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Mental Health Technologies: Designing With Consumers

Overview of attention for article published in JMIR Human Factors, January 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#28 of 607)
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

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28 X users
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1 Facebook page
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1 Google+ user

Citations

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45 Dimensions

Readers on

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83 Mendeley
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Title
Mental Health Technologies: Designing With Consumers
Published in
JMIR Human Factors, January 2016
DOI 10.2196/humanfactors.4336
Pubmed ID
Authors

Simone Orlowski, Ben Matthews, Niranjan Bidargaddi, Gabrielle Jones, Sharon Lawn, Anthony Venning, Philippa Collin

Abstract

Despite growing interest in the promise of e-mental and well-being interventions, little supporting literature exists to guide their design and the evaluation of their effectiveness. Both participatory design (PD) and design thinking (DT) have emerged as approaches that hold significant potential for supporting design in this space. Each approach is difficult to definitively circumscribe, and as such has been enacted as a process, a mind-set, specific practices/techniques, or a combination thereof. At its core, however, PD is a design research tradition that emphasizes egalitarian partnerships with end users. In contrast, DT is in the process of becoming a management concept tied to innovation with strong roots in business and education. From a health researcher viewpoint, while PD can be reduced to a number of replicable stages that involve particular methods, techniques, and outputs, projects often take vastly different forms and effective PD projects and practice have traditionally required technology-specific (eg, computer science) and domain-specific (eg, an application domain, such as patient support services) knowledge. In contrast, DT offers a practical off-the-shelf toolkit of approaches that at face value have more potential to have a quick impact and be successfully applied by novice practitioners (and those looking to include a more human-centered focus in their work). Via 2 case studies we explore the continuum of similarities and differences between PD and DT in order to provide an initial recommendation for what health researchers might reasonably expect from each in terms of process and outcome in the design of e-mental health interventions. We suggest that the sensibilities that DT shares with PD (ie, deep engagement and collaboration with end users and an inclusive and multidisciplinary practice) are precisely the aspects of DT that must be emphasized in any application to mental health provision and that any technology development process must prioritize empathy and understanding over innovation for the successful uptake of technology in this space.

X Demographics

X Demographics

The data shown below were collected from the profiles of 28 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Australia 1 1%
Unknown 81 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 18%
Researcher 10 12%
Student > Ph. D. Student 9 11%
Student > Bachelor 6 7%
Student > Doctoral Student 4 5%
Other 16 19%
Unknown 23 28%
Readers by discipline Count As %
Psychology 13 16%
Computer Science 11 13%
Medicine and Dentistry 10 12%
Nursing and Health Professions 7 8%
Social Sciences 4 5%
Other 12 14%
Unknown 26 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 May 2018.
All research outputs
#1,842,680
of 25,152,132 outputs
Outputs from JMIR Human Factors
#28
of 607 outputs
Outputs of similar age
#32,104
of 408,453 outputs
Outputs of similar age from JMIR Human Factors
#2
of 12 outputs
Altmetric has tracked 25,152,132 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 607 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 408,453 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.