Validating the Online Circle Test (OL-CT): Cross-Format Equivalence, One-Week Reliability, and Depressive Symptom Correlates in Japanese Undergraduates

Validating the Online Circle Test (OL-CT): Cross-Format Equivalence, One-Week Reliability, and Depressive Symptom Correlates in Japanese Undergraduates | 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 Validating the Online Circle Test (OL-CT): Cross-Format Equivalence, One-Week Reliability, and Depressive Symptom Correlates in Japanese Undergraduates HAILONG HAN, AKIRA MIDORIKAWA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8514018/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background The Circle Test is a drawing-based task for assessing time perspective, and its indices have been linked to psychological well-being, including depression symptoms. However, conventional paper-and-pencil administration requires substantial human resources for scoring and quantification, limiting its scalability for large or remote surveys to be conducted. We developed an online web-based circle test with automated scoring and evaluated its validity and reliability. Methods Thirty-six Japanese undergraduates completed both the Online Circle Test (OL-CT) and paper-and-pencil Circle Test (PP-CT) in a counterbalanced crossover design (Visit 1) and repeated the OL-CT after 7–9 days either in the laboratory or at home (Visit 2). Continuous indices included log-transformed absolute and proportional areas, and categorical indices included temporal dominance and temporal relatedness. Validity analyses evaluated (a) cross-format equivalence between PP-CT and OL-CT and (b) clinical validity through associations with depressive symptoms measured using the Japanese version of the Patient Health Questionnaire-9 (J-PHQ-9). Reliability analyses evaluated (a) the one-week test-retest reliability of the OL-CT and (b) the contextual stability across laboratory versus home administration. Results Cross-format equivalence was moderate for log-transformed absolute areas (ICC = .73-.77) but excellent for proportional areas (ICC = .89-.90). The categorical agreement was high (88.9%), with no systematic category shift. For clinical validity, higher J-PHQ-9 scores were associated with a greater representational emphasis on the past, whereas future indices were not significant predictors in this non-clinical sample. One-week test-retest reliability was moderate-to-excellent for absolute areas (ICC = .69-.89) and excellent for proportional areas (ICC = .93-.95). Categorical stability was also high (temporal dominance: 91.7%, κ = .88; temporal relatedness: 86.0%, κ = .76). The OL-CT indices did not differ between the laboratory and home retesting contexts. Conclusion The OL-CT demonstrates strong validity and reliability as a scalable digital time-perspective assessment, with particularly robust proportional indices and stable categorical profiles across formats, time, and testing contexts. Cottle’s Circle Test time perspective online assessment psychometric validation test–retest reliability depressive symptoms digital phenotyping Figures Figure 1 Introduction Time perspectives describe individuals’ views on the past, present, and future, which are relatively stable individual characteristics [ 1 ]. Recent reviews suggest that a balanced time perspective is broadly associated with higher subjective well-being, adaptive health behaviors, academic achievement, and lower psychopathology vulnerability [ 2 , 3 ]. Therefore, a variety of methods have been developed to measure time perspective. Time perspective has traditionally been assessed using questionnaire-based (self-report) and performance-based (projective) measures. Self-report instruments such as the Zimbardo Time Perspective Inventory (ZTPI)[ 4 ] and the Temporal Focus Scale [ 5 ] are easy to administer and psychometrically established. However, questionnaire methods are influenced by introspective ability and social desirability, which limits their applicability in clinical or screening contexts [ 6 ]. Performance-based measures have the potential to address these limitations; however, they are time-consuming to administer and are not well-suited for large-scale surveys. A widely used performance-based measure is Cottle’s circle test [ 7 ], in which participants were asked to draw three circles representing the past, present, and future respectively. Traditionally, there are two indexes based on drawings namely temporal dominance, and temporal relatedness. Temporal dominance refers to the relative preeminence of one temporal zone, as indicated by the circle size. Temporal relatedness refers