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Formative Evaluation of Digital Health Interventions (2226) Chatbots and Conversational Agents (473) Diabetes Self-Management (464) Health Coaching for Diabetes Patients (142)

Published on in Vol 6, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28091, first published .
Quantitative User Data From a Chatbot Developed for Women With Gestational Diabetes Mellitus: Observational Study

Quantitative User Data From a Chatbot Developed for Women With Gestational Diabetes Mellitus: Observational Study

Quantitative User Data From a Chatbot Developed for Women With Gestational Diabetes Mellitus: Observational Study

Authors of this article:

Mari Haaland Sagstad1, 2 Author Orcid Image ;   Nils-Halvdan Morken2, 3 Author Orcid Image ;   Agnethe Lund2, 3 Author Orcid Image ;   Linn Jannike Dingsør1 Author Orcid Image ;   Anne Britt Vika Nilsen1 Author Orcid Image ;   Linn Marie Sorbye2, 4 Author Orcid Image
Article Authors Cited by (9) Tweetations (3) Metrics

    Original Paper

    1Department of Health and Caring Sciences, Faculty of Health and Social Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    2Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway

    3Department of Clinical Science, University of Bergen, Bergen, Norway

    4Norwegian Research Centre for Womens´s Health, Rikshospitalet, Oslo University Hospital, Oslo, Norway

    *all authors contributed equally

    Corresponding Author:

    Mari Haaland Sagstad, MSc

    Department of Health and Caring Sciences

    Faculty of Health and Social Sciences

    Western Norway University of Applied Sciences

    28 Inndalsveien

    Bergen, 5063

    Norway

    Phone: 47 920 18489

    Email: mari_haaland@yahoo.no


    Background: The rising prevalence of gestational diabetes mellitus (GDM) calls for the use of innovative methods to inform and empower these pregnant women. An information chatbot, Dina, was developed for women with GDM and is Norway’s first health chatbot, integrated into the national digital health platform.

    Objective: The aim of this study is to investigate what kind of information users seek in a health chatbot providing support on GDM. Furthermore, we sought to explore when and how the chatbot is used by time of day and the number of questions in each dialogue and to categorize the questions the chatbot was unable to answer (fallback). The overall goal is to explore quantitative user data in the chatbot’s log, thereby contributing to further development of the chatbot.

    Methods: An observational study was designed. We used quantitative anonymous data (dialogues) from the chatbot’s log and platform during an 8-week period in 2018 and a 12-week period in 2019 and 2020. Dialogues between the user and the chatbot were the unit of analysis. Questions from the users were categorized by theme. The time of day the dialogue occurred and the number of questions in each dialogue were registered, and questions resulting in a fallback message were identified. Results are presented using descriptive statistics.

    Results: We identified 610 dialogues with a total of 2838 questions during the 20 weeks of data collection. Questions regarding blood glucose, GDM, diet, and physical activity represented 58.81% (1669/2838) of all questions. In total, 58.0% (354/610) of dialogues occurred during daytime (8 AM to 3:59 PM), Monday through Friday. Most dialogues were short, containing 1-3 questions (340/610, 55.7%), and there was a decrease in dialogues containing 4-6 questions in the second period (P=.013). The chatbot was able to answer 88.51% (2512/2838) of all posed questions. The mean number of dialogues per week was 36 in the first period and 26.83 in the second period.

    Conclusions: Frequently asked questions seem to mirror the cornerstones of GDM treatment and may indicate that the chatbot is used to quickly access information already provided for them by the health care service but providing a low-threshold way to access that information. Our results underline the need to actively promote and integrate the chatbot into antenatal care as well as the importance of continuous content improvement in order to provide relevant information.

    JMIR Form Res 2022;6(4):e28091

    doi:10.2196/28091

    Keywords

    chatbot; gestational diabetes mellitus; user data; log review; eHealth; diabetes; pregnancy; dialogue 


    Gestational diabetes mellitus (GDM) is defined as glucose intolerance that arises and is discovered during the second or third trimester of pregnancy [1]. Globally, it affects 1 in 7 pregnant women [2]. In 2019, 5.09% (2769 of 54,407) of pregnant women were diagnosed with GDM in Norway [3], but the condition is assumed to occur in up to 10% of all Norwegian pregnancies, varying by ethnic origin [4,5]. GDM is associated with numerous pregnancy complications affecting both the mother and the fetus [2,6-8], and women with GDM have an increased risk of later developing type 2 diabetes [9,10]. To ensure good health for both the mother and the fetus, a thorough follow-up of women with GDM is required. In reducing the consequences of GDM [7,8,11-13], antenatal training in self-managing blood glucose measurements and nutritional and physical activity education are cornerstones in current clinical care [8,14,15]. Follow-up is provided both by the primary and specialist health care based on blood glucose values [15]. Information provided should aim to strengthen the women’s autonomy to cope with the diagnosis, enabling them to make the best decisions for their own health [8,16]. Traditionally, information is provided in person by medical professionals in addition to written information and referral to official websites. Studies indicate that women with GDM experience a lack of personally adapted information, which may contribute to a sense of insecurity [17,18]. This calls for new ways of complementing the established care. Furthermore, the rising prevalence of GDM will likely increase the need for antenatal care consultations [15], and the use of new technologies like chatbots could be a valuable asset in future health care [19].

