Evidence map›Paper›PMID 34931992›Full record

ReviewJMIR mental health2021

The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review.

Shazmin Majid, Stuart Reeves, Grazziela Figueredo, Susan Brown, Alexandra Lang, Matthew Moore, Richard Morriss

Abstract readReview
In one paragraph

Review in JMIR mental health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
–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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  5. Development of an Essential Information Set for Supporting Life With Bipolar Disorder: A Modified Delphi Study With Patients, Families and Healthcare Professionals.Health expectations : an international journal of public participation in health care and health policy · 2025
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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

7 authors.

Shazmin Majid *School of Computer Science, Horizon Centre for Doctoral Training, University of Nottingham, Nottingham, United Kingdom.ORCID https://orcid.org/0000-0002-1061-6525
Stuart ReevesSchool of Computer Science, University of Nottingham, Nottingham, United Kingdom.ORCID https://orcid.org/0000-0001-7145-3320
Grazziela FigueredoSchool of Computer Science, University of Nottingham, Nottingham, United Kingdom.ORCID https://orcid.org/0000-0003-4094-7680
Susan BrownNational Institute for Health Research MindTech Medtech Co-operative, Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom.ORCID https://orcid.org/0000-0002-3403-3180
Alexandra LangHuman Factors Research Group, Faculty of Engineering, University of Nottingham, Nottingham, United Kingdom.ORCID https://orcid.org/0000-0002-7332-9443
Matthew MooreInstitute of Mental Health, University of Nottingham, Nottingham, United Kingdom.ORCID https://orcid.org/0000-0002-0774-7743
Richard MorrissNational Institute for Health Research Applied Research Collaboration East Midlands, Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom.ORCID https://orcid.org/0000-0003-2910-4121

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe number of self-monitoring apps for bipolar disorder (BD) is increasing. The involvement of users in human-computer interaction (HCI) research has a long history and is becoming a core concern for designers working in this space. The application of models of involvement, such as user-centered design, is becoming standardized to optimize the reach, adoption, and sustained use of this type of technology.

objectiveThis paper aims to examine the current ways in which users are involved in the design and evaluation of self-monitoring apps for BD by investigating 3 specific questions: are users involved in the design and evaluation of technology? If so, how does this happen? And what are the best practice ingredients regarding the design of mental health technology?

methodsWe reviewed the available literature on self-tracking technology for BD and make an overall assessment of the level of user involvement in design. The findings were reviewed by an expert panel, including an individual with lived experience of BD, to form best practice ingredients for the design of mental health technology. This combines the existing practices of patient and public involvement and HCI to evolve from the generic guidelines of user-centered design and to those that are tailored toward mental health technology.

resultsFor the first question, it was found that out of the 11 novel smartphone apps included in this review, 4 (36%) self-monitoring apps were classified as having no mention of user involvement in design, 1 (9%) self-monitoring app was classified as having low user involvement, 4 (36%) self-monitoring apps were classified as having medium user involvement, and 2 (18%) self-monitoring apps were classified as having high user involvement. For the second question, it was found that despite the presence of extant approaches for the involvement of the user in the process of design and evaluation, there is large variability in whether the user is involved, how they are involved, and to what extent there is a reported emphasis on the voice of the user, which is the ultimate aim of such design approaches. For the third question, it is recommended that users are involved in all stages of design with the ultimate goal of empowering and creating empathy for the user.

conclusionsUsers should be involved early in the design process, and this should not just be limited to the design itself, but also to associated research ensuring end-to-end involvement. Communities in health care-based design and HCI design need to work together to increase awareness of the different methods available and to encourage the use and mixing of the methods as well as establish better mechanisms to reach the target user group. Future research using systematic literature search methods should explore this further.

Indexed as

bipolar disorderhuman-computer interactionmobile phoneparticipatory designpatient and public involvementself-monitoring technologyuser-centered design

Identifiers

PMID34931992
PMCPMC8726024

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.