Objective
Long-acting reversible contraceptive (LARC) methods, such as the intrauterine device and implant, are highly effective but used by less than 10% of US women. The objective of our study was to improve LARC interest by enhancing clinic counseling.
Study Design
A quality improvement methodology was used to evaluate intrauterine device service delivery in 3 Chicago Title X clinics. To address identified barriers, we developed a theory-based iOS application (app) for patients to use in the clinic waiting room using human-centered design. The final prototype was tested in a randomized controlled trial in a Title X clinic with sexually active females (79% African American) under age 30 years. Our sample of 60 was chosen to detect an increase from 10% (baseline) to 45% (app intervention) in the proportion of patients expressing interest in discussing a LARC method during their clinic visit with 80% power and two-sided α = 0.05. After completing demographic and baseline knowledge questionnaires, participants were randomized 1:1 to intervention (app) or standard care arms. App users also completed a posttest. Our primary outcome was expressed interest in discussing a LARC method during the clinic visit. Secondary outcomes were contraceptive knowledge and LARC selection.
Results
App testers (n = 17) preferred interactive, visually appealing design and video testimonials. In the pilot randomized controlled trial (n = 52), app users had a significantly higher knowledge of contraceptive effectiveness (P = .0001) and increased interest in the implant (7.1-32.1%, P = .02) after the intervention. Users were highly satisfied with the app. Staff reported no problems using the app in the clinic.
Conclusion
Use of a theory-based counseling app offers a novel method to optimize wait time while minimizing clinic flow disruption. Preliminary data demonstrate that app use was associated with improvements in patients’ contraceptive knowledge and interest in the implant.
Key words
Long-acting reversible contraceptive (LARC) methods (ie, the intrauterine device [IUD] and contraceptive implant) require few use behaviors and are therefore highly effective. Rates of LARC use in the United States are low overall (<10%) and especially so among young women, who exhibit the highest rates of unintended pregnancy.
1
, 2
The lack of awareness of and misinformation about LARC may contribute to the low rates of use.3
, 4
In a 2009 survey of a nationally representative, racially diverse sample of unmarried adults aged 18-29 years (n = 1800), 87% of the women had heard of the IUD and 52% the implant.
5
However, knowledge was low: 56-80% of the respondents who had heard of the IUD knew “little or nothing” about it. Misconceptions about LARC were also highly prevalent. Thus, more effort is needed to provide accurate information and knowledge of LARC.Recent reports have called for focused application of behavioral theory to interventions for contraceptive use.
6
, 7
The transtheoretical model and its stages of change paradigm posit that in changing behaviors, individuals progress through sequential stages of precontemplation, contemplation, preparation, action, and maintenance and that transition between steps requires a core set of change processes.8
Patients presenting for contraception will most likely be in the precontemplation or contemplation stages for adopting LARC.For precontemplation, the key change process is enhancing awareness. The transtheoretical model is often coupled with the theory of planned behavior, which postulates that behavioral intention is informed by the following: (1) attitude toward the behavior and beliefs about outcomes associated with performing the behavior (behavioral beliefs); (2) subjective norms and beliefs about those norms; and (3) perceived behavioral control, or the perception that one has the requisite skills and resources to perform the behavior.
9
Thus, addressing common behavioral, normative, and control beliefs can help women advance from precontemplation or contemplation stages to action in adopting LARC.Interactive, multimedia, computer-based programs provide a novel platform for delivering information.
10
Tailored, theory-based, computer-based interventions have been effective in reducing repeat births among low-income, African American adolescent mothers,11
promoting adherence to antiretroviral medications,12
and supporting preventive health behaviors in vulnerable populations.13
, 14
In a recent randomized controlled trial (RCT), low-income family planning patients using a touch screen computer module were significantly more likely to choose highly effective contraception.15
This paper describes the development and evaluation of an iOS application (app) for use in the clinic waiting room to increase LARC awareness and interest prior to the clinic visit.
Materials and Methods
Assessment of IUD service barriers
From January to May 2011, barriers to IUD services for young (15-29 years old) women in 3 Chicago Title X family-planning clinics were assessed, using failure modes effects and criticality analysis. This failure modes effects and criticality analysis is 1 well-developed risk assessment method to systematically assess failure modes in systems of care. A failure mode is a point in a system in which progress toward the desired endpoint is hindered. The failure analysis informs the redesign processes to mitigate the failures.
