Mechanisms and Interventions for Physical Activity in Frail Cardiovascular-kidney-metabolic Syndrome Patients: A Temporal Self-Regulation Approach
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Cardiovascular-kidney-metabolic Syndrome. Basic parameters: 45 years — 60 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Research on the Mechanisms and Intervention Strategies for Physical Activity Promotion in Frail Patients With Cardiovascular-Kidney-Metabolic Syndrome From a Temporal Self-Regulation Perspective
Overview
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.
Detailed description
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)
Primary outcome measures
- Consideration of Future Consequences Scale (CFC-S) [Time frame: Baseline , 3 months, 6 months, 9 months]
- Global Physical Activity Questionnaire (GPAQ) [Time frame: Baseline , 3 months, 6 months, 9 months]
Secondary outcome measures (8)
- Monetary Choice Questionnaire (MCQ) [Time frame: Baseline , 3 months, 6 months, 9 months]
- Exercise Benefits/Barriers Scale (EBBS) [Time frame: Baseline , 3 months, 6 months, 9 months]
- Intentions [Time frame: Baseline , 3 months, 6 months, 9 months]
- Self-Report Habit Index (SRHI) [Time frame: Baseline , 3 months, 6 months, 9 months]
- Healthy Neighborhood Survey for Community-Dwelling Older Adults [Time frame: Baseline , 3 months, 6 months, 9 months]
- Brief Self-Control Scale (BSCS) [Time frame: Baseline , 3 months, 6 months, 9 months]
- Multidimensional Frailty [Time frame: baseline]
- Charlson Comorbidity Index (CCI) [Time frame: baseline]
Eligibility criteria
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).
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Other
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT07131488 · 20250731