The Chinese version of the Immediate Mood Scaler (IMS): a study evaluating its validity, reliability, and responsiveness in patients with MDD in China

The Chinese version of the Immediate Mood Scaler (IMS): a study evaluating its validity, reliability, and responsiveness in patients with MDD in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Chinese version of the Immediate Mood Scaler (IMS): a study evaluating its validity, reliability, and responsiveness in patients with MDD in China Xiongying Chen, Xu Chen, Zizhao Feng, Nanxi Li, Le Xiao, Xuequan Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4278887/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2025 Read the published version in BMC Psychiatry → Version 1 posted 16 You are reading this latest preprint version Abstract Background: It is important to timely capture the fluctuation of the symptoms related to MDD. However, most conventionally used assessment tools for MDD symptoms are not designed for real-time assessment. The Immediate Mood Scaler (IMS) is suitable for the real-time evaluation of the mood of patients with MDD. Methods: The original IMS was translated into Chinese and back-translated. At baseline, data from 368 patients with MDD, including demographic information and scores on the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and IMS, were collected. In total, 185 participants completed the retest at Week 2 which included the PHQ-9, GAD-7, and IMS. Internal structural validity, construct validity, and internal consistency were evaluated with the confirmatory factor analysis (CFA) and principal component analysis (PCA), the Pearson correlation, and Cronbach’s α, respectively. Responsiveness was anchored by the change of the PHQ-9 total scores from baseline to Week 2. Predictability was tested using the mixed models for repeated measure (MMRM). Results: PCA identified two factors with an eigenvalue greater than 1, corresponding to IMS-Depression and IMS-Anxiety subscales. The Cronbach’s α that evaluated internal consistency was 0.96, 0.95, and 0.92 for the scores of the IMS, IMS-Depression subscale, and IMS-Anxiety subscale at baseline, respectively. The depression and anxiety subscales at baseline showed high subscale-total correlations (r=0.96 for the depression subscale; r=0.89 for the anxiety subscale). The test-retest ICC (0.65, 95%CI: 0.53-0.73) of the IMS at baseline and Week 2 show high reliability. The total score of IMS had significant correlations with that of the PHQ-9 (r=0.52, P<.001) and GAD-7(r=0.43, P <.001), indicating high construct validity. In patients with MDD who showed changes in mood, the changes in total scores of the IMS from baseline to the retest were statistically significant with a mean difference of 13.3 (SD: 20.1), an ES of 0.66, and an SRM of 0.3, showing good responsiveness. Also, the baseline IMS-Depression subscale score could predict the change in the PHQ-9 score over the two weeks (t=2.19, P =.029). Conclusions: The Chinese version of the IMS is valid and reliable for the real-time assessment of mood in patients with MDD in China. major depressive disorder ecological momentary assessment mobile Figures Figure 1 Figure 2 Background Major depressive disorder (MDD) is a common psychiatric disease that causes heavy disease burden and life impairment both domestically in China and globally (Ferrari et al., 2013; Lopez & Mathers, 2006; Lu et al., 2021; Ren et al., 2020). Monitoring the symptoms and mood status of the patients could help to provide better care and treatment to them; it may also shed light on the etiology of MDD (Lanata et al., 2015). The scales conventionally used to evaluate depressive symptoms of patients, no matter clinician-rated or self-report ones, mostly rely on the patients’ recall of their mood status in the past, such as in the previous day, previous week or two weeks, or previous month. Therefore, these scales are unable to dynamically represent mood symptoms and may introduce measurement bias. The symptoms and mood of patients with MDD show significant nonlinear dynamic and unstable characteristics, which have a significant impact on the course and nature of the disease (Bowen et al., 2011, 2013; Broome et al., 2015; Koval et al., 2015). Currently, with conventional measurement tools, the mood fluctuation of patients with MDD is still often underreported, impeding both the understanding of the disease and treatment planning (Moore et al., 2016; Myin-Germeys et al., 2009; Nahum et al., 2017). Moreover, most scales measuring MDD symptoms or depressive mood rely mainly on patients’ recall of their past experiences, making the scales vulnerable to recall bias and emotional bias (Barrett, 1997; Cutler et al., 1996; Gentzler & Kerns, 2006; Safer & Keuler, 2002; Ruhe et al., 2019). Recall bias may happen when the patients are suffering from cognition disruptions during the evaluation (Ramponi et al., 2004), and the mood state at the time of recall may also cause memory bias which means depressed patients might recall more negative information than positive information (Kihlstrom et al., 2000). The length of recall interval of the scale is also a critical factor that influences the accuracy of mood assessment (Stull et al., 2009). Considering the characteristics of patients with MDD, a shorter recall interval or an immediate mood assessment may be more suitable (Armey et al., 2015; Panaite et al., 2020). A real-time report of the mood symptoms may also provide a more accurate and comprehensive mapping of the fluctuation of the symptoms in everyday life (Bauer et al., 2018; Pedrelli et al., 2020). Additionally, a passive, objective, momentary assessment of the patients’ mood may help improve treatment outcomes (Yim et al., 2020). A combination of active self-reported mood fluctuation and passive monitoring of behavioral and environmental indicators can provide more knowledge of the trajectory of the disease (Saeb et al., 2017; Yim et al., 2020). As previously mentioned, it is important to timely and accurately capture the fluctuation of the symptoms related to MDD. However, most conventionally used instruments for the assessment of MDD symptoms in either clinical or research settings, such as the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), or the Visual Analogue Scale (VAS) (Hung et al., 2016), are not designed for real-time assessment. Therefore, developing an easy-to-use, real-time assessment tool may be beneficial to both clinical practice and scientific research. The Immediate Mood Scaler (IMS) is a tool to assess momentary mood state; it was originally developed to be delivered through mobile devices so that it can be administered multiple times in one day and the users can rate how they feel at that moment in their natural settings. This self-report scale consists of 22 items measuring the current mood state related to depression or anxiety (e.g., pessimistic or optimistic, hopeless or hopeful). The users will be asked to rate their mood using 7-point Likert scales with a pair of opposite adjectives describing their mood state at opposite ends of each scale. Each item will derive an integer score between 1 to 7, and the total score of the IMS is the sum of all item scores. (Nahum et al., 2017). Valid real-time mood assessment tools are of great importance, especially for patients with MDD. To our best knowledge, the IMS is the only momentary assessment tool for mood symptoms related to depression. Moreover, to date, there is not a validated momentary assessment tool developed specifically for people with MDD in China or a translated version of such a tool. Therefore, this study aims to examine the psychometric properties of the Chinese version of the IMS in patients with MDD, so that there could be an instrument available for momentarily assessing mood symptoms in that group. Methods Study design and participants We used a two-step procedure to translate and validate the Chinese version of the IMS. This study was approved by the Human Research and Ethics Committee of Beijing Anding Hospital, Capital Medical University (Ethical approval number: 2018-119-201917FS-2). Written informed consents were obtained from all participants in the study. The participants were also informed that they could withdraw from the study at any time without any reason or consequence. The IMS was translated into Chinese with minor modifications for cultural adaptation by two authors. The back-translation was performed by a bilingual clinical psychologist without reading the original text. A preliminary translated version was administered to healthy individuals and a small cohort of patients with mood disorders. The authors then made minor revisions to the wording of two items based on the feedback of those participants and had the final version cross-checked again. The psychometric properties of the final version were then examined. Data collection and psychometric evaluation of the Chinese version of the IMS continued from February 2019 to April 2020. Participants of the study were recruited from the outpatients at Beijing Anding Hospital, a