Can a Mobile Game-like Intervention Help Women with Anxiety and Depression? Examining real world data of ‘OCD.app - Anxiety, Mood & Sleep’

Can a Mobile Game-like Intervention Help Women with Anxiety and Depression? 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Examining real world data of ‘OCD.app - Anxiety, Mood & Sleep’ Avi Gamoran, Anat Brunstein-klomek, Guy Doron This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2668691/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Anxiety and depression symptoms are a significant mental health challenge for women in the reproductive age and midlife. Cognitive behavioral therapy (CBT) based mobile health (mHealth) interventions may be a viable solution for addressing the treatment gap for women at these ages. We collected real world data of women using the CBT based app “OCD.app - Anxiety, Mood & Sleep” from October 2020 to January 2023. Women’s levels of anxiety (GAD-7) and depression (PHQ-9) were evaluated prior to the intervention (T0), at the payment barrier (T1), and upon completion of the intervention (T-Final). Women’s dropout rates were associated with younger age and more severe symptoms. Large effect-size reductions were found at T1 (n = 1,554; Cohen’s d = 0.702) and T-Final (n = 491; Cohen’s d = 0.774) with 37.9% reaching clinically significant improvement in anxiety symptoms (GAD-7 change > 4). Similar analyses of women’s PHQ-9 scores indicated small effect-size reductions at T1 (n = 512; Cohen’s d = 0.34) and moderate effect-size decreases at T-Final (n = 140; Cohen’s d = 0.489) with 23.6% of women reaching clinically significant improvement in depression symptoms (PHQ-9 change > 5). Results support the effectiveness of brief CBT-based mHealth interventions for women with depression and anxiety symptoms in real world settings. Biological sciences/Psychology Biological sciences/Psychology/Human behaviour Health sciences/Diseases/Psychiatric disorders/Anxiety Health sciences/Diseases/Psychiatric disorders/Depression Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Mood and anxiety disorders are common among women of reproductive age (between puberty and menopause, ages 15–44) 1 and midlife (ages 45–64) 2 – 4 . Both age groups mark a significant period of transition for women. During the reproductive years, women go through milestones of menstrual cycle, pregnancy, birth, maternity and parenting 5 . Midlife spans from the natural cessation of reproductive capacity (menopause) until retirement, and is accompanied by a range of family, work and physiological changes that can impact various aspects of a woman's life 6 . Changes among both age groups, therefore, often include shifts in family structure and dynamics, work responsibilities and satisfaction, relationship dynamics, redistribution of body weight, changes in sexual functioning ,and the emergence of new responsibilities 7 – 11 . Nevertheless, research suggests women show increased risk for depression and anxiety during midlife ages relative to reproductive years 12 . For instance, women during the menopause transition period that typically lasts anywhere from a few months to several years,are at higher risk for anxiety symptoms relative to premenopausal women 3 , 4 , particularly women showing low anxiety before menopause 2 . Women entering the menopause transition period are also twice as likely to develop significant depressive symptoms than women before menopause 4 , 13 – 15 although these differences seem to diminish post-menopause 14 . Cognitive behavioral therapy (CBT) is considered the “gold standard” non-pharmaceutical intervention for anxiety and depression 16 . Numerous studies have shown CBT efficacy during various life transitions and challenges including premenstrual dysphoria, postpartum depression, menopause related distress, trauma and grief 17 – 21 . CBT interventions use various methods (e.g., exposure, cognitive restructuring, behavioral experiments) to challenge maladaptive beliefs and appraisals of internal (e.g., physical sensations, thoughts, feelings, emotions) and external events (e.g., conflict at work, economic crisis) 16 . In the context of midlife, for instance, CBT interventions may target maladaptive beliefs about aging (e.g., “Aging means I’ll be abandoned”) or menopause (e.g., “Menopause symptoms are shameful”) 22 , 23 . Despite the evidence for its efficacy, several barriers may hinder women from seeking CBT treatment for mental health conditions.These include high cost of face-to-face CBT therapy, limited access to care (particularly for women living in rural or remote areas), transportation or scheduling conflicts (e.g., between work and child rearing commitments) that make it difficult to attend in-person appointments and embarrassment or fear of being stigmatized (e.g. when discussing postpartum depression or menopause-related issues) 24 , 25 . Digital technologies help overcome some of these barriers by providing easily accessible, continuous (24 hours a day), CBT-based interventions from anywhere (with internet connection), at a lower cost than face-to-face interventions that can often be provided anonymously 26 . Moreover, such interventions often include personalisation capacities and game-like interactive elements 27 , 28 ) making them attractive to a wide range of audiences. Digital interventions, therefore, have the potential to improve the delivery of evidence-based care to women in need of mental health assistance, thereby narrowing the disparity between the number of individuals who require psychological treatment and those who actually receive it 29 – 31 . Indeed, a substantial body of evidence suggests the efficacy of digital interventions such as computerized and internet-based CBT in treating anxiety and depression symptoms 32 – 34 . Studies have demonstrated that online CBT interventions for these disorders are equally effective as traditional face-to-face CBT treatments 35 . More recent findings support the efficacy and effectiveness of CBT-based mobile health (mHealth) digital technologies in reducing various mental health symptoms 36 . “OCD.app, Anxiety, Mood & Sleep'' (ocd.app) is a mobile app on the GGtude platform that includes brief CBT-based daily exercises targeting maladaptive beliefs associated with various mental health symptoms. Eight randomized controlled trials (RCTs) 37 – 44 in various countries (e.g., US, Italy, Spain, Turkey, Israel), as well as real world data analyses 45 , 46 have consistently linked training on the GGtude platform with significant reductions in a variety of mental health symptoms. For instance, in a recent fully remote crossover RCT in the USA, Ben-Zeev and colleagues 40 have shown that people with serious mental illness (SMI; n = 315) training daily with the GGtude platform for 30 days show reductions in self-reported depression, disability and anxiety as well as an increase in positive recovery attitudes and self-esteem. The waitlist control group participants showed comparable changes in outcome measures after cross over. Evaluating the effectiveness of training with the GGtude platform on participants high in COVID-19 related distress, Akin-Sari and colleagues 39 showed app use was associated with reductions in COVID-19 related distress, depression symptoms and associated maladaptive beliefs. The depression and anxiety modules of ocd.app target maladaptive beliefs that are commonly associated with anxiety and depression symptoms. These beliefs include but are not limited to, beliefs in change, over-monitoring of physical sensations, catastrophization of physical sensations and psychological experiences, overestimation of threat, helplessness and hopelessness, self-criticism, perfectionism and fear of being abandoned. Users engage in a comprehensive program consisting of brief daily exercises lasting only 3 minutes. By discarding anxiety and depression related cognitions (swiping them up) and embracing more adaptive statements (pulling them towards themselves), users increase accessibility of adaptive over maladaptive cognitions and learn to challenge their maladaptive beliefs. Systematically targeting different anxiety and depression related cognitions, in turn, is expected to reduce users’ maladaptive beliefs and related symptoms. Training on the GGtude platform has been suggested to decrease users' mental health symptoms by altering the balance between adaptive and maladaptive cognitions 45 . Users' ability to produce and retrieve adaptive self-statements may increase through repeated exposure to such self-statements. Prompting users with maladaptive beliefs while exposing them to unexpected competing cognitions may expedite their reflective processing to adjust their maladaptive beliefs 47 . Physical movements involved in the daily categorization exercises (i.e., swiping up or down of cognitions) may also lead to more distinct signals regarding the congruity of adaptive versus maladaptive cognitions to their mental health goals (i.e., embodied cognition) 48 . In addition, self-statements pairing self-referential pronouns with positive action words may enhance users' implicit positive self-concept 49 . Finally, brief psychoeducation scripts, such as "The world can feel dangerous. However, constantly searching for danger increases our fears and anxieties. Let's learn to reduce this tendency" may help users understand and reinforce basic principles of cognitive-behavioral therapy 50 . The reproductive and midlife periods include many life transitions that may activate maladaptive cognitions and increase the risk for depression and anxiety. Maladaptive cognitions are often associated with the various psychological experiences, physical sensations, potential threats and self-criticism which are integral part of women’s lives during these ages. We hypothesize that brief training exercises targeting maladaptive beliefs and cognitions will be feasible in reducing depression and anxiety symptoms among women in both these significant developmental periods. Despite different trajectories along the way both age groups we expect using the anxiety and depression modules of ocd.app would be associated with reductions in GAD-7 and PHQ-9 scores at both T1 and T-Final assessment points. Stronger effects are expected for women completing all levels of the corresponding modules as they trained longer and increased the activation of adaptive relative to maladaptive cognitions facilitating their retrieval as well as having learned to challenge a greater number of different maladaptive beliefs 45 , 47 . Results Baseline characteristics The initial pool of this study included 7,478 women that downloaded the “OCD.app, Anxiety, Mood & Sleep” and completed the main assessment measure (i.e., GAD-7 & PHQ-9) for the first time (T0: baseline assessment). Of these women, 2,111 selected the depression module (PHQ-9), and 5,367 selected the anxiety module (GAD-7). The mean age of women using the depression module was 28.7 (SD = 11.42; range 15–80), and the anxiety module was 28.0 (SD = 11.26; range 15–80). The numbers of women at each assessment stage are shown in Table 1 . Table 1 Women reaching each assessment point. Module Assessment point # Women % Remaining Days using app (± SD) Anxiety T0 5367 - - Anxiety T1 1554 29.0 14.2 (29) Anxiety TFinal 491 31.6 33.1(47.8) Depression T0 2111 - - Depression T1 512 24.3 24 (59.5) Depression TFinal 140 27.3 43.8 (67.7) Differences between completers and non-completers Anxiety Of the 5,367 women that completed the T0 assessment, 1,554 participants (29%) completed the GAD-7 a second time (T1). The mean number of days using the app between T0 and T1 was 14.2 (SD = 29). Of the participants completing the T1 assessment point, 491 participants (31.6%) passed the payment barrier and reached the final GAD-7 assessment (T-Final). The mean number of days using the app between T1 and T-Final was 33.1 (SD = 47.8). Significant age differences were found between T0 and T1 time points ( t (4035) = -9.28, p < .001). Participants at T0 were younger (M = 25.8, SD = 10.02) than participants who completed T1 (M = 31.21, SD = 11.29). Significant