Using the PIPOST model from the Evidence-Based Nursing Center at Fudan University, we identified the specific evidence-based problem (4). Inclusion criteria included: (1) Population: patients diagnosed with PD-MCI.(2) Intervention: non-pharmacological approaches, including cognitive training, physical exercise, and health education.(3) Professionals implementing the evidence: doctors, nurses, and rehabilitation specialists caring for PD patients.(4) Outcome measures: Overall cognitive function scores, along with objective neuropsychological test results for all cognitive domains (memory, attention, working memory, executive function, language ability, and visual–spatial ability). (5) Settings for evidence application: hospitals, communities, and rehabilitation centers. (6) Types of evidence: including clinical decision support, practice recommendations, guidelines, evidence summaries, expert consensus, systematic reviews, and randomized controlled trials.

Exclusion criteria included: (1) outdated or low-quality articles, (2) articles with incomplete information or those lacking full-text access, and (3) direct translations or guide interpretations of the articles.

The search was conducted from top to bottom according to the evidence resource “6S” evidence hierarchy model (5), systematic combing of the up-to-date database, BMJ Best Clinical Practice Database, International Guidelines Collaboration Network Platform, China Guide Network, the UK National Institute of Health and Clinical Optimization Database, the National Guide Database, Ontario, Canada, Scottish Medical School Guide Network, New Zealand Guide Collaboration, and the European Parkinson’s Disease Association Network. Further information search was conducted on the evidence related to the non-pharmacological intervention of PD-MCI in Weitong, Cochrane Library, PubMed, Embase, CINAHL, Web of Science, CNN, CNKI, CBM, Wanfang, Vip, and other databases. The time limit for retrieval will be until November 2024. In searching the guide network, the search terms included “Parkinson’s disease / cognitive impairment / mild cognitive impairment / non-pharmacological intervention / non-pharmacological treatment,” the English search included “Parkinson’s disease / cognitive impairment / mild cognitive impairment / non-pharmacological intervention / non-pharmacological therapy.” The Chinese database search strategy takes CNKI as an example, (theme: Parkinson’s disease + ‘Parkinson’s disease (pd)’ + Parkinson’s disease patients) AND (theme: mild cognitive impairment + mild cognitive impairment + “mild cognitive impairment (mci)’) AND (theme: guidelines + expert consensus 10 system evaluation + Meta analysis + evidence summary). The English database retrieval formula should be based on PubMed as an example, and the search formula is shown in Table 1.

Based on the type of research literature, appropriate evaluation tools are selected for assessing the quality. The guidelines use the Appraisal of Guidelines for Research and Evaluation II (AGREE II) (6) to conduct quality evaluation. AGREE II includes 23 items across 6 domains, with each item scored on a scale of 1 to 7, where 1 indicates “strongly disagree” and 7 indicates “strongly agree.” The standardized percentage for each domain score is calculated as follows: [(total score of all assessors–minimum possible score) / (maximum possible score–minimum possible score)] × 100%. The minimum possible score for each domain is the number of items scored 1 point, and the maximum possible score is the number of items scored 7 points (7). A higher standardized percentage indicates better guideline quality. The guideline recommendation grade is classified into three levels: A for a standardized percentage > 60%, B for a standardized percentage between 30 and 60% in 3 or more domains, and C for a standardized percentage < 30% in more than 3 domains (8). The quality evaluation of expert consensus, systematic reviews, meta-analyses, and randomized controlled trials was conducted according to the Australian JBI (2016) guidelines, with each item assessed as “yes,” “no,” “unclear,” or “not applicable” (9).

All members involved in the literature screening and quality evaluation underwent systematic training in evidence-based nursing science, and classified and summarized the evidence according to the theme.

A total of 763 articles were retrieved and imported into NoteExpress for screening. After excluding duplicates, 234 articles remained. Following the title and abstract review, 56 articles were selected. After full-text reading, 34 articles were further shortlisted; 3 studies were excluded because their study populations did not meet the inclusion criteria, 4 studies were excluded due to outdated publication dates, and 3 studies were excluded because of low methodological quality. Consequently, 13 articles were included: 2 clinical decisions, 5 clinical guidelines, 2 systematic reviews, 1 meta-analysis, and 3 randomized controlled trials (RCTs). The literature screening flowchart is presented in Figure 1, and the general characteristics of the included studies are summarized in Table 2.

