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Abstract

Although user involvement is generally considered critical to the effectiveness of digital behavior change interventions, there is a paucity of such data in pregnancy. Therefore, the aim of this study was to examine the relationship of user engagement with the HealthyMoms app with gestational weight gain, diet quality, and physical activity during pregnancy. This study involved secondary analysis of participant data from the intervention group (n = 134) in a randomized controlled trial to determine the effectiveness of the 6-month mHealth intervention (HealthyMoms app) on gestational weight gain, diet quality and physical activity. In the adjusted regression model, the total number of enrollments of the three self-monitoring features (i.e., for weight, diet, and physical activity) was associated with lower gestational weight gain (β =  − 0.18, P = 0.043) and increased diet quality (β = 0.17, P = 0.019). This finding was mainly due to the association of physical activity registration with lower pregnancy weight gain (β =  − 0.20, P = 0.026) and improved diet quality (β = 0.20, P = 0.006). However, the number of app sessions and page views is not associated with any results. Our results may motivate efforts to increase user engagement in digital lifestyle interventions in pregnancy. However, additional studies are needed to better elucidate the effect of different types of user involvement in digital pregnancy interventions on their effectiveness.

Trial registration: ClinicalTrials.gov (NCT03298555); https://clinicaltrials. See the article : Long-term weight loss associated with pharmacotherapy and lifestyle interventions.gov/ct2/show/NCT03298555 (registration date: October 2, 2017; date of first registered participant: October 24, 2017).

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Introduction

Excessive pregnancy weight gain is common worldwide and affects approximately 50% of women in the United States and Europe1,2. Preventing excessive gestational weight gain is important as it is a risk factor for several adverse pregnancy outcomes such as preeclampsia, gestational diabetes, cesarean delivery, and macrosomia3,4. Systematic reviews and meta-analyses have shown that interventions (generally traditional face-to-face interventions and supervised exercise sessions), intended to promote a healthy diet and physical activity can prevent excessive pregnancy weight gain5,6. For example, a Cochrane review and meta-analysis of 24 studies and 7096 participants showed that dietary and/or exercise interventions reduced the risk of excessive pregnancy weight gain by an average of 20% (risk ratio 0.80, 95% confidence interval 0.73 up to 0.87) 5.

Recently, interest in mobile health interventions (mHealth) to promote healthy weight gain, diet and physical activity in pregnancy has increased7. The mHealth intervention has several advantages over traditional, direct-delivered intervention programs in terms of reach and potential cost-effectiveness8. See the article : How Queer Chefs Reclaim bottom line food. Although involvement with and effectiveness of proprietary digital mHealth interventions varies widely between studies, several interventions have shown promise for reducing pregnancy weight gain and promoting a healthy diet during pregnancy7,8,9,10. For example, our previous randomized controlled trial, the HealthyMoms trial, showed that access to a smartphone app during pregnancy reduced weight gain in women who were overweight or obese before pregnancy and improved diet quality regardless of pre-pregnancy body mass index (BMI). pregnancy9.

Involvement in digital behavior change interventions has been defined as the “level (i.e., amount, frequency, duration, and depth) of use” but also as “a subjective experience characterized by attention, interest, and influence”11. Although user involvement in smartphone interventions has generally been associated with intervention effectiveness in nonpregnant adults12, such studies are sorely lacking in pregnancy. To the best of our knowledge, no previous mHealth study has examined how engagement with proprietary digital interventions is associated with effectiveness on pregnancy weight gain and health behaviors (eg, diet, physical activity) in pregnancy. Such information is important to understand the role of the amount and type of user engagement with the effectiveness of the intervention in digital interventions. Ultimately, the promotion of more rewarding patterns of user engagement (i.e., optimal amount and type of engagement) may result in more effective digital lifestyle interventions in pregnancy. Therefore, further research is needed to elucidate the role of engagement for the effectiveness of digital lifestyle interventions in pregnancy. Therefore, we used data from the HealthyMoms9 trial with the aim of examining the association of app user engagement with gestational weight gain, diet quality, and physical activity.