differences were also found between T1 and T-Final ( t (4288) = -20.947, p < .001). Participants at T1 were younger than participants who completed T-Final (M = 35.98, SD = 10.96). Significant differences between women reaching T0 and T1 were found in the baseline GAD-7 scores (at T0) ( t (4860) = 5.648, p < .001). Participants at T0 had higher GAD-7 scores (M = 15.75, SD = 4.47) than participants who completed T1 (M = 14.88, SD = 4.52). Significant differences in the baseline GAD-7 scores were also found between T1 and T-Final ( t (1552) = 3.137, p = .002). Participants at T1 were higher than participants who completed T-Final (M = 14.08, SD = 4.89). Significant differences between participants reaching T0 and T1 were found in the state mood scores (at T0) (t(4860) = -4.449, p < .001). Participants at T0 had lower state mood scores (M = 2.81, SD = 1.01) than participants who completed T1 (M = 2.97, SD = 1.02). Significant differences in the state mood scores (at T1) were also found between participants reaching T1 and T-Final (t(1552) = -4.571, p < .001). Participants at T1 had lower state mood scores (M = 3.12, SD = 0.99) than participants who completed T-Final (M = 3.35, SD = 0.86). Depression Of the 2,111 women that completed the T0 assessment, 512 participants (24.3%) completed the PHQ-9 a second time (T1). The mean number of days using the app between T0 and T1 was 24.03 (SD = 59.5). Of the participants completing the T1 assessment point, 140 participants (27.3%) passed the payment barrier and reached the final PHQ-9 assessment (T-Final). The mean number of days using the app between T1 and T-Final was 43.8 (SD = 67.7). Significant age differences were found between T0 and T1 time points ( t (1901) = -9.134, p < .001). Participants at T0 were younger (M = 26.52, SD = 10.31) than participants who completed T1 (M = 32.3, SD = 11.14). Significant differences were also found between T1 and T-Final ( t (1711) = -12.002, p < .001). Participants at T1 were younger than participants who completed T-Final (M = 37.5, SD = 11.06). Significant differences between participants reaching T0 and T1 were found in the baseline PHQ-9 scores (at T0) ( t (1901) = 7.321, p < .001). Participants at T0 had higher PHQ-9 scores (M = 16.58, SD = 6.45) than participants who completed T1 (M = 13.72, SD = 6.5). Significant differences in the baseline PHQ-9 scores were also found between T1 and T-Final ( t (468) = 3.428, p < .001). Participants at T1 were higher than participants who completed T-Final (M = 11.54, SD = 5.79). Outcomes at post-treatment Overall Anxiety improvement A paired t-test analysis of the 1,554 women reaching T1 suggested large effect-size reductions ( t (1553) = 27.682, p < .001, Cohen’s d = 0.702, Cohen’s d 95% CI: [0.647, 0.758]) in GAD-7 scores between T0 (M = 14.62, SD = 5.22) and T1 (M = 11.6, SD = 5.22). Similarly, a paired t-test analysis for the 491 women reaching T-Final indicated a large effect-size for reductions in GAD-7 scores ( t (490) = 17.127, p < .001, Cohen’s d = 0.774, Cohen’s d 95% CI: [0.672, 0.874]) between T0 (M = 14.08, SD = 4.89) and T-Final (M = 10.29, SD = 5.53). Overall Depression improvement A paired t-test analysis of the 512 women reaching T1 suggested small effect-size reductions ( t (511) = 7.695, p < .001, Cohen’s d = 0.34, Cohen’s d 95% CI: [0.251, 0.429]) in PHQ-9 scores between T0 (M = 13.17, SD = 6.35) and T1 (M = 11.61, SD = 6.27). Similarly, a paired t-test analysis of the 140 women reaching T-Final indicated medium effect-size for reductions in PHQ-9 scores ( t (139) = 5.761, p < .001, Cohen’s d = 0.489, Cohen’s d 95% CI: [0.312, 0.664]) between T0 (M = 11.54, SD = 5.79) and T-Final (M = 9.16, SD = 6.51). Anxiety improvement at T1 To control for intervening factors (e.g., age, mood), A Linear Mixed Model was conducted for GAD-7 anxiety scores between baseline assessment and T1. Women were split into two age groups: Reproductive Age (RA; 15–44), and Midlife (45–64). A base model (M0) included participants’ random intercepts, the fixed effects of age-group, mood (trait & state levels), and the two way interactions with group. The first model (M1) added the fixed effects of time (= assessment phase), and the two way, and three way interactions with time to M0. This model yielded a significantly better fit than M0 (M1-fit: -2LL = 17232.4, df = 14; χ 2 (6) = 642.67, p < .001). An additional model (M2) added the random effect of time to M1. This model yielded a better fit than M1 (M1-fit: -2LL = 17219.4, df = 15; χ 2 (1) = 13.05, p < .001). A significant main effect was found for time (b = -2.58, 95% CIs = -3.11 – -2.05, p < .001), demonstrating an overall effect of improvement from T0 to T1. The time X trait mood interaction was also significant, demonstrating women with more positive trait mood improved more between T0 and T1 (Fig. 1 ). A main effect for age-group (b = 1.12, 95% CIs = 0.54–1.70, p < .001) was also found demonstrating that women in the RA group had overall higher anxiety scores. Significant main effects were found for trait mood (b = -1.75, 95% CIs = -2.26 – -1.24, p < .001), and state mood scores (b = -0.65, 95% CIs = -1.25 – -0.05, p = .034; Supplementary Table 3), demonstrating that positive trait and state mood were linked to lower anxiety scores overall. Anxiety improvement at TFinal A similar Linear Mixed Model was conducted for GAD-7 anxiety scores between T0, and treatment completion at T-Final. M1, including the fixed effects of time, yielded a significantly better fit than the base model M0, including the effects of age-group, mood (state & trait), and participants’ random intercepts (M1-fit: -2LL = 8177.94, df = 14; χ 2 (6) = 318.98, p < .001). M2, added to M1 the random effect of time, yielding a significantly better fit than M1 (M1-fit: -2LL = 8167.57, df = 15; χ 2 (2) = 10.37, p < .001). A significant main effect was found for time (b = -1.89, 95% CIs = -2.32 – -1.46, p < .001), replicating the findings from T0 to T1, and demonstrating an overall reduction in GAD-7 scores from T0 to T-Final. Significant main effects were found for trait mood (b = -1.61, 95% CIs = -2.42 – -0.80, p < .001), and state mood scores (b = -0.83, 95% CIs = -1.65 – -0.02, p = .045), replicating the findings from T0 to T1, linking positive mood with overall lower anxiety scores. Depression improvement at T1 A Linear Mixed Model was conducted for PHQ-9 scores between baseline assessment, and the payment barrier at T1. M1, which added the fixed effects of time, yielded a significantly better fit than the base model M0, including the effects of age-group, mood (state & trait), and participants’ random intercepts (M1-fit: -2LL = 6005.9, df = 14; χ 2 (6) = 58.74, p < .001). M2, added to M1 the random effect of time. This model did not yield a better fit than M1 (M2-fit: -2LL = 6005.9, df = 15; χ 2 (1) = 0). The age-group X time interaction was significant (b = -1.15, 95% CIs = -2.16 – -0.14, p < .001), suggesting women in the RA group showed larger improvement between T0 and T1, compared with the Midlife group (Fig. 2 ). A significant main effect was found for the age-group (b = 3.16, 95% CIs = 1.94–4.39, p < .001), showing women in the RA group had higher PHQ-9 scores at both time points. A significant main effect was found for trait mood as well (b = -2.44, 95% CIs =-3.58 – -1.30, p < .001; Supplementary Table 1), showing positive trait mood was linked to lower PHQ-9 scores. Depression improvement at TFinal A similar Linear Mixed Model was conducted assessing PHQ-9 Scores between T0 and T-Final. M1, adding the fixed effects of time, yielded a significantly better fit than M0 which included effects of age-group, mood scores, and participants’ random intercepts (M1-fit: -2LL = 2716.15, df = 14; χ 2 (6) = 57.61, p < .001). M2, which further included the random effects of time, yielded a significantly better fit than M1 (M2-fit: -2LL = 2706.25, df = 12; χ 2 (2) = 9.90, p = .007). In this analysis, however, only a significant main effect for time was detected (b = -0.68, 95% CIs = -1.19 – -0.16, p = .010), suggesting an overall effect of improvement over assessment points (Fig. 3 , Supplementary table 2). Anxiety Clinically significant Improvement To evaluate the clinical significance of the app intervention, the proportion of women achieving a clinically significant improvement in GAD-7 scores (defined as a reduction in GAD-7 of > 4 51 ) was calculated. Of the 1554 women who completed T1, a total of 589 participants reached a clinically significant improvement (37.9%): 510 participants by T1 and an additional 79 participants by T-Final. The average number of days to reach a clinically significant improvement was 16.96 days (SD = 29.43). The numbers of women who achieved a clinically significant improvement, by age groups (RA / Midlife) are shown in Table 2 . Depression Clinically significant Improvement The proportion of women achieving a clinically significant improvement in PHQ-9 scores (defined as a reduction in PHQ-9 of > 5 52 ) was calculated. Of the 512 participants who completed T1, a total of 121 participants reached a clinically significant improvement (23.6%): 88 participants by T1 and an additional 33 participants by T-Final. The average number of days to reach clinically significant improvement was 31.88 days (SD = 55.4). The numbers of women that achieved clinically significant improvement, by age-group (RA / Midlife) are shown in Table 2 . Table 2 Clinically Significant Improvement. Module Scale Group Baseline Score (± SD) Total # Women at T1 # Women R-CSI % R-CSI # R-CSI by T1 # R-CSI by T-Final Mean Days till R-CSI (± SD) Anxiety GAD-7 RA 14.8 (4.6) 1286 485 37.7 429 56 17.4 (30.7) Anxiety GAD-7 Midlife 13.9 (4.7) 268 104 38.8 81 23 14.9 (22.8) Anxiety GAD-7 Total 14.6 (4.7) 1554 589 37.9 510 79 17 (29.4) Depression PHQ-9 RA 13.7 (6.4) 412 102 24.8 79 23 33.4 (58.7) Depression PHQ-9 Midlife 10.8 (5.8) 100 19 19.0 9 10 23.8 (32.1) Depression PHQ-9 Total 13.2 (6.4) 512 121 23.6 88 33 31.9 (55.4) Discussion Anxiety and depression symptoms are common in women in the reproductive and midlife age. Our main results demonstrate the effectiveness of an mHealth intervention comprising brief cognitive training for reducing anxiety and depression symptoms among women for both these age groups. Although the app was designed for the general audience it seems to be relevant for the two developmental stages of women. Women using the app showed large effect size reductions in anxiety scores following 14.2 days of app use at T1. Somewhat larger effect-size reductions were detected following 33.1 days of use at T-Final. The anxiety module of the GGtude platform seemed to be similarly effective for both age groups. The effects of app training on anxiety was moderated by trait mood, but only for women using the app until the payment barrier (T1). Indeed, higher mood has been associated with increased motivation and engagement with treatment. The range of individual differences in motivation, however, may have been significantly reduced following the payment barrier. Most significantly, around 38% of women using the anxiety module of the app showed clinically significant improvement in anxiety symptoms. Considering baseline anxiety scores were in the moderate-severe range at T0 (Mean GAD-7 = 14.6 53 ), such reductions in anxiety levels may have had a significant impact on the daily lives of around 600 women in this study. Our results suggest more diminished effects of the intervention on depression symptoms. Women reaching T1, showed small effect-size reductions in depression symptoms following 24 days of app use. These effects were moderated by group and suggested that women in the reproductive age group benefited from the intervention more than women in the midlife group. This finding may be attributed to the higher baseline (T0) depression scores found in the RA group reflecting a wider range of possible improvement, but it is also consistent with previous findings showing stronger effects of the GGtude intervention for users with more severe symptoms 45 . Group allocation, however, did not moderate the effects of the app on depression