Five guidelines were included in this study. The AGREE II standardized scores for each domain are shown in Table 3. Four of the guidelines are recommended for grade B, while one is recommended for grade A. Overall, the quality of the guidelines is high. All of them were included. Two clinical decisions were included in this study. In terms of recommendations, both articles in the latest column were rated as “no,” while the rest were rated as “yes” or “partially yes.” Specific details are shown in Table 4. Overall, the quality is high, and all were included. Two systematic reviews, one meta-analysis, and three randomized controlled trials (RCTs) were included in this study. After evaluation using the criteria from the Australian JBI Evidence-Based Healthcare Centers, the item “reduce data extraction error measures” from Hanna M. Gavelin’s study was rated as “unclear.” The remaining literature and corresponding criteria were all rated as “yes.” The study designs were relatively complete, and the overall quality of the literature was high. All were included.

Due to significant heterogeneity in intervention types (e.g., cognitive training protocols and exercise modalities) and outcome measures (e.g., MoCA and MDRS), quantitative pooling was not feasible. Instead, evidence was synthesized thematically, with quality assessed using AGREE II and JBI tools to ensure rigor. In addition, differences in intervention design (e.g., duration and frequency) and cultural adaptation (e.g., the use of Tai Chi in Chinese studies) led to heterogeneity. We addressed this issue by dividing the evidence into six themes and prioritizing high-quality guidelines (AFREE II ≥ 60%) and randomized controlled trials. A total of 22 relevant pieces of evidence were extracted from the 13 included studies on PD-MCI patients. The evidence was organized into six main themes: safety and effectiveness, assessment and screening, cognitive intervention, exercise, rTMS, health education, and multi-strategy interventions. The details are provided in Table 5.

In clinical practice, treatment of PD primarily targets alleviating motor symptoms. However, mild cognitive impairment (MCI) is insidious, often occurring early in the disease or even prior to motor symptoms, making it difficult to detect. If the disease progresses to PDD, it severely impacts patients’ ability to live independently and reduces their quality of life, while also increasing the caregiver burden. Evidence shows that PD-MCI is a major independent risk factor for progression to PDD. Diagnosis of PD-MCI can be made based on objective neuropsychological tests and clinical reports. Brief assessment methods can provide a preliminary evaluation of overall cognitive function; however, institutions with sufficient resources should use specific assessment scales to evaluate cognitive domain impairment and develop targeted interventions. Furthermore, studies have shown that PD patients with cognitive impairment often have co-occurring depression. Therefore, it is recommended to include depression screening in cognitive assessments to exclude risk factors and enable early intervention (10). Recent studies have suggested that objective physiological indicators, such as cerebrospinal fluid analysis, magnetic resonance imaging, and EEG, may be useful for diagnosing PD-MCI. These tests can be conducted based on patient preferences and the capabilities of medical institutions (11).

Cognitive intervention is increasingly recognized as the most effective and safest non-pharmacological strategy for PD-MCI and is gradually being tested and promoted. Cognitive interventions include cognitive training, cognitive rehabilitation, and cognitive stimulation. Among them, cognitive training, which includes traditional paper-based methods, computer-based training, and virtual reality-based training, is recommended for PD-MCI patients. Computer-based cognitive training is considered especially effective, as it integrates visual, auditory, and other sensory stimuli, which studies have shown improve focus and training outcomes (12). With the rise of big data and mobile health, cognitive training is likely to shift gradually from hospital-based to home-based rehabilitation. Cognitive training programs suitable for the elderly should be developed with the help of smartphones and other mobile devices. Additionally, big data should be used for dynamic evaluation and feedback to enhance the sustained effectiveness of cognitive intervention. Additionally, personalized cognitive training programs should be tailored to the specific symptoms of PD-MCI patients. These programs should offer diverse forms of training, promote social interaction, and ensure appropriate intensity and duration. Guidelines recommend at least 30 min per session, at least three times a week, with a total of no less than 20 h of continuous training.

