Evidence map›Paper›PMID 33808468›Full record

ArticleSensors (Basel, Switzerland)2021

Improving Stress Management and Sleep Hygiene in Intelligent Homes.

Asterios Leonidis, Maria Korozi, Eirini Sykianaki, Eleni Tsolakou, Vasilios Kouroumalis, Danai Ioannidi, Andreas Stavridakis, Margherita Antona, Constantine Stephanidis

Abstract 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 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–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

10 citing papers in PubMed.

  1. Article
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  4. Review
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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

9 authors.

Asterios LeonidisInstitute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Crete, Greece.ORCID 0000-0002-6800-3895
Maria KoroziInstitute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Crete, Greece.ORCID 0000-0001-6577-4339
Eirini SykianakiInstitute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Crete, Greece.
Eleni TsolakouInstitute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Crete, Greece.
Vasilios KouroumalisInstitute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Crete, Greece.
Danai IoannidiInstitute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Crete, Greece.
Andreas StavridakisInstitute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Crete, Greece.
Margherita AntonaInstitute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Crete, Greece.
Constantine StephanidisInstitute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Crete, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High stress levels and sleep deprivation may cause several mental or physical health issues, such as depression, impaired memory, decreased motivation, obesity, etc. The COVID-19 pandemic has produced unprecedented changes in our lives, generating significant stress, and worries about health, social isolation, employment, and finances. To this end, nowadays more than ever, it is crucial to deliver solutions that can help people to manage and control their stress, as well as to reduce sleep disturbances, so as to improve their health and overall quality of life. Technology, and in particular Ambient Intelligence Environments, can help towards that direction, when considering that they are able to understand the needs of their users, identify their behavior, learn their preferences, and act and react in their interest. This work presents two systems that have been designed and developed in the context of an Intelligent Home, namely CaLmi and HypnOS, which aim to assist users that struggle with stress and poor sleep quality, respectively. Both of the systems rely on real-time data collected by wearable devices, as well as contextual information retrieved from the ambient facilities of the Intelligent Home, so as to offer appropriate pervasive relaxation programs (CaLmi) or provide personalized insights regarding sleep hygiene (HypnOS) to the residents. This article will describe the design process that was followed, the functionality of both systems, the results of the user studies that were conducted for the evaluation of their end-user applications, and a discussion about future plans.

Indexed as

Sleep HygieneCOVID-19HumansPandemicsQuality of LifeRelaxation TherapySoftwareStress, PsychologicalWearable Electronic Devicesambient intelligencecontextual awarenesssleep hygienesleep monitoringsmart homestress management

Identifiers

PMID33808468
PMCPMC8036360

What OpenQuestion holds

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LicenceCC BY
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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.