The Design, Development, and Evaluation of a Qualitative Data Collection Application for Pregnant Women

Keedle, Hazel; Schmied, Virginia; Burns, Elaine; Dahlen, Hannah G

doi:10.1111/jnu.12344

Cited by 14 publications

(15 citation statements)

References 23 publications

(37 reference statements)

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“…A purpose-built app was designed, by the first author, to fulfil all the needs of data collection. The design and evaluation of the ‘myVBACapp’ is described in detail in a previous paper [30]. Using the ‘myVBACapp’ allowed for pregnant women (with a smartphone) across Australia, to access the study.…”

Section: Methodsmentioning

confidence: 99%

“…Using the ‘myVBACapp’ allowed for pregnant women (with a smartphone) across Australia, to access the study. Women were able to provide real-time accounts of their thoughts, feelings and experiences after an antenatal appointment in the form of an audio or video recording and this minimised issues of retrospective reporting or filtering and analysing effects of written responses [30]. This form of data collection aligned with feminist theory principles as the participant decided when and what to record and whether or not to send the recording to the researcher [18].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A narrative analysis of women's experiences of planning a vaginal birth after caesarean (VBAC) in Australia using critical feminist theory

Keedle

Schmied

Burns

et al. 2019

BMC Pregnancy Childbirth

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Background Most women who have a caesarean can safely have a vaginal birth after caesarean (VBAC) for their next birth, but more women have an elective repeat caesarean than a VBAC. Methods The aim of this qualitative study was to explore the experiences of women planning a vaginal birth after caesarean (VBAC) in Australia, the interactions with their health care providers and their thoughts, feelings and experiences after an antenatal appointment and following the birth. The study explored the effect of different models of care on women’s relationships with their health care provider using a feminist theoretical lens. Eleven women who had previously experienced a caesarean section and were planning a VBAC in their current pregnancy used the ‘myVBACapp’ to record their thoughts after their antenatal appointments and were followed up with in-depth interviews in the postnatal period. Results Fifty-three antenatal logs and eleven postnatal interviews were obtained over a period of eight months in 2017. Women accessed a variety of models of care. The four contextual factors found to influence whether a woman felt resolved after having a VBAC or repeat caesarean were: ‘having confidence in themselves and in their health care providers’, ‘having control’, ‘having a supportive relationship with a health care provider’ and ‘staying active in labour’. Conclusions The findings highlight that when women have high feelings of control and confidence; have a supportive continual relationship with a health care provider; and are able to have an active labour; it can result in feelings of resolution, regardless of mode of birth. Women’s sense of control and confidence can be undermined through the impact of paternalistic and patriarchal maternity systems by maintaining women’s subordination and lack of control within the system. Women planning a VBAC want confident, skilled, care providers who can support them to feel in control and confident throughout the birthing process. Continuity of care (CoC) provides a supportive relationship which some women in this study found beneficial when planning a VBAC. Electronic supplementary material The online version of this article (10.1186/s12884-019-2297-4) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A narrative analysis of women's experiences of planning a vaginal birth after caesarean (VBAC) in Australia using critical feminist theory

Keedle

Schmied

Burns

et al. 2019

BMC Pregnancy Childbirth

Self Cite

View full text Add to dashboard Cite

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“…Besides, developing easy-to-understand user interfaces is very challenging, depending on the respective application scenario [33]. Furthermore, we revealed communication issues between researchers and application developers that must be tackled, as described in [21]. More specifically, we realized that both parties used different languages (i.e., wording, or (graphical) notations) for the same aspects.…”

