Daily and hourly patterns of physical activity and sedentary behavior of older adults: Atherosclerosis risk in communities (ARIC) study

Invited commentary: improving spatial exposure data for everyone-life-course social context and ascertaining residential history

Measuring age-specific, contextual exposures is crucial for life-course epidemiology research. Longitudinal residential data offer a "golden ticket" to cumulative exposure metrics and can enhance our understanding of health disparities. Residential history can be linked to myriad spatiotemporal databases to characterize environmental, socioeconomic, and policy contexts that a person has experienced throughout life. However, obtaining accurate residential history is challenging in the United States due to the limitations of administrative registries and self-reports. In a recent article, Xu et al (Am J Epidemiol. 2024;193(2):348-359) detailed an approach to linking residential history sourced from LexisNexis Accurint to a Wisconsin-based research cohort, offering insights into challenges with collection of residential history data. Researchers must analyze the magnitude of selection and misclassification biases inherent to ascertaining residential history from cohort data. A life-course framework can provide insights into why the frequency and distance of moves is patterned by age, birth cohort, racial/ethnic identity, socioeconomic status, and urbanicity. Historical and contemporary migration patterns of marginalized people seeking economic and political opportunities must guide interpretations of residential history data. In this commentary, we outline methodological priorities for use of residential history in health disparities research, including contextualizing residential history data with determinants of residential moves, triangulating spatial exposure assessment methods, and transparently quantifying measurement error.

Objectively Measured Physical Activity and Sedentary Behaviors Among Older Adults in Assisted Living Facilities: A Scoping Review

Older adults in assisted living facilities (ALF) are at risk for low physical activity (PA) and high sedentary behavior (SB), both of which place them at risk for negative health outcomes. The purpose of this scoping review was to synthesize evidence describing the volume of device-measured PA/SB, factors associated with PA/SB, and interventions designed to change PA/SB in older adults living in ALF. Twenty articles representing 15 unique studies were identified from eight electronic databases and grey literature. Residents in ALF spent 96-201 min/day in light PA (n = 2 studies), 1-9.74 min/day in moderate to vigorous PA (n = 2 studies), and 8.5-11.01 hr/day of SB during waking hours (n = 3 studies). Factors associated with PA included 16 personal factors (n = 6 articles), one social factor (n = 2 articles), and two environmental factors (n = 2 articles). Factors associated with SB included 14 personal factors (n = 4 articles) and one social factor (n = 1 article). No intervention successfully changed PA/SB.

Hourly step recommendations to achieve daily goals for working and older adults

BACKGROUND

The widespread use of physical activity trackers enables the collection of high-resolution health data, such as hourly step counts, to evaluate health promotion programmes. We aim to investigate how participants meet their daily step goals.

METHODS

We used 24-h steps data from the National Steps ChallengeTM Season 3, wherein participants were rewarded with vouchers when achieving specified goals of 5000, 7500, and 10,000 steps per day. We extracted data from 3075 participants' including a total of 52,346 participant-days. We modelled the hourly step counts using a two-part model, in which the distribution for step counts was allowed to depend on the sum of step counts up to the previous hour and participant demographics.

RESULTS

Participants have a mean age of 44.2 years (standard deviation = 13.9), and 40.4% are males. We show that on weekdays, the hourly mean step counts among participants aged 60 and above are higher than participants aged 30 to 59 from the start of the day till 6 p.m. We also find that participants who accumulate at least 7000 steps by 7 p.m. are associated with higher success of achieving 10,000 steps.

CONCLUSIONS

We provide recommendations on the hourly targets to achieve daily goals, based on different participants' characteristics. Future studies could experimentally test if prompts and nudges at the recommended times of day could promote reaching step goals.

Validation of a Zio XT Patch Accelerometer for the Objective Assessment of Physical Activity in the Atherosclerosis Risk in Communities (ARIC) Study

BACKGROUND

Combination devices to monitor heart rate/rhythms and physical activity are becoming increasingly popular in research and clinical settings. The Zio XT Patch (iRhythm Technologies, San Francisco, CA, USA) is US Food and Drug Administration (FDA)-approved for monitoring heart rhythms, but the validity of its accelerometer for assessing physical activity is unknown.

OBJECTIVE

To validate the accelerometer in the Zio XT Patch for measuring physical activity against the widely-used ActiGraph GT3X.

METHODS

The Zio XT and ActiGraph wGT3X-BT (Actigraph, Pensacola, FL, USA) were worn simultaneously in two separately-funded ancillary studies to Visit 6 of the Atherosclerosis Risk in Communities (ARIC) Study (2016-2017). Zio XT was worn on the chest and ActiGraph was worn on the hip. Raw accelerometer data were summarized using mean absolute deviation (MAD) for six different epoch lengths (1-min, 5-min, 10-min, 30-min, 1-h, and 2-h). Participants who had ≥3 days of at least 10 h of valid data between 7 a.m-11 p.m were included. Agreement of epoch-level MAD between the two devices was evaluated using correlation and mean squared error (MSE).

RESULTS

Among 257 participants (average age: 78.5 ± 4.7 years; 59.1% female), there were strong correlations between MAD values from Zio XT and ActiGraph (average r: 1-min: 0.66, 5-min: 0.90, 10-min: 0.93, 30-min: 0.93, 1-h: 0.89, 2-h: 0.82), with relatively low error values (Average MSE × 106: 1-min: 349.37 g, 5-min: 86.25 g, 10-min: 56.80 g, 30-min: 45.46 g, 1-h: 52.56 g, 2-h: 54.58 g).

CONCLUSIONS

These findings suggest that Zio XT accelerometry is valid for measuring duration, frequency, and intensity of physical activity within time epochs of 5-min to 2-h.

Accelerometry-assessed physical activity and sedentary behavior patterns using single- and multi-component latent class analysis among postmenopausal women

BACKGROUND

Patterns of physical activity and sedentary behavior among postmenopausal women are not well characterized.

OBJECTIVES

To describe the patterns of accelerometer-assessed physical activity and sedentary behavior among postmenopausal women.

DESIGN

Cross-sectional study.

METHODS

Women 63-97 years (n = 6126) wore an ActiGraph GT3X + accelerometer on their hip for 1 week. Latent class analysis was used to classify women by patterns of percent of wake time in physical activity and sedentary behavior over the week.

RESULTS

On average, participants spent two-thirds of their day in sedentary behavior (62.3%), 21.1% in light low, 11.0% in light high, and 5.6% in moderate-to-vigorous physical activity. Five classes emerged for each single-component model for sedentary behavior and light low, light high, and moderate-to-vigorous physical activity. Six classes emerged for the multi-component model that simultaneously considered the four behaviors together.

CONCLUSION

Unique profiles were identified in both single- and multi-component models that can provide new insights into habitual patterns of physical activity and sedentary behavior among postmenopausal women.

IMPLICATIONS

The multi-component approach can contribute to refining public health guidelines that integrate recommendations for both enhancing age-appropriate physical activity levels and reducing time spent in sedentary behavior.