NUR 7110 Module 3 User-Centered Design Paper: A Postpartum Blood Pressure App Built With Patients Example

Designed by the Mothers Who Use It: A Postpartum Blood Pressure App Built Through Three Rounds of Testing in Newark

Student Name

M.S. in Nursing, Nursing Informatics, Thomas Edison State University

NUR-7110: Nursing Informatics: Consumer Informatics and Communications Technologies

Mentor Name

Month Day, Year

Designed by the Mothers Who Use It: A Postpartum Blood Pressure App Built Through Three Rounds of Testing in Newark

Why This App

The hospital and every participant in this example are invented. A hospital in Newark delivers about 3,600 babies a year, and many of its patients have high blood pressure during or after pregnancy. Dangerous blood pressure rises can happen in the days after discharge, when mothers are at home with a newborn and may not recognize warning signs. The hospital already sends some mothers home with a blood pressure cuff and asks them to call in readings, but few call. The obstetric nursing team proposed an app in which mothers enter readings and symptoms each day for two weeks, with nurses alerted when a reading or symptom needs action.

The informatics nurse was asked to lead the design. She insisted that mothers shape it from the first sketch.

Why People Use a Technology

Davis (1989) proposed the technology acceptance model to explain why some systems are taken up and others ignored. Perceived usefulness is how strongly someone expects the system to improve their work or life; perceived ease of use is how little effort they expect it to demand. Both beliefs tracked actual use in his studies, though usefulness tracked it more closely: people will put up with some difficulty for a tool that clearly helps them, but will not use an easy tool that does not. Ease of use worked partly by making a system seem more useful.

For postpartum mothers, the model suggested two design questions. Would mothers believe the app helps them and their baby, not only the hospital? And could a tired mother with a newborn use it in under a minute?

Measuring Usability

Brooke (1996) created the System Usability Scale as a quick, reliable way to measure perceived usability. It has ten statements, alternating positive and negative, rated on a five-point scale and combined into a score from 0 to 100. It was designed to be short enough to use after any test and general enough to compare different systems.

Bangor et al. (2008) analyzed a large collection of SUS results from many usability studies and found the scale reliable and sensitive across products and interfaces. Average scores across studies fell around 70, which gives designers a rough benchmark: a score well below that suggests serious problems, and a score in the 80s suggests a good experience.

Three Rounds

The first round changed the app's purpose in mothers' eyes. Mothers said that an app that only collected numbers felt like homework for the hospital. When each reading produced a plain message, such as "Your blood pressure is in a safe range today" or "This reading is high. A nurse will call you within an hour. If you have a severe headache, call 911," mothers described the app as something that would help them worry less. In the terms of Davis's model, perceived usefulness rose.

The second round found ease-of-use problems that the design team had not imagined. Mothers often held a baby in one arm, so small number fields were hard to tap. Spanish-speaking mothers wanted the whole app in Spanish, not only the instructions. A SUS score of 62 confirmed that the prototype was below the typical benchmark.

By the third round, with a large number pad, voice entry, full Spanish and reminders set at a time the mother chose, the SUS average rose to 81, and most mothers entered readings every day of the test week.

Behind the Screen

User-centered design extended to the nurses who receive alerts. Obstetric nurses tested the alert screen and asked that red readings appear on a single list with the mother's phone number and discharge summary, and that yellow readings go to a daily review rather than interrupting them. The hospital set a rule that red readings receive a call within an hour, every day of the week.

Equity in the Design

The informatics nurse recruited testers deliberately. Of the 30 mothers across the three rounds, more than half were Black or Hispanic, eleven had Medicaid and nine preferred Spanish, roughly matching the hospital's postpartum population with high blood pressure. Two testers had older phones with little storage, which revealed that the first build was too large to install. Without these testers, the team would have launched an app that worked well for mothers like its designers and poorly for the mothers at highest risk. The team also offered testers a gift card and paid for their data use, so that taking part did not cost them anything.

Launch Plan

Mothers with blood pressure problems during pregnancy or delivery will be offered the app and a cuff before discharge, with a nurse showing them how to take a reading and enter it. The app works on older phones and uses little data, and the hospital provides a loaner phone to mothers without one.

Measures

The hospital will track enrollment, daily entry rates, red readings and time to callback, emergency visits and readmissions for high blood pressure in the first two weeks and SUS scores at the end of the program.

Limits

Testing involved 30 mothers, and real use over two weeks may differ from a one-week test. The technology acceptance model explains intention to use but not every reason people stop. A high SUS score does not prove that the app improves health.

Conclusion

Mothers shaped an app that the hospital alone would have built differently. Davis's model explained why reassurance mattered as much as ease of entry, the SUS gave a quick measure in each round and a large body of SUS results provided a benchmark. Three rounds of testing moved the app from a score below average to one that mothers rated highly.

References

Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An empirical evaluation of the System Usability Scale. International Journal of Human-Computer Interaction, 24(6), 574-594. https://doi.org/10.1080/10447310802205776

Brooke, J. (1996). SUS: A "quick and dirty" usability scale. In P. W. Jordan, B. Thomas, B. A. Weerdmeester, & I. L. McClelland (Eds.), Usability evaluation in industry (pp. 189-194). Taylor & Francis.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008