in women reaching T-Final. Moreover, women reaching T-Final showed medium effect-size reductions in depression symptoms following 43.8 days of app use. Untreated depressive episodes become longer and more frequent with time. More intense treatment (higher dosage) may be more effective in relieving depressive symptoms. Indeed, previous findings supported a dose response relationship using the GGtude platform 46 . Importantly, however, a significant proportion of women benefited from training on the depression module of the intervention such that 23.6% reached clinically significant improvement. In this study, we evaluated improvement in anxiety and depression scores within participants (using paired t-tests). Our findings indicate that women improve compared to themselves, and increased app use is associated with greater improvement (albeit that some of the women with the most severe symptoms tended to drop out). The improvements found over assessment points can’t, therefore, be explained by pre-existing baseline differences in GAD-7 or PHQ-9 scores. Nevertheless, women using the app until T1 had higher anxiety and depression scores at T0 than women reaching T-Final. It could be that women with more severe symptoms found it more difficult to persist with app use. More severe symptoms are also associated with increased disability and lower socioeconomic status 54 . Women with more severe symptoms may have found it harder to afford paying for the app. Our study shows that scalable, low cost, low intensity, short duration interventions may be highly relevant for women in real-world settings. Between 23–38% of women achieved clinically significant improvement. Nevertheless, this study is based on real world data and lacks a control group. Therefore, our results do not permit refuting alternative interpretations of the effects found including the mere passage of time. The initial sample of our study was large with a significant dropout rate. Similar dropout rates, however, have been reported in studies on eHealth applications 55 – 57 . In these studies, dropouts are assumed as a feature of these types of interventions. In addition, use of the app past T1 was contingent on a payment fee. This could have contributed to the significant dropout rates from T1 to T-Final as well as the larger effect sizes detected in women reaching T-Final. Multiple RCTs have indicated that cognitive training targeting maladaptive beliefs delivered via the GGtude platform are associated with significant reductions in various symptoms and associated cognitions. Our real world data support the effectiveness of the GGtude platform for women with anxiety and depression symptoms. Considering women use apps more than men, especially mobile health apps 58 and that mHealth interventions are unrestricted by time and place, training on the GGtude platform may help women receive help while they are experiencing significant life changes such as menstrual cycles, pregnancy, childbirth, marriage/divorce and menopause. Indeed, brief daily training is very suitable for women who have busy schedules during these two life phases. The app may be part of self care that needs to be prioritized during these significant life transitions. Apps are especially important after COVID-19 that we have learned the intervention should be delivered remotely and in an independent manner. Numerous apps for depression are available, the investigated app, however, is focused on helping women who are “trapped” by their maladaptive beliefs. These beliefs can affect various aspects of a woman's life including biological and hormonal changes and psychological and interpersonal/social ones. Cognition during these developmental stages is important since the meaning of the relevant life events included is what makes a difference for women. In addition, the focus on cognition in both depression and anxiety is in line with a recent review and meta analysis which indicated iCBT programmes include training in a wide array of cognitive and behavioral skills, but should include only the beneficial components 59 . An app focusing on women's own cognitions lead to ripple effects on women’s surroundings including partners, family members and friends. Training on the GGtude platform can be recommended to women clients as a standalone tool (e.g. when they wait for a professional intervention) or integrated into the psychotherapy they receive. It can be part of stepped care of stratified care in women's mental health interventions. Moreover, by inserting the usage of the app we may be able to build a tailored treatment plan for each woman rather than asking them to conform to a preset model of care. The specific cognitions which were most helpful for each woman to discard or embrace can be continued to practice after completing the app usage. The desired abilities to discard and embrace cognitions can also become psychoeducation about women’s cognitive work needed during major life transitions in reproductive and midlife ages. Method Study design App Data included in this study was collected between October 2020 and January 2023, and was retrospectively analyzed.In this study the assessments were included for two separate treatment modules: Anxiety and Depression. Participants chose their modules during the app onboarding procedure. The analysis consists of comparison between An initial baseline assessment (T0) completed at the start of the relevant module and two follow-up assessments. The second assessment point (T1) is completed on the last level before the payment barrier of the app. The third assessment (T-Final) is completed upon completion of the final level of the module. The Depression module also includes an additional assessment point (T2) between T1 and T-Final, which was included in the linear mixed models analyses of improvement between T0 and T-Final. In each follow-up assessment (i.e., comparison of T0-T1 & T0-TFinal), only participants who completed all assessments up to that assessment point were included. Participants Women in the study downloaded the “OCD.app, Anxiety, Mood & Sleep '' through Google Play or Apple Store. We used the General Anxiety Disorder − 7 (GAD-7) 60 to assess General anxiety symptoms. Generalized anxiety disorder (GAD) is the most common of the anxiety disorders, with an estimated lifetime prevalence of 7.1% in adult women 61 . We used the Patient Health Questionnaire-9 (PHQ-9) 62 , 63 to assess depression symptoms. The GAD-7 and PHQ-9 were completed at three different times during use of the application: at baseline (T0 assessment; level 1), when reaching the payment barrier (T1 assessment; level 20 for anxiety; level 18 for depression) and upon completion of the anxiety module (T-Final assessment; level 48 for anxiety; level 66 for depression). Women in the depression completed the T2 assessment at level 48. Ethical considerations This study was approved by The Reichman University (IDC) Research Ethics Committee Ethical clearance number: P_2023036. All methods in the study were performed in accordance with the relevant guidelines and regulations set forth by the ethical committee. All data collected on the GGtude platform is completely anonymous (no user name or password required). Participants provide during app onboarding informed consent for analysis of app-use data and sharing of anonymized data from the mobile app including self-reported age & gender and mobile-app usage. Procedure All participants in this study used the English version of the Anxiety and Depression module of “OCD.app - Anxiety, Mood & Sleep” (Versions 3.0.6–3.4.4). After downloading the app, participants go through an onboarding procedure whereby they complete their age and gender, and go through a tutorial. They then complete the T0 assessment related to their chosen module for the first time (T0). The last level before the payment barrier includes the second assessment point (T1). The last level of the module (T-Final assessment point). “OCD.app - Anxiety, Mood & Sleep” is a smartphone app that uses the GGtude platform, a system designed to promote cognitive flexibility of individuals struggling with various mental health difficulties through brief daily training 38 , 40 38,40 . The anxiety and depression modules were specifically designed to help counteract cognitions relating to maladaptive beliefs shown to be linked to anxiety and depression symptoms. The intervention comprises daily brief game-like exercises designed to produce changes in the relative activation of adaptive and maladaptive beliefs such that adaptive beliefs would be more easily retrieved than maladaptive ones. The main gameplay of the intervention consists of users being taught to discard maladaptive cognitions by swiping them upwards (out of the top of the screen). Users are asked to swipe downwards adaptive cognition towards themselves and embrace them (downwards on the screen). The anxiety module includes 48 levels and the depression module includes 66 levels. Every 3 levels target a particular maladaptive belief and cognitions associated with it. Each level comprises several cognitions (in the form of statements) that are either consistent with their maladaptive belief (dysfunctional) or inconsistent with this belief (adaptive). For example, cognitions inconsistent with perfectionism include "Mistakes teach me how to overcome my fears" and "Imperfect is human" (Fig. 4 ). The push notifications are used to remind users to use the app each day. After completing three levels a day, a screen instructing the user to stop using the app for the day is presented. Outcomes The primary outcome measures were the GAD-7 60 for anxiety symptoms and PHQ-9 62,63 for depressive symptoms. Secondary outcomes included rates of ‘significant improvement’ clinical improvement, based on PHQ-9 (Change > 5 52 ) and GAD-7 (Change > 4 51 ) at TFinal. State and trait mood were assessed using a VAS depicting five faces, on a scale from 1 to 5, ranging from very sad to very happy 45 , 46 . Trait mood was calculated as the average of a women’’s daily answers which were mean-centered across women. State mood was calculated as the difference between a woman's response on the day of the assessment and the average of their daily answers. The GAD-7 and PHQ-9 were completed at three different times during use of the application: at baseline (T0 assessment; level 1), when reaching the payment barrier (T1 assessment; level 20 for anxiety; level 18 for depression) and upon completion of the anxiety module (T-Final assessment; level 48 for anxiety; level 66). Statistical analyses All statistical analyses were performed using R version 4.2.1 64 . Linear mixed models (LMMs) were conducted with the lme4 package 65 and significance testing for the linear mixed models were conducted with the lmerTest package 66 . Effect sizes for t-tests were calculated with the effectsize package 67 . Follow-up tests for linear mixed models were performed with the emmeans package 68 .The linear mixed models summary tables were drawn with the sjPlot package 69 . Declarations Data availability The dataset analyzed during the current study is available upon request to the corresponding author. Code availability The code used to generate the statistical outputs is available upon request to the corresponding author. Author Information Authors and Affiliations Ben Gurion University of the Negev, Department of Psychology Avi Gamoran Reichman University, Baruch Ivcher School of Psychology Anat Brunstein-klomek & Guy Doron GGtude Ltd., Tel Aviv, Israel Guy Doron Contributions A.G. conducted the statistical analysis and created the visualizations. G.D. conceptualized the study and obtained the data from GGtude Ltd. A.G, A.B.K & G.D. drafted and edited the article. All authors reviewed and approved the final version of the manuscript. Corresponding author Correspondence to Guy Doron, Baruch Ivcher School of Psychology Reichman University, Herzliya. P.O. Box 167, 46150 Herzliya, Israel. Email: [email protected] Competing interests G.D. is a co-developer of GG OCD. G.D. is also a co-founder of GGtude Ltd. GG OCD is the subject of this evaluation and therefore has financial interest to GGtude Ltd. A.G & A.B.K declare no competing interests. References Farr, S. L., Bitsko, R. H., Hayes, D. K. & Dietz, P. M. Mental health and access to services among US women of reproductive age. Am. J. Obstet. Gynecol. 