tertiary psychiatric hospital. The final sample included 368 participants. The inclusion criteria were: (1) an age of at least 18 years (outpatients); (2) a current diagnosis of an acute episode of MDD according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria, made by trained clinicians; (3) at least 9 years of education. Participants with a diagnosis of any psychotic disorder were excluded. After signing the written informed consents, the participants would be asked to download and log in to the app “Mood Mirror”, an app designed for recording the symptoms and behaviors of patients with MDD (see (Bai et al., 2021) for the details regarding the development of this app). Demographic information of the participants was collected at baseline. The participants were assessed with the IMS, PHQ-9, and GAD-7 at baseline and the follow-up at Week 2. See Figure 1 for a more detailed flowchart. Assessment tools in the study The Chinese version of the Immediate Mood Scaler (IMS-22) assesses current mood symptoms using 7-point scales. For each item, a pair of complementary antonyms (for instance, pessimistic versus optimistic, sleepy versus alert) were at opposite ends of the scale. The patients would be instructed to use the scales to rate how they felt at the moment. This original 22-item scale was found significantly correlated with other psychometrically sound measurement tools assessing anxiety and depression (Nahum et al., 2017). In the current study, the PHQ-9 and GAD-7 were used to test the psychometric properties of the Chinese version of the IMS-22. The PHQ-9 has been validated in patients with MDD in China (Cronbach’s alpha=0.88) (Feng et al., 2016) and is capable of assessing the severity of depression (Kroenke et al., 2001). The GAD-7 has also shown good reliability and validity for assessing the severity of anxiety symptoms and disorders (Cronbach’s alpha=0.95) (Wang et al., 2019). Statistical Analysis The characteristics of the participants were reported using descriptive statistics, categorical variables were reported using percentages, and continuous variables were reported using means and standard deviations (SDs). In the analysis of psychometric properties, we tested the internal structural and construct validity, internal consistency and test-retest reliability, and responsiveness and predictability. Confirmatory factor analysis (CFA) and principal component analysis (PCA) were used to test the internal structural validity and identify subscales corresponding to each measured dimension. To test the construct validity, we computed correlations of the PHQ-9 and GAD-7 with the IMS and its subscales using the Pearson r value. The test-retest reliability was evaluated by examining the stability of the IMS total scores from baseline to the retest at Week 2. Responsiveness refers to the sensitivity to change and reflects the ability of the IMS to accurately detect changes in mood over time. Based on the cut-off used in previous literature, a “change” in the PHQ-9 total score was defined as a difference of more than 0.5 SD of the total score at baseline, which equaled to 3.1 points in the current study (Sedaghat, 2019). Mean differences were standardized by the SD at baseline to derive effect sizes (ES) and standard response means (SRM). An ES or SRM of 0.2, 0.5, and 0.8 was considered as a cutoff for low, moderate, and large responsiveness, respectively (Streiner et al., 2016). Analyses were performed using the Statistical Analysis Software (SAS) version 9.4 (SAS Institute, Cary, N.C.) and MedCalc® Statistical Software version 20.106 (MedCalc Software Ltd, Ostend, Belgium; 2022). A p-value less than 0.05 was considered to indicate statistical significance. Results Participant characteristics Participant characteristics are reported in Table 1 . The participants were aged 18 to 64 years (mean [SD] age, 29.4 [9.8] years). Of them, 240 (65.2%) were female and 318 (86.4%) held a bachelor’s degree or above. In total, 185 participants completed the retest at Week 2. Those participants were aged 18 to 64 years (mean [SD], 30.0 [10.1]). Of them, 64.9% (120/185) were female and 86.5% (160/185) had education degree of bachelor’s degree or above. Internal structural validity Two factors with eigenvalues greater than 1 were identified using Promax rotation. The KMO test value of the IMS was 0.96, and the square value of Bartlett spherical test was statistically significant (c 2 =6415.02, P <.001). The cumulative contribution rate of variance explanation was 63.88%. All items included in each factor loading were more than 0.4. The specific item content of factor Ⅰ was depression symptoms, and that of factor Ⅱ was anxiety symptoms. Construct validity The total score of the IMS had significant correlations with that of the PHQ-9 (r=0.52, P <.001) (Figure 2A) and that of the GAD-7 (r=0.43, P <.001) (Figure 2C) at baseline. The IMS-Depression subscale was found significantly correlated with the PHQ-9 (r=0.57, P <.001) (Figure 2B) , and the IMS-Anxiety subscale was significantly correlated with the GAD-7 (r=0.41, P <.001) (Figure 2D) at baseline. Internal consistency and test-retest reliability Cronbach’s alpha indicated that the IMS had high internal consistency reliability and scale reliability (alpha=0.96), and all individual items had shown excellent reliability (alpha >0.96) with high item-total correlations (all ≥0.6). The Cronbach’s alphas of the IMS-Depression subscale and IMS-Anxiety subscale were 0.95 and 0.92, respectively. The IMS demonstrated strong test-retest reliability (ICC=0.65, 95%CI: 0.53-0.75) in 185 participants who completed the retest, and we also found that the IMS-Depression subscale (ICC=0.66, 95%CI: 0.66-0.75) and IMS-Anxiety subscale (ICC=0.61, 95%CI: 0.47-0.61) had good stability (Table 2) . Responsiveness and predictability Responsiveness of the Chinese version of the IMS was anchored by the change of the PHQ-9 total scores from baseline to the retest at Week 2 (in the current study, a “change” is defined as a difference in the PHQ-9 total scores of more than 3.1 points); it is quantified by the mean differences, ES, and SRM of the IMS total scores at baseline and the retest. Changes in the total scores of the IMS from baseline to the retest were statistically significant with a mean difference of 13.3 (SD: 20.1), an ES of 0.66, and an SRM of 0.3, suggesting that the IMS was sensitive to clinical change. The IMS-Depression subscale showed slightly larger responsiveness than the IMS, with an ES of 0.72 and an SRM of 0.41. On the other hand, responsiveness of the IMS-Anxiety subscale was low, with an ES of 0.37 and an SRM of 0.27 (Table 3) . The mixed models for repeated measure (MMRM) are presented in Table 4 . The change of total scores of the PHQ-9 from baseline to the Week-2 retest indicated a significant change in the severity of depressive symptoms (time effect in model 1 and model 2: all p-values <.001). The predictive model built based on the score of the IMS-Depression subscales could predict the change of PHQ-9 total scores over the two weeks (Model 2: t=2.19, P =.029), but the model based on the IMS could not (Model 1:t=1.71, P =0.088). (Table 4) Discussion In the current study, we translated the IMS into Chinese and examined the psychometric properties of the translated version. In total, 368 patients with MDD were included. Our results suggest that the Chinese version of the IMS has satisfactory psychometric properties in patients with MDD, indicating that it may be a valid tool to dynamically track and monitor depressive and anxiety symptoms in those patients. Similar to the original version of the IMS (Nahum et al., 2017), the Chinese version IMS was found to have strong correlations with the PHQ-9 and GAD-7, indicating that the scale can be used to measure depressive and anxiety symptoms. However, it should be admitted that the correlations of the changes in the symptoms over two weeks with the scores of the Chinese version IMS were weaker than that with the scores of the original version, indicating the Chinese version had a weaker ability to predict. This fact might result from the influence of anxiety combined with depression (FAVA et al., 2004), especially in patients with severe symptoms. It may also be explained by the fact that the severity of depression in our sample was higher than that in the sample in the study of the original IMS (the range of PHQ-9 score was 9 to 36 in our study, compared to 0 to 27 in the study of the original IMS) (Nahum et al., 2017). For responsiveness, we determined the minimal clinically important difference (MCID) for the change of MDD symptoms using the distribution-based method and set it at -3.1 in terms of the PHQ-9 score, which was a stricter threshold than the -1.7 used in a previous study in the UK (Kounali et al., 2022). The Chinese version of IMS was found able to reflect the clinically meaningful change in the total score of the PHQ-9 from the baseline to Week 2. In the current study, we found that the IMS-Depression subscale was more sensitive to changes in depression severity over two weeks