    Use of information and communications technology has the potential to improve public health by increasing efficiency, lowering costs, and improving quality of care [20]. Different health technology solutions may have positive effects on the self-management of diabetes [19], an asset especially important for women with GDM. Chatbots are conversational agents based on artificial intelligence that interact with users in a natural language, either text-based or voice driven, independent of time and location [21,22]. There is a growing number of health chatbots developed for different purposes [23]. Developed by health care personnel and users, Dina, Norway’s first health chatbot, was launched in 2018 and made freely available for women with GDM [24]. Dina provides information on GDM and relevant topics related to the condition in accordance with national recommendations. Chatbots providing health information and support regarding other specific conditions have been developed elsewhere [25-27].

    Health technology solutions such as chatbots are rarely implemented in health care after the initial pilot study phase [19]. Previous studies evaluating health chatbots have mostly used interviews or questionnaires and have not been based on quantitative analysis of chatbot logs [28]. Exploring chatbot dialogues may provide valuable information needed for further improvement [29]. In 2019, Dina the chatbot was integrated into the Norwegian official digital health platform and is thus an example of implementation of health technology solutions in clinical care.

    Our study aim is to provide a basis for improvement and further development of Dina the chatbot by exploring log data on what type of information the users’ seek, with the research question being as follows: What type of information do users seek in a health chatbot providing support on GDM? Further specific aims are to explore how many questions each dialogue contains and the time of day the chatbot is used. We subcategorized questions that led to a fallback message from the chatbot to obtain a deeper understanding of which type of questions the chatbot was unable to answer. This knowledge may provide insight into the use of health chatbots and potentially establish more general theoretical knowledge on this type of chatbot.


    Background and Settings for Developing the Chatbot

    In 2016, a pilot study for the project revealed an incoherent follow-up and lack of personally adapted information provided to women with GDM. Contrary to current practice when promoting health technology solutions [30], Dina the chatbot was developed after an observed and expressed need from both women with GDM and involved clinicians. Dina the chatbot was developed at Haukeland University Hospital in cooperation with Bergen municipality and Western Norway University of Applied Sciences. User involvement throughout development and evaluation of health technology is important [19], and a user-centered design [31] was applied throughout the development process. User representatives were involved from idea conception to evaluation [32,33] as was an interdisciplinary team of gynecologists, midwives, psychologists, nutritionists, endocrinologists, and information technology developers [34]. Initially, the chatbot was launched at its own website and was made available to all pregnant women in the country, but promotion was limited to Haukeland University Hospital and surrounding municipalities. In 2019, Dina the chatbot was implemented in the official Norwegian digital health platform, presented with an improved user interface (Dina 2.0), and hence made more available to all pregnant women in Norway. Of the 54,407 women who gave birth in Norway in 2019, the target population represented 2769 pregnant women diagnosed with GDM although a portion of these women might have had difficulty using the chatbot due to language barriers. The chatbot offers low-threshold access to quality-checked information, as login or a registered user account is not required. The overall goal for developing the chatbot was to provide reliable information to women with GDM, strengthen women’s knowledge about their own health, and improve their daily coping with the condition. Dina is intended as an addition to established care and was developed as an informational chatbot. Pregnant women frequently seek information online and from apps [35] and expect modern health service to provide integrated digital solutions in treatment and follow-up [36]. This chatbot could be an important supplement for pregnant women with GDM [35]. However, evaluation of its use based on objective data from the chatbot’s log is needed for further development. The results may also be beneficial in future development of similar informational chatbots created for other specific medical conditions. An observational study analyzing user data from Dina the chatbot was designed. Dialogues were collected from the chatbot’s log and platform over 20 weeks (from week 41 to 48 in 2018 and from week 47 in 2019 to week 6 in 2020). The management team of Dina added “test dialogues” to the chatbot for training and further development. However, these test dialogues were excluded from the collected data because they were not raised by the target population of Dina the chatbot and would have biased the results. A manual log review of the collected dialogues was performed. All data were anonymous, and the identification of users was not permitted. Thus, it was impossible to identify unique users or to determine if they visited the chatbot once or several times. Each dialogue served as the unit of analysis, and we prefer using the term “users of the chatbot” rather than “women with GDM.”

    Variables

    Questions from users to the chatbot were categorized (see Figure 1), and all categories were mutually exclusive [37].

    We identified the time of day that dialogues took place and the number of questions in each dialogue.

    Table 1 presents the variables in Dina the chatbot with explanations and categories.

    The time of day the dialogue took place was grouped into day (8 AM-3:59 PM), afternoon (4 PM-11:59 PM), and night (midnight-7:59AM), while the number of questions in each dialogue was categorized into 1-3, 4-6, 7-9, and ≥10 questions. We registered the frequency of questions the chatbot was unable to answer that resulted in a fallback message from the chatbot (eg, “I´m sorry, I cannot answer that at this point, could you rephrase the question?”). Questions resulting in a fallback message were subcategorized and counted numerically (see Figure 1) to explore which categories of questions the chatbot was unable to answer.