203, 542.e1–9 (2010). Bromberger, J. T. et al. Does risk for anxiety increase during the menopausal transition? Study of women’s health across the nation. Menopause 20, 488–495 (2013). Freeman, E. W. Associations of depression with the transition to menopause. Menopause 17, 823–827 (2010). Hart, J. Menopause: Shifting hormones linked to anxiety and depression symptoms. Focus Altern. Complement. Ther. (2019). Nelson, S. K., Kushlev, K. & Lyubomirsky, S. The pains and pleasures of parenting: When, why, and how is parenthood associated with more or less well-being? Psychol. Bull. 140, 846–895 (2014). Dennerstein, L., Guthrie, J. R., Clark, M., Lehert, P. & Henderson, V. W. 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Disord. 310, 429–440 (2022). Linardon, J., Cuijpers, P., Carlbring, P., Messer, M. & Fuller-Tyszkiewicz, M. The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials. World Psychiatry 18, 325–336 (2019). Aboody, D., Siev, J. & Doron, G. Building resilience to body image triggers using brief cognitive training on a mobile application: A randomized controlled trial. Behav. Res. Ther. 134, 103723 (2020). Akin-Sari, B. et al. Cognitive training via a mobile application to reduce obsessive-compulsive-related distress and cognitions during the COVID-19 outbreaks: A randomized controlled trial using a subclinical cohort. Behav. Ther. 53, 776–792 (2022). Akin-Sari, B. et al. Cognitive training using a mobile app as a coping tool against COVID-19 distress: A crossover randomized controlled trial. J. Affect. Disord. 311, 604–613 (2022). Ben-Zeev, D. et al. A Smartphone Intervention for People With Serious Mental Illness: Fully Remote Randomized Controlled Trial of CORE. J. Med. Internet Res. 23, e29201 (2021). Cerea, S. et al. Cognitive training via a mobile application to reduce some forms of body dissatisfaction in young females at high-risk for body image disorders: A randomized controlled trial. Body Image 42, 297–306 (2022). Cerea, S. et al. Cognitive Behavioral Training Using a Mobile Application Reduces Body Image-Related Symptoms in High-Risk Female University Students: A Randomized Controlled Study. Behav. Ther. 52, 170–182 (2021). Cerea, S. et al. Reaching reliable change using short, daily, cognitive training exercises delivered on a mobile application: The case of Relationship Obsessive Compulsive Disorder (ROCD) symptoms and cognitions in a subclinical cohort. J. Affect. Disord. 276, 775–787 (2020). Roncero, M., Belloch, A. & Doron, G. Can Brief, Daily Training Using a Mobile App Help Change Maladaptive Beliefs? Crossover Randomized Controlled Trial. JMIR Mhealth Uhealth 7, e11443 (2019). Gamoran, A. & Doron, G. Effectiveness of brief daily training using a mobile app in reducing obsessive compulsive disorder (OCD) symptoms: Examining real world data of ‘OCD.app - Anxiety, mood & sleep’. J. Obsessive Compuls. Relat. Disord. 36, 100782 (2023). Giraldo-O’Meara, M. & Doron, G. Can self-esteem be improved using short daily training on mobile applications? Examining real world data of GG Self-esteem users. Clin. Psychol. 25, 131–139 (2021). Brewin, C. R. Understanding cognitive behaviour therapy: A retrieval competition account. Behav. Res. Ther. 44, 765–784 (2006). Balcetis, E. & Cole, S. Body in mind: The role of embodied cognition in self-regulation. Soc. Personal. Psychol. Compass 3, 759–774 (2009). Baccus, J. R., Baldwin, M. W. & Packer, D. J. Increasing implicit self-esteem through classical conditioning. Psychol. Sci. 15, 498–502 (2004). Garner, D. M. Psychoeducational principles in treatment. Handbook of treatment for eating disorders., 2nd ed. 2, 147–177 (1997). Toussaint, A., Hüsing, P., Gumz, A. & Wingenfeld, K. Sensitivity to change and minimal clinically important difference of the 7-item Generalized Anxiety Disorder Questionnaire (GAD-7). J. Affect. Disord. (2020). Löwe, B., Unützer, J., Callahan, C. M., Perkins, A. J. & Kroenke, K. Monitoring depression treatment outcomes with the patient health questionnaire-9. Med. Care 42, 1194–1201 (2004). Plummer, F., Manea, L., Trepel, D. & McMillan, D. Screening for anxiety disorders with the GAD-7 and GAD-2: a systematic review and diagnostic metaanalysis. Gen. Hosp. Psychiatry 39, 24–31 (2016). Generaal, E., Timmermans, E. J., Dekkers, J. E. C., Smit, J. H. & Penninx, B. W. Not urbanization level but socioeconomic, physical and social neighbourhood characteristics are associated with presence and severity of depressive and anxiety disorders. Psychol. Med. 49, 149–161 (2019). Kelders, S. M., Van Gemert-Pijnen, J. E. W. C., Werkman, A., Nijland, N. & Seydel, E. R. Effectiveness of a Web-based intervention aimed at healthy dietary and physical activity behavior: a randomized controlled trial about users and usage. J. Med. Internet Res. 13, e32 (2011). Eysenbach, G. The law of attrition. J. Med. Internet Res. 7, e11 (2005). Ludden, G. D. S., van Rompay, T. J. L., Kelders, S. M. & van Gemert-Pijnen, J. E. W. C. How to Increase Reach and Adherence of Web-Based Interventions: A Design Research Viewpoint. J. Med. Internet Res. 17, e172 (2015). Escoffery, C. Gender Similarities and Differences for e-Health Behaviors Among U.S. Adults. Telemedicine and e-Health 24, 335–343 (2018). Furukawa, T. A. et al. Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: a systematic review and component network meta-analysis using individual participant data. Lancet Psychiatry 8, 500–511 (2021). Spitzer, R. L., Kroenke, K., Williams, J. B. W. & Löwe, B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch. Intern. Med. 166, 1092–1097 (2006). National Comorbidity Survey. https://www.hcp.med.harvard.edu/ncs/index.php . Kroenke, K., Spitzer, R. L. & Williams, J. B. W. The Patient Health Questionnaire (PHQ-9)--overview. J. Gen. Intern. Med. 16, 606–616 (2001). Kroenke, K., Spitzer, R. L. & Williams, J. B. The PHQ-9: validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613 (2001). R Core Team. R: A Language and Environment for Statistical Computing. Preprint at https://www.R-project.org/ (2021). Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software vol. 67 1–48 Preprint at https://doi.org/10.18637/jss.v067.i01 (2015). Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in Linear Mixed Effects Models. J. Stat. Softw. 82, 1–26 (2017). Ben-Shachar, M. S., Lüdecke, D. & Makowski, D. effectsize: Estimation of effect size indices and standardized parameters. Journal of Open Source Software 5, 2815 (2020). Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means. Preprint at https://CRAN.R-project.org/package=emmeans (2021). Lüdecke, D. et al. sjPlot: Data Visualization for Statistics in Social Science Version 2.8. 10. Preprint at (2021). Additional Declarations Competing interest reported. G.D. is a co-developer of GG OCD. G.D. is also a co-founder of GGtude Ltd. GG OCD is the subject of this evaluation and therefore has financial interest to GGtude Ltd. A.G & A.B.K declare no competing interests. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2668691","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":182781548,"identity":"4c187a53-4eb9-48e7-842f-bae3d6c6d556","order_by":0,"name":"Avi Gamoran","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Avi","middleName":"","lastName":"Gamoran","suffix":""},{"id":182781549,"identity":"c3ef2dee-a714-4054-92e6-389d8c7f8b88","order_by":1,"name":"Anat Brunstein-klomek","email":"","orcid":"","institution":"Reichman University","correspondingAuthor":false,"prefix":"","firstName":"Anat","middleName":"","lastName":"Brunstein-klomek","suffix":""},{"id":182781550,"identity":"2567e5c8-e498-4e70-bad0-3517779512d3","order_by":2,"name":"Guy Doron","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYFCCBBBhw8NwAMyTkCFWSxpcCw+xWg4zQLUwENYi75587MPHHedl+I4fYPzwg8GCsBbDM8+SZ848c5tH8kwCs2QPMQ4znJFjzMzbdpvH4AYDgzRRfoFqOQfSwvybKC3yEmAtB0Ba2IizxYDnWTLjzLZkoF8S2yx7DIixpT35MMPHNjt7vuOHD9/4UVEnR9iWA3AmYwOQS1AD0JYGIhSNglEwCkbBCAcAcOszPpGtu74AAAAASUVORK5CYII=","orcid":"","institution":"Reichman University","correspondingAuthor":true,"prefix":"","firstName":"Guy","middleName":"","lastName":"Doron","suffix":""}],"badges":[],"createdAt":"2023-03-08 08:59:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2668691/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2668691/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34239437,"identity":"6b5acea1-2a03-4c41-989b-de46d94b4dc6","added_by":"auto","created_at":"2023-03-14 14:56:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13520,"visible":true,"origin":"","legend":"\u003cp\u003eGAD-7 Scores by age-group, between baseline assessment (T0), and payment barrier (T1). Error bars represent 95% confidence intervals around the mean.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2668691/v1/14d1c75ac26c2d616c628179.png"},{"id":34239438,"identity":"712dd55a-4843-43f7-8667-3da6c4fa24d4","added_by":"auto","created_at":"2023-03-14 14:56:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16839,"visible":true,"origin":"","legend":"\u003cp\u003ePHQ-9 Scores by age-group, between baseline assessment (T0), and payment barrier (T1). Error bars represent 95% confidence intervals around the mean.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2668691/v1/b3cf68311296ccbd6f615e8a.png"},{"id":34241178,"identity":"c8af502d-cd5a-4b8a-951d-3750caf486a1","added_by":"auto","created_at":"2023-03-14 15:12:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16585,"visible":true,"origin":"","legend":"\u003cp\u003ePHQ-9 Scores by age-group, between baseline assessment (T0), and treatment completion (T-Final). Error bars represent 95% confidence intervals around the mean.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2668691/v1/cb1707746388f16a6ac72664.png"},{"id":34241179,"identity":"d0e37a74-0ead-4186-80ae-322a29f349c1","added_by":"auto","created_at":"2023-03-14 15:12:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":150261,"visible":true,"origin":"","legend":"\u003cp\u003eOCD.app - Anxiety, Mood \u0026amp; Sleep gameplay example screenshots.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2668691/v1/08052629462ec6429f80de21.png"},{"id":38874736,"identity":"f524fa4d-589b-42cb-954a-3c1f6a4eb77e","added_by":"auto","created_at":"2023-06-21 12:29:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":703423,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2668691/v1/865a715e-003b-43be-93f4-05dd61e07029.pdf"},{"id":34240688,"identity":"7f5a6ea8-ad94-4f2c-8cc7-ea8dfe6f6c04","added_by":"auto","created_at":"2023-03-14 15:04:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18279,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-2668691/v1/8bd1a76e08ce2df1def24605.docx"}],"financialInterests":"Competing interest reported. G.D. is a co-developer of GG OCD. G.D. is also a co-founder of GGtude Ltd. GG OCD is the subject of this evaluation and therefore has financial interest to GGtude Ltd. A.G \u0026 A.B.K declare no competing interests.","formattedTitle":"Can a Mobile Game-like Intervention Help Women with Anxiety and Depression? Examining real world data of ‘OCD.app - Anxiety, Mood \u0026 Sleep’","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMood and anxiety disorders are common among women of reproductive age (between puberty and menopause, ages 15\u0026ndash;44)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and midlife (ages 45\u0026ndash;64) \u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Both age groups mark a significant period of transition for women. During the reproductive years, women go through milestones of menstrual cycle, pregnancy, birth, maternity and parenting\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Midlife spans from the natural cessation of reproductive capacity (menopause) until retirement, and is accompanied by a range of family, work and physiological changes that can impact various aspects