Evidence map›Paper›PMID 35642184›Full record

ArticleInternational journal of nursing studies advances2022

Nurse-in-the-loop smart home detection of health events associated with diagnosed chronic conditions: A case-event series.

Roschelle Fritz, Katherine Wuestney, Gordana Dermody, Diane J Cook

Open access · goldAbstract read
In one paragraph

Article in International journal of nursing studies advances, 2022. 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
2.4field-weighted citation impact, top 10% 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

10 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
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  6. Article
  7. Smart Home Technologies for Monitoring Cancer Symptoms and Enhancing Palliative Care.Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2025
    Article
  8. Review
  9. Detecting the Impact of Older Adult Healthy Brain Aging Behaviour Adoption Using Smart Home Technology.Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2024
    Article
  10. Article
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 4 institutions in 2 countries.

Roschelle FritzCollege of Nursing, Washington State University, Vancouver, WA, United States of America.
Katherine WuestneyCollege of Nursing, Washington State University, Spokane, WA, United States of America.
Gordana DermodySchool of Nursing, Midwifery and Paramedicine, University of the Sunshine Coast, Queensland, Australia.
Diane J CookSchool of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States of America.
University of the Sunshine Coast · AUWashington State University · USWashington State University Spokane · USWashington State University Vancouver · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Telehealth and home-based care options significantly expanded during the SARS-CoV2 pandemic. Sophisticated, remote monitoring technologies now exist that support at-home care. Advances in the research of smart homes for health monitoring have shown these technologies are capable of recognizing and predicting health changes in near-real time. However, few nurses are familiar enough with this technology to use smart homes for optimizing patient care or expanding their reach into the home between healthcare touch points. Objective: The objective of this work is to explore a partnership between nurses and smart homes for automated remote monitoring and assessing of patient health. We present a series of health event cases to demonstrate how this partnership may be harnessed to effectively detect and report on clinically relevant health events that can be automatically detected by smart homes. Participants: 25 participants with multiple chronic health conditions. Methods: Ambient sensors were installed in the homes of 25 participants with multiple chronic health conditions. Motion, light, temperature, and door usage data were continuously collected from participants' homes. Descriptions of health events and participants' associated behaviors were captured via weekly nursing telehealth visits with study participants and used to analyze sensor data representing health events. Two cases of participants with congestive heart failure exacerbations, one case of urinary tract infection, two cases of bowel inflammation flares, and four cases of participants with sleep interruption were explored. Results: For each case, clinically relevant health events aligned with changes from baseline in behavior data patterns derived from sensors installed in the participant's home. In some cases, the detected event was precipitated by additional behavior patterns that could be used to predict the event. Conclusions: We found evidence in this case series that continuous sensor-based monitoring of patient behavior in home settings may be used to provide automated detection of health events. Nursing insights into smart home sensor data could be used to initiate preventive strategies and provide timely intervention. Tweetable abstract: Nurses partnered with smart homes could detect exacerbations of health conditions at home leading to early intervention.

Indexed as

Artificial intelligenceCase seriesChronic illnessNursing informaticsSmart home monitoring

Identifiers

PMID35642184
PMCPMC9132470
OpenAlexW4281570384

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.