Evidence map›Paper›PMID 40471295›Full record

Observational studyActa diabetologica2025

Trajectories of physical activity and influencing factors in patients with type 2 diabetes mellitus: a mixed-methods study.

YinShi Kan, Lin Liu, ShiHong Yuan, XiangNing Li, XiaoJuan Wan, Yan Zou, Yue Su, Yuying He, Yueqi Zhao, BeiXi Shi and 5 more

Abstract readObservational Study
PubMed Publisher
In one paragraph

Observational study in Acta diabetologica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

YinShi Kan *School of Nursing, Yangzhou University, 136 Jiangyang Middle Road, Yangzhou, Jiangsu, China.
Lin Liu *School of Nursing, Yangzhou University, 136 Jiangyang Middle Road, Yangzhou, Jiangsu, China.
ShiHong YuanSchool of Nursing, Yangzhou University, 136 Jiangyang Middle Road, Yangzhou, Jiangsu, China.
XiangNing LiSchool of Nursing, Yangzhou University, 136 Jiangyang Middle Road, Yangzhou, Jiangsu, China.
XiaoJuan WanSchool of Nursing, Yangzhou University, 136 Jiangyang Middle Road, Yangzhou, Jiangsu, China.
Yan ZouSchool of Nursing, Yangzhou University, 136 Jiangyang Middle Road, Yangzhou, Jiangsu, China.
Yue SuSchool of Nursing, Yangzhou University, 136 Jiangyang Middle Road, Yangzhou, Jiangsu, China.
Yuying HeSchool of Nursing, Yangzhou University, 136 Jiangyang Middle Road, Yangzhou, Jiangsu, China.
Yueqi ZhaoSchool of Nursing, Yangzhou University, 136 Jiangyang Middle Road, Yangzhou, Jiangsu, China.
BeiXi ShiSchool of Nursing, Yangzhou University, 136 Jiangyang Middle Road, Yangzhou, Jiangsu, China.
Mark HayterFaculty of Health and Education, Manchester Metropolitan University, Manchester, UK. M.Hayter@mmu.ac.uk.
Jing ChenDepartment of Endocrinology, Subei People's Hospital of Jiangsu Province, Yangzhou, China.
JinPing WangDepartment of Endocrinology, Affiliated Hospital of Yangzhou University, Yangzhou, China.
Li FangDepartment of Endocrinology, Subei People's Hospital of Jiangsu Province, Yangzhou, China.
Yu ZhangSchool of Nursing, Yangzhou University, 136 Jiangyang Middle Road, Yangzhou, Jiangsu, China. yizhangyu@yzu.edu.cn.ORCID http://orcid.org/0000-0002-4976-9158

Funding

Innovative Research Group Project of the National Natural Science Foundation of China 82100870The Postgraduate Research & Practice Innovation Program of Jiangsu Province P.R. China SJCX21_1652
6 · The paper itself

Abstract

aimTo explore the different trajectories of physical activtity (PA) change in Type 2 diabetes mellitus (T2DM) patients and the factors affecting them, and to delve deeper into the PA experiences and needs of the different trajectory groups.

designAn observational longitudinal study and a descriptive qualitative study.

methods276 patients with T2DM were recruited from two tertiary hospitals and followed them up in this longitudinal study. Over nine months, Latent Class Growth Modeling (LCGM) was used to identify distinct PA trajectories among patients with T2DM. The Attitude-Social Influence-Self-Efficacy (ASE) model provided the theoretical framework. Data were collected through General Information Questionnaires, Exercise Self-Efficacy Scales, Exercise Behavioral Attitude and Social Influence Scale, and Behavioural Planning Intention Scale. The analysis sought to identify factors affecting changes in PA trajectories. Subsequently, the PA experiences supportive/hindering factors and PA needs of T2DM patients with different trajectories were explored through qualitative interviews. The GRAMMS (Good Reporting of a Mixed Methods Study) checklist was used to guide the reporting of this study.

resultsPatients with T2DM were categorized into 3 distinct trajectories of total PA change: a low-level stabilization group (N = 181, 65.7%), a medium-level fluctuation group (N = 79, 28.7%), and a high-level decline group (N = 16, 5.6%). Logistic regression analysis identified self-efficacy, comorbidities, family environment, and intentions as significant predictors of these trajectories. Additionally, qualitative interviews with 17 participants revealed 7 experiential themes and 3 categories of need.

conclusionThree distinct PA trajectories were identified in T2DM patients, each with notable differences in PA experiences and needs. It is suggested that healthcare providers should therefore develop targeted, systematic health education and intervention programs tailored to individual patient characteristics to sustain PA behavior and achieve long-term PA benefits.

Indexed as

Diabetes Mellitus, Type 2ExerciseAdultAgedFemaleHumansLongitudinal StudiesMaleMiddle AgedSelf EfficacySurveys and QuestionnairesLCGMMixed-methods studyPhysical activityT2DMTrajectory

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.