Understanding adaptive tasks in cardiac rehabilitation among patients with acute myocardial infarction: a qualitative study

While Cardiac Rehabilitation (CR) programs have shown effectiveness in improving cardiac outcomes, there is limited understanding of how patients perceive and adapt to these interventions. Furthermore, alternative modes of delivering CR that have received positive evaluations from participants remain underexplored, yet they have the potential to enhance CR uptake. To explore the patient experience in CR programmes following Acute Myocardial Infarction (AMI) and describe their adaptive processing. This qualitative study was conducted at a nationally certified centre in China between July 2021 and September 2022, encompassing three stages: in-hospital, centre-based, and home-based CR programs. Purposive sampling was used to select eligible AMI patients for in-depth semi-structured interviews. The interview outline and analytical framework were aligned with the key concepts derived from the middle-range theory of adaptation to chronic illness and the normalization process theory. The findings were reported following the Consolidated Criteria for Reporting Qualitative Research checklist. Forty AMI patients were recruited. Four main themes describing the process of AMI patients normalizing CR intervention were identified, including (1) experiencing CR service driving by role’s responsibilities, (2) engaging in collaborative relationship based on interpersonal trust, (3) exploring a personalized rehabilitation plan by complex integration, and (4) expecting a promised outcome to shape decision-making. Integrated care interventions for AMI patients could benefit from a collaborative co-designed approach to ensure that CR interventions are normalized and fit into patients’ daily lives. Organizational-level CR services should align with the rehabilitation needs and expectations of patients.</p

Hydroxyl Radical-Dominated Catalytic Oxidation in Neutral Condition by Axially Coordinated Iron Phthalocyanine on Mercapto-Functionalized Carbon Nanotubes

The ligands and protein surroundings are important in peroxidase processes with iron porphyrins as catalysts. Similarly, two bioinspired composite catalysts made from iron phthalocyanine with axial ligands, 4-aminopyridine and 2-aminoethanethiol, were anchored on multiwalled carbon nanotubes to degrade some pollutants to the water environment, such as 4-chlorophenol, dyes, and so on. The effect of pH and sustained catalytic stability were investigated in the presence of two catalysts. Different axial ligands and carbon nanotubes that synergistically donated electrons to the central iron of iron phthalocyanine significantly improved the catalytic activity and stability during hydrogen peroxide activation. Electron paramagnetic resonance spin-trapping experiments indicated that catalytic oxidation is dominated by hydroxyl radicals in both catalytic systems, which is different from the high-valent metal-oxo generated in common biomimetic catalytic systems with iron porphyrins in the presence of the fifth ligands. The high catalytic activity and strong durability are distinct from traditional peroxide-activating catalysts of metal complexes dominated by hydroxyl radicals, where catalysts have poor stability and are self-destructive in repetitive cyclic oxidation. In our catalytic system, the axial ligand and carbon nanotubes together affect the electronic structure of the central iron in which electron-donor substituents shift the Fe^III/II potential to more negative values, which make the activation process of hydrogen peroxide occur at neutral pH, and increase the rate of the step from Fe^III to Fe^II. However, the reaction takes place under acidic conditions, and Fe^III/Fe^II cycling occurs slowly in the traditional Fenton system with hydrogen peroxide

Logistic regression models fitting results of the association between tea concentration and cognitive impairment ¹.

<p>¹Binary logistic regression analysis was used to calculate ORs and 95% CIs for cognitive impairment related with tea concentration which assessed with CCM, with non-consumption group treated as reference.</p><p>² P value were determined by logistic regressions in which tea concentration was treated as non-ordinal categorical variable.</p><p>³Crude model.</p><p>⁴ Adjusted for age, sex, race, education, marriage, tea consumption volume and tea categories.</p><p>⁵ Adjusted for variables in model 2 plus physical examinations (BMI, WHR, SBP, DBP), family status (family income, have children or not) and disease situation (history of present illness and family history of hypertension, diabetes, CHD, AD, PD).</p><p>⁶ Adjusted for variables in model 3 plus behavioral risk factors (cigarette smoking, alcohol consumption, and physical activities), dietary intake (vegetables, fruits, red meat, fish, beans, milk).</p><p>⁷Adjusted for variables in model 4 plus nutrition supplement, depression and ADL.</p><p>Logistic regression models fitting results of the association between tea concentration and cognitive impairment <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137781#t005fn001" target="_blank">¹</a>.</p

Logistic regression models fitting results of the association between tea categories and cognitive impairment ¹.

<p>¹Binary logistic regression analysis was used to calculate ORs and 95% CIs for tea categories related to cognitive impairment which assessed with CCM, with non-consumption group treated as reference.</p><p>² P value were tested by logistic regressions in which tea category was treated as categorical variable.</p><p>³ Crude model.</p><p>⁴ Adjusted for age, sex, race, education, marriage, tea consumption volume and tea concentration.</p><p>⁵Adjusted for variables in model 2 plus physical examinations (BMI, WHR, SBP, DBP), family status (family income, have children or not) and disease situation (history of present illness and family history of hypertension, diabetes, CHD, AD, PD).</p><p>⁶ Adjusted for variables in model 3 plus behavioral risk factors (cigarette smoking, alcohol consumption, and physical activities), dietary intake (vegetables, fruits, meat, fish, beans, milk).</p><p>⁷Adjusted for variables in model 4 plus nutrition supplement, depression and ADL.</p><p>Logistic regression models fitting results of the association between tea categories and cognitive impairment <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137781#t004fn001" target="_blank">¹</a>.</p

Frequency of tea consumption among study participants.

<p>Frequency of tea consumption among study participants.</p

Characteristics of study participants by volume of tea consumption.

<p>¹ Based on ANOVA, chi-square test or Kruskal-Wallis test.</p><p>² Under the CCM of cognitive impairment.</p><p>³ Under the commonly used MMSE cut-off worldwide of cognitive impairment.</p><p>Characteristics of study participants by volume of tea consumption.</p