Evidence map›Paper›PMID 35062531›Full record

SynthesisSensors (Basel, Switzerland)2022

A Systematic Review of Wearable Sensors for Monitoring Physical Activity.

Annica Kristoffersson, Maria Lindén

Abstract readSystematic Review
In one paragraph

Synthesis in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 4 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

51 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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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.

Annica KristofferssonSchool of Innovation, Design and Engineering, Mälardalen University, Box 883, 721 23 Västerås, Sweden.ORCID 0000-0002-4368-4751
Maria LindénSchool of Innovation, Design and Engineering, Mälardalen University, Box 883, 721 23 Västerås, Sweden.ORCID 0000-0003-1940-1747

Funding

Swedish Knowledge Foundation 20180158
6 · The paper itself

Abstract

This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing.

Indexed as

Wearable Electronic DevicesAccidental FallsAgedAged, 80 and overExerciseGaitHumansLongitudinal StudiesMaledata setsfall detectiongait and balanceneurological diseasesphysical activity recognitionrehabilitationresearch shortcomingssensor systemsuser demographywearable sensors

Identifiers

PMID35062531
PMCPMC8778538

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.