    ‎
    Figure 1. Categorization of questions for Dina the chatbot. HbA1c: hemoglobin A1c.
    View this figure
    Table 1. Variables with explanations for Dina the chatbot.
    VariableExplanationCategorization
    Dialogue (the unit of analysis)Question sequence between the user and the chatbotN/Aa
    PeriodData collection periodPeriod 1: week 41 to 48 in 2018 Period 2: week 47 in 2019 to week 6 in 2020
    Week numberWeek the dialogue took placeWeeks 41 to 48 in 2018 Week 47 in 2019 to week 6 in 2020
    Day of the weekDay of the week the dialogue took placeMonday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday
    DateDate the dialogue took placeDay, month, year
    Time of dayTime of day the dialogue took place8 AM-3:59 PM 4 PM-11:59 PM Midnight-7:59AM
    Total number of questions in each dialogueNumber of questions from the user to the chatbot in each dialogue1-3, 4-6, 7-9, ≥10
    Blood glucoseNumber of questions from the user regarding blood glucoseNumerical
    DietNumber of questions from the user regarding diet and nutritionNumerical
    GDMbNumber of questions from the user regarding the GDM diagnosisNumerical
    Information about the chatbotNumber of maneuvers to “navigate” in the chatbot (theme button, general information about the chatbot, privacy in use, greetings)Numerical
    Physical activityNumber of questions from the user regarding physical activityNumerical
    HbA1cc (average blood glucose levels)Number of questions from the user regarding HbA1c (specific test used to diagnose pre-existing diabetes before the 16th week of pregnancy)Numerical
    OGTTdNumber of questions from the user regarding OGTTNumerical
    MedicationsNumber of questions from the user regarding medications used in the treatment of GDMNumerical
    Birth/postpartum/babyNumber of questions from the user regarding birth, postpartum period, and/or the babyNumerical
    About the pregnancyNumber of questions from the user regarding the pregnancy in generalNumerical
    VideoLink to informational videosNumerical
    Social health benefitsNumber of questions from the user regarding sick leave and appointments with midwife or doctorNumerical
    FallbackNumber of questions in free text from the user the chatbot failed to answer that resulted in a fallback message from the chatbot (eg, “I’m sorry, I cannot answer that at the time, please contact your physician or midwife for further information.”)Numerical
    1. Questions about GDM
      2. ‎
    2. Questions about the pregnancy in general
      3. ‎
    3. Questions the chatbot did not understand (eg, questions with several spelling errors, questions asked in a foreign language)
      4. ‎
    4. Nonsensical questions (eg, “Who is the king of Denmark?”, “When does my bus leave?”)
      5. ‎

    aN/A: not applicable.

    bGDM: gestational diabetes mellitus.

    cHbA1c: hemoglobin A1c.

    dOGTT: oral glucose tolerance test.

    Data Analyses

    The unit of analysis was the individual dialogue. Dialogues were manually registered and thoroughly read by the first author (MHS). In dialogues, users could either type their questions in free text or click theme buttons. Dialogues could therefore consist of free-text questions, theme button questions, or a mix of the 2. All dialogues are displayed the same way in the chatbots log, making it impossible to distinguish between the predefined questions and free-text questions. Therefore, it was not possible to determine if a user chose to spell “blood sugar” in free text or if they pressed the theme button “blood sugar.” Both free-text questions and predefined questions were counted as a whole, but questions that led to fallback would naturally be free-text questions, as the chatbot offers answers to the questions that are predefined. Differentiation between free-text and theme button questions will be considered in the future development of the chatbot. Variables were coded as nominal or interval. We used descriptive statistics: frequencies, proportions, and percentages. For continuous variables, we have reported mean as the central tendency with SD. Normal distribution was tested by Q-Q plot. Chi-square tests were performed to explore associations between variables, and independent t tests were used for comparisons of means. Results are presented visually in charts [37]. IBM SPSS Statistics (version 26) was used for all analyses. The significance level was set at 5%.

    Ethical Considerations

    This study was conducted as a collaboration between Haukeland University Hospital, Western Norway University of Applied Sciences, and Bergen municipality. The chatbot’s platform and technological design have been validated in a risk and vulnerability analysis by the technical department at Haukeland University Hospital, and users’ data are protected according to the General Data Protection Regulation [38]. Data are stored at a secure server, and access to data are limited to authorized personnel only. Users were informed by the chatbot about anonymity, asked not to leave any personal information, and told about potential future use of data for scientific purposes. This study was presented to the regional ethics committee of Western Norway (approval #167012, 12.08.2020) and found exempt from extended application, as all data are completely anonymous. The data protection officer of Haukeland University Hospital approved the study on August 25, 2020 (ID 1555).


    A total of 610 dialogues containing 2838 questions were registered during data collection. In the first period, 288 dialogues were registered, containing 1329 questions, while in the in the second period, 322 dialogues were registered, containing 1509 questions.

    The Users’ Informational Needs

    Questions by category and period are presented in Figure 2.

    Questions on blood glucose, diet, the GDM diagnosis, and physical activity accounted for 58.81% (1669/2838) of all questions, with little variation by period. The most frequent single category was questions on blood glucose levels, accounting for 24.07% (683/2838) of all questions. Questions on maneuvering and orienting in the chatbot (information about the chatbot) decreased from 18.28% (243/1329) in the first period, to 6.69% (101/1509) in the second period (P<.001). The remaining categories involving screening for GDM (oral glucose tolerance test and hemoglobin A1c), birth and postpartum period, treatment (medications), general information concerning the pregnancy, informational videos, and questions on social health benefits represented 17.58% (499/2838) of questions.