Clinical evidence indicates that detrimental lifestyle factors, including smoking, sedentary behavior, and alcohol abuse, may increase the risk of cognitive decline in PD patients by 23–41% (13). Comorbidities such as sleep disorders, diabetes, and hypercholesterolemia have also been shown to significantly accelerate cognitive deterioration (14, 15). Targeting these modifiable risk factors, health education and lifestyle interventions have emerged as pivotal strategies to delay the progression of PD-MCI:(1) Stratified Health Education: Tailored to patients’ cognitive levels and educational backgrounds, we use visual teaching tools (infographics/instructional videos) for disease education, emphasizing the clinical manifestations of PD-MCI, modifiable risk factors, and the potential progression to PDD. Behavioral interventions (smoking cessation, alcohol restriction, and sleep regulation) have demonstrated efficacy in decelerating cognitive decline (16). (2) Structured Exercise: Physical exercise exerts neuroprotective effects by promoting neuronal proliferation and enhancing cerebral function, thereby improving cognitive performance in PD patients (17). Current evidence-based exercise modalities for PD-MCI include Tai Chi, aerobic training, tango dancing, and recumbent cycling. While Tai Chi—originating from China—has demonstrated cognitive benefits across diverse populations (e.g., Spain, France, and South Korea) in dementia rehabilitation (18–20), most non-Chinese trials remain limited in scale. Large-scale multinational randomized controlled trials are warranted to standardize protocols and validate outcomes across heterogeneous healthcare systems. As summarized in Table 6, exercise frequency guidelines for cognitive training, aerobic exercise, and endurance training are aligned with current clinical recommendations.(3) Precision Nutrition Intervention: Dietary factors play a critical role in PD pathogenesis and management. Both Mediterranean diets and low-carbohydrate diets have shown significant improvements in executive function and verbal fluency (21, 22). We recommend increased dietary fiber intake to prevent constipation and vitamin D supplementation (400–800 IU/day). Vitamin D deficiency correlates with accelerated cognitive decline, while its anti-inflammatory and antioxidant properties may mitigate dementia risk (23). (4) Synergistic Effects: Multimodal interventions exhibit synergistic benefits for cognitive enhancement. Health education establishes a cognitive framework for disease understanding, exercise enhances cerebral function through neuroprotective mechanisms, and precision nutrition delays decline through metabolic modulation. Collectively, multidimensional lifestyle interventions regulate neuroplasticity and metabolic homeostasis, offering a safe and effective non-pharmacological approach for PD-MCI management.

PD-MCI exhibits heterogeneous cognitive impairment subtypes (e.g., attention deficit, executive dysfunction, and memory impairment) and significant interindividual variability in comorbid symptoms (e.g., depression and sleep disorders). Monotherapy approaches often fail to address these multifaceted needs, necessitating personalized intervention plans tailored to individual clinical profiles (24). Repetitive transcranial magnetic stimulation (rTMS), a non-invasive neuromodulation technique, has been investigated for addressing cognitive deficits in PD. In PD patients, rTMS protocols demonstrate therapeutic potential not only for motor symptom improvement but also for alleviating non-motor symptoms, including depression and cognitive dysfunction (24). Dual-task training, which requires simultaneous performance of two distinct tasks, enhances attentional control, processing speed, and cognitive flexibility. For PD patients, this approach can be adapted to functional scenarios such as ambulation, stair negotiation, and dressing. Ecologically relevant activities—including simulated driving and shopping tasks—not only improve cognitive performance but also enhance gait stability, balance control, and self-efficacy during daily activities, thereby effectively reducing fall risk (25). Future research should focus on developing precision intervention frameworks that integrate individualized cognitive profiles, neurophysiological biomarkers, and real-world functional demands to optimize therapeutic synergy in PD-MCI management.

However, studies on its effect on overall cognitive dysfunction remain inconclusive. Although some studies suggest that repeated rTMS may benefit cognitive function in PD patients, its effects remain unclear. Therapeutic targets and parameters are inconsistent, and further clinical studies are needed for confirmation. While Chinese studies contributed valuable insights (e.g., Tai Chi and transcranial needle stimulation), regional differences must be acknowledged. China’s healthcare system often integrates traditional practices into rehabilitation, which may not be directly applicable in Western settings. Additionally, cultural acceptance of home-based training and family-led care differs from individual-centric models elsewhere. Demographic factors (e.g., older age at diagnosis in Chinese cohorts) may also influence outcomes. Future multinational trials are needed to validate these interventions across diverse populations. While cognitive scores remain primary endpoints, patient-reported outcomes (e.g., daily functioning) are equally vital. Health education improved self-management, yet no trials have assessed long-term social participation. Future studies should adopt holistic metrics such as the WHOQOL-BREF. Based on the aforementioned research findings, a dual-track model of “disciplinary collaboration + intelligent monitoring” can be established in the future. On one hand, this involves developing multidisciplinary teams in neurology, rehabilitation, nutrition, and psychology to formulate comprehensive plans that include medication guidance, physical function exercises, and health education. On the other hand, it strengthens family and community support by using smart bracelets, APPs, and other devices to monitor patients’ activities and cognitive states in real-time, providing remote training guidance, and regularly revising the plan through follow-up visits. This integrated management system of “hospital-community-family” is expected to break through the time and space limitations of traditional interventions, achieving precise and continuous rehabilitation for PD-MCI patients.