ArticlemedRxiv : the preprint server for health sciences2024
Impact of automated data flow and reminders on adherence and resource utilization for remotely monitoring physical activity in individuals with stroke or chronic obstructive pulmonary disease.
Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Self-Reported Acceptance of a Wearable Activity Monitor in Persons With Stroke: Usability Study.JMIR cardio · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
As rehabilitation advances into the era of digital health, remote monitoring of physical activity via wearable devices has the potential to change how we provide care. However, uncertainties about patient adherence and the significant resource requirements needed create challenges to adoption of remote monitoring into clinical care. Here we aim to determine the impact of a novel digital application to overcome these barriers. The Rehabilitation Remote Monitoring Application (RRMA) automatically extracts data about physical activity collected via a Fitbit device, screens the data for adherence, and contacts the participant if adherence is low. We compare adherence and estimate the resources required (i.e., time and financial) to perform remote monitoring of physical activity with and without the RRMA in two patient groups. Seventy-three individuals with stroke or chronic obstructive pulmonary disease completed 28 days of monitoring physical activity with the RRMA, while 62 individuals completed 28 days with the data flow processes being completed manually. Adherence (i.e., the average percentage of the day that the device was worn) was similar between groups (p=0.85). However, the RRMA saved an estimated 123.8 minutes or $50.24 per participant month when compared to manual processes. These results demonstrate that automated technologies like the RRMA can maintain patient adherence to remote monitoring of physical activity while reducing the time and financial resources needed. Applications like the RRMA can facilitate the adoption of remote monitoring in rehabilitation by reducing barriers related to adherence and resource requirements.
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.