    ‎
    Figure 2. Number of questions by category and period (period 1: weeks 41 to 48 in 2018 with 1329 questions; period 2: week 47 in 2019 to week 6 in 2020 with 1509 questions) for Dina the chatbot.
    View this figure

    When and How the Chatbot Was Used

    The number of dialogues per week ranged from 5 to 92 across the 20 weeks of registration, with a mean value of 36 (SD 19.26) and 26.8 (SD 24.34) for the first and second period, respectively. The dialogues by day of the week and time of day for the 2 periods combined are presented in Figure 3.

    In total, 90.7% (553/610) of all dialogues took place Monday through Friday, and 58% (354/610) took place during the daytime (8 AM-3:59 PM). The dialogues registered during the afternoon accounted for 28.2% (172/610) of dialogues Monday through Friday. There was little registered activity during weekends.

    The number of questions in each dialogue ranged from 1 to 38, with a mean value of 4.65 for the 20 weeks of registration. Short dialogues (1-3 questions) were most frequent, both in the first (153/288, 53.1%) and second period (187/322, 58.1%; see Figure 4).

    There was a decrease in number of dialogues containing 4-6 questions from the first period (75/288, 26.0%) to the second period (57/322, 17.7%; P=.013). Long dialogues of >7 questions were stable across the 2 periods.

    ‎
    Figure 3. Number and percentage of dialogues in Dina the chatbot (n=610) by weekday and time of day for period 1 and period 2 combined.
    View this figure
    ‎
    Figure 4. Dialogues by number of questions and period (period 1: weeks 41 to 48 in 2018, 288 dialogues; period 2: week 47 in 2019 to week 6 in 2020, 322 dialogues) for Dina the chatbot.
    View this figure

    Ability To Answer Questions and Fallback by the Chatbot

    Overall, Dina the chatbot was able to answer 88.15% (2512/2838) of all questions asked by users. Figure 5 shows the types of fallback questions by period.

    Fallback questions on GDM increased from 26.3% (41/156) to 42.4% (72/170) from the first to the second period (P=.002), while the fallback questions the chatbot did not understand decreased from 32.1% (50/156) to 18.2% (31/170; P=.004). Fallback questions about pregnancy in general and nonsensical questions showed small variations between the 2 periods.

    ‎
    Figure 5. Types of fallback question by period (period 1: weeks 41 to 48 in 2018, n=156; period 2: week 47 in 2019 to week 6 in 2020, n=170) for Dina the chatbot.
    View this figure

    Principal Findings

    Nearly 60% (1669/2838) of all questions from users were on blood glucose, diet, GDM, and physical activity, and the chatbot was able to answer 88.51% (2512/2838) of all posed questions. The chatbot was most frequently used during the daytime, Monday through Friday, and most dialogues were short, containing 1-3 questions. However, the mean number of dialogues per week was 36 in the first period and 26.83 in the second period.

    Most Frequently Asked Questions to Dina the Chatbot

    Few prior studies have evaluated health chatbots based on log reviews [28]. Inkster et al [27] explored log data on self-reported symptoms of depression, using questionnaires integrated in their chatbot, Wysa, but with a different focus than ours. To the extent of our knowledge, our study is the first study aiming to explore what users ask a health chatbot integrated into a national health service platform by categorizing incoming questions. Dina the chatbot provides the opportunity to click on related themes and questions, “guiding” the user through the conversation. This might have increased conversation efficiency and influenced findings in this study by making some information more available for the users than other topics. Our results indicate that users mainly seek information with high relevance for their currently experienced issues related to GDM (questions regarding blood glucose levels and diet) as opposed to relevant information on future events like the postpartum period or contextual factors such as social health benefits. The information sought also seems to overlap with information available through the established antenatal health care program in Norway, as distribution of the most frequent questions mirrors the cornerstones in the treatment of GDM [14,15]. This indicates that the chatbot could serve as a low-threshold addition to the already-established health care service. The chatbot may enhance the treatment of GDM, promote stable blood glucose, and thereby prevent the development of adverse outcomes for the mother and the fetus. Qualitative studies have shown that women diagnosed with GDM may perceive a lack of personally adapted information, contributing to a feeling of insecurity [17,33] in which managing blood glucose measurements and changing diets are the main challenges [18]. Information provided from a chatbot can thus serve as a reminder or a confirmation for the user on already-received information from medical professionals [33] and not as a substitute for the traditional face-to-face consultation [39]. For users, adding technology like informational chatbots to the standard patient care may reduce insecurities [33,40] and potentially contribute to increased self-efficacy [19].

    What Can User Behavior in the Chatbot Tell Us?

    Despite some unanswered questions, we found that exploring user behavior in the chatbot will provide useful information for planning and organizing future antenatal care. A previous study on a comparable supportive chatbot developed for patients with breast cancer explored user behavior by asking a weekly question and by observing the retention rate among users [25]. As all data in our study were anonymous, we were unable to explore retention rate, and we treated each dialogue as the unit of analysis. We anticipated a higher frequency of use out of office hours, when medical professionals are less available. Surprisingly, the chatbot was most frequently used during the daytime, Monday through Friday. Even though a great advantage of chatbots is their 24-7 availability, frequent daytime use provides valuable insight for planning future antenatal care. To our knowledge, prior studies on health chatbots have not explored this issue before. The frequent use during the daytime may be a result of the users needing to quickly access information already provided for them by the health care service. With the timeframe of consultations often being limited [41], questions may arise before or after consultations [33]. The chatbot may provide reassurance for managing the condition of variable validity as an alternative to Google or other internet sources [33]. A chatbot may also provide answers to questions that appear too insignificant or embarrassing to ask health personnel directly [33], potentially reducing the barriers for contacting the health care service [33,42,43].

