenges. Research has shed light on the complexities of conveying numerical data and statistical probabilities to individuals.[16] To address this, experts emphasize the need for precision in risk communication.[17] This entails incorporating absolute risk figures, utilizing intuitive visual representations, and embracing a collaborative decision-making process that acknowledges the inherent uncertainties associated with probability-based assessments. ALGORITHM-RECOMMENDED INTERVENTION PLAN The provision of an algorithm-recommended intervention plan that is based on the integration of advanced data-driven methods, such as predictive modeling, person-centered analyses, and machine learning that recognizes neuroimaging patterns, offers the possibilities of individually tailored dementia prevention strategies. Latent transition analysis (LTA) extends LPA and LCA by modeling cognitive status transitions over time.[18] Cross-sectional models may be evaluated longitudinally to observe intrapersonal changes on cognitive statuses. This algorithm development method allows the testing of subgroup-specific expectations about change and the probability of change and has been applied to examine health profile and intervention utilization trajectories in several neurodegenerative disorders including stroke.[19] Furthermore, LTA provides a framework for investigating the temporal evolution and characteristics of groups identified as comprising at-risk individuals. When predictive modeling and LTA are used together in algorithm development, transition patterns of combined modifiable lifestyle risk characteristics and individual nonmodifiable but covarying characteristics (e.g., age and sex) over time and how these patterns predict distal cognitive outcomes may become examined.[20] The synchronous use of predictive modeling and LTA also permits the generation of algorithms in which combinative change patterns of specific cognitive abilities over time are predicted by individual modifiable and nonmodifiable risk characteristics. For instance, among persons with a perceptual-motor function impairment status at baseline, those who read had higher odds of progressing to normal cognition as opposed to maintaining the same cognitive status.[21] Machine learning, using magnetic resonance imaging (MRI) data, has made remarkable progress in characterizing individuals with mild cognitive impairment (MCI), purportedly due to anatomical changes in the brain reflective of a stable state of MCI.[22] However, predictive accuracies for MRI-associated models can vary,[23] and challenges such as reproducibility, data leakage, and interpretability persist.[24] These challenges underscore the need for a comprehensive approach that integrates various data sources and methodologies. MOTIVATIONAL STRATEGIES Addressing individual motivation and preferences is crucial in the effective implementation of multidomain lifestyle interventions.[1] Motivational interviewing (MI) is a client-centered and evidence-based counseling technique.[25] Through a collaborative approach between therapist and client, MI explores and resolves ambivalence toward change, thereby enhancing the likelihood of sustained behavioral modification. It aligns with the self-determination theory, emphasizing the need for autonomy, competence, and relatedness in fostering intrinsic motivation for behavioral change.[26] MI has improved adherence and engagement in various health-related behavior change programs effectively, indicating its potential utility in dementia prevention interventions.[27] For example, a recently published integrative review found that MI significantly improved treatment adherence among patients with inflammatory bowel disease (IBD).[28] This is particularly important for managing chronic conditions like IBD, which requires consistent treatment. Various MI adaptations have been developed using technological platforms, suggesting the feasibility of these adaptations in dementia prevention.[29] A scoping review found that technology-delivered adaptations of MI, delivered via text messaging, web-based platforms, or apps, showed statistically significant improvements in health outcomes, making them a promising tool for managing and preventing chronic diseases, including cognitive decline and dementia.[30] Future research should integrate these MI adaptations with multidomain lifestyle intervention components, incorporate fidelity measures, address sociocontextual barriers to utilization, and recommend best practices for clinical practice implementation. GAMIFICATION While MI offers techniques for promoting self-reflection and facilitating the conscious satisfaction of psychological needs, gamification can create opportunities for individuals to experience autonomy, competence, and relatedness through enjoyable and thrilling activities.[31] Gamification, the utilization of game features such as points, badges, visualizations, challenges, and surprises in nongame situations, is progressively captivating consumers in the process of behavior modification.[32] Research on the gamification of health-related behavior change interventions has not been frequently reported due to variations in terminology across disciplines and a scarcity of randomized controlled trials.[33] However, a systematic review and meta-analysis of 12 studies revealed that serious games enhanced cognitive function and diminished depression in persons with dementia.[34] Games often do not compel users to contemplate their motivations for their actions, but instead rely on inherent incentives, such as challenges and unexpected events, which can lead to spontaneous changes in behavior. Users may participate in goal setting and self-monitoring of physical activity due to the visualization of goals and accomplishments, rather than health objectives. An exemplary instance is Pokémon Go, an augmented reality game developed by Microsoft Inc. in the United States that increased users’ daily step count through its game mechanics.[35] Game elements, such as progression, rewards, and feedback, could be personalized to suit individual necessities and capabilities in goal attainment. With machine learning, personalization could keep improving the more the game is played. Games could also integrate with other digital tools and devices, such as wearable trackers, to collect lifestyle data and communicate dementia risk score changes in real time. Because smartphone apps with game elements use more behavioral change techniques than health apps in general,[30] N-of-1 randomized controlled trials are urgently needed to design these apps for individuals at-risk of dementia in ways that optimize motivation, engagement in health-promoting lifestyle activities, and cognitive stimulation.[36] PERSONALIZED DIGITAL THERAPEUTICS Emerging digital therapeutics offer transformative potential for integrating personalized multidomain lifestyle interventions for individuals at risk of cognitive decline. Recent advances in wearable devices allow continuous real-time data collection, which, when integrated with machine learning, facilitates the delivery of tailored interventions that adapt dynamically to users’ changing needs.[37] These tools provide personalized risk communication that is interactive and easier for individuals to comprehend.[17] In addition, adaptive cognitive training, based on users’ progress, has demonstrated effectiveness in sustaining engagement.[37] Artificial intelligence-powered virtual coaching is another promising approach, offering real-time feedback and support, thereby increasing motivation and adherence to interventions.[37] Furthermore, gamification strategies, customized to individual preferences, have been shown to improve both adherence and long-term behavioral change.[33,34] Together, digital therapeutics represent a valuable complement to traditional interventions, enhancing both the precision and scalability of dementia prevention efforts. CONCLUSIONS Table 1 summarizes four personalization approaches to multidomain lifestyle interventions that hold great promise in effectiveness enhancement and challenges in their implementation. Future dementia risk communication tools should be designed to optimize precision, comprehensibility, and motivation effect. Developing accurate and reliable algorithms for intervention plans requires big datasets and sophisticated analytical techniques. The effectiveness of technology-delivered motivational strategies and gamification in promoting behavior change associated with cognitive change and integrating personalization approaches also requires further investigation.Table 1: Personalization approaches to multidomain lifestyle interventions for dementia preventionNonetheless, the potential of personalized multidomain lifestyle interventions in dementia prevention should be given thoughtful consideration. Tailoring such interventions to individual needs and preferences through these four approaches is expected to enhance the effectiveness and scalability of dementia prevention strategies for at-risk populations. Conflicts of interest Dr. Wayne Freeman Weien Chong is a Youth Editorial Board Member of Heart and Mind. The article was subject to the journal’s standard procedures, with peer review handled independently of Dr. Wayne Freeman Weien Chong and the research groups. There are no conflicts of interest.
Source: Heart and Mind
Author: Wayne Freeman Weien Chong
Institution: Nanyang Technological University
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4405277952