16
During 13 meetings with clinicians, staff, and patients, 33 failures in the processes of care that could undermine IUD service delivery were identified and further substantiated by clinic administrative data. It revealed that contraceptive counseling was limited by the lack of time, the nonuse of waiting room pamphlets, and failure to counsel all women on LARC. Finally, waiting room down time was recognized as a missed opportunity for patient engagement. We therefore opted to create a counseling app to promote interest in and awareness of LARC.
App development
The app development was guided by the principles of human-centered design, the theory of planned behavior, and our failure analysis findings, along with continued input from clinic staff and clinicians. Human-centered design is an innovative approach to designing interventions whereby the designer and end user are brought together early in the design process. Design solutions are created in an iterative process of rapid, low-fidelity prototyping (eg, pen and paper) with key stakeholder participation.
The goal was to create an app that accomplished the following: (1) reflected the tenets of the theory of planned behavior; (2) filled gaps in LARC awareness while providing information on the full range of contraceptive options; (3) appealed to a variety of learning styles with text, video, and imagery; (4) featured unbiased, evidence-based content; and (5) complemented existing clinic flow.
Simple app prototypes were created using Microsoft PowerPoint (Richmond, WA) to test the individual aspects of the app in a controlled setting with targeted users by observing, recording, and taking notes about the user’s experience. From August 2012 to March 2013, iterative rounds of usability testing were conducted at the University of Chicago and at another Chicago-based family-planning clinic.
Testers (n = 17) were African American women demographically similar to patients of the Title X clinics included in the failure analysis. As testers used the app, they narrated their experience and gave their opinions regarding the specific areas being studied. Design adjustments were made following each round of testing. Once tester feedback became consistent, a programming team at the University of Chicago built a native iOS app prototype. Video testimonials were filmed with 2 young women recruited from the study clinics who were using the IUD or implant. Videos were inserted into the tablet prototype and assessed in the final rounds of usability testing.
App evaluation
Procedure
An RCT was conducted to evaluate the app’s impact on contraceptive knowledge, LARC interest, and selection of a LARC method. Eligible participants were sexually experienced, English-speaking women, aged 15-30 years presenting for contraceptive services at a Chicago Title X clinic. Exclusion criteria were currently pregnant, desiring pregnancy within the next 12 months, currently using LARC, scheduled for a LARC-related visit, and reliance on male partner sterilization for contraception. A sample size of 60 was chosen to detect an increase from 10% (baseline) to 45% (app intervention) in the proportion of patients expressing interest in discussing a LARC method during their clinic visit with 80% power and 2-sided α = 0.05 based on a checklist of methods.
Potential study participants were identified by clinic staff, based on age and appointment type, and referred to on-site research staff who then confirmed eligibility, explained the study, and obtained informed consent. All participants completed a baseline survey after which they were randomized to the intervention (app) or standard care. Randomization was performed in advance using a random numbers generator (www.random.org), with assignments concealed in sequentially numbered opaque envelopes.
Women randomized to the intervention arm were given a tablet computer programmed with the app and instructions to use the app for up to 15 minutes. After completing a brief postintervention survey, participants returned to the clinic waiting room for standard care. Standard care included contraceptive counseling by a clinic counselor followed by a visit with a nurse practitioner to receive (or be prescribed) their chosen method. Participants randomized to standard care returned to the clinic waiting room following completion of the baseline survey for the same sequence of care. All participants received $10 for participation.
After the visit, clinic staff reviewed the participant’s medical chart and her chosen contraceptive method, reconfirmed the inclusion criteria, and relayed results to the research team. Clinic counselors and clinicians were blinded to participants’ treatment assignment throughout. Study data were managed using REDCap electronic data capture tools
18
hosted at the University of Chicago. On March 28, 2013, the study, number 13-0145, was approved by the University of Chicago Institutional Review Board, which also provided a waiver of parental consent for minors under the age of 18 years.Measures
The baseline survey collected demographic information; mobile phone use; sexual and reproductive health history (sexual experience and pregnancy history); and contraception-related variables (awareness of methods, knowledge of effectiveness, past and current method use, self-efficacy, method interest). Contraceptive knowledge was measured by 3 questions in which the participants were presented with a method pair (eg, oral contraceptives [OCs] and condoms) and asked which method was more effective.
