Disability measures in multiple sclerosis (MS) rely heavily on ambulatory function, and current metrics fail to capture potentially important variability in walking behavior. We sought to determine whether remote step count monitoring using a consumer-friendly accelerometer (Fitbit Flex) can enhance MS disability assessment. 99 adults with relapsing or progressive MS able to walk ≥2-min were prospectively recruited. At 4 weeks, study retention was 97% and median Fitbit use was 97% of days. Substudy validation resulted in high interclass correlations between Fitbit, ActiGraph and manual step count tally during a 2-minute walk test, and between Fitbit and ActiGraph (ICC = 0.76) during 7-day home monitoring. Over 4 weeks of continuous monitoring, daily steps were lower in progressive versus relapsing MS (mean difference 2546 steps, p < 0.01). Lower average daily step count was associated with greater disability on the Expanded Disability Status Scale (EDSS) (p < 0.001). Within each EDSS category, substantial variability in step count was apparent (i.e., EDSS = 6.0 range 1097-7152). Step count demonstrated moderate-strong correlations with other walking measures. Lower average daily step count is associated with greater MS disability and captures important variability in real-world walking activity otherwise masked by standard disability scales, including the EDSS. These results support remote step count monitoring as an exploratory outcome in MS trials.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5292081 | PMC |
http://dx.doi.org/10.1007/s00415-016-8334-6 | 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.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!