Evidence map›Paper›PMID 21989632›Full record

ReviewNeurorehabilitation and neural repair

The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors.

Bruce H Dobkin, Andrew Dorsch

4 registry-linked trialsAbstract readReview
In one paragraph

Review in Neurorehabilitation and neural repair. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 4 registered trials, which are not on this map. Cited by 124 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
124citing papers in PubMed, 8 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

NCT01246882 phase3completednot on this map

Stroke Inpatient Rehabilitation Reinforcement of ACTivty: An International Multisite Clinical Trial

TypeinterventionalSponsorUniversity of California, Los AngelesRan2011 to 2012Enrolled140ConditionsStrokeArmsAugmented activity feedback, Speed-only feedback
NCT02741895 completednot on this map

Using Wearable Biosensors to Monitor Ambulation After Major Surgery: Optimizing Efficiency of Postoperative Recovery

TypeobservationalSponsorCedars-Sinai Medical CenterRan2016 to 2017Enrolled135ConditionsGeneral SurgeryArmsFitbit monitoring
NCT03500393 naterminatednot on this map

A Remotely Supervised Exercise Program for Lung Cancer Patients Undergoing Chemoradiation

TypeinterventionalSponsorDartmouth-Hitchcock Medical CenterRan2018 to 2021Enrolled4ConditionsLung CancerArmsRemotely Supervised Exercise (REM), Unsupervised Exercise (UNSUP)
NCT04576611 nacompletednot on this map

Behavioral Activation and Exercise Protocol for Chronic Pain Patients Based on the Use of New Technologies (Smartphone)

TypeinterventionalSponsorUniversity of the Balearic IslandsRan2019 to 2020Enrolled59ConditionsLow Back PainArmsface-to-face protocol, self-managed protocol
3 · Its place in the literature

Who cites it

124 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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64 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Bruce H DobkinDepartment of Neurology, Geffen UCLA School of Medicine, Los Angeles, CA, USA. BDobkin@mednet.ucla.edu
Andrew Dorsch

Funding

Sensor Activity Monitoring, Feedback, and Outcome Measures, Stroke RehabilitationR01HD071809 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DOBKIN, BRUCE H · 2012 to 2014
$1.2M
NICHD NIH HHS R01 HD071809
6 · The paper itself

Abstract

Mobile health tools that enable clinicians and researchers to monitor the type, quantity, and quality of everyday activities of patients and trial participants have long been needed to improve daily care, design more clinically meaningful randomized trials of interventions, and establish cost-effective, evidence-based practices. Inexpensive, unobtrusive wireless sensors, including accelerometers, gyroscopes, and pressure-sensitive textiles, combined with Internet-based communications and machine-learning algorithms trained to recognize upper- and lower-extremity movements, have begun to fulfill this need. Continuous data from ankle triaxial accelerometers, for example, can be transmitted from the home and community via WiFi or a smartphone to a remote data analysis server. Reports can include the walking speed and duration of every bout of ambulation, spatiotemporal symmetries between the legs, and the type, duration, and energy used during exercise. For daily care, this readily accessible flow of real-world information allows clinicians to monitor the amount and quality of exercise for risk factor management and compliance in the practice of skills. Feedback may motivate better self-management as well as serve home-based rehabilitation efforts. Monitoring patients with chronic diseases and after hospitalization or the start of new medications for a decline in daily activity may help detect medical complications before rehospitalization becomes necessary. For clinical trials, repeated laboratory-quality assessments of key activities in the community, rather than by clinic testing, self-report, and ordinal scales, may reduce the cost and burden of travel, improve recruitment and retention, and capture more reliable, valid, and responsive ratio-scaled outcome measures that are not mere surrogates for changes in daily impairment, disability, and functioning.

Indexed as

Activities of Daily LivingOutcome Assessment, Health CareBiosensing TechniquesHumansMonitoring, AmbulatoryRandomized Controlled Trials as TopicRemote Sensing TechnologyTelemedicine

Identifiers

PMID21989632
PMCPMC4098920

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.