than the IMS. The relatively lower responsiveness of the IMS, judged with the ES or SRM, may result from the small sample size and the stricter threshold set for the MCID. As previously mentioned, a real-time, dynamic assessment of the patients’ mood might allow clinicians and researchers to track the fluctuation of symptoms and the prognosis of the disease over the treatment course. Furthermore, we will investigate whether a trajectory recorded daily could predict the prognosis for the patient in future research. Another point to be noted is the practical significance of the Chinese version IMS. Same as the original version IMS, the Chinese version IMS can be administered through mobile devices, which makes it suitable and convenient for the real-time assessment of outpatients. The current study still has several limitations. One of the limitations stems from the nature of the assessment tool we chose: the IMS focuses mainly on negative mood such as depression or anxiety but does not assess or track positive mood. Focusing only on negative affect might limit its capacity to draw a whole picture of the mood state of the patients. The other limitation is the reduced interpretability in clinical practice, resulted from the small sample size and not using a patient-centered anchor-based method (which takes account of patients’ subjective reports of symptom alleviation in the evaluation of clinically meaningful change in symptoms) in the study. Conclusion Overall, our study showed that the Chinese version of the IMS had good psychometric properties in patients with MDD in China. It can be a useful tool for the real-time assessment of the mood of patients with MDD which can help clinicians to track the condition of the patients in a timely manner. Moreover, as an assessment tool that can be delivered through mobile devices, the Chinese version IMS would be able to help draw a picture of patients’ mood fluctuation in a natural setting and thus provide information on the condition and prognosis of patients outside of inpatient care. Abbreviations IMS: Immediate Mood Scaler; MDD: major depressive disorder; PHQ-9: Patient Health Questionnaire-9; GAD-7: Generalized Anxiety Disorder-7; VAS: Visual Analogue Scale; SD: standard deviation; CFA: confirmatory factor analysis; PCA: principal component analysis; ES: effect size; SRM: standard response means; SAS: Statistical Analysis Software; MMRM: mixed models for repeated measure Declarations Ethics approval and consent to participate This study was approved by the Human Research and Ethics Committee of Beijing Anding Hospital, Capital Medical University (Ethical approval number: 2018-119-201917FS-2). Written informed consents were obtained from all participants in the study. The participants were also informed that they could withdraw from the study at any time without any reason or consequence. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from the authors upon reasonable request. Competing interests None. Funding This study was supported by the Capital’s Funds for Health Improvement and Research Program (2022-4-2125) and the Beijing Municipal Health Commission of Beijing demonstration research ward (BCRW202009). Authors' contributions XZ designed the study. XC and NL implemented the study and collected the data. XZ performed the statistical analysis and interpreted the results. XC and ZF wrote the first draft of the manuscript. XZ, XC and LX revised the manuscript. All authors contributed significantly to this work. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Armey, M. F., Schatten, H. 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J., Ikani, N., Tyborowska, A., Browning, M., Vrijsen, J. N., Harmer, C. J., & Schene, A. H. (2019). Emotional Biases and Recurrence in Major Depressive Disorder. Results of 2.5 Years Follow-Up of Drug-Free Cohort Vulnerable for Recurrence. Frontiers in Psychiatry , 10 , 145. https://doi.org/10.3389/fpsyt.2019.00145 Saeb, S., Lattie, E. G., Kording, K. P., & Mohr, D. C. (2017). Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. JMIR mHealth and uHealth , 5 (8), e112. https://doi.org/10.2196/mhealth.7297 Safer, M. A., & Keuler, D. J. (2002). Individual differences in misremembering pre-psychotherapy distress: Personality and memory distortion. Emotion , 2 (2), 162–178. https://doi.org/10.1037/1528-3542.2.2.162 Sedaghat, A. R. (2019). Understanding the Minimal Clinically Important Difference (MCID) of Patient‐Reported Outcome Measures. Otolaryngology–Head and Neck Surgery , 161 (4), 551–560. https://doi.org/10.1177/0194599819852604 Streiner, D. L., Norman, G. R., & Cairney, J. (2016). Health measurement scales: A practical guide to their development and use (5th edition). Australian and New Zealand Journal of Public Health , 40 (3), 294–295. https://doi.org/10.1111/1753-6405.12484 Stull, D. E., Leidy, N. K., Parasuraman, B., & Chassany, O. (2009). Optimal recall periods for patient-reported outcomes: Challenges and potential solutions. Current Medical Research and Opinion , 25 (4), 929–942. https://doi.org/10.1185/03007990902774765 Wang, Y.-Y., Dong, M., Zhang, Q., Xu, D.-D., Zhao, J., Ng, C. H., Ungvari, G. S., Jia, F.-J., & Xiang, Y.-T. (2019). Suicidality and clinical correlates in Chinese men who have sex with men (MSM) with HIV infection. Psychology, Health & Medicine , 24 (2), 137–143. https://doi.org/10.1080/13548506.2018.1515495 Yim, S. J., Lui, L. M. W., Lee, Y., Rosenblat, J. D., Ragguett, R.-M., Park, C., Subramaniapillai, M., Cao, B., Zhou, A., Rong, C., Lin, K., Ho, R. C., Coles, A. S., Majeed, A., Wong, E. R., Phan, L., Nasri, F., & McIntyre, R. S. (2020). The utility of smartphone-based, ecological momentary assessment for depressive symptoms. Journal of Affective Disorders , 274 , 602–609. https://doi.org/10.1016/j.jad.2020.05.116 Tables Table 1. Characteristics of participants at baseline and the retest at week 2 Characteristics Whole sample （ n=368 ） Test-retest group (N=185) Change group (N=81) Age, yrs. Mean (SD) 29.4(9.8) 30.0(10.1) 29.0(9.5) Range 18.0-64.1 18.0-64.1 18.0-60.0 Sex, n (%) Male 128(34.8) 65(35.1) 29(35.8) Female 240(65.2) 120(64.9) 52(64.2) Educational level, n (%) High school and below 50(13.6) 25(13.5) 10(12.3) Bachelor’s degree and above 318(86.4) 160(86.5) 71(87.7) Medical history First episode, n (%) 227(61.7) 120(64.9) 51(63.0) The PHQ-9 total score at baseline Mean (SD) 23.5(6.2) 23.9(6.2) 26.6(5.0) Range 9-36 9-36 15-35 The GAD-7 total score at baseline Mean (SD) 9.6(5.3) 10.0(5.4) 11.0(5.2) Range 0-21 0-21 1-21 The IMS total score at baseline Mean (SD) 103.9(24.1) 106.3(23.4) 111.2(20.2) Range 22-154 22-154 47-146 The PHQ-9 total score at retest Mean (SD) a N/A 20.9(5.9) 18.9(4.3) Range a N/A 9-36 11-28 The GAD-7 total score at retest Mean (SD) a N/A 8.0(5.3) 7.0(4.7) Range N/A 0-21 0-21 The IMS total score at retest Mean (SD) a N/A 100.0(22.9) 97.9(20.0) Range a N/A 30-154 59-139 a N/A: not applicable. Table 2. Descriptive Statistics, Reliability, And Factor Loadings Item Mean (SD) Item-total correlation Test-retest reliability Factor1 Factor2 Depressed-Happy 4.7(1.37) 0.73 0.59 0.78 Distracted-Focused 4.6(1.46) 0.70 0.61 0.67 Worthless-Valuable 4.73(1.57) 0.74 0.63 0.85 Lonely- Engaged 4.85(1.59) 0.67 0.65 0.80 Sleepy-Alert 5.07(1.5) 0.69 0.58 0.79 Slow-Speedy 4.95(1.42) 0.71 0.61 0.82 Tired-Energetic 5.27(1.5) 0.70 0.58 0.81 Pessimistic-Optimistic 4.96(1.46) 0.77 0.65 0.81 Apathetic-Motivated 4.93(1.44) 0.78 0.53 0.78 Guilty-Proud 4.73(1.31) 0.69 0.53 0.57 Numb- Interested 4.89(1.41) 0.74 0.52 0.64 Withdraw-Welcoming 4.83(1.44) 0.79 0.61 0.73 Frustrated-Peaceful 4.73(1.51) 0.77 0.51 0.58 Impulsive-Careful 4.04(1.43) 0.49 0.42 0.71 Moody-Stable 4.45(1.56) 0.76 0.46 0.64 Hopeless-Hopeful 4.76(1.48) 0.78 0.57 0.56 Irritable-Easy-Going 4.18(1.62) 0.60 0.44 0.82 Tense-Relaxed 4.62(1.42) 0.75 0.53 0.80 Worried-Untroubled 4.86(1.45) 0.72 0.66 0.69 Fearful-Fearless 4.55(1.4) 0.69 0.51 0.81 Anxious-Peaceful 4.78(1.56) 0.74 0.54 0.80 Restless-Calm 4.41(1.5) 0.72 0.63 0.83 Scale IMS-Depression 68.02(16.11) 0.96 0.66 IMS-Anxiety 35.89(9.62) 0.89 0.61 Table 3. Responsiveness in patients anchored by the change in depressive severity (n=85) Responsiveness Mean (SD) a Effect size Standardized Response Mean IMS 13.3(20.1) 0.66(0.36-0.93) 0.39(0.29-0.44) IMS-depression 10.0(13.3) 0.72(0.41-1.01) 0.41(0.32-0.44) IMS-anxiety 3.2(8.8) 0.37(0.11-0.62) 0.27(0.10-0.37) a Baseline -retest Table 4 Predictability of the PHQ-9 by IMS and IMS-Depression subscale evaluated using MMRM (n=185) Estimate SE t P Model 1: Y=PHQ-9 at week 2 Intercept 3.06 0.63 4.85 <.001 Time effect -1.36 0.13 -10.22 <.001 PHQ-9 at baseline 0.82 0.02 33.89 <.001 IMS at baseline 0.01 0.01 1.71 0.088 Model 2: Y=PHQ-9 at week 2 Intercept 2.95 0.62 4.80 <.001 Time effect -1.36 0.13 -10.27 <.001 PHQ-9 at baseline 0.81 0.03 32.34 <.001 IMS-depression at baseline 0.02 0.01 2.19 0.029 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2025 Read the published version in BMC Psychiatry → Version 1 posted Editorial decision: Revision requested 24 Oct, 2024 Reviews received at journal 22 Jul, 2024 Reviews