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Methods

Study design and participants

This study involved secondary analysis of participant data from the intervention group (n = 134) in a randomized controlled trial to determine the effectiveness of the 6-month mHealth intervention (HealthyMoms app) on gestational weight gain, diet quality and physical activity. Detailed information on the HealthyMoms trial and outcomes for intervention effectiveness has been published elsewhere9,13. Briefly, 305 women were randomized to the intervention (n = 152) or control group (n = 153) after taking baseline measurements at gestational week 14 (13.8 ± 0.6 weeks gestation). Women randomized to the control group received standard antenatal care, while women in the intervention group received standard antenatal care as well as the HealthyMoms app. At follow-up at 37 weeks’ gestation (36.4 ± 0.4 weeks gestation), a total of 271 women (89% of the original sample) were measured. To address the purpose of this study, user engagement data from women randomized to the HealthyMoms application (n = 134) were used.

The HealthyMoms app and user engagement data

The HealthyMoms app is a comprehensive program aimed at promoting a healthy weight, diet and physical activity during pregnancy9,13. Briefly, this application has seven features, which include: (1) an information theme that changes every second week; (2) three self-monitoring features in which participants were able to record their weight, diet, and physical activity during the intervention period; (3) push notifications; (4) exercise guides (eg, aerobics and resistance training, training programs and videos); (5) recipes; (6) pregnancy calendar; and (7) application libraries (eg, frequently asked questions, practical tips). Women were asked to register their weight, diet and physical activity according to their own preferences. Figure 1 shows a screenshot of the HealthyMoms app to visualize the homepage and signup features. On the same subject : HEALTH RESOURCES: Money or food? Why not two?. Women were asked to use the accelerometer (for physical activity measurement) 1-2 weeks before the follow-up measurement at 37 gestational week. Therefore, we selected data for the first 20 weeks of application use (ie the first 10 themes).

HealthyMoms app screenshot (translated from Swedish to English). Left panel: HealthyMoms app home with access to registration of self-monitoring features for weight, diet, and physical activity; Middle panel: Example of a diet registration feature; Right panel: Sample feedback for physical activity registration.

We considered two types of engagement data in the current study (registration and application use). First, we used data from the HealthyMoms app to derive the number of registrations of the self-monitoring feature for registration of weight, diet, and physical activity (hereinafter referred to as enrollment) (n = 134) during the first 20 weeks of application use. We considered the number of enrollments for weight, diet, and physical activity separately, but also the total number of enrollments for the three categories. Second, through Google Analytics, we also use data related to app usage which is assessed as the number of app sessions (representing one period of user interaction with the app) and page views from the first 20 weeks of the intervention. We were able to use Google Analytics data for women who started the intervention in January 2019 onwards (n = 60). Five women had no registration and thus the Google Analytics sample consisted of 55 women. We only include data regarding app sessions and pageviews from sessions that are 5 s to exclude sessions that are too short and possibly accidental.

Effectiveness outcomes

Outcome measures were evaluated at baseline and follow-up measurements and have been described in detail previously9,13. In summary, body weight was measured in underwear using standard procedures. Consequently, gestational weight gain was calculated as weight at follow-up minus body weight at baseline. Women were classified as having excessive pregnancy weight gain using the cut-off from the National Academy of Medicine, USA (formerly the Institute of Medicine) for recommended weight gain in the second and third trimesters according to the BMI group before pregnancy (underweight:  ≥ 0, 59 kg per week; normal weight: 0.51 kg per week; overweight: 0.34 kg per week; and obese: 0.28 kg/week)14. Diet was measured using Riksmaten FLEX which has been developed by the Swedish National Food Agency15. This method uses web-based data collected from three repetitions of the 24-hour diet which was then linked to the Swedish National Food Composition Database. Diet quality was assessed using the Swedish Healthy Eating Index Score which is based on the Nordic Nutrition Recommendations9,16. The score consists of 9 components; (1) fruit and vegetables; (2) fish and shellfish; (3) red meat; (4) fiber; (5) whole grain; (6) polyunsaturated fat; (7) monounsaturated fat; (8) saturated fat; and (9) sucrose. Each item can result in a score between 0 and 1 and thus the total score can range between 0 and 9 with higher scores indicating better diet quality. Physical activity was assessed using the ActiGraph wGT3x-BT accelerometer (ActiGraph, Pensacola, FL). The women were instructed to wear the accelerometer on the non-dominant wrist 24 hours a day for 7 consecutive days. Women who cannot wear accelerometers on the wrist (for example, due to hygiene restrictions at work) are instructed to wear accelerometers tar on the hip (baseline, n = 9; follow-up, n = 8). Data were collected at 100 Hz and participants filled out diaries to record non-wear and sleep times. Women with at least one valid day were included in the analysis and valid days were defined as 1/3 of the 24-hour period was wear time, 2/3 of wake time was wear time, and 2/3 of bedtime was wear time9. Cut-off by Hildebrand et al. (ie, wrist: 100 mg; hip: 70 mg) was applied to determine moderate to vigorous physical activity17. Moderate to vigorous daily physical activity was calculated as the average weekday and weekend weight9. Physical activity data processing was carried out using the R software program (v. 4.1.0, https://www.cran.r-project.org/)18 and the GGIR19 package.

