Association Between Physical Activity and Fitness in Patients with Heart Failure and Type 2 Diabetes Mellitus: Influence of a Telemedicine Program

<b>Background.</b> Few studies have evaluated physical fitness (PF) and physical activity (PA) in individuals with heart failure and type 2 diabetes mellitus and the possibility that some telemedicine programs (TMPs) may impact them. This post hoc subgroup analysis of an RCT study aimed to describe PF and PA in this population before and after a TMP. <b>Methods.</b> We evaluated (a) PF as distance, assessed via the 6-Minute Walking Test (6MWT), and PA as daily step count in this population before and after a TMP; (b) their relationship; and (c) the patient characteristics that influence PF and PA. <b>Results.</b> Fifty-eight patients (aged 71.31 ± 7.92 years old, 84% male, BMI 28.01 ± 4.70 Kg/m<sup>2</sup>, ejection fraction 48.64 ± 10.64%) were enrolled between August 2022 and September 2024. All patients received a six-month TMP (nurse teleassistance, telemonitoring, a dedicated app, and PA evaluation using a tracker bracelet and step count goals). The 6MWT improved (from 418 ± 113 to 439 ± 120 m, <i>p</i> < 0.001), while PA remained unchanged after the TMP (from 7181 ± 4149 to 7229 ± 4947 steps/day, <i>p</i> = 0.92). The PA and PF association ranged from moderate at baseline (rho = 0.4958, <i>p</i> < 0.001) to strong at the end of the study (rho = 0.6807, <i>p</i> < 0.001). The regression model shows that following the TMP, PA(y) was associated with baseline 6MWT [β= 8.5, 95%IC -0.31; 17.24], age (β = -144.0, 95%IC -262.14; -25.90)), baseline daily steps (β = 0.5967, 95%IC 0.37; 0.82), and HDL cholesterol (β = 119.7, 95%IC 39.07; 200.31) (R<sup>2</sup>= 0.6580, F(4.53) = 25.49, <i>p</i> < 0.001), while PF(y) was associated with BMI (β= -2.04, 95%IC -4.30; 0.22)), age (β= 0.90, 95%IC -4.4; 1.32), and baseline 6MWT (β = 0.90, 95%IC 0.79; 1.00) (R<sup>2</sup> = 0.9007, F(3.54) = 163.29, <i>p</i> < 0.001). <b>Conclusions</b>. Our TMP led to a statistically significant but clinically modest improvement in PF but did not impact PA despite there being variability among patients. PA and PF appear to be interdependent. PF, age, HDL cholesterol, and baseline PA were key predictors of PA, while BMI, age, and baseline 6MWT impacted PF at the end of the TMP.

Institutions

Publication Details

Journal
Healthcare
Published
2025-12-11
DOI
https://doi.org/10.3390/healthcare13243250
Primary Topic
Physical Activity and Health
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Association Between Physical Activity and Fitness in Patients with Heart Failure and Type 2 Diabetes Mellitus: Influence of a Telemedicine Program

Healthcare
Physical Activity and Health
article

Association Between Physical Activity and Fitness in Patients with Heart Failure and Type 2 Diabetes Mellitus: Influence of a Telemedicine Program

article en

Abstract

<b>Background.</b> Few studies have evaluated physical fitness (PF) and physical activity (PA) in individuals with heart failure and type 2 diabetes mellitus and the possibility that some telemedicine programs (TMPs) may impact them. This post hoc subgroup analysis of an RCT study aimed to describe PF and PA in this population before and after a TMP. <b>Methods.</b> We evaluated (a) PF as distance, assessed via the 6-Minute Walking Test (6MWT), and PA as daily step count in this population before and after a TMP; (b) their relationship; and (c) the patient characteristics that influence PF and PA. <b>Results.</b> Fifty-eight patients (aged 71.31 ± 7.92 years old, 84% male, BMI 28.01 ± 4.70 Kg/m<sup>2</sup>, ejection fraction 48.64 ± 10.64%) were enrolled between August 2022 and September 2024. All patients received a six-month TMP (nurse teleassistance, telemonitoring, a dedicated app, and PA evaluation using a tracker bracelet and step count goals). The 6MWT improved (from 418 ± 113 to 439 ± 120 m, <i>p</i> < 0.001), while PA remained unchanged after the TMP (from 7181 ± 4149 to 7229 ± 4947 steps/day, <i>p</i> = 0.92). The PA and PF association ranged from moderate at baseline (rho = 0.4958, <i>p</i> < 0.001) to strong at the end of the study (rho = 0.6807, <i>p</i> < 0.001). The regression model shows that following the TMP, PA(y) was associated with baseline 6MWT [β= 8.5, 95%IC -0.31; 17.24], age (β = -144.0, 95%IC -262.14; -25.90)), baseline daily steps (β = 0.5967, 95%IC 0.37; 0.82), and HDL cholesterol (β = 119.7, 95%IC 39.07; 200.31) (R<sup>2</sup>= 0.6580, F(4.53) = 25.49, <i>p</i> < 0.001), while PF(y) was associated with BMI (β= -2.04, 95%IC -4.30; 0.22)), age (β= 0.90, 95%IC -4.4; 1.32), and baseline 6MWT (β = 0.90, 95%IC 0.79; 1.00) (R<sup>2</sup> = 0.9007, F(3.54) = 163.29, <i>p</i> < 0.001). <b>Conclusions</b>. Our TMP led to a statistically significant but clinically modest improvement in PF but did not impact PA despite there being variability among patients. PA and PF appear to be interdependent. PF, age, HDL cholesterol, and baseline PA were key predictors of PA, while BMI, age, and baseline 6MWT impacted PF at the end of the TMP.

HealthcareVol. 13(24)
Istituti Clinici Scientifici Maugeri (IT), Azienda Ospedaliera Bolognini Seriate (IT), Ospedale Papa Giovanni XXIII (IT)
Ministero della Salute
Good health and well-being
Openalex Percentile: Top 35%
Physical Activity and Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.