received at journal 22 Jul, 2024 Reviews received at journal 17 Jul, 2024 Reviews received at journal 15 Jul, 2024 Reviewers agreed at journal 03 Jul, 2024 Reviewers agreed at journal 02 Jul, 2024 Reviewers agreed at journal 02 Jul, 2024 Reviews received at journal 28 Jun, 2024 Reviewers agreed at journal 24 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers invited by journal 19 Jun, 2024 Editor assigned by journal 14 Jun, 2024 Editor invited by journal 19 Apr, 2024 Submission checks completed at journal 19 Apr, 2024 First submitted to journal 16 Apr, 2024 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-4278887","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294212856,"identity":"4658b174-0a78-494a-97b3-88fc3c85a2b4","order_by":0,"name":"Xiongying Chen","email":"","orcid":"","institution":"Beijing Anding Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiongying","middleName":"","lastName":"Chen","suffix":""},{"id":294212858,"identity":"f9812875-d98b-42c9-a084-7253653176c0","order_by":1,"name":"Xu Chen","email":"","orcid":"","institution":"Beijing Anding Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Chen","suffix":""},{"id":294212860,"identity":"436464af-53b4-40fc-a77b-b105af587453","order_by":2,"name":"Zizhao Feng","email":"","orcid":"","institution":"Beijing Anding Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zizhao","middleName":"","lastName":"Feng","suffix":""},{"id":294212862,"identity":"9173ee29-f06d-4130-a031-9c4aa7a3273d","order_by":3,"name":"Nanxi Li","email":"","orcid":"","institution":"Beijing Anding Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nanxi","middleName":"","lastName":"Li","suffix":""},{"id":294212863,"identity":"3e27ac91-6015-4ea9-9f4d-f7fb2e4b9bb4","order_by":4,"name":"Le Xiao","email":"","orcid":"","institution":"Beijing Anding Hospital","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"","lastName":"Xiao","suffix":""},{"id":294212864,"identity":"4cdb255f-4d70-4d84-afcb-86d1c58645bc","order_by":5,"name":"Xuequan Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYDADAwbmA1BmAtFa2GBKidfCY0CcFoMbyQdv89TcsTeXyPn44WfOYQZ+9hwDhp87cGuRnJGWbM1z7Bmz5YzczZK92w4zSPa8MWDsPYNbC79Ejpk0D9thNoMbuduYGYFaDG7kGDAztuHWwiaR/02a599hHqDKZ2At9oS0AG1hk+ZtOywB1MIGsUWCgBbJnmfGlnP7DhsYnHlmDPRLOo/EmWcFB3vxaDE4nvzwxptvh+1BjA8/t1nL8bcnb3zwE48WEJBA5vCAiAP4NaBpGQWjYBSMglGAAQCfvE1f6LPJlgAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Anding Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xuequan","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-04-17 02:44:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4278887/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4278887/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12888-024-06418-3","type":"published","date":"2025-01-07T15:56:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55511139,"identity":"fe652851-d94b-414b-9c02-178633b90658","added_by":"auto","created_at":"2024-04-29 12:33:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21882,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4278887/v1/bd4653d918d8c50481d7c915.png"},{"id":55511140,"identity":"85cbacda-4748-48db-aca1-4b4e883ca0b9","added_by":"auto","created_at":"2024-04-29 12:33:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3766688,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations among the scores on the five scales and subscales at baseline\u003c/p\u003e\n\u003cp\u003eFigure 2A. Correlation between the IMS total score and the PHQ-9 total score at baseline\u003c/p\u003e\n\u003cp\u003eFigure 2B. Correlation between the IMS-Depression subscale score and the PHQ-9 total score at baseline\u003c/p\u003e\n\u003cp\u003eFigure 2C. Correlation between the IMS total score and the GAD-7 total score at baseline\u003c/p\u003e\n\u003cp\u003eFigure 2D. Correlation between the IMS-Anxiety subscale score and the GAD-7 total score at baseline\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4278887/v1/0eee33216985aa7af41e8772.png"},{"id":73693712,"identity":"4e042b90-a16b-498f-bcd0-636895a68b1f","added_by":"auto","created_at":"2025-01-13 16:00:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5157524,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4278887/v1/c7715f77-0dbb-46c7-90d2-57a557676c15.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Chinese version of the Immediate Mood Scaler (IMS): a study evaluating its validity, reliability, and responsiveness in patients with MDD in China","fulltext":[{"header":"Background","content":"\u003cp\u003eMajor depressive disorder (MDD) is a common psychiatric disease that causes heavy disease burden and life impairment both domestically in China and globally (Ferrari et al., 2013; Lopez \u0026amp; Mathers, 2006; Lu et al., 2021; Ren et al., 2020). Monitoring the symptoms and mood status of the patients could help to provide better care and treatment to them; it may also shed light on the etiology of MDD (Lanata et al., 2015). The scales conventionally used to evaluate depressive symptoms of patients, no matter clinician-rated or self-report ones, mostly rely on the patients\u0026rsquo; recall of their mood status in the past, such as in the previous day, previous week or two weeks, or previous month. Therefore, these scales are unable to dynamically represent mood symptoms and may introduce measurement bias.\u003c/p\u003e\n\u003cp\u003eThe symptoms and mood of patients with MDD show significant nonlinear dynamic and unstable characteristics, which have a significant impact on the course and nature of the disease (Bowen et al., 2011, 2013; Broome et al., 2015; Koval et al., 2015). Currently, with conventional measurement tools, the mood fluctuation of patients with MDD is still often underreported, impeding both the understanding of the disease and treatment planning (Moore et al., 2016; Myin-Germeys et al., 2009; Nahum et al., 2017). Moreover, most scales measuring MDD symptoms or depressive mood rely mainly on patients\u0026rsquo; recall of their past experiences, making the scales vulnerable to recall bias and emotional bias (Barrett, 1997; Cutler et al., 1996; Gentzler \u0026amp; Kerns, 2006; Safer \u0026amp; Keuler, 2002; Ruhe et al., 2019). Recall bias may happen when the patients are suffering from cognition disruptions during the evaluation (Ramponi et al., 2004), and the mood state at the time of recall may also cause memory bias which means depressed patients might recall more negative information than positive information (Kihlstrom et al., 2000). The length of recall interval of the scale is also a critical factor that influences the accuracy of mood assessment (Stull et al., 2009). Considering the characteristics of patients with MDD, a shorter recall interval or an immediate mood assessment may be more suitable (Armey et al., 2015; Panaite et al., 2020). A real-time report of the mood symptoms may also provide a more accurate and comprehensive mapping of the fluctuation of the symptoms in everyday life (Bauer et al., 2018; Pedrelli et al., 2020). Additionally, a passive, objective, momentary assessment of the patients\u0026rsquo; mood may help improve treatment outcomes (Yim et al., 2020). A combination of active self-reported mood fluctuation and passive monitoring of behavioral and environmental indicators can provide more knowledge of the trajectory of the disease (Saeb et al., 2017; Yim et al., 2020).\u003c/p\u003e\n\u003cp\u003eAs previously mentioned, it is important to timely and accurately capture the fluctuation of the symptoms related to MDD. However, most conventionally used instruments for the assessment of MDD symptoms in either clinical or research settings, such as the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), or the Visual Analogue Scale (VAS) (Hung et al., 2016), are not designed for real-time assessment. Therefore, developing an easy-to-use, real-time assessment tool may be beneficial to both clinical practice and scientific research. The Immediate Mood Scaler (IMS) is a tool to assess momentary mood state; it was originally developed to be delivered through mobile devices so that it can be administered multiple times in one day and the users can rate how they feel at that moment in their natural settings. This self-report scale consists of 22 items measuring the current mood state related to depression or anxiety (e.g., pessimistic or optimistic, hopeless or hopeful). The users will be asked to rate their mood using 7-point Likert scales with a pair of opposite adjectives describing their mood state at opposite ends of each scale. Each item will derive an integer score between 1 to 7, and the total score of the IMS is the sum of all item scores. (Nahum et al., 2017).\u003c/p\u003e\n\u003cp\u003eValid real-time mood assessment tools are of great importance, especially for patients with MDD. To our best knowledge, the IMS is the only momentary assessment tool for mood symptoms related to depression. Moreover, to date, there is not a validated momentary assessment tool developed specifically for people with MDD in China or a translated version of such a tool. Therefore, this study aims to examine the psychometric properties of the Chinese version of the IMS in patients with MDD, so that there could be an instrument available for momentarily assessing mood symptoms in that group.