Covariates

At the initial measurement, participants filled out a questionnaire regarding their age, educational attainment, and the number of children they had given birth to (parity). Next, women answered questions about their perceived competence to have a healthy diet and physical activity. Questions were based on the original perceived competence questionnaire20,21 and modified to assess diet and physical activity (translation of the questionnaire is presented in Table S1).

Statistical analysis

Differences in user engagement with user characteristics (i.e. age, educational attainment, parity, BMI before pregnancy and perceived competence) were examined with the Mann-Whitney U test because these variables were not normally distributed (all P < 0.05 using Kolmogorov – Smirnov test). Associations of user involvement with outcome variables of weight gain, diet quality and physical activity were examined by linear regression and two regression models were created. Model 1 includes only adjustments for baseline values ​​for the outcome variable (analysis with gestational weight gain adjusted for baseline BMI). Model 2 was adjusted for age, parity (0 vs.-1), educational attainment (high school vs. university degree), perceived competence for a healthy diet and physical activity at baseline, baseline BMI and baseline for outcome variables (for analysis with Swedish Healthy Eating Index and moderate to vigorous physical activity). The HealthyMoms trial was measured to detect a 1.5 kg difference in gestational weight gain between the intervention and control groups at 80% power (α = 0.05, two-tailed). In this secondary analysis, sample sizes of 134 and 55 women provided 80% power (α = 0.05, two-tailed) to observe standard regression coefficients 0.24 and 0.36, respectively. No violations of the regression model assumptions were observed22. Double-sided P value < 0.05 was considered statistically significant and statistical analysis was performed in SPSS (SPSS IMB Statistics, version 26, IBM Corp., NY, USA) and R (v. 4.1.0, https://www.cran.r-project. org/).

Ethics approval and consent to participate

The HealthyMoms trial received approval from the Regional Ethical Review Board in Linköping, Sweden (reference numbers 2017/112-31 and 2018/262-32). All women gave written consent before entering the trial.

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Results

Participants’ characteristics and user engagement data

Participants’ characteristics and overall user engagement data are presented in Table 1. Additional descriptive data from participants in the intervention and control groups are presented in Table S2. Overall, user engagement was high with an average number of application and signup sessions 66 and 63 over 20 weeks, respectively. Figure 2 shows user interactions over time, presented as the number of application and registration sessions for each of the 10 two-week themes (detailed data in Table S3). Although engagement decreased over time, overall engagement remained relatively high throughout the intervention period. For example, at least 60% of participants had one or more application or registration sessions for each theme (two-week period) during the intervention.

Number of registrations and application sessions per theme using a box-and-whisker plot. Enrollment refers to the total number of registrations of the self-monitoring feature for weight, diet, and physical activity in the HealthyMoms app whereas app sessions represent a single period of user interaction with the app. The box visualizes the values ​​for the first quartile, median and third quartile while the whiskers represent the first quartile minus 1.5 times the interquartile range and the third quartile plus 1.5 times the interquartile range. Each theme represents a two-week period.

Correlates of user engagement

User engagement by participant characteristics is presented in Table 2. There were no statistically significant differences in the number of application and enrollment sessions according to age, educational attainment, and perceived competence for a healthy diet. However, women with perceived healthy physical activity competence above the median had more enrollment than women who matched below the median (P = 0.003). Furthermore, women who are primiparous (ie, expecting their first child) or have a BMI < 25.0 kg/m2 had a higher number of application sessions than women who had children or had a BMI ≥ 25.0 kg/m2 before pregnancy (P = 0.032 and 0.041, respectively).