of a woman's life\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Changes among both age groups, therefore, often include shifts in family structure and dynamics, work responsibilities and satisfaction, relationship dynamics, redistribution of body weight, changes in sexual functioning ,and the emergence of new responsibilities \u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eNevertheless, research suggests women show increased risk for depression and anxiety during midlife ages relative to reproductive years\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. For instance, women during the menopause transition period that typically lasts anywhere from a few months to several years,are at higher risk for anxiety symptoms relative to premenopausal women\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, particularly women showing low anxiety before menopause\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Women entering the menopause transition period are also twice as likely to develop significant depressive symptoms than women before menopause\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e although these differences seem to diminish post-menopause\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCognitive behavioral therapy (CBT) is considered the \u0026ldquo;gold standard\u0026rdquo; non-pharmaceutical intervention for anxiety and depression\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Numerous studies have shown CBT efficacy during various life transitions and challenges including premenstrual dysphoria, postpartum depression, menopause related distress, trauma and grief \u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCBT interventions use various methods (e.g., exposure, cognitive restructuring, behavioral experiments) to challenge maladaptive beliefs and appraisals of internal (e.g., physical sensations, thoughts, feelings, emotions) and external events (e.g., conflict at work, economic crisis)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In the context of midlife, for instance, CBT interventions may target maladaptive beliefs about aging (e.g., \u0026ldquo;Aging means I\u0026rsquo;ll be abandoned\u0026rdquo;) or menopause (e.g., \u0026ldquo;Menopause symptoms are shameful\u0026rdquo;)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the evidence for its efficacy, several barriers may hinder women from seeking CBT treatment for mental health conditions.These include high cost of face-to-face CBT therapy, limited access to care (particularly for women living in rural or remote areas), transportation or scheduling conflicts (e.g., between work and child rearing commitments) that make it difficult to attend in-person appointments and embarrassment or fear of being stigmatized (e.g. when discussing postpartum depression or menopause-related issues)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDigital technologies help overcome some of these barriers by providing easily accessible, continuous (24 hours a day), CBT-based interventions from anywhere (with internet connection), at a lower cost than face-to-face interventions that can often be provided anonymously\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Moreover, such interventions often include personalisation capacities and game-like interactive elements\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e) making them attractive to a wide range of audiences. Digital interventions, therefore, have the potential to improve the delivery of evidence-based care to women in need of mental health assistance, thereby narrowing the disparity between the number of individuals who require psychological treatment and those who actually receive it\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIndeed, a substantial body of evidence suggests the efficacy of digital interventions such as computerized and internet-based CBT in treating anxiety and depression symptoms\u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Studies have demonstrated that online CBT interventions for these disorders are equally effective as traditional face-to-face CBT treatments\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. More recent findings support the efficacy and effectiveness of CBT-based mobile health (mHealth) digital technologies in reducing various mental health symptoms\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e\u0026ldquo;OCD.app, Anxiety, Mood \u0026amp; Sleep'' (ocd.app) is a mobile app on the GGtude platform that includes brief CBT-based daily exercises targeting maladaptive beliefs associated with various mental health symptoms. Eight randomized controlled trials (RCTs)\u003csup\u003e\u003cspan additionalcitationids=\"CR38 CR39 CR40 CR41 CR42 CR43\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e in various countries (e.g., US, Italy, Spain, Turkey, Israel), as well as real world data analyses \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e have consistently linked training on the GGtude platform with significant reductions in a variety of mental health symptoms.\u003c/p\u003e \u003cp\u003eFor instance, in a recent fully remote crossover RCT in the USA, Ben-Zeev and colleagues\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e have shown that people with serious mental illness (SMI; n\u0026thinsp;=\u0026thinsp;315) training daily with the GGtude platform for 30 days show reductions in self-reported depression, disability and anxiety as well as an increase in positive recovery attitudes and self-esteem. The waitlist control group participants showed comparable changes in outcome measures after cross over. Evaluating the effectiveness of training with the GGtude platform on participants high in COVID-19 related distress, Akin-Sari and colleagues\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e showed app use was associated with reductions in COVID-19 related distress, depression symptoms and associated maladaptive beliefs.\u003c/p\u003e \u003cp\u003eThe depression and anxiety modules of ocd.app target maladaptive beliefs that are commonly associated with anxiety and depression symptoms. These beliefs include but are not limited to, beliefs in change, over-monitoring of physical sensations, catastrophization of physical sensations and psychological experiences, overestimation of threat, helplessness and hopelessness, self-criticism, perfectionism and fear of being abandoned. Users engage in a comprehensive program consisting of brief daily exercises lasting only 3 minutes. By discarding anxiety and depression related cognitions (swiping them up) and embracing more adaptive statements (pulling them towards themselves), users increase accessibility of adaptive over maladaptive cognitions and learn to challenge their maladaptive beliefs. Systematically targeting different anxiety and depression related cognitions, in turn, is expected to reduce users\u0026rsquo; maladaptive beliefs and related symptoms.\u003c/p\u003e \u003cp\u003eTraining on the GGtude platform has been suggested to decrease users' mental health symptoms by altering the balance between adaptive and maladaptive cognitions\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Users' ability to produce and retrieve adaptive self-statements may increase through repeated exposure to such self-statements. Prompting users with maladaptive beliefs while exposing them to unexpected competing cognitions may expedite their reflective processing to adjust their maladaptive beliefs\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePhysical movements involved in the daily categorization exercises (i.e., swiping up or down of cognitions) may also lead to more distinct signals regarding the congruity of adaptive versus maladaptive cognitions to their mental health goals (i.e., embodied cognition)\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In addition, self-statements pairing self-referential pronouns with positive action words may enhance users' implicit positive self-concept\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Finally, brief psychoeducation scripts, such as \"The world can feel dangerous. However, constantly searching for danger increases our fears and anxieties. Let's learn to reduce this tendency\" may help users understand and reinforce basic principles of cognitive-behavioral therapy\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe reproductive and midlife periods include many life transitions that may activate maladaptive cognitions and increase the risk for depression and anxiety. Maladaptive cognitions are often associated with the various psychological experiences, physical sensations, potential threats and self-criticism which are integral part of women\u0026rsquo;s lives during these ages. We hypothesize that brief training exercises targeting maladaptive beliefs and cognitions will be feasible in reducing depression and anxiety symptoms among women in both these significant developmental periods. Despite different trajectories along the way both age groups we expect using the anxiety and depression modules of ocd.app would be associated with reductions in GAD-7 and PHQ-9 scores at both T1 and T-Final assessment points. Stronger effects are expected for women completing all levels of the corresponding modules as they trained longer and increased the activation of adaptive relative to maladaptive cognitions facilitating their retrieval as well as having learned to challenge a greater number of different maladaptive beliefs \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline characteristics\u003c/p\u003e \u003cp\u003eThe initial pool of this study included 7,478 women that downloaded the \u0026ldquo;OCD.app, Anxiety, Mood \u0026amp; Sleep\u0026rdquo; and completed the main assessment measure (i.e., GAD-7 \u0026amp; PHQ-9) for the first time (T0: baseline assessment). Of these women, 2,111 selected the depression module (PHQ-9), and 5,367 selected the anxiety module (GAD-7). The mean age of women using the depression module was 28.7 (SD\u0026thinsp;=\u0026thinsp;11.42; range 15\u0026ndash;80), and the anxiety module was 28.0 (SD\u0026thinsp;=\u0026thinsp;11.26; range 15\u0026ndash;80). The numbers of women at each assessment stage are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWomen reaching each assessment point.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModule\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssessment point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e# Women\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% Remaining\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDays using app (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.2 (29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTFinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.1(47.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (59.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTFinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.8 (67.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDifferences between completers and non-completers\u003c/h2\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003cp\u003eOf the 5,367 women that completed the T0 assessment, 1,554 participants (29%) completed the GAD-7 a second time (T1). The mean number of days using the app between T0 and T1 was 14.2 (SD\u0026thinsp;=\u0026thinsp;29). Of the participants completing the T1 assessment point, 491 participants (31.6%) passed the payment barrier and reached the final GAD-7 assessment (T-Final). The mean number of days using the app between T1 and T-Final was 33.1 (SD\u0026thinsp;=\u0026thinsp;47.8).\u003c/p\u003e \u003cp\u003eSignificant age differences were found between T0 and T1 time points (\u003cem\u003et\u003c/em\u003e(4035) = -9.28, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Participants at T0 were younger (M\u0026thinsp;=\u0026thinsp;25.8, SD\u0026thinsp;=\u0026thinsp;10.02) than participants who completed T1 (M\u0026thinsp;=\u0026thinsp;31.21, SD\u0026thinsp;=\u0026thinsp;11.29). Significant differences were also found between T1 and T-Final (\u003cem\u003et\u003c/em\u003e(4288) = -20.947, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Participants at T1 were younger than participants who completed T-Final (M\u0026thinsp;=\u0026thinsp;35.98, SD\u0026thinsp;=\u0026thinsp;10.96).