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Using Mobile Phones to Collect Patient Data: Lessons Learned From the SIMPle Study

Overview of attention for article published in JMIR Research Protocols, April 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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Title
Using Mobile Phones to Collect Patient Data: Lessons Learned From the SIMPle Study
Published in
JMIR Research Protocols, April 2017
DOI 10.2196/resprot.6389
Pubmed ID
Authors

Sinead Duane, Meera Tandan, Andrew W Murphy, Akke Vellinga

Abstract

Mobile phones offer new opportunities to efficiently and interactively collect real-time data from patients with acute illnesses, such as urinary tract infections (UTIs). One of the main benefits of using mobile data collection methods is automated data upload, which can reduce the chance of data loss, an issue when using other data collection methods such as paper-based surveys. The aim was to explore differences in collecting data from patients with UTI using text messaging, a mobile phone app (UTI diary), and an online survey. This paper provides lessons learned from integrating mobile data collection into a randomized controlled trial. Participants included UTI patients consulting in general practices that were participating in the Supporting the Improvement and Management of UTI (SIMPle) study. SIMPle was designed to improve prescribing antimicrobial therapies for UTI in the community. Patients were invited to reply to questions regarding their UTI either via a prospective text message survey, a mobile phone app (UTI diary), or a retrospective online survey. Data were collected from 329 patients who opted in to the text message survey, 71 UTI patients through the mobile phone UTI symptom diary app, and 91 online survey participants. The age profile of UTI diary app users was younger than that of the text message and online survey users. The largest dropout for both the text message survey respondents and UTI diary app users was after the initial opt-in message; once the participants completed question 1 of the text message survey or day 2 in the UTI diary app, they were more likely to respond to the remaining questions/days. This feasibility study highlights the potential of using mobile data collection methods to capture patient data. As well as improving the efficiency of data collection, these novel approaches highlight the advantage of collecting data in real time across multiple time points. There was little variation in the number of patients responding between text message survey, UTI diary, and online survey, but more patients participated in the text message survey than the UTI diary app. The choice between designing a text message survey or UTI diary app will depend on the age profile of patients and the type of information the researchers' desire. ClinicalTrials.gov NCT01913860; https://clinicaltrials.gov/ct2/show/NCT01913860 (Archived by WebCite at http://www.webcitation.org/6pfgCztgT).

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X Demographics

The data shown below were collected from the profiles of 18 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Norway 1 3%
Unknown 29 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 20%
Student > Ph. D. Student 4 13%
Student > Master 3 10%
Student > Doctoral Student 2 7%
Student > Postgraduate 2 7%
Other 6 20%
Unknown 7 23%
Readers by discipline Count As %
Nursing and Health Professions 8 27%
Medicine and Dentistry 6 20%
Psychology 3 10%
Business, Management and Accounting 1 3%
Mathematics 1 3%
Other 4 13%
Unknown 7 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 25 October 2017.
All research outputs
#2,799,467
of 23,758,334 outputs
Outputs from JMIR Research Protocols
#199
of 3,191 outputs
Outputs of similar age
#51,520
of 311,063 outputs
Outputs of similar age from JMIR Research Protocols
#9
of 72 outputs
Altmetric has tracked 23,758,334 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,191 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 93% 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 311,063 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.