From sedentary volume to sedentary content in older inpatients with type 2 diabetes: a multicenter study.

Two-thirds of older inpatients with T2DM were classified as having high sedentary time.

• Of 419 participants, 279 (66.6%) were classified as having high sedentary time.
• Participants were recruited from three tertiary hospitals in northwestern, central, and southern China.
• The study used a multicenter cross-sectional design.
• Sedentary time was assessed using a validated questionnaire covering 10 types of sedentary content.

Among those with high sedentary time, a majority had a high proportion of screen-based sedentary time.

• Within the high-sedentary-time subgroup (n=279), 165 (59.1%) had a high proportion of screen-based sedentary time.
• Screen-based sedentary content included watching television, using computers, and using mobile phones.
• A two-stage sequential modeling approach was used, with Model II examining factors associated with high screen tendency in the high-sedentary-time subgroup.

High sedentary time status was associated with physical function, living context, and exercise-related cognition.

• Model I used hierarchical logistic regression to examine factors associated with high sedentary time status (n=419).
• The Nagelkerke R² for Model I was 0.383, indicating moderate explanatory power.
• Factors included personal, social, environmental, and exercise-related psychological variables obtained from medical records, field-based assessments, and standardized instruments.
• Continuous-outcome sensitivity analyses broadly supported the overall patterns.

High screen tendency was associated with age, postprandial glucose level, and living arrangement, with gait speed showing only a category-specific contrast.

• Model II used binary logistic regression in the high-sedentary-time subgroup (n=279) to examine factors associated with high screen tendency.
• The Nagelkerke R² for Model II was 0.218.
• Postprandial glucose level was identified as a metabolic factor associated with screen-based sedentary content.
• Living arrangement was identified as a social/environmental factor associated with high screen tendency.

Participants with high screen tendency differed in their distribution of sedentary activities compared to those with low screen tendency.

• Participants with high screen tendency spent more time watching television, using computers, and using mobile phones.
• Participants with high screen tendency spent less time chatting, eating, engaging in hobbies, napping, and doing seated work.
• The Mann-Whitney U test was used to descriptively compare activity composition between the high- and low-screen-tendency groups.
• Item-level analysis of all 10 sedentary content types was conducted.

The study used a two-stage sequential modeling approach to separately examine determinants of sedentary volume and sedentary content.

• Model I (n=419) applied hierarchical logistic regression to identify factors associated with high sedentary time.
• Model II (n=279) applied binary logistic regression within the high-sedentary-time subgroup to identify factors associated with high screen tendency.
• Personal, social, environmental, and exercise-related psychological factors were assessed.
• Continuous-outcome sensitivity analyses were conducted to assess robustness to the operational classifications used.

The authors suggest that intervention strategies targeting sedentary behavior in this population should address both total sedentary time and the type of sedentary activity.

• Functional assessment, environmental support, and strategies related to exercise outcome expectations and self-efficacy were identified as potential intervention components.
• The authors note these components 'better reflect daily life, although their effects require confirmation in longitudinal and interventional studies.'
• The cross-sectional design limits causal inference.
• The authors call for future longitudinal and interventional studies to confirm these findings.