\u003c/p\u003e \u003cp\u003eSignificant differences between women reaching T0 and T1 were found in the baseline GAD-7 scores (at T0) (\u003cem\u003et\u003c/em\u003e(4860)\u0026thinsp;=\u0026thinsp;5.648, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Participants at T0 had higher GAD-7 scores (M\u0026thinsp;=\u0026thinsp;15.75, SD\u0026thinsp;=\u0026thinsp;4.47) than participants who completed T1 (M\u0026thinsp;=\u0026thinsp;14.88, SD\u0026thinsp;=\u0026thinsp;4.52). Significant differences in the baseline GAD-7 scores were also found between T1 and T-Final (\u003cem\u003et\u003c/em\u003e(1552)\u0026thinsp;=\u0026thinsp;3.137, p\u0026thinsp;=\u0026thinsp;.002). Participants at T1 were higher than participants who completed T-Final (M\u0026thinsp;=\u0026thinsp;14.08, SD\u0026thinsp;=\u0026thinsp;4.89).\u003c/p\u003e \u003cp\u003eSignificant differences between participants reaching T0 and T1 were found in the state mood scores (at T0) (t(4860) = -4.449, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Participants at T0 had lower state mood scores (M\u0026thinsp;=\u0026thinsp;2.81, SD\u0026thinsp;=\u0026thinsp;1.01) than participants who completed T1 (M\u0026thinsp;=\u0026thinsp;2.97, SD\u0026thinsp;=\u0026thinsp;1.02). Significant differences in the state mood scores (at T1) were also found between participants reaching T1 and T-Final (t(1552) = -4.571, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Participants at T1 had lower state mood scores (M\u0026thinsp;=\u0026thinsp;3.12, SD\u0026thinsp;=\u0026thinsp;0.99) than participants who completed T-Final (M\u0026thinsp;=\u0026thinsp;3.35, SD\u0026thinsp;=\u0026thinsp;0.86).\u003c/p\u003e \u003cp\u003eDepression\u003c/p\u003e \u003cp\u003eOf the 2,111 women that completed the T0 assessment, 512 participants (24.3%) completed the PHQ-9 a second time (T1). The mean number of days using the app between T0 and T1 was 24.03 (SD\u0026thinsp;=\u0026thinsp;59.5). Of the participants completing the T1 assessment point, 140 participants (27.3%) passed the payment barrier and reached the final PHQ-9 assessment (T-Final). The mean number of days using the app between T1 and T-Final was 43.8 (SD\u0026thinsp;=\u0026thinsp;67.7).\u003c/p\u003e \u003cp\u003eSignificant age differences were found between T0 and T1 time points (\u003cem\u003et\u003c/em\u003e(1901) = -9.134, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Participants at T0 were younger (M\u0026thinsp;=\u0026thinsp;26.52, SD\u0026thinsp;=\u0026thinsp;10.31) than participants who completed T1 (M\u0026thinsp;=\u0026thinsp;32.3, SD\u0026thinsp;=\u0026thinsp;11.14). Significant differences were also found between T1 and T-Final (\u003cem\u003et\u003c/em\u003e(1711) = -12.002, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Participants at T1 were younger than participants who completed T-Final (M\u0026thinsp;=\u0026thinsp;37.5, SD\u0026thinsp;=\u0026thinsp;11.06).\u003c/p\u003e \u003cp\u003eSignificant differences between participants reaching T0 and T1 were found in the baseline PHQ-9 scores (at T0) (\u003cem\u003et\u003c/em\u003e(1901)\u0026thinsp;=\u0026thinsp;7.321, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Participants at T0 had higher PHQ-9 scores (M\u0026thinsp;=\u0026thinsp;16.58, SD\u0026thinsp;=\u0026thinsp;6.45) than participants who completed T1 (M\u0026thinsp;=\u0026thinsp;13.72, SD\u0026thinsp;=\u0026thinsp;6.5). Significant differences in the baseline PHQ-9 scores were also found between T1 and T-Final (\u003cem\u003et\u003c/em\u003e(468)\u0026thinsp;=\u0026thinsp;3.428, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Participants at T1 were higher than participants who completed T-Final (M\u0026thinsp;=\u0026thinsp;11.54, SD\u0026thinsp;=\u0026thinsp;5.79).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eOutcomes at post-treatment\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section4\"\u003e \u003ch2\u003eOverall Anxiety improvement\u003c/h2\u003e \u003cp\u003eA paired t-test analysis of the 1,554 women reaching T1 suggested large effect-size reductions (\u003cem\u003et\u003c/em\u003e(1553)\u0026thinsp;=\u0026thinsp;27.682, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.702, Cohen\u0026rsquo;s d 95% CI: [0.647, 0.758]) in GAD-7 scores between T0 (M\u0026thinsp;=\u0026thinsp;14.62, SD\u0026thinsp;=\u0026thinsp;5.22) and T1 (M\u0026thinsp;=\u0026thinsp;11.6, SD\u0026thinsp;=\u0026thinsp;5.22). Similarly, a paired t-test analysis for the 491 women reaching T-Final indicated a large effect-size for reductions in GAD-7 scores (\u003cem\u003et\u003c/em\u003e(490)\u0026thinsp;=\u0026thinsp;17.127, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.774, Cohen\u0026rsquo;s d 95% CI: [0.672, 0.874]) between T0 (M\u0026thinsp;=\u0026thinsp;14.08, SD\u0026thinsp;=\u0026thinsp;4.89) and T-Final (M\u0026thinsp;=\u0026thinsp;10.29, SD\u0026thinsp;=\u0026thinsp;5.53).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section4\"\u003e \u003ch2\u003eOverall Depression improvement\u003c/h2\u003e \u003cp\u003eA paired t-test analysis of the 512 women reaching T1 suggested small effect-size reductions (\u003cem\u003et\u003c/em\u003e(511)\u0026thinsp;=\u0026thinsp;7.695, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.34, Cohen\u0026rsquo;s d 95% CI: [0.251, 0.429]) in PHQ-9 scores between T0 (M\u0026thinsp;=\u0026thinsp;13.17, SD\u0026thinsp;=\u0026thinsp;6.35) and T1 (M\u0026thinsp;=\u0026thinsp;11.61, SD\u0026thinsp;=\u0026thinsp;6.27). Similarly, a paired t-test analysis of the 140 women reaching T-Final indicated medium effect-size for reductions in PHQ-9 scores (\u003cem\u003et\u003c/em\u003e(139)\u0026thinsp;=\u0026thinsp;5.761, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.489, Cohen\u0026rsquo;s d 95% CI: [0.312, 0.664]) between T0 (M\u0026thinsp;=\u0026thinsp;11.54, SD\u0026thinsp;=\u0026thinsp;5.79) and T-Final (M\u0026thinsp;=\u0026thinsp;9.16, SD\u0026thinsp;=\u0026thinsp;6.51).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section4\"\u003e \u003ch2\u003eAnxiety improvement at T1\u003c/h2\u003e \u003cp\u003eTo control for intervening factors (e.g., age, mood), A Linear Mixed Model was conducted for GAD-7 anxiety scores between baseline assessment and T1. Women were split into two age groups: Reproductive Age (RA; 15\u0026ndash;44), and Midlife (45\u0026ndash;64). A base model (M0) included participants\u0026rsquo; random intercepts, the fixed effects of age-group, mood (trait \u0026amp; state levels), and the two way interactions with group. The first model (M1) added the fixed effects of time (=\u0026thinsp;assessment phase), and the two way, and three way interactions with time to M0. This model yielded a significantly better fit than M0 (M1-fit: -2LL\u0026thinsp;=\u0026thinsp;17232.4, df\u0026thinsp;=\u0026thinsp;14; χ\u003csup\u003e2\u003c/sup\u003e(6)\u0026thinsp;=\u0026thinsp;642.67, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). An additional model (M2) added the random effect of time to M1. This model yielded a better fit than M1 (M1-fit: -2LL\u0026thinsp;=\u0026thinsp;17219.4, df\u0026thinsp;=\u0026thinsp;15; χ\u003csup\u003e2\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;13.05, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eA significant main effect was found for time (b = -2.58, 95% CIs = -3.11 \u0026ndash; -2.05, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), demonstrating an overall effect of improvement from T0 to T1. The time X trait mood interaction was also significant, demonstrating women with more positive trait mood improved more between T0 and T1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A main effect for age-group (b\u0026thinsp;=\u0026thinsp;1.12, 95% CIs\u0026thinsp;=\u0026thinsp;0.54\u0026ndash;1.70, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) was also found demonstrating that women in the RA group had overall higher anxiety scores. Significant main effects were found for trait mood (b = -1.75, 95% CIs = -2.26 \u0026ndash; -1.24, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and state mood scores (b = -0.65, 95% CIs = -1.25 \u0026ndash; -0.05, p\u0026thinsp;=\u0026thinsp;.034; Supplementary Table\u0026nbsp;3), demonstrating that positive trait and state mood were linked to lower anxiety scores overall.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section4\"\u003e \u003ch2\u003eAnxiety improvement at TFinal\u003c/h2\u003e \u003cp\u003eA similar Linear Mixed Model was conducted for GAD-7 anxiety scores between T0, and treatment completion at T-Final. M1, including the fixed effects of time, yielded a significantly better fit than the base model M0, including the effects of age-group, mood (state \u0026amp; trait), and participants\u0026rsquo; random intercepts (M1-fit: -2LL\u0026thinsp;=\u0026thinsp;8177.94, df\u0026thinsp;=\u0026thinsp;14; χ\u003csup\u003e2\u003c/sup\u003e(6)\u0026thinsp;=\u0026thinsp;318.98, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). M2, added to M1 the random effect of time, yielding a significantly better fit than M1 (M1-fit: -2LL\u0026thinsp;=\u0026thinsp;8167.57, df\u0026thinsp;=\u0026thinsp;15; χ\u003csup\u003e2\u003c/sup\u003e(2)\u0026thinsp;=\u0026thinsp;10.37, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eA significant main effect was found for time (b = -1.89, 95% CIs = -2.32 \u0026ndash; -1.46, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), replicating the findings from T0 to T1, and demonstrating an overall reduction in GAD-7 scores from T0 to T-Final. Significant main effects were found for trait mood (b = -1.61, 95% CIs = -2.42 \u0026ndash; -0.80, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and state mood scores (b = -0.83, 95% CIs = -1.65 \u0026ndash; -0.02, p\u0026thinsp;=\u0026thinsp;.045), replicating the findings from T0 to T1, linking positive mood with overall lower anxiety scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section4\"\u003e \u003ch2\u003eDepression improvement at T1\u003c/h2\u003e \u003cp\u003eA Linear Mixed Model was conducted for PHQ-9 scores between baseline assessment, and the payment barrier at T1. M1, which added the fixed effects of time, yielded a significantly better fit than the base model M0, including the effects of age-group, mood (state \u0026amp; trait), and participants\u0026rsquo; random intercepts (M1-fit: -2LL\u0026thinsp;=\u0026thinsp;6005.9, df\u0026thinsp;=\u0026thinsp;14; χ\u003csup\u003e2\u003c/sup\u003e(6)\u0026thinsp;=\u0026thinsp;58.74, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). M2, added to M1 the random effect of time. This model did not yield a better fit than M1 (M2-fit: -2LL\u0026thinsp;=\u0026thinsp;6005.9, df\u0026thinsp;=\u0026thinsp;15; χ\u003csup\u003e2\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;0).\u003c/p\u003e \u003cp\u003eThe age-group X time interaction was significant (b = -1.15, 95% CIs = -2.16 \u0026ndash; -0.14, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting women in the RA group showed larger improvement between T0 and T1, compared with the Midlife group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A significant main effect was found for the age-group (b\u0026thinsp;=\u0026thinsp;3.16, 95% CIs\u0026thinsp;=\u0026thinsp;1.94\u0026ndash;4.39, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), showing women in the RA group had higher PHQ-9 scores at both time points. A significant main effect was found for trait mood as well (b = -2.44, 95% CIs =-3.58 \u0026ndash; -1.30, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; Supplementary Table\u0026nbsp;1), showing positive trait mood was linked to lower PHQ-9 scores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003eDepression improvement at TFinal\u003c/h2\u003e \u003cp\u003eA similar Linear Mixed Model was conducted assessing PHQ-9 Scores between T0 and T-Final. M1, adding the fixed effects of time, yielded a significantly better fit than M0 which included effects of age-group, mood scores, and participants\u0026rsquo; random intercepts (M1-fit: -2LL\u0026thinsp;=\u0026thinsp;2716.15, df\u0026thinsp;=\u0026thinsp;14; χ\u003csup\u003e2\u003c/sup\u003e(6)\u0026thinsp;=\u0026thinsp;57.61, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). M2, which further included the random effects of time, yielded a significantly better fit than M1 (M2-fit: -2LL\u0026thinsp;=\u0026thinsp;2706.25, df\u0026thinsp;=\u0026thinsp;12; χ\u003csup\u003e2\u003c/sup\u003e(2)\u0026thinsp;=\u0026thinsp;9.90, p\u0026thinsp;=\u0026thinsp;.007).