Evidence map›Paper›PMID 36800211›Full record

ReviewJMIR mHealth and uHealth2023

The Feasibility of Using Smartphone Sensors to Track Insomnia, Depression, and Anxiety in Adults and Young Adults: Narrative Review.

Doaa Alamoudi, Emma Breeze, Esther Crawley, Ian Nabney

Open access · goldAbstract readReview
In one paragraph

Review in JMIR mHealth and uHealth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
6.1field-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

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

  1. Pooled it
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  10. Telemedicine and Its Application in Cystic Fibrosis.Journal of personalized medicine · 2023
    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

4 authors at 1 institution in 1 country.

Doaa Alamoudi *Department of Computer Science, University of Bristol, Bristol, United Kingdom.ORCID 0000-0001-6406-9742
Emma BreezeBristol Medical School, University of Bristol, Bristol, United Kingdom.ORCID 0000-0002-9163-8151
Esther CrawleyCentre for Academic Child Health, University of Bristol Medical School, University of Bristol, Bristol, United Kingdom.ORCID 0000-0002-2521-0747
Ian NabneyDepartment of Computer Science, University of Bristol, Bristol, United Kingdom.ORCID 0000-0001-7382-2855
University of Bristol · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince the era of smartphones started in early 2007, they have steadily turned into an accepted part of our lives. Poor sleep is a health problem that needs to be closely monitored before it causes severe mental health problems, such as anxiety or depression. Sleep disorders (eg, acute insomnia) can also develop to chronic insomnia if not treated early. More specifically, mental health problems have been recognized to have casual links to anxiety, depression, heart disease, obesity, dementia, diabetes, and cancer. Several researchers have used mobile sensors to monitor sleep and to study changes in individual mood that may cause depression and anxiety.

objectiveExtreme sleepiness and insomnia not only influence physical health, they also have a significant impact on mental health, such as by causing depression, which has a prevalence of 18% to 21% among young adults aged 16 to 24 in the United Kingdom. The main body of this narrative review explores how passive data collection through smartphone sensors can be used in predicting anxiety and depression.

methodsA narrative review of the English language literature was performed. We investigated the use of smartphone sensors as a method of collecting data from individuals, regardless of whether the data source was active or passive. Articles were found from a search of Google Scholar records (from 2013 to 2020) with keywords including "mobile phone," "mobile applications," "health apps," "insomnia," "mental health," "sleep monitoring," "depression," "anxiety," "sleep disorder," "lack of sleep," "digital phenotyping," "mobile sensing," "smartphone sensors," and "sleep detector."

resultsThe 12 articles presented in this paper explain the current practices of using smartphone sensors for tracking sleep patterns and detecting changes in mental health, especially depression and anxiety over a period of time. Several researchers have been exploring technological methods to detect sleep using smartphone sensors. Researchers have also investigated changes in smartphone sensors and linked them with mental health and well-being.

conclusionsThe conducted review provides an overview of the possibilities of using smartphone sensors unobtrusively to collect data related to sleeping pattern, depression, and anxiety. This provides a unique research opportunity to use smartphone sensors to detect insomnia and provide early detection or intervention for mental health problems such as depression and anxiety if insomnia is detected.

Indexed as

Sleep Initiation and Maintenance DisordersSmartphoneAnxietyDepressionFeasibility StudiesHumansYoung Adultanxietycardiovascular diseasedementiadepressiondiabetesdigitalhealthheart diseaseinsomniainterventionmenmental healthmHealthmortalityobesityrisksensorsleepsleep disordertechnologyyoung adult

Identifiers

PMID36800211
PMCPMC9984993
OpenAlexW4319151392

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.