Section: Questionsys Framework Background Informationmentioning

confidence: 94%

Measuring Mental Effort for Creating Mobile Data Collection Applications

Schobel

Probst

Reichert

et al. 2020

IJERPH

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To deal with drawbacks of paper-based data collection procedures, the QuestionSys approach empowers researchers with none or little programming knowledge to flexibly configure mobile data collection applications on demand. The mobile application approach of QuestionSys mainly pursues the goal to mitigate existing drawbacks of paper-based collection procedures in mHealth scenarios. Importantly, researchers shall be enabled to gather data in an efficient way. To evaluate the applicability of QuestionSys, several studies have been carried out to measure the efforts when using the framework in practice. In this work, the results of a study that investigated psychological insights on the required mental effort to configure the mobile applications are presented. Specifically, the mental effort for creating data collection instruments is validated in a study with N = 80 participants across two sessions. Thereby, participants were categorized into novices and experts based on prior knowledge on process modeling, which is a fundamental pillar of the developed approach. Each participant modeled 10 instruments during the course of the study, while concurrently several performance measures are assessed (e.g., time needed or errors). The results of these measures are then compared to the self-reported mental effort with respect to the tasks that had to be modeled. On one hand, the obtained results reveal a strong correlation between mental effort and performance measures. On the other, the self-reported mental effort decreased significantly over the course of the study, and therefore had a positive impact on measured performance metrics. Altogether, this study indicates that novices with no prior knowledge gain enough experience over the short amount of time to successfully model data collection instruments on their own. Therefore, QuestionSys is a helpful instrument to properly deal with large-scale data collection scenarios like clinical trials.

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“…Although the participants involved in respective scenarios gave positive feedback, several shortcomings could still be observed. The latter include, for example, high development costs, the need for skilled app developers, or the common business-IT alignment gap (ie, domain experts being unable to express what developers shall realize) [13]. When relying purely on smart mobile devices for data collection purposes, specific participant groups may be excluded (eg, elderly) [14].…”

Section: Introductionmentioning

confidence: 99%

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

Schobel¹,

Pryss²,

Probst³

et al. 2018

JMIR Mhealth Uhealth

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BackgroundMany research domains still heavily rely on paper-based data collection procedures, despite numerous associated drawbacks. The QuestionSys framework is intended to empower researchers as well as clinicians without programming skills to develop their own smart mobile apps in order to collect data for their specific scenarios.ObjectiveIn order to validate the feasibility of this model-driven, end-user programming approach, we conducted a study with 80 participants.MethodsAcross 2 sessions (7 days between Session 1 and Session 2), participants had to model 10 data collection instruments (5 at each session) with the developed configurator component of the framework. In this context, performance measures like the time and operations needed as well as the resulting errors were evaluated. Participants were separated into two groups (ie, novices vs experts) based on prior knowledge in process modeling, which is one fundamental pillar of the QuestionSys framework.ResultsStatistical analysis (t tests) revealed that novices showed significant learning effects for errors (P=.04), operations (P<.001), and time (P<.001) from the first to the last use of the configurator. Experts showed significant learning effects for operations (P=.001) and time (P<.001), but not for errors as the experts’ errors were already very low at the first modeling of the data collection instrument. Moreover, regarding the time and operations needed, novices got significantly better at the third modeling task than experts were at the first one (t tests; P<.001 for time and P=.002 for operations). Regarding errors, novices did not get significantly better at working with any of the 10 data collection instruments than experts were at the first modeling task, but novices’ error rates for all 5 data collection instruments at Session 2 were not significantly different anymore from those of experts at the first modeling task. After 7 days of not using the configurator (from Session 1 to Session 2), the experts’ learning effect at the end of Session 1 remained stable at the beginning of Session 2, but the novices’ learning effect at the end of Session 1 showed a significant decay at the beginning of Session 2 regarding time and operations (t tests; P<.001 for time and P=.03 for operations).ConclusionsIn conclusion, novices were able to use the configurator properly and showed fast (but unstable) learning effects, resulting in their performances becoming as good as those of experts (which were already good) after having little experience with the configurator. Following this, researchers and clinicians can use the QuestionSys configurator to develop data collection apps for smart mobile devices on their own.

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The Design, Development, and Evaluation of a Qualitative Data Collection Application for Pregnant Women

Cited by 14 publications

References 23 publications

A narrative analysis of women's experiences of planning a vaginal birth after caesarean (VBAC) in Australia using critical feminist theory

A narrative analysis of women's experiences of planning a vaginal birth after caesarean (VBAC) in Australia using critical feminist theory

Measuring Mental Effort for Creating Mobile Data Collection Applications

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

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