    As there is currently a lack of a standardized methods for evaluating health chatbots, a comparison of chatbots performances may be challenging [25,28]. In general, metrics used to measure chatbot performance depend on which purpose the chatbot is designed to serve; still, most developers aim to keep the conversations short and effective [44]. Keeping this in mind, our results may prove an effective change in user interface in Dina version 2.0, as findings indicate that this version requires fewer conversational steps from the user, evidenced by both fewer questions from users on maneuvering in the chatbot and a decrease in dialogues containing 4-6 questions. This is supported by findings from a previous qualitative study, in which participants stated that they perceived Dina version 2.0 to be effective in providing answers [33]. However, short dialogues could also be an indication of users “giving up” and leaving the conservation; nonetheless, efficiency and the ability to provide a fast answer are important for the intention to use a chatbot [33].

    Despite the fact that Dina the chatbot was integrated into the Norwegian digital health platform and made available for all pregnant Norwegian women between the 2 periods of registration, we found that the weekly mean number of dialogues was 36 in the first period and 26.83 in the second period. Although this change was not significant, it could be explained by a possible insufficient promotion of the chatbot among both pregnant women and health care personnel [33]. Previous studies have described several obstacles like organizational, economic, and knowledge barriers when implementing new technology in the health care service [45]. It is our view that the promotion of the chatbot should be a priority going forward to increase the chance of implementation of the chatbot in Norwegian antenatal care.

    The Chatbot´s Ability To Answer and Need for Further Development

    As the chatbot currently does not provide users with the opportunity to express if they are satisfied with the answer, we used the percentage of questions that the chatbot was unable to answer (fallback) as a measure of how well it operates. Our findings showed that a fallback message was given in 11.49% (326/2838) of all questions asked to Dina the chatbot. The goal is to keep the percentage of fallback as low as possible in order to meet user satisfaction [46]. In a previous study on conversational repair in a chatbot developed for customer service, the fallback percentage was reported to be 15% [47]. However, we did not consider “false positive” responses, where the chatbot seemingly provided an answer but not the answer the user sought. This would have provided more insight into the chatbots ability to answer and will be important to consider in future analyses. We categorized questions resulting in a fallback message to discover problem areas that need further development [48] and to satisfy our specific aim of determining what type of questions the chatbot is unable to answer. Our results (Figure 5) suggested an increase in the fallback category “GDM” between the 2 periods of registration. These are questions the chatbot should be able to answer, and calls for further training and development of content regarding information on GDM are warranted. Notably, we found that that number of questions that the chatbot did not understand decreased in the second period, which may be an indication of increased functionality in Dina version 2.0, as the chatbot may provide more options to click on related themes, thereby guiding the user in a more efficient way. Nearly 1 in 4 questions resulting in a fallback message were related to pregnancy in general, and this may be viewed as an expression of an interest from users and possibly serve as an idea for future development. Nonsensical questions represented 16.9% (55/326) of the overall questions resulting in a fallback message from the chatbot, and one could therefore consider the real fallback rate for the chatbot to be lower than 11.5% if one disregarded these questions.

    There are several challenges when it comes to integrating this type of new technology into the established health care [39]. Studies outline several reasons as to why users of health technology tools such as chatbots can lose interest, among them being frustrations with the technology and losing the in-person contact with the caregiver [49,50]. A chatbot is not a finished product once it is deployed, and it is important to continuously monitor and add information to improve the chatbot’s ability to function optimally [51]. By highlighting areas in Dina the chatbot that need improvement, we hope this study may serve as a contribution for further improvement and implementation.

    Strength and Limitations

    The strengths of this study are its use of the chatbot log data from 2 different periods during the course of continuous development and maintenance of the chatbot [52]. Utilization of user data is a cost-effective approach, providing important insight needed for further development. This study also has some limitations. We were unable to assess if users were women with GDM; however, the chatbot could also be a resource for health personnel or partners or next of kin. As all questions to the chatbot are displayed as dialogues in the chatbot’s log, we were not able to stratify our analysis according to which method the user decide upon (ie, free text, theme buttons, or a mix of the 2). A registration of users’ preferred way of asking questions should be considered in the future development of the chatbot. Furthermore, we could not identify if the user visited the chatbot once or several times. There is currently no way for the users to express whether they received the answer to their question in the dialogue, and obtaining this information would have been a useful addition to the fallback percentage acquired in analyses. Moreover, it would be useful to explore “false positive” responses by the chatbot (the chatbot provides an answer but not the answer the user is seeking). This would have provided more insight into the functionality of the chatbot and will be considered in future studies. The chatbot is currently only available in Norwegian language, limiting the external validity of the knowledge. In addition, not all women may feel comfortable trusting a chatbot on health issues, which might have potentially excluded some women and introduced selection bias into our study.

    Implications

    Our findings indicate that users seek information on topics relevant to them at the time, such as blood glucose, diet, and physical activity, and that the most frequently asked questions mirror the cornerstones of GDM treatment. This may indicate that the chatbot is used to quickly access information already provided to users by the health care service, but the chatbot offers a low-threshold way to access that information.

