Longitudinal physical activity data and associated factors were collected at baseline (diagnosis), 3-month, 6-month, and 9-month follow-ups in cardiovascular-kidney-metabolic syndrome patients.

This study targets the frail population with early-middle-age CKM syndrome. Grounded in the Time-limited Self-regulation Theory (TST), it employs behavioral data analysis, theoretical variable modeling, and intervention strategy development to systematically identify risk trajectories and influencing pathways of physical activity (PA) insufficiency, thereby formulating stratified and classified intervention strategies to enhance PA levels. The research comprises three key components:

Research Component 1: Risk Prediction Model for PA Insufficiency in Frail CKM Syndrome Patients Early-middle-age frail CKM syndrome patients exhibit significant PA insufficiency and behavioral variability at the disease onset. Early identification of PA evolution trends and high-risk groups is crucial for timely intervention and precise resource allocation.

Objective:

To determine "who is more likely to sustain PA insufficiency" by constructing a trajectory identification model and risk prediction tool using longitudinal data, analyzing dynamic PA behavior patterns, and quantifying multifactorial risk probabilities to support subsequent intervention mechanisms and strategy classification.

Study Design:

Exploring PA Trajectories in Frail CKM Syndrome Patients

Target Population: Early-middle-age frail CKM patients (aligned with AHA lifestyle management guidelines).

Method: Prospective longitudinal study with multi-timepoint data collection.

Analysis: Group-based trajectory modeling (GBTM) to delineate PA dynamics, identifying high-risk trends (e.g., persistent insufficiency, steep decline).

Developing a Risk Prediction Model for PA Insufficiency

Dependent Variable: PA trajectory classification (e.g., stable-high, persistent-low, fluctuating).

Predictors: Sociodemographics, health behaviors, and environmental factors.

Method: Multi-class machine learning (XGBoost) to identify key predictors and quantify risk probabilities.

Output: Interactive visualization tool for community-level screening of high-risk individuals.

Research Component 2: TST-Based Mechanisms of PA Promotion in Frail CKM Syndrome Patients

To elucidate the key determinants and moderators of PA insufficiency, this study leverages TST's six core variables:

Behavioral intention

Consistency beliefs

Self-control capacity

Delay discounting tendency

Environmental cue perception

Habit formation strength

Methodological Approach:

Dynamic Feature Engineering:

Multi-timepoint measurement of TST variables → Derived metrics (baseline level, trend, mean, variability).

Dimensionality Reduction:

LASSO regression + PCA to mitigate multicollinearity.

Predictive Modeling:

XGBoost classification (trajectory groups as outcomes) + SHAP analysis to rank variable contributions.

Causal Pathway Analysis:

Generalized structural equation modeling (GSEM) to identify mechanistic pathways.

Outcome:

Prioritized modifiable factors (e.g., self-control > environmental cues) for tailored interventions.

Research Component 3: TST-Driven PA Promotion Strategies for Frail CKM Syndrome Patients Goal: Translate mechanistic insights into actionable, precision strategies for behavior change.

Strategy Development Framework:

Intervention Targets:

Mechanistic variables (e.g., enhancing self-control in "persistent-low" trajectory patients).

Three Strategy Archetypes:

Intrinsic Motivation Modulation (e.g., goal-setting interventions)

Social Support Activation (e.g., peer coaching)

Environmental Cue Optimization (e.g., neighborhood walkability enhancements)

Strategy Prioritization:

Analytic Hierarchy Process (AHP) to weight strategies by feasibility, acceptability, and efficacy.

Deliverable:

A modular "PA Promotion Toolkit" for phased, adaptive community interventions.

Inclusion Criteria:

• Aged ≥45 years and <60 years;

Frailty status assessed by Fried phenotype criteria, including those diagnosed with frailty or pre-frailty;

Meeting AHA guideline criteria for Stage 1-2 CKM syndrome (diagnostic details in Table 1);

Ability to communicate normally and use smartphones or wearable devices;

Willingness to participate in surveys and follow-up;

Additional Note on PREVENT Equation:

The PREVENT equation, developed by the American Heart Association (AHA), is a cardiovascular disease (CVD) risk prediction tool. It calculates 10-year CVD risk based on:

Age, sex

Total cholesterol, HDL-C

Systolic blood pressure, BMI

eGFR

History of diabetes, smoking

Use of antihypertensive or lipid-lowering medications

Exclusion Criteria:

• Severe cognitive impairment (e.g., MMSE score <10 or clinical dementia diagnosis);

Severe psychiatric/psychological disorders (e.g., schizophrenia, major depressive disorder with suicidal ideation);

Advanced chronic diseases including:

End-stage renal disease (eGFR <15 mL/min/1.73m² or on dialysis)

Metastatic cancer (stage IV per AJCC criteria)

Cardiorespiratory insufficiency (NYHA Class III-IV heart failure or COPD GOLD Stage D)

Severe osteoarthritis (Kellgren-Lawrence Grade 4 with functional limitation)

Major surgery or acute illness within 3 months (e.g., myocardial infarction, stroke, or major trauma requiring hospitalization);

Physical disabilities impairing mobility (e.g., amputation, paralysis, or severe Parkinsonism with Hoehn & Yahr Stage ≥3).

This study was conducted at the First Affiliated Hospital of Nanjing Medical University (Jiangsu Province Hospital), enrolling 614 middle-aged adults with frailty and cardiovascular-kidney-metabolic (CKM) syndrome. Participants who met the inclusion/exclusion criteria and provided written informed consent were prospectively followed for 12 months.