Individual question scores (1, correct; 0, incorrect) were combined into a single summary score ranging from 0 to 3. A 5-item contraceptive self-efficacy scale previously validated by Galavotti et al
19
(α = 0.84) with responses measured on 5-point Likert scales (anchors: not at all confident to extremely confident) was also administered. Participants indicated all of the contraceptive methods that they were interested in discussing during counseling. The postintervention survey reassessed contraceptive knowledge and the indicated methods that they wanted to discuss during counseling and gauged intervention satisfaction. Participants indicated their agreement with 7 statements (eg, “The app was easy to use,” “I would recommend to a friend”) using 5-point Likert scales (strongly disagree to strongly agree). Selection of contraceptive method was ascertained through chart review 1 month after the visit. The method selected by the participant, as well as how the method was provided (dispensed directly, prescribed, or referred for follow-up visit), was obtained.Statistical analysis
Descriptive statistics were used to assess the satisfaction with the app. Baseline survey responses and the selection of a LARC method were compared using the Wilcoxon-Mann-Whitney test for ordinal or continuous variables and the χ2 test statistic for categorical variables between the 2 groups. Within the intervention arm, pre-post differences in contraceptive knowledge and LARC interest were evaluated using a Wilcoxon’s signed-rank test (if ordinal) or McNemar’s χ2 (if binary). For self-efficacy, summary scores ranging from 0 to 25 were computed; the interitem correlation was assessed using a standardized Cronbach’s alpha. We performed a per-protocol analysis, excluding ineligible patients who were mistakenly enrolled. However, for the outcomes of LARC interest and LARC selection, we also present findings from an intent-to-treat (ITT) analysis.
Results
App development
The primary app design challenge faced by the research team and clinic staff was to present content that was LARC focused yet comprehensive for all contraceptive methods. The app needed to provide an enriching experience for clients who were interested in LARC as well as those seeking non-LARC methods. The extent to which the app screen directed the user from 1 screen to the next vs allowing free navigation was also taken into consideration. The final app prototype began with prescribed pathways in which all users see a landing page comparing the effectiveness of all methods and then view LARC peer testimonials but allowed for self-navigation of all other information (Figure 1).
Figure 1Waiting room app home page and IUD page screen shots
Show full caption
The figure shows a screen shot of the contraceptive counseling iOS app home page (top). Users can navigate from this page to learn about various contraceptive methods such as the IUD (bottom) in which they can then click on method-specific questions.
IUD, intrauterine device.
Gilliam. Waiting room app in Title X. Am J Obstet Gynecol 2014.
The human-centered design process revealed a number of desired features. Testers preferred contraceptive effectiveness conveyed in absolute numbers (eg, <1 pregnancy per 100 women) rather than percentages (eg, >99% effective). They rated peer testimonials very highly and related well to the stories. Testers were presented with multiple options for the information categories to be included on all method-specific pages. The most popular categories (“How does it work,” “What can I expect,” and “What will he think?”) were incorporated into the final design.
RCT
Study flow
Research staff screened 73 presumptively eligible patients (Figure 2) referred by the clinic staff. Of these, 4 declined to participate and 7 were ineligible because of an age older than 30 years. On subsequent chart review by clinic staff following the visit, an additional 10 participants were excluded from analysis because of missing or ineligible age (n = 4), current implant use (n = 3), currently desiring pregnancy (n = 2), and presenting for IUD insertion (n = 1). Thus, the final per-protocol sample was comprised of 52 participants (intervention, n = 28; standard care, n = 24).
Figure 2Study participant flow chart
Show full caption
Gilliam. Waiting room app in Title X. Am J Obstet Gynecol 2014.
