Evidence map›Paper›PMID 33805217›Full record

ArticleSensors (Basel, Switzerland)2021

Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation.

Fatemeh Sarhaddi, Iman Azimi, Sina Labbaf, Hannakaisa Niela-Vilén, Nikil Dutt, Anna Axelin, Pasi Liljeberg, Amir M Rahmani

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
5.7field-weighted citation impact, top 3% of its field
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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.

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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

8 authors at 2 institutions in 2 countries.

Fatemeh SarhaddiDepartment of Computing, University of Turku, 20014 Turku, Finland.ORCID 0000-0002-5750-5793
Iman AzimiDepartment of Computing, University of Turku, 20014 Turku, Finland.ORCID 0000-0001-5003-299X
Sina LabbafDepartment of Computer Science, University of California, Irvine, CA 92697-3435, USA.ORCID 0000-0002-9478-2546
Hannakaisa Niela-VilénDepartment of Nursing Science, University of Turku, 20520 Turku, Finland.ORCID 0000-0002-8646-6551
Nikil DuttDepartment of Computer Science, University of California, Irvine, CA 92697-3435, USA.ORCID 0000-0002-3060-8119
Anna AxelinDepartment of Nursing Science, University of Turku, 20520 Turku, Finland.ORCID 0000-0003-2743-3589
Pasi LiljebergDepartment of Computing, University of Turku, 20014 Turku, Finland.ORCID 0000-0002-9392-3589
Amir M RahmaniDepartment of Computer Science, University of California, Irvine, CA 92697-3435, USA.ORCID 0000-0003-0725-1155
University of Turku · FIUniversity of California, Irvine · US

Funding

Academy of Finland 316810 and 316811National Science Foundation SCC CNS-1831918
6 · The paper itself

Abstract

Pregnancy is a unique time when many mothers gain awareness of their lifestyle and its impacts on the fetus. High-quality care during pregnancy is needed to identify possible complications early and ensure the mother's and her unborn baby's health and well-being. Different studies have thus far proposed maternal health monitoring systems. However, they are designed for a specific health problem or are limited to questionnaires and short-term data collection methods. Moreover, the requirements and challenges have not been evaluated in long-term studies. Maternal health necessitates a comprehensive framework enabling continuous monitoring of pregnant women. In this paper, we present an Internet-of-Things (IoT)-based system to provide ubiquitous maternal health monitoring during pregnancy and postpartum. The system consists of various data collectors to track the mother's condition, including stress, sleep, and physical activity. We carried out the full system implementation and conducted a real human subject study on pregnant women in Southwestern Finland. We then evaluated the system's feasibility, energy efficiency, and data reliability. Our results show that the implemented system is feasible in terms of system usage during nine months. We also indicate the smartwatch, used in our study, has acceptable energy efficiency in long-term monitoring and is able to collect reliable photoplethysmography data. Finally, we discuss the integration of the presented system with the current healthcare system.

Indexed as

ExerciseLife StyleFemaleFinlandHumansInfantMonitoring, PhysiologicPregnancyReproducibility of ResultsInternet of Thingsmaternal healthremote health monitoringwearable device

Identifiers

PMID33805217
PMCPMC8036648
OpenAlexW3142808426

What OpenQuestion holds

Textmetadata
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.