An official website of the United States government
Prolonged sedentary behavior poses significant health risks, calling for interventions that promote active lifestyles. For older adults, every physical activity, no matter how small or significant, plays a vital role in their quality of life. However, many interventions aimed at reducing sedentary behavior have overlooked the unique needs and preferences of older adults. In this study, we explore design opportunities for supporting behavior displacement---replacing sedentary time with active movements---as a potential strategy for intervening sedentary time among older adults. Through a 7-day diary study and interviews with 13 participants, we uncovered key factors, such as attention demand, productivity and quality of activities, physical fatigue, as well as social norms, that influence their decisions to engage in displacement. We also identified sequential and concurrent displacement strategies and the contexts in which each was employed. Our findings highlight the need for designing personalized, adaptive interventions that respect the diverse preferences and agency of older adults.
more »
« less
Pre- Versus Postmeal Sedentary Duration—Impact on Postprandial Glucose in Older Adults With Overweight or Obesity
Introduction: Reducing sedentary time is associated with improved postprandial glucose regulation. However, it is not known if the timing of sedentary behavior (i.e., pre- vs. postmeal) differentially impacts postprandial glucose in older adults with overweight or obesity.Methods: In this secondary analysis, older adults (≥65 years) with overweight and obesity (body mass index ≥ 25 kg/m2) wore a continuous glucose monitor and a sedentary behavior monitor continuously in their real-world environments for four consecutive days on four separate occasions. Throughout each 4-day measurement period, participants followed a standardized eucaloric diet and recorded mealtimes in a diary. Glucose, sedentary behavior, and meal intake data were fused using sensor and diary timestamps. Mixed-effect linear regression models were used to evaluate the impact of sedentary timing relative to meal intake.Results: Premeal sedentary time was significantly associated with both the increase from premeal glucose to the postmeal peak (ΔG) and the percent of premeal glucose increase that was recovered 1-hr postmeal glucose peak (%Baseline Recovery;p < .05), with higher levels of premeal sedentary time leading to both a larger ΔGand a smaller %Baseline Recovery. Postmeal sedentary time was significantly associated with the time from meal intake to glucose peak (ΔT;p < .05), with higher levels of postmeal sedentary time leading to a longer time to peak.Conclusions: Pre- versus postmeal sedentary behavior differentially impacts postprandial glucose response in older adults with overweight or obesity, suggesting that the timing of sedentary behavior reductions might play an influential role on long-term glycemic control.
more »
« less
- PAR ID:
- 10499396
- Publisher / Repository:
- International Society for the Measurement of Physical Behaviour
- Date Published:
- Journal Name:
- Journal for the Measurement of Physical Behaviour
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2575-6605
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Diet monitoring is an important component of interventions in type 2 diabetes, but is time intensive and often inaccurate. To address this issue, we describe an approach to monitor diet automatically, by analyzing fluctuations in glucose after a meal is consumed. In particular, we evaluate three standardization techniques (baseline correction, feature normalization, and model personalization) that can be used to compensate for the large individual differences that exist in food metabolism. Then, we build machine learning models to predict the amounts of macronutrients in a meal from the associated glucose responses. We evaluate the approach on a dataset containing glucose responses for 15 participants who consumed 9 meals. Three techniques improve the accuracy of the models: subtracting the baseline glucose, performing z-score normalization, and scaling the amount of macronutrients by each individuals’ body mass index.more » « less
-
null (Ed.)Measuring dietary intake is a major challenge in the management of chronic diseases. Current methods rely on self-report measures, which are cumbersome to obtain and often unreliable. This article presents an approach to estimate dietary intake automatically by analyzing the post-prandial glucose response (PPGR) of a meal, as measured with continuous glucose monitors. In particular, we propose a sparse-coding technique that can be used to estimate the amounts of macronutrients (carbohydrates, protein, fat) in a meal from the meal’s PPGR. We use Lasso regularization to represent the PPGR of a new meal as a sparse combination of PPGRs in a dictionary, then combine the sparse weights with the macronutrient amounts in the dictionary’s meals to estimate the macronutrients in the new meal. We evaluate the approach on a dataset containing nine standardized meals and their corresponding PPGRs, consumed by fifteen participants. The proposed technique consistently outperforms two baseline systems based on ridge regression and nearest-neighbors, in terms of correlation and normalized root mean square error of the predictions.more » « less
-
The focus of this paper is on the characterization of the uncertainties in the evolving states of a diabetic model, to permit a study of the impact of the time interval between insulin bolusing and meal initiation on hypo- and hyperglycemic events. A polynomial chaos based approach is used to characterize the independent uncertainties in the initial condition and meal size. Galerkin projection of the resulting equations reduce the stochastic differential equations to a set of deterministic equations. This forms the framework to optimize for the post bolusing time to initiate the meal. Two cost functions are considered which correspond to the postprandial hypoand hyperglycemic excursions of the blood glucose. Numerical results from the minimal Bergman model suggest a 13 and 14 minute interval between bolusing and the initiation of the meal.more » « less
-
ABSTRACT Interactions between sleep and feeding behaviors are critical for adaptive fitness. Diverse species suppress sleep when food is scarce to increase the time spent foraging. Postprandial sleep, an increase in sleep time following a feeding event, has been documented in vertebrate and invertebrate animals. While interactions between sleep and feeding appear to be highly conserved, the evolution of postprandial sleep in response to changes in food availability remains poorly understood. Multiple populations of the Mexican cavefish,Astyanax mexicanus, have independently evolved sleep loss and increased food consumption compared to surface‐dwelling fish of the same species, providing the opportunity to investigate the evolution of interactions between sleep and feeding. Here, we investigate the effects of feeding on sleep in larval and adult surface fish, and in two parallelly evolved cave populations ofA. mexicanus. Larval surface and cave populations ofA. mexicanusincrease sleep immediately following a meal, providing the first evidence of postprandial sleep in a fish model. The amount of sleep was not correlated to meal size and occurred independently of feeding time. In contrast to larvae, postprandial sleep was not detected in adult surface or cavefish, which can survive for months without food. Together, these findings reveal that postprandial sleep is present in multiple short‐sleeping populations of cavefish, suggesting sleep‐feeding interactions are retained despite the evolution of sleep loss. These findings raise the possibility that postprandial sleep is critical for energy conservation and survival in larvae that are highly sensitive to food deprivation.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1123/jmpb.2023-0032