    Furthermore, results indicate that Dina version 2.0 guides the user in a more efficient way. However, the low mean number of dialogues per week in the second period (26.83) suggests further efforts should be put into promoting and integrating the chatbot into Norwegian antenatal care. We view our findings as potentially relevant to future development of informational and supportive health chatbots. The authors finds a low-threshold design is an advantage, as this will provide easy access to information the user is also provided through other channels in the health service to further support self-efficacy. As our society quickly becomes more digital, there is a call for the health care service to keep up with the rapid development [36]. We see the need for an informational tool like Dina the chatbot to contribute to increased self-efficacy and coping with GDM, considering the rising prevalence of the condition [2,4,5]. The next step for us with Dina the chatbot would be to continue the ongoing work of further development and to improve promotion to increase its use. We believe that when chatbots are a more integrated part of the health care service, they may serve as a positive contribution to antenatal care.

    Conclusions

    The majority of posed questions pertained to blood glucose, diet, the GDM diagnosis, and physical activity, and the chatbot was able to answer about 9 out of 10 of all questions from the users. The most frequent use was during the daytime, Monday through Friday, and the majority of dialogues were short, containing 1-3 questions. However, the mean number of dialogues per week was 36 in the first period and 26.83 in the second period.

    Acknowledgments

    The authors would like to thank Asbjørn Følstad at SINTEF for valuable input and contribution to the paper. Furthermore, we would like to thank the managing team of Dina the chatbot and Western Norway University of Applied Sciences for valuable cooperation in the process. The Research and Development department at Haukeland University Hospital founded this study through a short-term scholarship.

    Authors' Contributions

    The study was designed by MHS, NHM, AL, LJD, ABVN, and LMS. AL further obtained access to the data. MHS and LJD performed the data collection, and LJD especially contributed to the background material for this manuscript. MHS and LJD performed the statistical analysis with support from LMS and ABVN, and MHS drafted the first version of this manuscript. LMS contributed to project administration, and NHM provided important insight regarding the methodology and statistical analysis. MHS, NHM, AL, LJD, ABVN, and LMS all contributed to the interpretation of the data and the visualization of tables and figures and have critically revised the manuscript throughout the process and approved the final manuscript. The first author, MHS confirms that all listed authors meet authorship criteria.

    Conflicts of Interest

    Contributing author AL is the founder and concept developer of Dina the chatbot. The other authors have no conflicts of interest to declare.

