ArticlePhysiological measurement2016
Actigraphy features for predicting mobility disability in older adults.
Article in Physiological measurement, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01814072 (Opt-IN), which is not on this map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Opt-IN: Optimization of Remotely Delivered Intensive Lifestyle Treatment for Obesity
Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- A Case for Promoting Movement Medicine: Preventing Disability in the LIFE Randomized Controlled Trial.The journals of gerontology. Series A, Biological sciences and medical sciences · 2019Trial
- Article
- Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study.Aging clinical and experimental research · 2022Article
- A comparison of accelerometry analysis methods for physical activity in older adult women and associations with health outcomes over time.Journal of sports sciences · 2019Article
- A smartwatch-based framework for real-time and online assessment and mobility monitoring.Journal of biomedical informatics · 2019Article
- Multimodal Imaging of Brain Activity to Investigate Walking and Mobility Decline in Older Adults (Mind in Motion Study): Hypothesis, Theory, and Methods.Frontiers in aging neuroscience · 2019Article
- Wrist accelerometer shape feature derivation methods for assessing activities of daily living.BMC medical informatics and decision making · 2018Article
- A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses.Physiological measurement · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 10 institutions in 1 country.
Funding
Abstract
Actigraphy has attracted much attention for assessing physical activity in the past decade. Many algorithms have been developed to automate the analysis process, but none has targeted a general model to discover related features for detecting or predicting mobility function, or more specifically, mobility impairment and major mobility disability (MMD). Men (N = 357) and women (N = 778) aged 70-89 years wore a tri-axial accelerometer (Actigraph GT3X) on the right hip during free-living conditions for 8.4 ± 3.0 d. One-second epoch data were summarized into 67 features. Several machine learning techniques were used to select features from the free-living condition to predict mobility impairment, defined as 400 m walking speed <0.80 m s
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.