\u003c/p\u003e \u003cp\u003eIn this analysis, however, only a significant main effect for time was detected (b = -0.68, 95% CIs = -1.19 \u0026ndash; -0.16, p\u0026thinsp;=\u0026thinsp;.010), suggesting an overall effect of improvement over assessment points (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary table 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnxiety Clinically significant Improvement\u003c/h2\u003e \u003cp\u003eTo evaluate the clinical significance of the app intervention, the proportion of women achieving a clinically significant improvement in GAD-7 scores (defined as a reduction in GAD-7 of \u0026gt;\u0026thinsp;4\u003csup\u003e51\u003c/sup\u003e) was calculated. Of the 1554 women who completed T1, a total of 589 participants reached a clinically significant improvement (37.9%): 510 participants by T1 and an additional 79 participants by T-Final. The average number of days to reach a clinically significant improvement was 16.96 days (SD\u0026thinsp;=\u0026thinsp;29.43). The numbers of women who achieved a clinically significant improvement, by age groups (RA / Midlife) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDepression Clinically significant Improvement\u003c/h2\u003e \u003cp\u003eThe proportion of women achieving a clinically significant improvement in PHQ-9 scores (defined as a reduction in PHQ-9 of \u0026gt;\u0026thinsp;5\u003csup\u003e52\u003c/sup\u003e) was calculated. Of the 512 participants who completed T1, a total of 121 participants reached a clinically significant improvement (23.6%): 88 participants by T1 and an additional 33 participants by T-Final. The average number of days to reach clinically significant improvement was 31.88 days (SD\u0026thinsp;=\u0026thinsp;55.4). The numbers of women that achieved clinically significant improvement, by age-group (RA / Midlife) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinically Significant Improvement.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModule\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBaseline Score (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal # Women at T1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e# Women R-CSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e% R-CSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e# R-CSI by T1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e# R-CSI by T-Final\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMean Days till R-CSI (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAD-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.8 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e17.4 (30.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAD-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMidlife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.9 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e14.9 (22.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAD-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.6 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e17 (29.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHQ-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.7 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e33.4 (58.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHQ-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMidlife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.8 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e23.8 (32.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHQ-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.2 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e31.9 (55.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAnxiety and depression symptoms are common in women in the reproductive and midlife age. Our main results demonstrate the effectiveness of an mHealth intervention comprising brief cognitive training for reducing anxiety and depression symptoms among women for both these age groups. Although the app was designed for the general audience it seems to be relevant for the two developmental stages of women.\u003c/p\u003e \u003cp\u003eWomen using the app showed large effect size reductions in anxiety scores following 14.2 days of app use at T1. Somewhat larger effect-size reductions were detected following 33.1 days of use at T-Final. The anxiety module of the GGtude platform seemed to be similarly effective for both age groups. The effects of app training on anxiety was moderated by trait mood, but only for women using the app until the payment barrier (T1). Indeed, higher mood has been associated with increased motivation and engagement with treatment. The range of individual differences in motivation, however, may have been significantly reduced following the payment barrier. Most significantly, around 38% of women using the anxiety module of the app showed clinically significant improvement in anxiety symptoms. Considering baseline anxiety scores were in the moderate-severe range at T0 (Mean GAD-7\u0026thinsp;=\u0026thinsp;14.6\u003csup\u003e53\u003c/sup\u003e), such reductions in anxiety levels may have had a significant impact on the daily lives of around 600 women in this study.\u003c/p\u003e \u003cp\u003eOur results suggest more diminished effects of the intervention on depression symptoms. Women reaching T1, showed small effect-size reductions in depression symptoms following 24 days of app use. These effects were moderated by group and suggested that women in the reproductive age group benefited from the intervention more than women in the midlife group. This finding may be attributed to the higher baseline (T0) depression scores found in the RA group reflecting a wider range of possible improvement, but it is also consistent with previous findings showing stronger effects of the GGtude intervention for users with more severe symptoms\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGroup allocation, however, did not moderate the effects of the app on depression in women reaching T-Final. Moreover, women reaching T-Final showed medium effect-size reductions in depression symptoms following 43.8 days of app use. Untreated depressive episodes become longer and more frequent with time. More intense treatment (higher dosage) may be more effective in relieving depressive symptoms. Indeed, previous findings supported a dose response relationship using the GGtude platform\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Importantly, however, a significant proportion of women benefited from training on the depression module of the intervention such that 23.6% reached clinically significant improvement.\u003c/p\u003e \u003cp\u003eIn this study, we evaluated improvement in anxiety and depression scores within participants (using paired t-tests). Our findings indicate that women improve compared to themselves, and increased app use is associated with greater improvement (albeit that some of the women with the most severe symptoms tended to drop out). The improvements found over assessment points can\u0026rsquo;t, therefore, be explained by pre-existing baseline differences in GAD-7 or PHQ-9 scores. Nevertheless, women using the app until T1 had higher anxiety and depression scores at T0 than women reaching T-Final. It could be that women with more severe symptoms found it more difficult to persist with app use. More severe symptoms are also associated with increased disability and lower socioeconomic status\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Women with more severe symptoms may have found it harder to afford paying for the app.\u003c/p\u003e \u003cp\u003eOur study shows that scalable, low cost, low intensity, short duration interventions may be highly relevant for women in real-world settings. Between 23\u0026ndash;38% of women achieved clinically significant improvement. Nevertheless, this study is based on real world data and lacks a control group. Therefore, our results do not permit refuting alternative interpretations of the effects found including the mere passage of time. The initial sample of our study was large with a significant dropout rate. Similar dropout rates, however, have been reported in studies on eHealth applications \u003csup\u003e\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. In these studies, dropouts are assumed as a feature of these types of interventions. In addition, use of the app past T1 was contingent on a payment fee. This could have contributed to the significant dropout rates from T1 to T-Final as well as the larger effect sizes detected in women reaching T-Final.\u003c/p\u003e \u003cp\u003eMultiple RCTs have indicated that cognitive training targeting maladaptive beliefs delivered via the GGtude platform are associated with significant reductions in various symptoms and associated cognitions. Our real world data support the effectiveness of the GGtude platform for women with anxiety and depression symptoms. Considering women use apps more than men, especially mobile health apps \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e and that mHealth interventions are unrestricted by time and place, training on the GGtude platform may help women receive help while they are experiencing significant life changes such as menstrual cycles, pregnancy, childbirth, marriage/divorce and menopause. Indeed, brief daily training is very suitable for women who have busy schedules during these two life phases. The app may be part of self care that needs to be prioritized during these significant life transitions. Apps are especially important after COVID-19 that we have learned the intervention should be delivered remotely and in an independent manner.\u003c/p\u003e \u003cp\u003eNumerous apps for depression are available, the investigated app, however, is focused on helping women who are \u0026ldquo;trapped\u0026rdquo; by their maladaptive beliefs. These beliefs can affect various aspects of a woman's life including biological and hormonal changes and psychological and interpersonal/social ones. Cognition during these developmental stages is important since the meaning of the relevant life events included is what makes a difference for women. In addition, the focus on cognition in both depression and anxiety is in line with a recent review and meta analysis which indicated iCBT programmes include training in a wide array of cognitive and behavioral skills, but should include only the beneficial components\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. An app focusing on women's own cognitions lead to ripple effects on women\u0026rsquo;s surroundings including partners, family members and friends.\u003c/p\u003e \u003cp\u003eTraining on the GGtude platform can be recommended to women clients as a standalone tool (e.g. when they wait for a professional intervention) or integrated into the psychotherapy they receive. It can be part of stepped care of stratified care in women's mental health interventions. Moreover, by inserting the usage of the app we may be able to build a tailored treatment plan for each woman rather than asking them to conform to a preset model of care. The specific cognitions which were most helpful for each woman to discard or embrace can be continued to practice after completing the app usage. The desired abilities to discard and embrace cognitions can also become psychoeducation about women\u0026rsquo;s cognitive work needed during major life transitions in reproductive and midlife ages.