Participant characteristics
Participants had a median age of 22.5 years (Table 1). The majority (78.9%) self-identified as black/African American with half reporting their relationship status as single. Most of the participants had mobile phones and had experience using mobile apps. Almost all participants (98%) were sexually experienced, with a median age of first sex of 16 years. Pregnancy histories were similar between the 2 arms, although intervention participants were significantly younger at their first pregnancy. Method-specific contraceptive awareness was high for non-LARC methods (>85%, data not shown) and 69% and 50% for the IUD and implant, respectively. Less than half (45.1%) reported currently using birth control, with condoms being the most commonly used method.
Table 1Participant characteristics at baseline (n = 52)
| Characteristic | Total (n = 52) | Intervention (n = 28) | Standard care (n = 24) | P value |
|---|---|---|---|---|
| Sociodemographic | ||||
| Age, y (median, range) | 22.5 (16–30) | 23 (17–30) | 21.5 (16–30) | .36 |
| Race/ethnicity | .62 | |||
| African American, non-Hispanic | 41 (78.9) | 23 (82.1) | 18 (75.0) | |
| White, non-Hispanic | 6 (11.5) | 3 (10.7) | 3 (12.5) | |
| Hispanic | 3 (5.8) | 2 (7.1) | 1 (4.2) | |
| Multiple/other/unknown | 2 (3.9) | 0 (0.0) | 2 (8.3) | |
| Relationship status | .12 | |||
| Living with partner or spouse | 4 (7.7) | 4 (14.3) | 0 (0.0) | |
| Dating/casual partner(s) | 22 (42.3) | 13 (46.4) | 9 (37.5) | |
| Single | 26 (50.0) | 11 (39.3) | 15 (62.5) | |
| Education status | .42 | |||
| High school graduate or less | 11 (21.2) | 4 (14.3) | 7 (29.2) | |
| Some college | 27 (51.9) | 16 (57.1) | 11 (45.8) | |
| College degree or higher | 14 (26.9) | 8 (28.6) | 6 (25.0) | |
| Owns mobile phone | 48 (92.3) | 27 (96.4) | 21 (87.5) | .32 |
| Uses mobile apps | 44 (91.7) | 24 (88.9) | 20 (95.2) | .62 |
| Pregnancy/sexual history | ||||
| Ever had sexual intercourse | 51 (98.1) | 27 (96.4) | 24 (100.0) | 1.00 |
| Age at first intercourse, y (median) | 16 | 16 | 16 | .58 |
| Pregnancy history | .56 | |||
| Never been pregnant | 31 (59.6) | 15 (53.6) | 16 (66.7) | |
| 1 pregnancy | 9 (17.3) | 5 (17.9) | 4 (16.7) | |
| ≥2 pregnancies | 12 (23.1) | 8 (28.6) | 4 (16.6) | |
| Age at first pregnancy, y, median | 19 | 17 | 20 | .034 |
| Parity | .12 | |||
| No births | 31 (73.8) | 15 (71.4) | 16 (76.2) | |
| 1 birth | 5 (11.9) | 1 (4.8) | 4 (19.1) | |
| ≥2 births | 6 (14.3) | 5 (23.8) | 1 (4.8) | |
| Contraception: awareness | ||||
| Ever heard of IUD | 36 (69.2) | 20 (71.4) | 16 (66.7) | .71 |
| Ever heard of implant | 26 (50.0) | 13 (46.4) | 13 (54.2) | .58 |
| Contraception: past use | ||||
| IUD | 2 (3.9) | 2 (7.1) | 0 (0.0) | .49 |
| Implant | 1 (1.9) | 0 (0.0) | 1 (4.2) | .46 |
| DMPA | 16 (30.8) | 4 (14.3) | 12 (50.0) | .005 |
| OCs | 29 (55.8) | 17 (60.7) | 12 (50.0) | .44 |
| Patch | 12 (23.1) | 8 (28.6) | 4 (16.7) | .31 |
| Ring | 8 (15.4) | 4 (14.3) | 4 (16.7) | .81 |
| Condom | 49 (94.2) | 27 (96.4) | 22 (91.7) | .59 |
| Contraception: current use (primary method) | ||||
| DMPA | 4 (7.8) | 1 (3.6) | 3 (13.0) | .50 |
| OCs | 6 (11.8) | 4 (14.3) | 2 (8.7) | |
| Condom | 13 (25.5) | 6 (21.4) | 7 (30.4) | |
| None | 28 (54.9) | 17 (60.7) | 11 (47.8) | |
| Contraception: intention | ||||
| Intends to start in next 30 d | .51 | |||
| Yes | 36 (70.6) | 20 (74.1) | 16 (66.7) | |
| No | 4 (7.8) | 1 (3.7) | 3 (12.5) | |
| Unsure | 11 (21.6) | 6 (22.2) | 5 (20.8) | |
| Contraception: self-efficacy | ||||
| Five-item score: contraceptive use, median 20 | 20 | 19 | 20 | .17 |
Data are listed as n (percentage) unless otherwise specified.
