The extent to which light-intensity physical activity contributes to health in older adults is not well known. The authors examined associations between physical activity across the intensity spectrum (sedentary to vigorous) and health and well-being variables in older adults. Two 7-day assessments of accelerometry from 2005 to 2007 were collected 6 months apart in the observational Senior Neighborhood Quality of Life Study of adults aged >65 years in Baltimore, Maryland, and Seattle, Washington. Self-reported health and psychosocial variables (e.g., lower-extremity function, body weight, rated stress) were also collected. Physical activity based on existing accelerometer thresholds for moderate/vigorous, high-light, low-light, and sedentary categories were examined as correlates of physical health and psychosocial well-being in mixed-effects regression models. Participants (N = 862) were 75.4 (standard deviation, 6.8) years of age, 56% female, 71% white, and 58% overweight/obese. After adjustment for study covariates and time spent in moderate/vigorous physical activity and sedentary behavior, low-light and high-light physical activity were positively related to physical health (all P < 0.0001) and well-being (all P < 0.001). Additionally, replacing 30 minutes/day of sedentary time with equal amounts of low-light or high-light physical activity was associated with better physical health (all P < 0.0001). Objectively measured light-intensity physical activity is associated with physical health and well-being variables in older adults.

Sleep and sedentary and active behaviors are linked to cardiovascular disease risk biomarkers, and across a 24-hour day, increasing time in 1 behavior requires decreasing time in another. We explored associations of reallocating time to sleep, sedentary behavior, or active behaviors with biomarkers. Data (n = 2,185 full sample; n = 923 fasting subanalyses) from the cross-sectional 2005–2006 US National Health and Nutrition Examination Survey were analyzed. The amounts of time spent in sedentary behavior, light-intensity activity, and moderate-to-vigorous physical activity (MVPA) were derived from ActiGraph accelerometry (ActiGraph LLC, Pensacola, Florida), and respondents reported their sleep duration. Isotemporal substitution modeling indicated that, independent of potential confounders and time spent in other activities, beneficial associations (P < 0.05) with cardiovascular disease risk biomarkers were associated with the reallocation of 30 minutes/day of sedentary time with equal time of either sleep (2.2% lower insulin and 2.0% lower homeostasis model assessment of β-cell function), light-intensity activity (1.9% lower triglycerides, 2.4% lower insulin, and 2.2% lower homeostasis model assessment of β-cell function), or MVPA (2.4% smaller waist circumference, 4.4% higher high-density lipoprotein cholesterol, 8.5% lower triglycerides, 1.7% lower glucose, 10.7% lower insulin, and 9.7% higher homeostasis model assessment of insulin sensitivity. These findings provide evidence that MVPA may be the most potent health-enhancing, time-dependent behavior, with additional benefit conferred from light-intensity activities and sleep duration when reallocated from sedentary time.

The physiologic mechanisms by which the four activities of sleep, sedentary behavior, light-intensity physical activity, and moderate-to-vigorous physical activity (MVPA) affect health are related, but these relationships have not been well explored in adults. Research studies have commonly evaluated how time spent in one activity affects health. Because one can only increase time in one activity by decreasing time in another, such studies cannot determine the extent that a health benefit is due to one activity versus due to reallocating time among the other activities. For example, interventions to improve sleep possibly also increase time spent in MVPA. If so, the overall effect of such interventions on risk of premature mortality is due to both more MVPA and better sleep. Further, the potential for interaction between activities to affect health outcomes is largely unexplored. For example, is there a threshold of MVPA minutes per day, above which adverse health effects of sedentary behavior are eliminated? This article considers the 24-h Activity Cycle (24-HAC) model as a paradigm for exploring inter-relatedness of health effects of the four activities. It discusses how to measure time spent in each of the four activities, as well as the analytical and statistical challenges in analyzing data based on the model, including the inevitable challenge of confounding among activities. The potential usefulness of this model is described by reviewing selected research findings that aided in the creation of the model and discussing future applications of the 24-HAC model.

Ivity (MVPA)—may now be feasible using small wearable devices. Purpose This study compared nine devices for accuracy in a 24-h activity measurement. Methods Adults (n = 40, 47% male) wore nine devices for 24 h: ActiGraph GT3X+, activPAL, Fitbit One, GENEactiv, Jawbone Up, LUMOback, Nike Fuelband, Omron pedometer, and Z-Machine. Comparisons (with standards) were made for total sleep time (Z-machine), time spent in SED (activPAL), LPA (GT3X+), MVPA (GT3X+), and steps (Omron). Analysis included mean absolute percent error, equivalence testing, and Bland–Altman plots. Results Error rates ranged from 8.1% to 16.9% for sleep, 9.5% to 65.8% for SED, 19.7% to 28.0% for LPA, 51.8% to 92% for MVPA, and 14.1% to 29.9% for steps. Equivalence testing indicated that only two comparisons were significantly equivalent to standards: the LUMOback for SED and the GT3X+ for sleep. Bland–Altman plots indicated GT3X+ had the closest measurement for sleep, LUMOback for SED, GENEactiv for LPA, Fitbit for MVPA, and GT3X+ for steps. Conclusions Currently, no device accurately captures activity data across the entire 24-h day, but the future of activity measurement should aim for accurate 24-h measurement as a goal. Researchers should continue to select measurement devices on the basis of their primary outcomes of interest....

Researchers in HCI and behavioral science are increasingly exploring the use of technology to support behavior change in domains such as health and sustainability. This work, however, remain largely siloed within the two communities. We begin to address this silo problem by attempting to build a bridge between the two disciplines at the level of behavioral theory. Specifically, we define core theoretical terms to create shared understanding about what theory is, discuss ways in which behavioral theory can be used to inform research on behavior change technologies, identify shortcomings in current behavioral theories, and outline ways in which HCI researchers can not only interpret and utilize behavioral science theories but also contribute to improving them.