ArticleSpinal cord2020
Comparative validity of energy expenditure prediction algorithms using wearable devices for people with spinal cord injury.
Article in Spinal cord, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Wearable-Enhanced mHealth Intervention to Promote Physical Activity in Manual Wheelchair Users: Single-Group Pre-Post Feasibility Study.JMIR rehabilitation and assistive technologies · 2025Article
- Predicting physical activity intensity using raw accelerometer signals in manual wheelchair users with spinal cord injury.Spinal cord · 2022Article
- Validation of Using Smartphone Built-In Accelerometers to Estimate the Active Energy Expenditures of Full-Time Manual Wheelchair Users with Spinal Cord Injury.Sensors (Basel, Switzerland) · 2021Article
- The Validity of MotionSense HRV in Estimating Sedentary Behavior and Physical Activity under Free-Living and Simulated Activity Settings.Sensors (Basel, Switzerland) · 2021Article
- Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems.IEEE sensors journal · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
STUDY
designCross-sectional validation study.
objectivesTo conduct a literature search for existing energy expenditure (EE) predictive algorithms using ActiGraph activity monitors for manual wheelchairs users (MWUs) with spinal cord injury (SCI), and evaluate their validity using an out-of-sample dataset.
settingResearch institution in Pittsburgh, USA.
methodsA literature search resulted in five articles containing five sets of predictive equations using an ActiGraph activity monitor for MWUs with SCI. Out-of-sample data were collected from 29 MWUs with chronic SCI who were asked to follow an activity protocol while wearing an ActiGraph GT9X Link on the dominant wrist. They also wore a portable metabolic cart which provided the criterion measure for EE. The out-of-sample dataset was used to evaluate the validity of the five sets of EE predictive equations.
resultsNone of the five sets of predictive equations demonstrated equivalence within 20% of the criterion measure based on an equivalence test. The mean absolute error for the five sets of predictive equations ranged from 0.87 to 6.41 kilocalories per minute (kcal min
conclusionsThe existing EE predictive equations based on ActiGraph monitors for MWUs with SCI showed varied performance when compared with the criterion measure. Their accuracies may not be sufficient to support future clinical and research use. More work is needed to develop more accurate EE predictive equations for this population.
Indexed as
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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.