DMPA, depot medroxyprogesterone acetate; IUD, intrauterine device; LARC, long-acting reversible contraceptive; OC, oral contraceptive.
Gilliam. Waiting room app in Title X. Am J Obstet Gynecol 2014.
Contraceptive knowledge
Use of the app translated to improvements in knowledge of contraceptive effectiveness over baseline (Table 2). After the intervention, app participants had significantly higher scores on 2 of the 3 knowledge questions as well as higher total knowledge scores (P = .0001).
Table 2Pre/post outcomes: contraceptive knowledge and LARC interest
| Variable | Intervention (n = 28) | Standard care (n = 24) | P value | P value | |
|---|---|---|---|---|---|
| Baseline | After intervention | Baseline | |||
| Knowledge of contraceptive effectiveness | |||||
| Question 1: OCs vs condom (correct) | 10 (35.7) | 19 (67.9) | 8 (33.3) | .86 | .04 |
| Question 2: IUD vs DMPA (correct) | 4 (14.3) | 18 (64.3) | 1 (4.2) | .22 | .0001 |
| Question 3: IUD vs implant (correct) | 2 (7.1) | 8 (28.6) | 6 (25.0) | .08 | .11 |
| Total score, median | 1 | 1.5 | 1 | .83 | .0001 |
| Interest in discussing LARC at visit | |||||
| IUD | 8 (28.6) | 8 (28.6) | 4 (16.7) | .31 | 1.00 |
| Implant | 2 (7.1) | 9 (32.1) | 3 (12.5) | .51 | .02 |
| Any LARC | 8 (28.6) | 13 (46.4) | 7 (29.2) | .96 | .18 |
Data are listed is n (percentage) unless otherwise specified.
DMPA, depot medroxyprogesterone acetate; IUD, intrauterine device; LARC, long-acting reversible contraceptive; OC, oral contraceptive.
Gilliam. Waiting room app in Title X. Am J Obstet Gynecol 2014.
a Intervention vs standard care arms at baseline
b Baseline vs after intervention in intervention arm.
LARC interest
Baseline interest in the IUD was higher than for the implant in both study arms (Table 2). Among intervention participants, there were no pre-post differences in IUD interest (28.6% and 25.8% at both time points in per-protocol and ITT analyses, respectively). However, there was a significant increase in the proportion of women interested in discussing the implant during contraceptive counseling (per-protocol: 7.1-32.1%, P = .02; ITT: 6.5-29.0%, P = .02).
LARC selection
There were no significant differences in selection of a LARC method between intervention and standard care arms (per-protocol: 25.0% vs 20.8%, P = .72; ITT: 22.6% vs 25.8%, P = .77). Most intervention and standard care participants who selected LARC chose the IUD (per-protocol: 71.4% and 60.0%, respectively).
App satisfaction
Users (n = 27) rated the app very highly, with greater than 90% agreeing or strongly agreeing that the app was easy to use, taught them new things about birth control, provided them with information that they would use in the future, and was a good use of their time. All but 1 participant indicated that they would recommend the app to a friend and that they liked the app overall. Ratings for the videos were favorable: 74.1% agreed or strongly agreed that the videos were interesting.
Comment
Providing high-quality contraceptive care to low-income women is a critical challenge. Nationally, 49% of conceptions are unintended, with high proportions for women aged 15-19 years (82%), women aged 20-24 years (64%), and African Americans of all ages (67%).