    1. American Diabetes Association. (2) Classification and diagnosis of diabetes. Diabetes Care 2015 Jan;38 Suppl:S8-S16. [CrossRef] [Medline]
    2. Sadikot S, Purandare CN, Cho NH, Hod M. FIGO-IDF joint statement and declaration on hyperglycemia in pregnancy. Diabetes Res Clin Pract 2018 Nov;145:1-4. [CrossRef] [Medline]
    3. Medical Birth Registry of Norway. Statistics Bank. Norwegian Institute of Public Health. 2019.   URL: https://www.fhi.no/en/hn/statistics/statistics-from-niph/statistikkbanker/ [accessed 2021-02-10]
    4. Svangerskapsdiabetes - bør alle gravide screenes? Cochrane: Kort oppsummert. Folkehelseinstituttet. 2019.   URL: https:/​/www.​fhi.no/​publ/​2019/​svangerskapsdiabetes---bor-alle-gravide-screenes-cochrane-kort-oppsummert/​ [accessed 2021-02-10]
    5. Jenum AK, Richardsen KR, Berntsen S, Mørkrid K. Gestational diabetes, insulin resistance and physical activity in pregnancy in a multi-ethnic population – a public health perspective. Nor J Epidemiol 2013 Jun 03;23(1):45-54. [CrossRef]
    6. Durnwald C, Nathan DM, Werner EF. Diabetes mellitus in pregnancy: Screening and diagnosis. Up To Date. 2020.   URL: https://tinyurl.com/2p8r42fc [accessed 2021-02-10]
    7. HAPO Study Cooperative Research Group, Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, et al. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med 2008 May 08;358(19):1991-2002. [CrossRef] [Medline]
    8. Hod M, Kapur A, Sacks DA, Hadar E, Agarwal M, Di Renzo GC, et al. The International Federation of Gynecology and Obstetrics (FIGO) Initiative on gestational diabetes mellitus: A pragmatic guide for diagnosis, management, and care. Int J Gynaecol Obstet 2015 Oct;131 Suppl 3:S173-S211. [CrossRef] [Medline]
    9. Bellamy L, Casas J, Hingorani AD, Williams D. Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. Lancet 2009 May 23;373(9677):1773-1779. [CrossRef] [Medline]
    10. Song C, Lyu Y, Li C, Liu P, Li J, Ma RC, et al. Long-term risk of diabetes in women at varying durations after gestational diabetes: a systematic review and meta-analysis with more than 2 million women. Obes Rev 2018 Mar;19(3):421-429. [CrossRef] [Medline]
    11. Falavigna M, Schmidt MI, Trujillo J, Alves LF, Wendland ER, Torloni MR, et al. Effectiveness of gestational diabetes treatment: a systematic review with quality of evidence assessment. Diabetes Res Clin Pract 2012 Dec;98(3):396-405. [CrossRef] [Medline]
    12. Cremona A, O'Gorman C, Cotter A, Saunders J, Donnelly A. Effect of exercise modality on markers of insulin sensitivity and blood glucose control in pregnancies complicated with gestational diabetes mellitus: a systematic review. Obes Sci Pract 2018 Oct;4(5):455-467 [FREE Full text] [CrossRef] [Medline]
    13. Viana L, Gross J, Azevedo M. Dietary intervention in patients with gestational diabetes mellitus: a systematic review and meta-analysis of randomized clinical trials on maternal and newborn outcomes. Diabetes Care 2014 Dec;37(12):3345-3355. [CrossRef] [Medline]
    14. Baz B, Riveline J. Gestational diabetes mellitus: definition, aetiological and clinical aspects. Eur J Endocrinol 2016;174(2):43-51. [CrossRef]
    15. Nasjonal faglig retningslinje for svangerskapsdiabetes. Helsedirektoratet. Norway; 2017.   URL: https://www.helsedirektoratet.no/retningslinjer/svangerskapsdiabetes [accessed 2022-04-01]
    16. Nasjonal faglig retningslinje for svangerskapsomsorgen. Helsedirektoratet. 2018.   URL: https://helsedirektoratet.no/Retningslinjer/Svangerskapsomsorgen.pdf [accessed 2021-02-10]
    17. Devsam BU, Bogossian FE, Peacock AS. An interpretive review of women's experiences of gestational diabetes mellitus: proposing a framework to enhance midwifery assessment. Women Birth 2013 Jun;26(2):e69-e76. [CrossRef] [Medline]
    18. Draffin C, Alderdice F, McCance D, Maresh M, Harper Md Consultant Physician R, McSorley O, et al. Exploring the needs, concerns and knowledge of women diagnosed with gestational diabetes: A qualitative study. Midwifery 2016 Sep;40:141-147. [CrossRef] [Medline]
    19. Larbi D, Randine P, Årsand E, Antypas K, Bradway M, Gabarron E. Methods and evaluation criteria for apps and digital interventions for diabetes self-management: systematic review. J Med Internet Res 2020 Jul 06;22(7):e18480 [FREE Full text] [CrossRef] [Medline]
    20. Granja C, Janssen W, Johansen MA. Factors Determining the Success and Failure of eHealth Interventions: Systematic Review of the Literature. J Med Internet Res 2018 May 01;20(5):e10235 [FREE Full text] [CrossRef] [Medline]
    21. Dale R. The return of the chatbots. Nat. Lang. Eng 2016 Sep 13;22(5):811-817. [CrossRef]
    22. Følstad A, Brandtzæg P. Chatbots and the new world of HCI. Interactions 2017 Jun 23;24(4):38-42 [FREE Full text] [CrossRef]
    23. Milne-Ives M, de Cock C, Lim E, Shehadeh MH, de Pennington N, Mole G, et al. The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review. J Med Internet Res 2020 Oct 22;22(10):e20346 [FREE Full text] [CrossRef] [Medline]
    24. Lanserte Norges første helse-chatbot. Bergen Kommune. 2018.   URL: https://www.bergen.kommune.no/hvaskjer/tema/smart-omsorg/10573/article-153256 [accessed 2022-04-01]
    25. Chaix B, Bibault J, Pienkowski A, Delamon G, Guillemassé A, Nectoux P, et al. When Chatbots Meet Patients: One-Year Prospective Study of Conversations Between Patients With Breast Cancer and a Chatbot. JMIR Cancer 2019 May 02;5(1):e12856 [FREE Full text] [CrossRef] [Medline]
    26. Fulmer R, Joerin A, Gentile B, Lakerink L, Rauws M. Using Psychological Artificial Intelligence (Tess) to Relieve Symptoms of Depression and Anxiety: Randomized Controlled Trial. JMIR Ment Health 2018 Dec 13;5(4):e64 [FREE Full text] [CrossRef] [Medline]
    27. Inkster B, Sarda S, Subramanian V. An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study. JMIR Mhealth Uhealth 2018 Nov 23;6(11):e12106 [FREE Full text] [CrossRef] [Medline]