to the degree of perceived linkage among the three temporal zones. Because the Circle Test explores implicit aspects of time perspective [ 8 ], its utility has been demonstrated across diverse populations and outcomes [ 9 , 10 ]. However, because the original scoring methods require considerable effort, they are unsuitable for large-scale surveys. The use of digital technologies can be a useful way to address these challenges. Web-based online versions of the circle test allow participants to complete the task and automatically record geometric information, such as circle centers, radii, and final layouts [ 11 ], while other versions can also compute the absolute area [ 12 ]. However, these studies rely on manual judgment for scoring of temporal dominance and temporal relatedness, therefore scoring still requires considerable effort. In addition, these studies have not confirmed the equivalence between the original paper-based and online versions, test-retest reliability, and validity for clinically relevant outcomes. To overcome these challenges, we newly developed a web-based online circle test (OL-CT) that automatically scores temporal dominance and temporal relatedness. In addition, the system enables automated capture of both end-state geometric features of the three circles and process-level drawing data (e.g., latencies and kinematic traces); however, the present study focuses on the psychometric evaluation of the end-state indices. The OL-CT has at least three advantages. First, the administration and scoring can be conducted automatically. It requires no rater training and can relieve heavy burdens on human raters. Second, uniform instructions are provided by the browser and therefore reduce the variability caused by inter-rater differences and environmental inconsistencies. Third, OL-CT scores can be provided and used directly for the interpretation of results and further data analyses. Thus, the OL-CT shows great feasibility in large-scale cohort studies and also remote research contexts. However, the utility of the OL-CT is constrained owing to the unknown psychometric properties of the target population. Drawing inspiration from contemporary digital validation frameworks [ 13 ]), we prespecified two objectives with a strict analysis–reporting correspondence: Objective 1 (Validity)—(a) evaluate cross-format equivalence between the paper-and-pencil Circle Test (PP-CT) and the OL-CT, and (b) evaluate clinical validity via associations with depressive symptoms; Objective 2 (Reliability)—(a) estimate one-week test–retest reliability of the OL-CT, and (b) assess contextual stability across laboratory versus home administration. Method Study design The study consisted of two visits. In visit 1, participants completed both the paper-and-pencil Circle Test (PP-CT) and the online Circle Test (OL-CT). They were randomly allocated either OL-CT-first or PP-CT-first. After finishing the circle tests, the Japanese version of the Patient Health Questionnaire-9 (J-PHQ-9)[14] was administered. Participants returned for Visit 2, 7–9 days after the first visit, to repeat the OL-CT to provide data for test–retest reliability analyses. At the same time, to examine test-retest reliability, they were block randomized, considering sex and age, to complete the retest in the laboratory or at home under standardized instructions, and the J-PHQ-9 was again administered. All other procedures (instructions, timing, and scoring pipelines) were identical across visits and order groups. Study population Participants were recruited over a two-week period in April 2025 through in-class announcements in psychology and general education courses, as well as the distribution of flyers on campus (convenience sampling). Interested individuals scheduled an appointment via an online registration form or email for Visit 1 and a second session approximately 7–9 days later (Visit 2). The second session was conducted either in the laboratory or at home, depending on the assigned context. After completing Visit 1, each participant received a snack valued at approximately ¥500 as an incentive. Eligibility criteria included: (1) being 18 years or older, (2) providing written informed consent, (3) understanding Japanese and being able to follow task instructions, and (4) no motor or visual limitations (corrected vision permitted) that would preclude drawing the circles, operating a mouse/trackpad, or viewing the on-screen materials. Data collection PP-CT Task (Visit 1 only) The PP-CT followed Cottle’s procedure [7] and administered on A4 landscape paper and the subjects were asked to draw three circles