Associations of user engagement with gestational weight gain, diet and physical activity

Table 3 presents the relationship between user engagement and pregnancy weight gain as well as changes in diet and physical activity quality between gestational weeks 14 and 37. The total number of enrollments (number of enrollments in weight, diet and physical activity) was associated with improvements in diet quality (β = 0.16, P = 0.025) in model 1 and with lower gestational weight gain (β =  − 0.18, P = 0.043) and improved diet quality (β = 0.17, P = 0.019) in model 2. Total physical activity enrollment was associated with increased lower gestational weight (β =  − 0.20, P = 0.026) and improved diet quality (β = 0.20, P = 0.006) in model 2, whereas no statistically significant association was observed for weight registration and diets. However, there was no statically significant relationship of the number of application sessions or page views with pregnancy weight gain or changes in diet quality and moderate to strong physical activity in any of the regression models.

We performed additional and sensitivity analyzes to further assess the robustness of our results. First, we performed logistic regression to examine the association of user engagement with the likelihood of excessive pregnancy weight gain (Table S4). In general, the results are quite comparable to our main results (i.e. when analyzing gestational weight gain as a continuous variable). For example, in model 2, the odds ratios for excessive pregnancy weight gain per 1 SD increase in total enrollment and physical activity enrollment were 0.67 (95% CI: 0.44–1.02) and 0.61 (95% CI : 0.39-0.95), respectively. Second, we explored whether the associations for physical activity were comparable if only including participants with wrist data and 4 valid days (including 1 weekend day) of accelerometer data, but estimates remained comparable to our main results.

Discussion

Main results

The purpose of this study was to examine the relationship of user engagement with the HealthyMoms application with gestational weight gain, diet quality, and physical activity during pregnancy. The main finding was that more registrations in the HealthyMoms app were associated with decreased pregnancy weight and improved diet quality. This result was mainly due to the number of physical activity enrollments, which were associated with pregnancy weight gain and diet quality. However, the number of application sessions and page views was not related to pregnancy weight, diet quality, and physical activity.

User engagement

Previous independent mHealth interventions in pregnancy generally reported relatively high levels of user engagement as demonstrated by app use, weight and physical activity logging, and response rates to messages8,10,23,24. In contrast, online interventions (generally consisting of access to web pages)25,26 and multi-component interventions27,28 (where the mHealth intervention was provided in conjunction with the face-to-face intervention) generally showed lower engagement. Thus, it appears that user involvement is higher in stand-alone mobile phone interventions than online and multicomponent interventions, which may be due to several reasons. It may be speculated that application engagement might be lower in the multi-component intervention if participants were already satisfied with the intervention content delivered in the non-mHealth intervention section. Additionally, apps can increase user engagement compared to websites because apps may be more accessible and often include push notifications reminding users to engage with the app.

Several studies have examined whether user involvement in digital interventions in pregnancy differs according to user characteristics. A previous study in the US reported that women from ethnic minorities and with low incomes generally had lower levels of engagement in online interventions29. Our results observed no difference in user engagement according to educational attainment, age, and perceived competence for a healthy diet. However, there is some evidence that women who are multiparous, were overweight before pregnancy or have low competence for healthy physical activity have lower engagement with the HealthyMoms app. These findings can be compared with data from a nonpregnant population that have reported a positive association with self-efficacy and user engagement and an inverse relationship between body weight and user engagement11. Furthermore, our qualitative evaluation of the HealthyMoms30 app showed that multiparous women generally had a lower need for pregnancy-related content on the app and less time spent on such apps. Nevertheless, further research is needed to identify and understand the differences in user engagement and how they affect the effectiveness of digital interventions during pregnancy.