    28. Abd-Alrazaq A, Safi Z, Alajlani M, Warren J, Househ M, Denecke K. Technical Metrics Used to Evaluate Health Care Chatbots: A Scoping Review. J Med Internet Res 2020 Apr 15:e18301 [FREE Full text] [CrossRef] [Medline]
    29. Kvale K, Sell O, Hodnebrog S, Følstad A. Improving conversations: lessons learnt from manual analysis of chatbot dialogues. Chatbot Research and Design 2020 Jan 19:187-200 [FREE Full text] [CrossRef]
    30. Tomlinson M, Rotheram-Borus MJ, Swartz L, Tsai AC. Scaling up mHealth: where is the evidence? PLoS Med 2013 Feb;10(2):e1001382 [FREE Full text] [CrossRef] [Medline]
    31. User-centered design basics. US Department of Health & Human Services.   URL: https://www.usability.gov/what-and-why/user-centered-design.html [accessed 2022-04-01]
    32. Sharma S. mHealth Design for patients with gestational diabetes. Master thesis. Bergen: The University of Bergen; 2017.   URL: https://bora.uib.no/bora-xmlui/handle/1956/17417 [accessed 2022-03-01]
    33. Corneliussen S. Factors affecting intention to use chatbots for health information. Master thesis.: University of Oslo; 2020.   URL: https:/​/www.​duo.uio.no/​bitstream/​handle/​10852/​79932/​Susanna-CorneliussenV2020.​pdf?sequence=1&isAllowed=y [accessed 2022-03-01]
    34. Dina-chatbot for gravide med svangerskapsdiabetes. Helse Bergen. 2018.   URL: https://helse-bergen.no/avdelinger/kvinneklinikken/spor-dina#om-prosjektet-og-kontaktpersonar [accessed 2022-04-01]
    35. Wallwiener S, Müller M, Doster A, Laserer W, Reck C, Pauluschke-Fröhlich J, et al. Pregnancy eHealth and mHealth: user proportions and characteristics of pregnant women using Web-based information sources-a cross-sectional study. Arch Gynecol Obstet 2016 Apr 15:937-944. [CrossRef] [Medline]
    36. Nasjonal e-helsestrategi og mål 2017-2022. Direktoratet for e-helse. 2019.   URL: https://www.ehelse.no/strategi/nasjonal-e-helsestrategi#Nasjonal%20e-helsestrategi%202017-2022 [accessed 2022-04-01]
    37. Polit D, Beck C. Nursing Research: Generating and Assessing Evidence for Nursing Practice. 9th edition. Philadelphia, PA: Wolters Kluwer Health; 2012.
    38. Hva er nytt med personvernforordningen? Datatilsynet. 2018.   URL: https://www.datatilsynet.no/regelverk-og-verktoy/lover-og-regler/hva-er-nytt/ [accessed 2022-04-01]
    39. Powell J. Trust Me, I'm a Chatbot: How Artificial Intelligence in Health Care Fails the Turing Test. J Med Internet Res 2019 Oct 28;21(10):e16222 [FREE Full text] [CrossRef] [Medline]
    40. Hirst JE, Mackillop L, Loerup L, Kevat DA, Bartlett K, Gibson O, et al. Acceptability and user satisfaction of a smartphone-based, interactive blood glucose management system in women with gestational diabetes mellitus. J Diabetes Sci Technol 2015 Jan;9(1):111-115 [FREE Full text] [CrossRef] [Medline]
    41. Forbrukerrådets pasientundersøkelse. En kvalitativ studie av pasientopplevelser. Forbrukerrådet. 2016.   URL: https://fil.forbrukerradet.no/wp-content/uploads/2016/12/pasientundersokelse-web.pdf [accessed 2022-04-01]
    42. Neumann B, Stefanik M, Gonzalvo J, Weber Z. Diabetes mHealth Applications. AADE in Practice 2016 Aug 16;4(5):28-32. [CrossRef] [Medline]
    43. Lucas GM, Gratch J, King A, Morency L. It’s only a computer: virtual humans increase willingness to disclose. Computers in Human Behavior 2014 Aug;37:94-100. [CrossRef]
    44. Przegalinska A, Ciechanowski L, Stroz A, Gloor P, Mazurek G. In bot we trust: a new methodology of chatbot performance measures. Business Horizons 2019 Nov;62(6):785-797. [CrossRef]
    45. Bashshur RL, Shannon G, Krupinski EA, Grigsby J. Sustaining and realizing the promise of telemedicine. Telemed J E Health 2013 May;19(5):339-345 [FREE Full text] [CrossRef] [Medline]
    46. Patel S. REVE Chat. 2020.   URL: https://www.revechat.com/blog/chatbot-analytics-metrics/ [accessed 2022-04-01]
    47. Følstad A, Taylor C. Conversational repair in chatbots for customer service: the effect of expressing uncertainty and suggesting alternatives for customer service: the effect of expressing uncertainty and suggesting alternatives. In: Følstad A, Araujo T, PapadopoulosEffie S, Lai-Chong Law E, Granmo OC, Luger E, et al, editors. Chatbot Research and Design. Amsterdam, The Netherlands: Springer International Publishing; 2020:201-214.
    48. Jassova, B. Key Chatbot KPIs: How to Measure Chatbot Performance? Landbot. 2020 Jan 31.   URL: https://landbot.io/blog/chatbot-metrics-kpi/ [accessed 2022-04-01]
    49. Lie SS, Karlsen B, Oord ER, Graue M, Oftedal B. Dropout From an eHealth Intervention for Adults With Type 2 Diabetes: A Qualitative Study. J Med Internet Res 2017 May 30;19(5):e187 [FREE Full text] [CrossRef] [Medline]
    50. Fitzpatrick KK, Darcy A, Vierhile M. Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial. JMIR Ment Health 2017 Jun 06;4(2):e19 [FREE Full text] [CrossRef] [Medline]
    51. The ultimate guide into 22 metrics for chatbot analytics in 2022. AI Multiple. 2022 Feb 10.   URL: https://research.aimultiple.com/chatbot-analytics/ [accessed 2022-04-01]
    52. Radziwill N, Benton BM. Evaluating quality of chatbots and intelligent conversational agents. Software Quality Professional 2017;19(3):25-36. [CrossRef]

    ‎
    GDM: gestational diabetes mellitus

    Edited by A Mavragani; submitted 20.02.21; peer-reviewed by P Randine, K Fitzner; comments to author 19.05.21; revised version received 24.06.21; accepted 19.02.22; published 18.04.22

    Copyright

    ©Mari Haaland Sagstad, Nils-Halvdan Morken, Agnethe Lund, Linn Jannike Dingsør, Anne Britt Vika Nilsen, Linn Marie Sorbye. Originally published in JMIR Formative Research (https://formative.jmir.org), 18.04.2022.

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