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eStudy design\u003c/p\u003e \u003cp\u003eApp Data included in this study was collected between October 2020 and January 2023, and was retrospectively analyzed.In this study the assessments were included for two separate treatment modules: Anxiety and Depression. Participants chose their modules during the app onboarding procedure. The analysis consists of comparison between An initial baseline assessment (T0) completed at the start of the relevant module and two follow-up assessments. The second assessment point (T1) is completed on the last level before the payment barrier of the app. The third assessment (T-Final) is completed upon completion of the final level of the module. The Depression module also includes an additional assessment point (T2) between T1 and T-Final, which was included in the linear mixed models analyses of improvement between T0 and T-Final. In each follow-up assessment (i.e., comparison of T0-T1 \u0026amp; T0-TFinal), only participants who completed all assessments up to that assessment point were included.\u003c/p\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003cp\u003eWomen in the study downloaded the \u0026ldquo;OCD.app, Anxiety, Mood \u0026amp; Sleep '' through Google Play or Apple Store. We used the General Anxiety Disorder \u0026minus;\u0026thinsp;7 (GAD-7)\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e to assess General anxiety symptoms. Generalized anxiety disorder (GAD) is the most common of the anxiety disorders, with an estimated lifetime prevalence of 7.1% in adult women\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. We used the Patient Health Questionnaire-9 (PHQ-9)\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e to assess depression symptoms. The GAD-7 and PHQ-9 were completed at three different times during use of the application: at baseline (T0 assessment; level 1), when reaching the payment barrier (T1 assessment; level 20 for anxiety; level 18 for depression) and upon completion of the anxiety module (T-Final assessment; level 48 for anxiety; level 66 for depression). Women in the depression completed the T2 assessment at level 48.\u003c/p\u003e \u003cp\u003eEthical considerations\u003c/p\u003e \u003cp\u003e This study was approved by The Reichman University (IDC) Research Ethics Committee Ethical clearance number: P_2023036. All methods in the study were performed in accordance with the relevant guidelines and regulations set forth by the ethical committee. All data collected on the GGtude platform is completely anonymous (no user name or password required). Participants provide during app onboarding informed consent for analysis of app-use data and sharing of anonymized data from the mobile app including self-reported age \u0026amp; gender and mobile-app usage.\u003c/p\u003e \u003cp\u003eProcedure\u003c/p\u003e \u003cp\u003eAll participants in this study used the English version of the Anxiety and Depression module of \u0026ldquo;OCD.app - Anxiety, Mood \u0026amp; Sleep\u0026rdquo; (Versions 3.0.6\u0026ndash;3.4.4). After downloading the app, participants go through an onboarding procedure whereby they complete their age and gender, and go through a tutorial. They then complete the T0 assessment related to their chosen module for the first time (T0). The last level before the payment barrier includes the second assessment point (T1). The last level of the module (T-Final assessment point).\u003c/p\u003e \u003cp\u003e\u0026ldquo;OCD.app - Anxiety, Mood \u0026amp; Sleep\u0026rdquo; is a smartphone app that uses the GGtude platform, a system designed to promote cognitive flexibility of individuals struggling with various mental health difficulties through brief daily training \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e 38,40\u003c/sup\u003e. The anxiety and depression modules were specifically designed to help counteract cognitions relating to maladaptive beliefs shown to be linked to anxiety and depression symptoms. The intervention comprises daily brief game-like exercises designed to produce changes in the relative activation of adaptive and maladaptive beliefs such that adaptive beliefs would be more easily retrieved than maladaptive ones.\u003c/p\u003e \u003cp\u003eThe main gameplay of the intervention consists of users being taught to discard maladaptive cognitions by swiping them upwards (out of the top of the screen). Users are asked to swipe downwards adaptive cognition towards themselves and embrace them (downwards on the screen). The anxiety module includes 48 levels and the depression module includes 66 levels. Every 3 levels target a particular maladaptive belief and cognitions associated with it. Each level comprises several cognitions (in the form of statements) that are either consistent with their maladaptive belief (dysfunctional) or inconsistent with this belief (adaptive). For example, cognitions inconsistent with perfectionism include \"Mistakes teach me how to overcome my fears\" and \"Imperfect is human\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The push notifications are used to remind users to use the app each day. After completing three levels a day, a screen instructing the user to stop using the app for the day is presented.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003cp\u003eThe primary outcome measures were the GAD-7\u003csup\u003e60\u003c/sup\u003e for anxiety symptoms and PHQ-9\u003csup\u003e62,63\u003c/sup\u003e for depressive symptoms. Secondary outcomes included rates of \u0026lsquo;significant improvement\u0026rsquo; clinical improvement, based on PHQ-9 (Change\u0026thinsp;\u0026gt;\u0026thinsp;5\u003csup\u003e52\u003c/sup\u003e) and GAD-7 (Change\u0026thinsp;\u0026gt;\u0026thinsp;4\u003csup\u003e51\u003c/sup\u003e) at TFinal. \u003cem\u003eState and trait mood\u003c/em\u003e were assessed using a VAS depicting five faces, on a scale from 1 to 5, ranging from \u003cem\u003every sad to very happy\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e.\u003cem\u003eTrait mood\u003c/em\u003e was calculated as the average of a women\u0026rsquo;\u0026rsquo;s daily answers which were mean-centered across women. \u003cem\u003eState mood\u003c/em\u003e was calculated as the difference between a woman's response on the day of the assessment and the average of their daily answers.\u003c/p\u003e \u003cp\u003eThe GAD-7 and PHQ-9 were completed at three different times during use of the application: at baseline (T0 assessment; level 1), when reaching the payment barrier (T1 assessment; level 20 for anxiety; level 18 for depression) and upon completion of the anxiety module (T-Final assessment; level 48 for anxiety; level 66).\u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R version 4.2.1\u003csup\u003e64\u003c/sup\u003e. Linear mixed models (LMMs) were conducted with the lme4 package\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e and significance testing for the linear mixed models were conducted with the lmerTest package\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Effect sizes for t-tests were calculated with the effectsize package\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Follow-up tests for linear mixed models were performed with the emmeans package\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e.The linear mixed models summary tables were drawn with the sjPlot package\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eData availability\u003c/h3\u003e\u003cp\u003eThe dataset analyzed during the current study is available upon request to the corresponding author.\u003c/p\u003e\u003ch3\u003eCode availability\u003c/h3\u003e\u003cp\u003eThe code used to generate the statistical outputs is available upon request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBen Gurion University of the Negev, Department of Psychology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAvi Gamoran\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReichman University, Baruch Ivcher School of Psychology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnat Brunstein-klomek \u0026amp; Guy Doron\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGGtude Ltd., Tel Aviv, Israel\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuy Doron\u003c/p\u003e\n\u003ch3\u003eContributions\u003c/h3\u003e\n\u003cp\u003eA.G. conducted the statistical analysis and created the visualizations. G.D. conceptualized the study and obtained the data from GGtude Ltd. A.G, A.B.K \u0026amp; G.D. drafted and edited the article.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch3\u003eCorresponding author\u003c/h3\u003e\n\u003cp\u003eCorrespondence to Guy Doron, Baruch Ivcher School of Psychology Reichman University, Herzliya.\u003cbr\u003e\u0026nbsp;P.O. Box 167, 46150 Herzliya, Israel.\u003cbr\u003e\u0026nbsp;Email: [email protected]\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eG.D. is a co-developer of GG OCD. G.D. is also a co-founder of GGtude Ltd. GG OCD is the subject of this evaluation and therefore has financial interest to GGtude Ltd.\u003c/p\u003e\n\u003cp\u003eA.G \u0026amp; A.B.K declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFarr, S. L., Bitsko, R. H., Hayes, D. K. \u0026amp; Dietz, P. M. 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Preprint at (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2668691/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2668691/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnxiety and depression symptoms are a significant mental health challenge for women in the reproductive age and midlife. Cognitive behavioral therapy (CBT) based mobile health (mHealth) interventions may be a viable solution for addressing the treatment gap for women at these ages. We collected real world data of women using the CBT based app \u0026ldquo;OCD.app - Anxiety, Mood \u0026amp; Sleep\u0026rdquo; from October 2020 to January 2023. Women\u0026rsquo;s levels of anxiety (GAD-7) and depression (PHQ-9) were evaluated prior to the intervention (T0), at the payment barrier (T1), and upon completion of the intervention (T-Final). Women\u0026rsquo;s dropout rates were associated with younger age and more severe symptoms. Large effect-size reductions were found at T1 (n\u0026thinsp;=\u0026thinsp;1,554; Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.702) and T-Final (n\u0026thinsp;=\u0026thinsp;491; Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.774) with 37.9% reaching clinically significant improvement in anxiety symptoms (GAD-7 change\u0026thinsp;\u0026gt;\u0026thinsp;4). Similar analyses of women\u0026rsquo;s PHQ-9 scores indicated small effect-size reductions at T1 (n\u0026thinsp;=\u0026thinsp;512; Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.34) and moderate effect-size decreases at T-Final (n\u0026thinsp;=\u0026thinsp;140; Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.489) with 23.6% of women reaching clinically significant improvement in depression symptoms (PHQ-9 change\u0026thinsp;\u0026gt;\u0026thinsp;5). Results support the effectiveness of brief CBT-based mHealth interventions for women with depression and anxiety symptoms in real world settings.\u003c/p\u003e","manuscriptTitle":"Can a Mobile Game-like Intervention Help Women with Anxiety and Depression? Examining real world data of ‘OCD.app - Anxiety, Mood \u0026amp; Sleep’","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-14 14:56:33","doi":"10.21203/rs.3.rs-2668691/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e9532092-36cf-431e-b4b6-8c3acb4728f6","owner":[],"postedDate":"March 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":19851296,"name":"Biological sciences/Psychology"},{"id":19851297,"name":"Biological sciences/Psychology/Human behaviour"},{"id":19851298,"name":"Health sciences/Diseases/Psychiatric disorders/Anxiety"},{"id":19851299,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"}],"tags":[],"updatedAt":"2023-06-21T12:29:21+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-14 14:56:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2668691","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2668691","identity":"rs-2668691","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}