Social, economic, and environmental disparities in device-measured 24-hour movement behaviours in a nationally representative cohort of older English adults

This study of a nationally representative cohort of older English adults reveals significant social, economic, and environmental disparities in device-measured 24-hour movement behaviors, showing that older, unmarried, and lower-wealth individuals tend to be less active and more sedentary, while rural residents are more active than urban dwellers.

The Gist

Imagine your day as a 24-hour pie. Every single slice of that pie is filled with something you do: walking, sitting, sleeping, or just lying in bed. For a long time, scientists only looked at a tiny crumb of that pie (how much you exercise) and asked people to guess the rest. But people are terrible at guessing; they often forget they sat on the couch for three hours or overestimate how much they ran.

This new study is like handing out super-accurate, invisible cameras (wristwatches) to thousands of older adults in England. These watches didn't just count steps; they watched the entire pie, 24 hours a day, for a week. They measured exactly how much time was spent moving, sitting, and sleeping, without anyone having to guess.

Here is what the researchers discovered about how life circumstances change the shape of that daily pie:

1. The "Wealth and Education" Gap

Think of movement behaviors like fuel for a car.

• The Richer & More Educated Drivers: People with more money and higher education levels had "full tanks." They drove further (took more steps), drove faster (did more vigorous exercise), and spent less time idling in traffic (sitting still).
• The Disadvantaged Drivers: Those with less money or education often had "empty tanks." They took fewer steps, sat more, and slept less.
• The Analogy: It's not just that they chose to sit more; it's that life is harder when you have fewer resources. Maybe they work harder physical jobs earlier in life and are too tired to move later, or maybe they can't afford a gym or safe parks. The study suggests that if we want to fix health inequalities, we need to help these drivers get their fuel back.

2. The "Marriage" Factor

Being married seemed to act like a safety net.

• Married people generally moved more, sat less, and slept better.
• Unmarried people (single, divorced, or widowed) were more likely to be "stuck in traffic" (sedentary) and had shorter sleep.
• Why? It's like having a co-pilot. A partner might encourage you to go for a walk, remind you to get up, or just provide a reason to get out of bed. Without that co-pilot, it's easier to drift into a routine of sitting and sleeping less.

3. The "City vs. Country" Surprise

You might think city life is full of walking, but the study found the opposite.

• Country Dwellers: They were the "hikers." They took more steps and did more vigorous activity.
• City Dwellers: They were the "commuters." They took fewer steps.
• The Analogy: In the country, you might have to walk to the shop, tend a garden, or walk the dog because there are no sidewalks or elevators. In the city, everything is right there, or you take a bus. Sometimes, the convenience of the city actually makes us lazier!

4. The "Gender" Puzzle

Men and women filled their 24-hour pie differently.

• Men: They were like sprinters. They did fewer steps overall, but when they moved, they moved faster (more intense exercise). However, they also sat still for longer periods.
• Women: They were like marathon walkers. They took more steps overall and did more "light" movement (like housework or caregiving), but they did less of the intense "sprinting." They also slept a bit longer.
• The Takeaway: If you only counted "sprinting" (gym workouts), you'd think men were healthier. But if you look at the whole day, women were actually moving more total time, just in smaller bursts.

5. The "Age" Curve

As people got older, their pie changed shape.

• Older adults took fewer steps, sat more, and slept less.
• The Analogy: Imagine a battery that slowly loses its charge over the years. The older you get, the less "energy" you have to fill the "active" slices of the pie, so the "sitting" and "sleeping" slices get bigger.

The Big Picture

This study is a wake-up call. It shows that your health isn't just about your choices; it's about your circumstances.

If you are older, unmarried, poor, or live in a city, your daily routine naturally leans toward sitting more and moving less. This isn't just a personal failure; it's a societal pattern.

The Solution?
We can't just tell people to "move more." We need to build better parks in cities, support people who live alone, and create programs that help those with less money get moving. By understanding the whole 24-hour pie, we can finally start slicing it in a way that helps everyone stay healthy as they age.

Technical Summary

1. Problem Statement

While insufficient physical activity, excessive sedentary time, and suboptimal sleep are linked to premature mortality and chronic disease, most existing evidence relies on self-reported data, which is prone to recall and social desirability biases. Furthermore, previous device-measured studies have limitations:

• Scope: Most focus only on physical activity rather than the full 24-hour movement spectrum (including sleep).
• Device Placement: Waist-worn accelerometers (common in older studies) require removal for sleeping and bathing, preventing continuous 24-hour monitoring.
• Data Processing: Many studies rely on proprietary metrics rather than raw acceleration data, limiting harmonization.
• Population Gaps: There is a scarcity of nationally representative, device-measured data specifically for older adults (≥50 years), a demographic with distinct movement patterns.
• Disparity Analysis: Existing nationally representative studies often lack comprehensive analysis of social determinants beyond age, sex, and education (e.g., marital status, wealth, urbanicity).