representing past, present, and future. The left half of the sheet printed the Japanese instruction text identical in content to the online version, and, beneath it, a demographic panel with fields for sex, age, birth month/day (MMDD), and the last three digits of the mobile number was provided. From these entries, an anonymous participant ID (MMDD-XXX) was formed and written on the sheet. The right half contained a 120*120 mm blank drawing frame printed in thin black lines, centered vertically with margins > 10 mm to the page edges. Completed sheets were digitized using a flatbed scanner at 600 dpi, and images were processed with ImageJ (v1.54) [15] to extract calibrated radii, areas, centroids, and pairwise spatial indices (center-to-center distances and overlap areas) of the three circles. From these geometric features, we derived (a) continuous indices: absolute areas (mm²) and proportional areas (each circle’s area divided by the sum of the three), and (b) two qualitative typologies followed Cottle’s procedure [7]: temporal dominance and temporal relatedness. Temporal dominance was assigned by comparing proportional areas: cases were labeled Past-, Present-, or Future-dominant when one time zone’s proportional area clearly exceeded the other two by a pre-specified margin. Drawings were labeled Balanced when all three shares were within the pre-specified tolerance of an equal one-third split (i.e., each circle’s radius deviated by less than 10% from the mean radius of the three circles). Temporal relatedness was coded based on the pattern and magnitude of pairwise overlaps: Atomicity (no meaningful overlap among any pair), Continuity (exactly one substantive pairwise overlap forming a “chain” with the third circle separate), Integration/Projection (two or more substantive overlaps, including triple-overlap configurations or instances where one circle is substantially projected into another), and Other for atypical or ambiguous arrangements (e.g., tangencies, severe distortions, or labeling anomalies). Two independent, trained raters blinded to participant identity and any online outputs coded all PP-CT drawings for both typologies. Discrepancies were resolved by consensus after computing inter-rater agreement, which was 88.9% for dominance (κ = .84) and 100% for relatedness (κ = 1.00). OL-CT Task (Visit 1 and Visit 2) Participants completed the OL-CT independently in a browser-based application on laboratory-standardized laptops (Lenovo IdeaPad 16 Pro) with external mice. The program (https://reurl.cc/4LZNyK) proceeded as follows: (1) a full-screen 800*800px canvas with high-precision event logging; (2) demographic entry and automatic anonymous ID (MMDD-XXX); (3) two-point screen calibration (200/250px) using a ruler to compute pixel-to-mm, with >±5% ratio error triggering repeat; (4) brief guided practice (create/move/resize/toggle/delete); and (5) asked to draw three circles for Past, Present, and Future and labels each. The system records geometry, overlaps/proportions, dominance/relatedness, and kinematics (Fig. 1). (6) automatic export (static CSV, dynamic CSV, JPEG) and secure upload with ZIP fallback (A step-by-step description of the OL-CT program flow is provided in Appendix 1). The OL-CT platform generated the same geometric feature set (radii, areas, centroids, and pairwise overlap areas), from which identical dominance and relatedness rules were algorithmically applied to assign categorical labels. For quality control, one trained human rater—blinded to the algorithm’s output—independently coded each OL-CT image using the same codebook. Human–algorithm concordance was perfect (κ = 1.00 for both dominance and relatedness), confirming the functional equivalence of the automated scoring to expert judgment. Questionnaire We used the Japanese version of the Patient Health Questionnaire-9 (J-PHQ-9) as a brief self-reported measure of depressive symptom severity. The instrument comprises nine items reflecting DSM-consistent symptoms over the past two weeks, each rated on a 0–3 scale (“not at all” to “nearly every day”), yielding a total score of 0–27 points. Scoring followed the standard sum of the item responses. The J-PHQ-9 was administered in both visits. In both visits, participants completed the questionnaire digitally immediately after finishing the OL-CT session, using the same secure browser interface as the online task. Statistical methods Analyses were organized to mirror the prespecified objectives: Objective 1(Validity) (a) evaluate cross-format equivalence between the paper and pencil circle test (PP-CT) and the online circle test (OL-CT), and (b) evaluate