ArticleJMIR rehabilitation and assistive technologies2023
Using Wearable Technology to Quantify Physical Activity Recovery: Secondary Report From the AFTER (App-Facilitated Tele-Rehabilitation) Program for COVID-19 Survivors Randomized Study.
Article in JMIR rehabilitation and assistive technologies, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04663945 (Predictors of Recovery and the App-Facilitated Tele-Rehabilitation), which is not on this map. Cited by 3 papers.
What it found
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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.
Predictors of Recovery and the App-Facilitated Tele-Rehabilitation (AFTER) Program for COVID Survivors
Who cites it
3 citing papers in PubMed.
- Animals as Architects: Building the Future of Technology-Supported Rehabilitation with Biomimetic Principles.Biomimetics (Basel, Switzerland) · 2024Review
- Exploring the Use of Mobile Health for the Rehabilitation of Long COVID Patients: A Scoping Review.Healthcare (Basel, Switzerland) · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundKnowledge on physical activity recovery after COVID-19 survival is limited. The AFTER (App-Facilitated Tele-Rehabilitation) program for COVID-19 survivors randomized participants, following hospital discharge, to either education and unstructured physical activity or a telerehabilitation program. Step count data were collected as a secondary outcome, and we found no significant differences in total step count trajectories between groups at 6 weeks. Further step count data were not analyzed.
objectiveThe purpose of this analysis was to examine step count trajectories and correlates among all participants (combined into a single group) across the 12-week study period.
methodsLinear mixed models with random effects were used to model daily steps over the number of study days. Models with 0, 1, and 2 inflection points were considered, and the final model was selected based on the highest log-likelihood value.
resultsParticipants included 44 adults (41 with available Fitbit [Fitbit LLC] data). Initially, step counts increased by an average of 930 (95% CI 547-1312; P<.001) steps per week, culminating in an average daily step count of 7658 (95% CI 6257-9059; P<.001) at the end of week 3. During the remaining 9 weeks of the study, weekly step counts increased by an average of 67 (95% CI -30 to 163; P<.001) steps per week, resulting in a final estimate of 8258 (95% CI 6933-9584; P<.001) steps.
conclusionsParticipants showed a marked improvement in daily step counts during the first 3 weeks of the study, followed by more gradual improvement in the remaining 9 weeks. Physical activity data and step count recovery trajectories may be considered surrogates for physiological recovery, although further research is needed to examine this relationship.
trial registrationClinicalTrials.gov NCT04663945; https://tinyurl.com/2p969ced.
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