20
Each year, Title X family-planning clinics collectively serve 5 million clients, the majority of whom are women.21
Given limited counseling time, brief interventions hold promise for reaching large numbers of women. A growing body of research suggests that brief interventions relying on more than just information can be successful.US Department of Health and Human Services. Title X family planning, 2013. Available at: http://www.hhs.gov/opa/title-x-family-planning/. Accessed Nov. 1, 2013.
7
, 11
, 22
, 23
, 24
, 25
, 26
, 27
, 28
Interventions with strong behavioral models, focused information, and opportunity to address potential barriers to change tend to report stronger effects.22
, 26
This study presents an innovative process for developing and testing a contraceptive counseling tool. Principles of human centered design were used to include patients and clinic staff throughout the design and development process. Iterative prototyping enabled immediate design modifications in response to comments. In addition, behavioral theory was incorporated to address factors that can hinder behavioral change. The final app complemented the clinical visit, took advantage of the clinic wait time, shifted some of the counseling burden, and did not interfere with clinic flow.
In our sample, baseline awareness of LARC methods was fairly high at 69.2% for the IUD and 50% for the implant, similar to rates found in a larger, national survey of young unmarried women.
5
However, as the survey’s authors noted, high levels of LARC awareness can mask inaccurate knowledge and negative attitudes toward these methods. Relative knowledge questions are a gold standard for gauging women’s comprehension of materials that communicate contraceptive effectiveness.29
Here brief (<15 minutes) app sessions significantly improved the women’s knowledge scores on the relative effectiveness of LARC compared with other methods. Although the app users reported an increased interest in the contraceptive implant, the app had no discernible impact on the women’s interest in the IUD.
This study has a number of strengths. First, human-centered design was used to engage low-income women in all aspects of the project from defining the problem to designing the solution. Second, a theory-based intervention was created in which peers delivered video testimonials eliciting and addressing normative beliefs and control beliefs. In addition, some elements requested by the app testers would not have been included had the team designed it on its own (eg, “What will he think?”).
The limitations of this study must be noted. First, the study was implemented in a highly skilled, clinical setting in which providers have expertise lowering barriers to family planning. Indeed, baseline rates of LARC interest and uptake were higher than anticipated. Similarly, despite interest in implants, the rates of IUD uptake were higher at baseline, suggesting the positive effect of clinic personnel. Hence, the findings might not generalize to all settings and more integration of the app and counseling might be needed. Second, consistent with the transtheoretical model and theory of planned behavior, the app was intended to raise awareness and interest in LARC methods. Additional intervention components may be needed to help women advance beyond the contemplation stage. Furthermore, a larger RCT could help assess which users responded to which aspects of the intervention. Lastly, enrollment of ineligible women was identified only toward the end of the study, thus reducing the sample size for analysis.
This study demonstrates that an app can be highly acceptable, informative, and easily integrated into the Title X setting for use by low-income women. Women self-counseled by the app had increased knowledge, awareness, and interest in highly effective contraception before even meeting with a health care provider. Although the study was clinic based, given the high access to Smartphones with app capability in this community, it is conceivable that the app could be downloaded by women onto their Smartphone in advance of the visit. Thus, apps could be a mechanism for implementing timely, evidenced-based educational information to a wide network of clinics and clients.
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Article Info
Publication History
Published online: May 29, 2014
Accepted:
May 24,
2014
Received in revised form:
April 25,
2014
Received:
March 28,
2014
Footnotes
This study was supported by grant 6FPRPA006053-03-02 (principal investigator: M.L.G.) from the Office of Population Affairs . During the preparation of the manuscript, S.L.M. was supported by grant T76-MC00005 (principal investigator: W. Hellerstedt) from the Maternal and Child Health Bureau, Health Resources and Services Administration, US Department of Health and Human Services .
The authors report no conflict of interest.
Cite this article as: Gilliam ML, Martins SL, Bartlett E, et al. Development and testing of an iOS waiting room “app” for contraceptive counseling in a Title X family planning clinic. Am J Obstet Gynecol 2014;211:481.e1-8.
Identification
Copyright
© 2014 Elsevier Inc. Published by Elsevier Inc. All rights reserved.
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