clinical validity with depressive symptom using the J-PHQ-9; Objective 2 (Reliability) (a) one week test–retest reliability of the OL-CT, and (b) assess contextual stability across laboratory vs. home administration. Although the OL-CT logs process-level variables, these data were not analyzed in the present report and are retained for future work. For all analysis, a significant level of α = .05 (two-tailed) was applied, and 95% confidence intervals (CIs) were reported where appropriate. In regard to quantitative analysis of the circle test, data preparation followed a two-pronged approach. First, the absolute areas (mm²) of the Past, Present, and Future circles, as well as their total area were extracted, and natural logarithm transformations were applied to each area (log-Past, log-Present, log-Future) to reduce positive skewness and accommodate multiplicative scale differences, following common recommendations for log-transforming skewed psychometric variables [16, 17]; the log-transformed total area was used to index each participant’s overall drawing scale. Second, proportional areas for Past, Present, and Future were calculated dividing each circle's area by the total three areas. In regard to qualitative analysis of the circle test, based on Cottle’s original distinction [7]; temporal dominance profiles and temporal-relatedness configurations of the three circles were coded as categorical indices based on the spatial arrangement of the circles. The statistical analyses in this study encompassed both continuous and categorical dimensions. For quantitative indices, including log-transformed absolute and proportional areas, intraclass correlation coefficients (ICC 3,1) were employed to evaluate cross-format equivalence and one-week test–retest reliability. Categorical indices—temporal dominance and relatedness—were summarized using frequency distributions and assessed for within-person stability across formats and time using McNemar–Bowker tests. To establish clinical validity, hierarchical multiple regression models were used to examine the predictive utility of the circle indices for J-PHQ-9 depressive symptom severity. Additionally, independent-samples t-tests served as randomization checks for the counterbalanced administration order, while contextual stability between the laboratory and home settings was evaluated through mean comparisons with 90% confidence intervals and chi-square tests of independence. In order to evaluate the validity and reliability of the Circle Test, we conducted the following analyses; Objective 1(a): To examine the validity of cross format equivalence between the paper and online version for quantitative outcomes (absolute and proportional areas; within subjects), data obtained from Visit 1 were utilized. Intraclass correlation coefficients (ICCs) were calculated using ICC (3,1) (two-way mixed effects, absolute agreement) with 95% confidence intervals. For categorical comparability (temporal dominance and relatedness), PP-CT classifications were cross-tabulated with OL-CT classifications and evaluated using the McNemar–Bowker test of symmetry for paired multi-category data. Objective 1(b): To evaluate the association between depressive symptoms and circular indices using data collected during Visit 2, we employed a consistent hierarchical multiple regression framework for both the absolute and proportional metrics. First, for log-transformed absolute areas, we assessed whether the specific sizes of the Past and Future circles predicted J-PHQ-9 scores after strictly controlling for the overall drawing size. In Step 1, the administration context (laboratory vs. home) and log-transformed Total Area were entered as control variables. The former was included to account for any potential environmental variance, and the latter served to control for individual differences in the overall drawing scale, thereby isolating the unique predictive power of specific temporal zones, independent of total figure size. In Step 2, the log-transformed areas of the Past and Future circles were added to the model. The Present circle area was excluded from this analysis for two primary reasons. First, statistically, preliminary diagnostics indicated that including all three temporal zones alongside the total area introduced severe multicollinearity (Variance Inflation Factors > 10), whereas excluding the present dimension reduced collinearity to acceptable levels. Second, theoretically, depressive symptomatology is most strongly linked to distortions in past (e.g., rumination) and future (e.g., hopelessness) perspectives [18]. Second, for proportional