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used a two-step procedure to translate and validate the Chinese version of the IMS. This study was approved by the Human Research and Ethics Committee of Beijing Anding Hospital, Capital Medical University (Ethical approval number: 2018-119-201917FS-2). Written informed consents were obtained from all participants in the study. The participants were also informed that they could withdraw from the study at any time without any reason or consequence.\u003c/p\u003e\n\u003cp\u003eThe IMS was translated into Chinese with minor modifications for cultural adaptation by two authors. The back-translation was performed by a bilingual clinical psychologist without reading the original text. A preliminary translated version was administered to healthy individuals and a small cohort of patients with mood disorders. The authors then made minor revisions to the wording of two items based on the feedback of those participants and had the final version cross-checked again. The psychometric properties of the final version were then examined.\u003c/p\u003e\n\u003cp\u003eData collection and psychometric evaluation of the Chinese version of the IMS continued from February 2019 to April 2020. Participants of the study were recruited from the outpatients at Beijing Anding Hospital, a tertiary psychiatric hospital. The final sample included 368 participants. The inclusion criteria were: (1) an age of at least 18 years (outpatients); (2) a current diagnosis of an acute episode of MDD according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria, made by trained clinicians; (3) at least 9 years of education. Participants with a diagnosis of any psychotic disorder were excluded.\u003c/p\u003e\n\u003cp\u003eAfter signing the written informed consents, the participants would be asked to download and log in to the app \u0026ldquo;Mood Mirror\u0026rdquo;, an app designed for recording the symptoms and behaviors of patients with MDD (see (Bai et al., 2021) for the details regarding the development of this app). Demographic information of the participants was collected at baseline. The participants were assessed with the IMS, PHQ-9, and GAD-7 at baseline and the follow-up at Week 2. See \u003cstrong\u003eFigure 1\u003c/strong\u003e for a more detailed flowchart.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment tools in the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Chinese version of the Immediate Mood Scaler (IMS-22) assesses current mood symptoms using 7-point scales. For each item, a pair of complementary antonyms (for instance, pessimistic versus optimistic, sleepy versus alert) were at opposite ends of the scale. The patients would be instructed to use the scales to rate how they felt at the moment. This original 22-item scale was found significantly correlated with other psychometrically sound measurement tools assessing anxiety and depression (Nahum et al., 2017).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;In the current study, the PHQ-9 and GAD-7 were used to test the psychometric properties of the Chinese version of the IMS-22. The PHQ-9 has been validated in patients with MDD in China (Cronbach\u0026rsquo;s alpha=0.88) (Feng et al., 2016) and is capable of assessing the severity of depression (Kroenke et al., 2001). The GAD-7 has also shown good reliability and validity for assessing the severity of anxiety symptoms and disorders (Cronbach\u0026rsquo;s alpha=0.95) (Wang et al., 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe characteristics of the participants were reported using descriptive statistics, categorical variables were reported using percentages, and continuous variables were reported using means and standard deviations (SDs).\u003c/p\u003e\n\u003cp\u003eIn the analysis of psychometric properties, we tested the internal structural and construct validity, internal consistency and test-retest reliability, and responsiveness and predictability.\u003c/p\u003e\n\u003cp\u003eConfirmatory factor analysis (CFA) and principal component analysis (PCA) were used to test the internal structural validity and identify subscales corresponding to each measured dimension. To test the construct validity, we computed correlations of the PHQ-9 and GAD-7 with the IMS and its subscales using the Pearson r value.\u003c/p\u003e\n\u003cp\u003eThe test-retest reliability was evaluated by examining the stability of the IMS total scores from baseline to the retest at Week 2.\u003c/p\u003e\n\u003cp\u003eResponsiveness refers to the sensitivity to change and reflects the ability of the IMS to accurately detect changes in mood over time. Based on the cut-off used in previous literature, a \u0026ldquo;change\u0026rdquo; in the PHQ-9 total score was defined as a difference of more than 0.5 SD of the total score at baseline, which equaled to 3.1 points in the current study (Sedaghat, 2019). Mean differences were standardized by the SD at baseline to derive effect sizes (ES) and standard response means (SRM). An ES or SRM of 0.2, 0.5, and 0.8 was considered as a cutoff for low, moderate, and large responsiveness, respectively (Streiner et al., 2016).\u003c/p\u003e\n\u003cp\u003eAnalyses were performed using the Statistical Analysis Software (SAS) version 9.4 (SAS Institute, Cary, N.C.) and MedCalc\u0026reg; Statistical Software version 20.106 (MedCalc Software Ltd, Ostend, Belgium; 2022). A p-value less than 0.05 was considered to indicate statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipant characteristics are reported in \u003cstrong\u003eTable 1\u003c/strong\u003e. The participants were aged 18 to 64 years (mean [SD] age, 29.4 [9.8] years). Of them, 240 (65.2%) were female and 318 (86.4%) held a bachelor\u0026rsquo;s degree or above. In total, 185 participants completed the retest at Week 2. Those participants were aged 18 to 64 years (mean [SD], 30.0 [10.1]). Of them, 64.9% (120/185) were female and 86.5% (160/185) had education degree of bachelor\u0026rsquo;s degree or above.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInternal structural validity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo factors with eigenvalues greater than 1 were identified using Promax rotation. The KMO test value of the IMS was 0.96, and the square value of Bartlett spherical test was statistically significant (c\u003csup\u003e2\u003c/sup\u003e=6415.02, P \u0026lt;.001). The cumulative contribution rate of variance explanation was 63.88%. All items included in each factor loading were more than 0.4. The specific item content of factor Ⅰ\u0026nbsp;was depression symptoms, and that of factor Ⅱ\u0026nbsp;was anxiety symptoms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruct validity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe total score of the IMS had significant correlations with that of the PHQ-9 (r=0.52, P \u0026lt;.001) \u003cstrong\u003e(Figure 2A)\u0026nbsp;\u003c/strong\u003eand that of the GAD-7 (r=0.43, P \u0026lt;.001) \u003cstrong\u003e(Figure 2C)\u003c/strong\u003e at baseline. The IMS-Depression subscale was found significantly correlated with the PHQ-9 (r=0.57, P \u0026lt;.001) \u003cstrong\u003e(Figure 2B)\u003c/strong\u003e, and the IMS-Anxiety subscale was significantly correlated with the GAD-7 (r=0.41, P \u0026lt;.001) \u003cstrong\u003e(Figure 2D)\u0026nbsp;\u003c/strong\u003eat baseline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInternal consistency and test-retest reliability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCronbach\u0026rsquo;s alpha indicated that the IMS had high internal consistency reliability and scale reliability (alpha=0.96), and all individual items had shown excellent reliability (alpha \u0026gt;0.96) with high item-total correlations (all \u0026ge;0.6). The Cronbach\u0026rsquo;s alphas of the IMS-Depression subscale and IMS-Anxiety subscale were 0.95 and 0.92, respectively. The IMS demonstrated strong test-retest reliability (ICC=0.65, 95%CI: 0.53-0.75) in 185 participants who completed the retest, and we also found that the IMS-Depression subscale (ICC=0.66, 95%CI: 0.66-0.75) and IMS-Anxiety subscale (ICC=0.61, 95%CI: 0.47-0.61) had good stability \u003cstrong\u003e(Table 2)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResponsiveness and predictability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResponsiveness of the Chinese version of the IMS was anchored by the change of the PHQ-9 total scores from baseline to the retest at Week 2 (in the current study, a \u0026ldquo;change\u0026rdquo; is defined as a difference in the PHQ-9 total scores of more than 3.1 points); it is quantified by the mean differences, ES, and SRM of the IMS total scores at baseline and the retest. Changes in the total scores of the IMS from baseline to the retest were statistically significant with a mean difference of 13.3 (SD: 20.1), an ES of 0.66, and an SRM of 0.3, suggesting that the IMS was sensitive to clinical change.