Objectives: Higher steps are associated with lower mortality and cardiovascular event rates. We previously demonstrated that tailored physician-delivered step count prescriptions successfully increased steps/day in adults with type 2 diabetes mellitus (T2DM) and/or hypertension. In the present analysis, we examined patterns of step count change and the factors that influence different responses.
Design: Longitudinal observational study METHODS: Active arm participants (n=118) recorded steps/day. They received a step count prescription from their physician every 3-4 months. We computed mean steps/day and changes from baseline for sequential 30-day periods. Group-based trajectory modeling was applied.
Results: Four distinct trajectories of mean steps/day emerged, distinguishable by differences in baseline steps/day: sedentary (19%), low active (40%), somewhat active (30%) and active (11%). All four demonstrated similar upward slopes. Three patterns emerged for the change in steps from baseline: gradual decrease (30%), gradual increase with late decline (56%), and rapid increase with midpoint decline (14%); thus 70% had an increase from baseline. T2DM (odd ratios [OR]: 3.7, 95% CI 1.7, 7.7) and age (OR per 10-year increment: 2, 95% CI 1.3, 2.8) were both associated with starting at a lower baseline but participants from these groups were no less likely than others to increase steps/day.
Conclusions: T2DM and older age were associated with lower baseline values but were not indicators of likelihood of step count increases. A physician-delivered step count prescription and monitoring strategy has strong potential to be effective in increasing steps irrespective of baseline counts and other clinical and demographic characteristics.
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http://dx.doi.org/10.1016/j.jsams.2020.04.010 | DOI Listing |
JMIR Form Res
January 2025
1, Department of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Changjogwan, Yonseidae-gil 1, Wonju, 26493, Republic of Korea, +82 (0) 33-760-2257.
Background: Diabetes is prevalent in older adults, and machine learning algorithms could help predict diabetes in this population.
Objective: This study determined diabetes risk factors among older adults aged ≥60 years using machine learning algorithms and selected an optimized prediction model.
Methods: This cross-sectional study was conducted on 3084 older adults aged ≥60 years in Seoul from January to November 2023.
Menopause
January 2025
Department of Child, Family, and Population Health Nursing, University of Washington, Seattle, WA.
Objective: This study aimed to determine whether exposure to traffic-related air pollution (TRAP) is associated with depressive symptoms while also characterizing the contribution of key explanatory factors related to sociodemographics and health. In addition, it aimed to also explore the role of reproductive health as a pathway through which exposure to TRAP may relate to depressive symptoms.
Methods: Participants were 688 healthy reproductive-age women in the Ovarian Aging Study.
Dev Psychol
January 2025
Department of Psychology, University of California, San Diego.
Numerate adults know that when two sets are equal, they should be labeled by the same number word. We explored the development of this principle-sometimes called "cardinal extension"-and how it relates to children's other numerical abilities. Experiment 1 revealed that 2- to 5-year-old children who could accurately count large sets often inferred that two equal sets should be labeled with the same number word, unlike children who could not accurately count large sets.
View Article and Find Full Text PDFJ Behav Med
January 2025
Department of Psychiatry, Center for Health Outcomes and Interdisciplinary Research, Massachusetts General Hospital, One Bowdoin Square, 1st Floor, Suite 100, Boston, MA, 02114, USA.
Multimodal digital health assessments overcome the limitations of patient-reported outcomes by allowing for continuous and passive monitoring but remain underutilized in older adult lifestyle interventions for brain health. Therefore, we aim to (1) report ecological momentary assessment (EMA) and ActiGraph adherence among older adults during a lifestyle intervention; and (2) use dynamic data collected via EMA and ActiGraph to examine person-specific patterns of mindfulness, steps, and sleep throughout the intervention. We analyzed EMA and ActiGraph data from a pilot study of the 8-week My Healthy Brain program (N = 10) lifestyle group for older adults (60+) with subjective cognitive decline.
View Article and Find Full Text PDFJ Food Prot
January 2025
Department of Food Science & Technology, University of Nebraska-Lincoln. Lincoln, NE 68588 USA; The Food Processing Center, University of Nebraska-Lincoln. Lincoln, NE 68588, USA. Electronic address:
The presence of Listeria monocytogenes in the dairy environment remains a food safety challenge. The source of microbial contamination may include employees and their personal protective equipment (PPE). This study investigated the effectiveness of cleaning protocols (i.
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