2. Methodology

Study Design and Population

• Cohort: The study utilized the 2021–23 accelerometry sub-study of the English Longitudinal Study of Ageing (ELSA), a nationally representative prospective cohort of adults aged ≥50 years.
• Sampling: Of 7,482 Wave 10 participants, 5,382 (71.9%) were invited to wear an accelerometer; 4,354 (80.9%) agreed.
• Final Analytic Sample: After data cleaning and exclusion for insufficient wear time or device errors, 3,161 participants (mean age 68.5 ± 9.3 years; 44.3% men) were included in the movement behavior analyses.

Data Collection

• Device: Axivity AX3 wrist-worn triaxial accelerometers.
• Protocol: Participants wore the device 24 hours/day for 8 consecutive days on the dominant wrist.
• Parameters: Data recorded at 100 Hz with a dynamic range of ±8 g.
• Demographics: Social (sex, age, marital status), economic (education, household non-housing wealth), and environmental (urbanicity) factors were collected via interviews.

Data Processing
Raw acceleration data were processed using three open-source, machine-learning-based algorithms:

1. Step Count: Derived using a hybrid machine learning and peak detection algorithm (stepcount).
2. Sleep Duration: Derived using a sleep staging algorithm (asleep) classifying epochs into wake, REM, and NREM.
3. Movement Behaviors: Time in moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary time, and time in bed were derived using the Biobank Accelerometer Analysis Tool.

• Wear Time Criteria: Strict criteria were applied (e.g., ≥3 days of data with 24-hour coverage; non-wear defined as <13 mg SD for ≥90 mins). Imputation was used for diurnal bias.

Statistical Analysis

• Models: Linear regression models were used to examine cross-sectional associations between movement behaviors and disparities.
• Adjustments: Models were sequentially adjusted for sex, age, education, marital status, and wealth to isolate specific effects.
• Trends: Linear trends were assessed for age groups and wealth quintiles.
• Sensitivity: A sensitivity analysis applied core wave 10 cross-sectional weights to ensure representativeness.

3. Key Contributions

• First Nationally Representative 24-Hour Profile: Provides the first device-measured, nationally representative data on the full 24-hour movement spectrum (steps, MVPA, LPA, sedentary time, sleep, time in bed) for older English adults.
• Advanced Processing: Utilizes raw acceleration data and validated open-source machine learning algorithms, ensuring high comparability with other major cohorts (e.g., UK Biobank).
• Comprehensive Disparity Analysis: Expands the scope of inequality research to include marital status, wealth, and urbanicity, not just standard demographics.
• High Adherence: Demonstrated high response (81%) and adherence (87%) rates in an older population using wrist-worn devices, validating the feasibility of 24-hour monitoring in this demographic.

4. Key Results

Demographic and Wear Time

• Older and wealthier participants had higher wear times.
• Wear time was slightly higher on weekdays than weekends but showed no significant difference by time of day.

Movement Behavior Disparities

• Age: Older adults were less active, more sedentary, and slept less. (e.g., Step count decreased by ~1,505 steps/day per 10-year increase in age).
• Sex:
  • Women: Accumulated fewer steps and less MVPA, but more LPA and longer sleep duration than men.
  • Men: More sedentary time and shorter sleep.
• Socioeconomic Status (SES):
  • Education/Wealth: Lower education and lower wealth were strongly associated with lower physical activity (steps, MVPA, LPA) and higher sedentary time.
  • Sleep: Lower wealth and education were associated with shorter sleep duration and less time in bed.
• Marital Status: Unmarried participants were less active, more sedentary, and slept less than married participants.
• Urbanicity:
  • Rural vs. Urban: Rural participants were significantly more active (higher steps and MVPA) than urban participants.
  • No significant differences were found for sedentary time or sleep between rural and urban settings.

Temporal Patterns

• Participants took more steps and engaged in more MVPA during the day (12:00–17:59) and less at night.
• Sleep duration was slightly longer on weekends compared to weekdays.

5. Significance and Implications

• Targeted Interventions: The study identifies specific high-risk groups for health interventions: older, unmarried, socioeconomically disadvantaged, and urban-dwelling adults. These groups exhibit the most unfavorable 24-hour movement profiles.
• Health Inequalities: The findings suggest that 24-hour movement behaviors are a mechanism through which social determinants of health (wealth, education, marital status) translate into health inequalities.
• Policy and Surveillance: The data supports the need for national surveillance systems to monitor the full 24-hour spectrum using device-measured data rather than self-reports to accurately track population health trends.
• Future Research: The study establishes a baseline for future longitudinal analyses within ELSA to determine how these disparities influence healthy aging and mortality over time.

Conclusion:
The study confirms that significant social, economic, and environmental disparities exist across the entire 24-hour movement spectrum in older English adults. Addressing these disparities requires targeted strategies that go beyond general physical activity promotion to include sleep optimization and sedentary reduction, specifically tailored to disadvantaged and urban populations.