\u003c/p\u003e\n\u003cp\u003eThe IMS-Depression subscale showed slightly larger responsiveness than the IMS, with an ES of 0.72 and an SRM of 0.41. On the other hand, responsiveness of the IMS-Anxiety subscale was low, with an ES of 0.37 and an SRM of 0.27\u003cstrong\u003e\u0026nbsp;(Table 3)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe mixed models for repeated measure (MMRM) are presented in \u003cstrong\u003eTable 4\u003c/strong\u003e. The change of total scores of the PHQ-9 from baseline to the Week-2 retest indicated a significant change in the severity of depressive symptoms (time effect in model 1 and model 2: all p-values \u0026lt;.001). The predictive model built based on the score of the IMS-Depression subscales could predict the change of PHQ-9 total scores over the two weeks (Model 2: t=2.19, P =.029), but the model based on the IMS could not (Model 1:t=1.71, P =0.088). \u003cstrong\u003e(Table 4)\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the current study, we translated the IMS into Chinese and examined the psychometric properties of the translated version. In total, 368 patients with MDD were included. Our results suggest that the Chinese version of the IMS has satisfactory psychometric properties in patients with MDD, indicating that it may be a valid tool to dynamically track and monitor depressive and anxiety symptoms in those patients.\u003c/p\u003e\n\u003cp\u003eSimilar to the original version of the IMS (Nahum et al., 2017), the Chinese version IMS was found to have strong correlations with the PHQ-9 and GAD-7, indicating that the scale can be used to measure depressive and anxiety symptoms. However, it should be admitted that the correlations of the changes in the symptoms over two weeks with the scores of the Chinese version IMS were weaker than that with the scores of the original version, indicating the Chinese version had a weaker ability to predict. This fact might result from the influence of anxiety combined with depression (FAVA et al., 2004), especially in patients with severe symptoms. It may also be explained by the fact that the severity of depression in our sample was higher than that in the sample in the study of the original IMS (the range of PHQ-9 score was 9 to 36 in our study, compared to 0 to 27 in the study of the original IMS) (Nahum et al., 2017).\u003c/p\u003e\n\u003cp\u003eFor responsiveness, we determined the minimal clinically important difference (MCID) for the change of MDD symptoms using the distribution-based method and set it at -3.1 in terms of the PHQ-9 score, which was a stricter threshold than the -1.7 used in a previous study in the UK (Kounali et al., 2022). The Chinese version of IMS was found able to reflect the clinically meaningful change in the total score of the PHQ-9 from the baseline to Week 2. In the current study, we found that the IMS-Depression subscale was more sensitive to changes in depression severity over two weeks than the IMS. The relatively lower responsiveness of the IMS, judged with the ES or SRM, may result from the small sample size and the stricter threshold set for the MCID.\u003c/p\u003e\n\u003cp\u003eAs previously mentioned, a real-time, dynamic assessment of the patients\u0026rsquo; mood might allow clinicians and researchers to track the fluctuation of symptoms and the prognosis of the disease over the treatment course. Furthermore, we will investigate whether a trajectory recorded daily could predict the prognosis for the patient in future research.\u003c/p\u003e\n\u003cp\u003eAnother point to be noted is the practical significance of the Chinese version IMS. Same as the original version IMS, the Chinese version IMS can be administered through mobile devices, which makes it suitable and convenient for the real-time assessment of outpatients.\u003c/p\u003e\n\u003cp\u003eThe current study still has several limitations. One of the limitations stems from the nature of the assessment tool we chose: the IMS focuses mainly on negative mood such as depression or anxiety but does not assess or track positive mood. Focusing only on negative affect might limit its capacity to draw a whole picture of the mood state of the patients. The other limitation is the reduced interpretability in clinical practice, resulted from the small sample size and not using a patient-centered anchor-based method (which takes account of patients\u0026rsquo; subjective reports of symptom alleviation in the evaluation of clinically meaningful change in symptoms) in the study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, our study showed that the Chinese version of the IMS had good psychometric properties in patients with MDD in China. It can be a useful tool for the real-time assessment of the mood of patients with MDD which can help clinicians to track the condition of the patients in a timely manner. Moreover, as an assessment tool that can be delivered through mobile devices, the Chinese version IMS would be able to help draw a picture of patients\u0026rsquo; mood fluctuation in a natural setting and thus provide information on the condition and prognosis of patients outside of inpatient care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIMS: Immediate Mood Scaler; MDD: major depressive disorder; PHQ-9: Patient Health Questionnaire-9; GAD-7: Generalized Anxiety Disorder-7; VAS: Visual Analogue Scale; SD: standard deviation; CFA: confirmatory factor analysis; PCA: principal component analysis; ES: effect size; SRM: standard response means; SAS: Statistical Analysis Software; MMRM: mixed models for repeated measure\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Human Research and Ethics Committee of Beijing Anding Hospital, Capital Medical University (Ethical approval number: 2018-119-201917FS-2). Written informed consents were obtained from all participants in the study. The participants were also informed that they could withdraw from the study at any time without any reason or consequence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Capital\u0026rsquo;s Funds for Health Improvement and Research Program (2022-4-2125) and the Beijing Municipal Health Commission of Beijing demonstration research ward (BCRW202009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXZ designed the study. XC and NL implemented the study and collected the data. XZ performed the statistical analysis and interpreted the results. XC and ZF wrote the first draft of the manuscript. XZ, XC and LX revised the manuscript. All authors contributed significantly to this work. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArmey, M. F., Schatten, H. T., Haradhvala, N., \u0026amp; Miller, I. W. (2015). 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J., Ikani, N., Tyborowska, A., Browning, M., Vrijsen, J. N., Harmer, C. J., \u0026amp; Schene, A. H. (2019). Emotional Biases and Recurrence in Major Depressive Disorder. Results of 2.5 Years Follow-Up of Drug-Free Cohort Vulnerable for Recurrence. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 145. https://doi.org/10.3389/fpsyt.2019.00145\u003c/li\u003e\n\u003cli\u003eSaeb, S., Lattie, E. G., Kording, K. P., \u0026amp; Mohr, D. C. (2017). Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. \u003cem\u003eJMIR mHealth and uHealth\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(8), e112. https://doi.org/10.2196/mhealth.7297\u003c/li\u003e\n\u003cli\u003eSafer, M. A., \u0026amp; Keuler, D. J. (2002). Individual differences in misremembering pre-psychotherapy distress: Personality and memory distortion. \u003cem\u003eEmotion\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(2), 162\u0026ndash;178. https://doi.org/10.1037/1528-3542.2.2.162\u003c/li\u003e\n\u003cli\u003eSedaghat, A. R. (2019). Understanding the Minimal Clinically Important Difference (MCID) of Patient‐Reported Outcome Measures. \u003cem\u003eOtolaryngology\u0026ndash;Head and Neck Surgery\u003c/em\u003e, \u003cem\u003e161\u003c/em\u003e(4), 551\u0026ndash;560. https://doi.org/10.1177/0194599819852604\u003c/li\u003e\n\u003cli\u003eStreiner, D. L., Norman, G. R., \u0026amp; Cairney, J. (2016). Health measurement scales: A practical guide to their development and use (5th edition). \u003cem\u003eAustralian and New Zealand Journal of Public Health\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(3), 294\u0026ndash;295. https://doi.org/10.1111/1753-6405.12484\u003c/li\u003e\n\u003cli\u003eStull, D. E., Leidy, N. K., Parasuraman, B., \u0026amp; Chassany, O. (2009). Optimal recall periods for patient-reported outcomes: Challenges and potential solutions. \u003cem\u003eCurrent Medical Research and Opinion\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(4), 929\u0026ndash;942. https://doi.org/10.1185/03007990902774765\u003c/li\u003e\n\u003cli\u003eWang, Y.-Y., Dong, M., Zhang, Q., Xu, D.-D., Zhao, J., Ng, C. H., Ungvari, G. S., Jia, F.-J., \u0026amp; Xiang, Y.