IoTMindCare: An Integrative Reference Architecture for Safe and Personalized IoT-Based Depression Management.

Sanaz Zamani, Roopak Sinha, Samaneh Madanian, Minh Nguyen

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Sanaz ZamaniDepartment of Computer Science and Software Engineering, Auckland University of Technology, Auckland 1010, New Zealand.ORCID 0009-0006-7182-7525

Roopak SinhaSchool of Information Technology, Deakin University, Burwood, VIC 3125, Australia.ORCID 0000-0001-9486-7833

Samaneh MadanianDepartment of Data Science and Artificial Intelligence, Auckland University of Technology, Auckland 1010, New Zealand.ORCID 0000-0001-6430-9611

Minh NguyenDepartment of Computer Science and Software Engineering, Auckland University of Technology, Auckland 1010, New Zealand.ORCID 0000-0002-2757-8350

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Depression affects millions of people worldwide. Traditional management relies heavily on periodic clinical assessments and self-reports, which lack real-time responsiveness and personalization. Despite numerous research prototypes exploring Internet of Things (IoT)-based mental health support, almost none have translated into practical, mainstream solutions. This gap stems from several interrelated challenges, including the absence of robust, flexible, and safe architectural frameworks; the diversity of IoT device ownership; the need for further research across many aspects of technology-based depression support; highly individualized user needs; and ongoing concerns regarding safety and personalization. We aim to develop a reference architecture for IoT-based safe and personalized depression management. We introduce

Indexed as

DepressionInternet of ThingsPrecision MedicineHumansdepression managementhealth safetyInternet of Thingspersonalized healthsmart homesystem architecture

Identifiers

PMID41305201

DOI10.3390/s25226994

PMCPMC12656082

What OpenQuestion holds

Textmetadata

LicenceCC BY

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Registered trials

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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.