Associations of user engagement with intervention effectiveness

This is, to the best of our knowledge, the first study to examine the association of user engagement with the effectiveness of a standalone smartphone application on weight gain, diet quality, and physical activity in pregnancy. However, our results are comparable with the PEARS randomized controlled trial examining the effectiveness of a multi-component intervention that includes an antenatal behavior change intervention in combination with application27. When the authors analyzed the differences of a comprehensive set of diet and physical activity variables between app users and non-users, only the glycemic index and energy proportion of free dietary sugars were statistically significantly lower among app users (P = 0.032– 0.041)27 . While the results are difficult to compare because the PEARS trial was multi-component, our results are partially agreeable because we did not identify any relationship between app usage (assessed as number of page views and app sessions) and quality of diet or physical activity. However, we identified a statistically significant association of number of enrollments with changes in diet quality and gestational weight gain. This suggests that different indicators of user engagement (app usage vs. registration) can have different implications for app effectiveness. Furthermore, recording health behaviors (eg, through goal setting and self-monitoring) has been suggested as an important behavior change technique in weight management31, also in pregnancy32. In addition, the lack of relationship between user engagement and physical activity can also be seen as interesting. One reason for this finding is that the HealthyMoms trial did not have any effect on physical activity which we previously hypothesized could be partly due to delayed follow-up in pregnancies where the number of physical activity has generally decreased9. Another interesting finding was that primarily physical activity registration was associated with lower pregnancy weight gain and better diet quality. One reason for this finding may be that physical activity registration has greater variation and is the most used registration feature with 62% of all enrollments. In comparison, only 9% of enrollments were dieters. Indeed, our qualitative evaluation of the HealthyMoms app showed that some participants found diet registration less motivating because it was difficult to remember food intake and that feedback was sometimes ambiguous30. In addition, it is also relevant to consider the differences in the way registration is carried out. For example, physical activity registrations, unlike diet and weight registrations, were only performed when “healthy” behaviors had been performed which may explain why this registration underlies the association with intervention effectiveness. It is also possible that physical activity registration reflects greater overall and deeper engagement with applications that has not been captured by other variables. Clearly, more research is needed to elucidate the effect of different types of user involvement in exclusive digital interventions on pregnancy.

Strengths and limitations

This study has several strengths such as being objective and accurate with regard to user engagement (eg, registration, application use, and page views) and intervention outcomes (eg, objectively and accurately measured weight gain and physical activity). Furthermore, the HealthyMoms trial had a very low dropout rate (11% in the intervention group included in the study). Finally, another important strength is that we can adjust our results for a range of relevant confounders. For example, it has been hypothesized that the association of user involvement in digital interventions with intervention effectiveness could be confounded by unmeasured factors11 such as baseline motivation, self-efficacy, or perceived competence that some previous studies have taken into account. For example, women with higher motivation, self-efficacy, or perceived competence may be less likely to experience excessive pregnancy weight gain but may have a greater interest in engaging with mHealth applications. Thus, it is a strength of the study that we were able to adjust estimates for perceived baseline competence of a healthy diet and physical activity.

Our study also has some limitations that should be considered. First, our sample is generally highly educated and reports high perceived baseline competencies for a healthy diet and physical activity which may influence the generalizability of our findings. Although we adjusted our results for perceived educational attainment and competence, with little effect on estimates, further research is needed in other populations. Furthermore, because the HealthyMoms intervention only reduced gestational weight gain in women who were overweight or obese before pregnancy, our ability to detect an association with gestational weight gain may be limited even though we were able to detect such an association. Finally, our relatively small sample size (n = 55) regarding usage levels (i.e., application sessions and page views) motivated further studies.

Implications

Our results provide some evidence for a beneficial association of higher user engagement with the HealthyMoms app with lower pregnancy weight gain and better diet quality. These results are in agreement with previous results in the nonpregnant population which have generally shown an association between higher user involvement in digital interventions and weight loss12. Therefore, our results suggest that a high level of user involvement is also desirable in lifestyle interventions in pregnancy and that efforts can be made to increase user involvement in such interventions. However, although statistically significant, the associations identified in this study generally have relatively small effect sizes indicating that the results may be more relevant for larger groups of individuals than single individuals. Indeed, as previously hypothesized11 individuals may respond differently despite being involved at the same level and the optimal dose of engagement may vary depending on the characteristics of the user. Therefore, more knowledge in this area enables the identification of patterns of individual engagement that are beneficial for digital behavior change interventions. Thus, further research is needed to examine whether different types of users respond differently to different levels and types of involvement in digital interventions in pregnancy.

Conclusion

A greater total number of enrollments (weight, diet, and physical enrollment) in the HealthyMoms app was associated with decreased pregnancy weight and improved diet quality. However, the number of application sessions and page views had no relationship with weight gain, diet quality, and physical activity. Additional studies are needed to better elucidate the effect of different types of user involvement in digital pregnancy interventions on their effectiveness.

Data availability

The data set used and analyzed during the current study is available from the relevant authors upon reasonable request.

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Kleinbaum, D.G., Kupper, L.L., Nizam, A. & Muller, K. E. Applied Regression Analysis and Other Multivariable Methods (Thomson, 2008).

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Choi, J., Lee, J.H., Vittinghoff, E. & Fukuoka, Y. mHealth physical activity intervention: A randomized pilot study in physically inactive pregnant women. mother. Child Health J. 20, 1091-1101 (2016).

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