-T. (2019). Suicidality and clinical correlates in Chinese men who have sex with men (MSM) with HIV infection. \u003cem\u003ePsychology, Health \u0026amp; Medicine\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(2), 137\u0026ndash;143. https://doi.org/10.1080/13548506.2018.1515495\u003c/li\u003e\n\u003cli\u003eYim, S. J., Lui, L. M. W., Lee, Y., Rosenblat, J. D., Ragguett, R.-M., Park, C., Subramaniapillai, M., Cao, B., Zhou, A., Rong, C., Lin, K., Ho, R. C., Coles, A. S., Majeed, A., Wong, E. R., Phan, L., Nasri, F., \u0026amp; McIntyre, R. S. (2020). The utility of smartphone-based, ecological momentary assessment for depressive symptoms. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e, \u003cem\u003e274\u003c/em\u003e, 602\u0026ndash;609. https://doi.org/10.1016/j.jad.2020.05.116\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of participants at baseline and the retest at week 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhole sample\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=368\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest-retest group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=185)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(N=81)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, yrs.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e29.4(9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e30.0(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e29.0(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e18.0-64.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e18.0-64.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e18.0-60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e128(34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e65(35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e29(35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e240(65.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e120(64.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e52(64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational level, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eHigh school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e50(13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e25(13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e10(12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s degree and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e318(86.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e160(86.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e71(87.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFirst episode, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e227(61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e120(64.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e51(63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe PHQ-9 total score at baseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e23.5(6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e23.9(6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e26.6(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e9-36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e9-36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e15-35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe GAD-7 total score at baseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e9.6(5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e10.0(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e11.0(5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e1-21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe IMS total score at baseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e103.9(24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e106.3(23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e111.2(20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e22-154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e22-154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e47-146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;The PHQ-9 total score at retest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e20.9(5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e18.9(4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e9-36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e11-28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe GAD-7 total score at retest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e8.0(5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e7.0(4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0-21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe IMS total score at retest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e100.0(22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e97.9(20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e30-154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e59-139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eN/A: not applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Descriptive Statistics, Reliability, And Factor Loadings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem-total correlation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest-retest reliability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepressed-Happy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.7(1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistracted-Focused\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.6(1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorthless-Valuable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.73(1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.85\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLonely- Engaged\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.85(1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleepy-Alert\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e5.07(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.79\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlow-Speedy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.95(1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.82\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTired-Energetic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e5.27(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.81\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePessimistic-Optimistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.96(1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.81\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eApathetic-Motivated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.93(1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGuilty-Proud\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.73(1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumb- Interested\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.89(1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithdraw-Welcoming\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.83(1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.73\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrustrated-Peaceful\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.73(1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.58\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImpulsive-Careful\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.04(1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.71\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMoody-Stable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.45(1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHopeless-Hopeful\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.76(1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIrritable-Easy-Going\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.18(1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.82\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTense-Relaxed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.62(1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorried-Untroubled\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.86(1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFearful-Fearless\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.55(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.81\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxious-Peaceful\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.78(1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRestless-Calm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.41(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.83\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScale\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMS-Depression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e68.02(16.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMS-Anxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e35.89(9.