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The development and feasibility of a personal health-optimization system for people with bipolar disorder

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2017
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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7 X users
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1 Facebook page

Citations

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176 Mendeley
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Title
The development and feasibility of a personal health-optimization system for people with bipolar disorder
Published in
BMC Medical Informatics and Decision Making, July 2017
DOI 10.1186/s12911-017-0481-x
Pubmed ID
Authors

Øystein Eiring, Kari Nytrøen, Simone Kienlin, Soudabeh Khodambashi, Magne Nylenna

Abstract

People with bipolar disorder often experience ill health and have considerably reduced life expectancies. Suboptimal treatment is common and includes a lack of effective medicines, overtreatment, and non-adherence to medical interventions and lifestyle measures. E- and m-health applications support patients in optimizing their treatment but often exhibit conceptual and technical shortcomings. The objective of this work was to develop and test the usability of a system targeting suboptimal treatment and compare the service to other genres and strategies. Based on the frameworks of shared decision-making, multi-criteria decision analysis, and single-subject research design, we interviewed potential users, reviewed research and current approaches, and created a first version using a rapid prototyping framework. We then iteratively improved and expanded the service based on formative usability testing with patients, healthcare providers, and laypeople from Norway, the UK, and Ukraine. The evidence-based health-optimization system was developed using systematic methods. The System Usability Scale and a questionnaire were administered in formative and summative tests. A comparison of the system to current standards for clinical practice guidelines and patient decision aids was performed. Seventy-eight potential users identified 82 issues. Driven by user feedback, the limited first version was developed into a more comprehensive system. The current version encompasses 21 integrated core features, supporting 6 health-optimization strategies. One crucial feature enables patients and clinicians to explore the likely value of treatments based on mathematical integration of self-reported and research data and the patient's preferences. The mean ± SD (median) system usability score of the patient-oriented subsystem was 71 ± 18 (73). The mean ± SD (median) system usability score in the summative usability testing was 78 ± 18 (75), well above the norm score of 68. Feedback from the questionnaire was generally positive. Eighteen out of 23 components in the system are not required in international standards for patient decision aids and clinical practice guidelines. We have developed the first evidence-based health-optimization system enabling patients, clinicians, and caregivers to collaborate in optimizing the patient's health on a shared platform. User tests indicate that the feasibility of the system is acceptable.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 176 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 176 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 16%
Student > Master 24 14%
Student > Ph. D. Student 18 10%
Student > Bachelor 17 10%
Student > Postgraduate 8 5%
Other 28 16%
Unknown 52 30%
Readers by discipline Count As %
Medicine and Dentistry 27 15%
Psychology 20 11%
Nursing and Health Professions 18 10%
Computer Science 15 9%
Social Sciences 8 5%
Other 30 17%
Unknown 58 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 12 September 2017.
All research outputs
#6,011,911
of 23,881,329 outputs
Outputs from BMC Medical Informatics and Decision Making
#527
of 2,030 outputs
Outputs of similar age
#91,213
of 314,377 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#11
of 43 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 2,030 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 74% 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 314,377 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.