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eResponsiveness in patients anchored by the change in depressive severity\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(n=85)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponsiveness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized Response Mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e13.3(20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.66(0.36-0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e0.39(0.29-0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMS-depression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e10.0(13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.72(0.41-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e0.41(0.32-0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMS-anxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e3.2(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.37(0.11-0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e0.27(0.10-0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Baseline -retest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4 Predictability of the PHQ-9 by IMS and IMS-Depression subscale evaluated using MMRM (n=185)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.003502626970224%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.259194395796847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.309982486865149%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.003502626970224%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1: Y=PHQ-9 at week 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.259194395796847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.309982486865149%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.003502626970224%\" valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.259194395796847%\" valign=\"top\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.309982486865149%\" valign=\"top\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.003502626970224%\" valign=\"top\"\u003e\n \u003cp\u003eTime effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.259194395796847%\" valign=\"top\"\u003e\n \u003cp\u003e-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.309982486865149%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e-10.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.003502626970224%\" valign=\"top\"\u003e\n \u003cp\u003ePHQ-9 at baseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.259194395796847%\" valign=\"top\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.309982486865149%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e33.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.003502626970224%\" valign=\"top\"\u003e\n \u003cp\u003eIMS at baseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.259194395796847%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.309982486865149%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.003502626970224%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2: Y=PHQ-9 at week 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.259194395796847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.309982486865149%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.003502626970224%\" valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.259194395796847%\" valign=\"top\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.309982486865149%\" valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.003502626970224%\" valign=\"top\"\u003e\n \u003cp\u003eTime effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.259194395796847%\" valign=\"top\"\u003e\n \u003cp\u003e-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.309982486865149%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e-10.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.003502626970224%\" valign=\"top\"\u003e\n \u003cp\u003ePHQ-9 at baseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.259194395796847%\" valign=\"top\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.309982486865149%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e32.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.003502626970224%\" valign=\"top\"\u003e\n \u003cp\u003eIMS-depression at baseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.259194395796847%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.309982486865149%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\" valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"major depressive disorder, ecological momentary assessment, mobile","lastPublishedDoi":"10.21203/rs.3.rs-4278887/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4278887/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e It is important to timely capture the fluctuation of the symptoms related to MDD. However, most conventionally used assessment tools for MDD symptoms are not designed for real-time assessment. The Immediate Mood Scaler (IMS) is suitable for the real-time evaluation of the mood of patients with MDD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe original IMS was translated into Chinese and back-translated. At baseline, data from 368 patients with MDD, including demographic information and scores on the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and IMS, were collected. In total, 185 participants completed the retest at Week 2 which included the PHQ-9, GAD-7, and IMS. Internal structural validity, construct validity, and internal consistency were evaluated with the confirmatory factor analysis (CFA) and principal component analysis (PCA), the Pearson correlation, and Cronbach’s α, respectively. Responsiveness was anchored by the change of the PHQ-9 total scores from baseline to Week 2. Predictability was tested using the mixed models for repeated measure (MMRM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e PCA identified two factors with an eigenvalue greater than 1, corresponding to IMS-Depression and IMS-Anxiety subscales. The Cronbach’s α that evaluated internal consistency was 0.96, 0.95, and 0.92 for the scores of the IMS, IMS-Depression subscale, and IMS-Anxiety subscale at baseline, respectively. The depression and anxiety subscales at baseline showed high subscale-total correlations (r=0.96 for the depression subscale; r=0.89 for the anxiety subscale). The test-retest ICC (0.65, 95%CI: 0.53-0.73) of the IMS at baseline and Week 2 show high reliability. The total score of IMS had significant correlations with that of the PHQ-9 (r=0.52, P\u0026lt;.001) and GAD-7(r=0.43, P \u0026lt;.001), indicating high construct validity. In patients with MDD who showed changes in mood, the changes in total scores of the IMS from baseline to the retest were statistically significant with a mean difference of 13.3 (SD: 20.1), an ES of 0.66, and an SRM of 0.3, showing good responsiveness. Also, the baseline IMS-Depression subscale score could predict the change in the PHQ-9 score over the two weeks (t=2.19, P =.029).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The Chinese version of the IMS is valid and reliable for the real-time assessment of mood in patients with MDD in China.\u003c/p\u003e","manuscriptTitle":"The Chinese version of the Immediate Mood Scaler (IMS): a study evaluating its validity, reliability, and responsiveness in patients with MDD in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-29 12:33:50","doi":"10.21203/rs.3.rs-4278887/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-24T12:00:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-22T19:02:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-22T06:58:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-17T11:38:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-16T01:39:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231378658604991698491006865479124281930","date":"2024-07-03T05:31:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69894711400754637836675624598487056436","date":"2024-07-02T12:39:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42151968796367134675868146490474113248","date":"2024-07-02T07:21:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-29T02:49:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112020571463553659102966065230457465908","date":"2024-06-25T01:32:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137425367861185072413525660455728456729","date":"2024-06-20T01:32:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-20T01:08:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-15T02:38:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-19T06:13:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-19T06:11:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2024-04-17T02:38:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e62ecf3d-0b55-4ebf-8eb6-de35e98df8bd","owner":[],"postedDate":"April 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T15:58:14+00:00","versionOfRecord":{"articleIdentity":"rs-4278887","link":"https://doi.org/10.1186/s12888-024-06418-3","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2025-01-07 15:56:50","publishedOnDateReadable":"January 7th, 2025"},"versionCreatedAt":"2024-04-29 12:33:50","video":"","vorDoi":"10.1186/s12888-024-06418-3","vorDoiUrl":"https://doi.org/10.1186/s12888-024-06418-3","workflowStages":[]},"version":"v1","identity":"rs-4278887","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4278887","identity":"rs-4278887","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}