areas, a similar hierarchical approach was used to predict the J-PHQ-9 scores. Objective 2(a) Test–retest reliability (within subjects) between Visit 1 and Visit 2 for quantitative indices was calculated using ICC(3,1) with 95% confidence intervals, and test–retest stability for categorical dominance and relatedness classifications was examined using McNemar–Bowker tests. Objective 2(b): To assess the contextual stability of the OL-CT (between subjects), we evaluated whether the administration environment influenced the outcomes by comparing the mean values and 90% confidence intervals (CIs) of continuous indices (absolute and proportional areas) between the laboratory and home settings at Visit 2. In accordance with equivalence testing standards (α = 0.05), overlapping 90% CIs and the absence of substantial mean differences were interpreted as evidence that the physical environment did not exert a systematic effect on the metrics. For categorical indices (dominance and relatedness), we employed chi-square tests of independence to compare the frequency distributions of classifications across the two contexts. Results 3.1. Sample description A total of 36 Japanese undergraduates participated (23 women, 13 men; M age = 20.78, SD = 2.15) and completed both visits without attrition. In Visit 1 (laboratory), participants were randomized to two order groups: OL-CT-first (PP-CT-second) ( n = 18; 9 women, 9 men; M age = 21.56, SD = 2.55) and PP-CT-first (OL-CT-second) ( n = 18; 14 women, 4 men; M age = 20.00, SD = 1.33). In Visit 2 (7–9 days later), the same participants were block-randomized by sex and age into laboratory ( n = 18; 11 women; M age = 20.61, SD = 2.17) and home ( n = 18; 12 women; M age = 20.94, SD = 2.18) retest contexts. All datasets met the prespecified quality-control criteria. 3.2. Objective 1a — Cross-format equivalence 3.2.1. Order randomization checks To evaluate whether the counterbalanced administration order introduced systematic differences in OL-CT performance, independent-samples t tests compared Visit 1 OL-CT indices between participants who completed PP-CT first (PP-first; n = 18) and those who completed OL-CT first (OL-first; n = 18). For log-transformed absolute areas, there were no significant order effects for the Past or Future circles, nor for the total OL-CT area (Past: t (34) = –0.28, p = .78; Future: t (34) = –0.40, p = .69; total: t (34) = –1.25, p = .22). The log-transformed Present circle area was larger when OL-CT was administered first than when it followed PP-CT, t (34) = –2.28, p = .03. For proportional indices, no order differences reached conventional significance (Past: t (34) = 0.75, p = .46; Present: t (34) = –1.71, p = .10; Future: t (34) = 0.45, p = .65). To account for individual differences in baseline drawing style, we also computed within-person format-difference scores for the Present circle, defined as the log-transformed difference between OL-CT and PP-CT areas. An independent-samples t test on these change scores revealed no significant order effect, t (34) = –0.66, p = .52, indicating that the apparent raw group difference in OL-CT Present area was largely attributable to pre-existing between-group variability rather than a systematic administration-order bias. 3.2.2. Cross-format agreement for continuous indices Results for cross-format agreement are summarized in Table 1. Across log-transformed absolute areas, the ICCs (.73–.77) indicate moderate cross-version agreement: each significantly exceeded zero yet remained below the .75 benchmark typically considered “good” for single observations. When areas were expressed as proportions of total drawing space, agreement rose sharply; ICCs for Past (.89), Present (.90), and Future (.89) all surpassed the “good” threshold and approached the .90 criterion for excellence, with narrow confidence intervals entirely above .75. Hence, while log-transformed absolute area metrics are reasonably consistent, proportional scores exhibit excellent reliability and may be regarded as fully interchangeable between PP and OL administrations. Therefore, subsequent analyses relied on proportional indices as the primary continuous outcomes. Table 1 Cross-Version Intraclass Correlations for Log-transformed absolute area and Proportion Scores Measure Time-zone ICC(3,1) 95 % CI F (35, 35) p Log-transformed absolute area Past .77 .60 – .88 7.88 < .001 Present .76 .57 – .87 7.17 < .001 Future .73 .53 – .85 6.39 < .001 Proportion Past .89 .80 – .94 17.43 < .001 Present .90 .81 – .95 17.73 < .001 Future .89 .80 – .94 17.45 < .001 Note . ICC= intraclass correlation coefficient (single-measure, absolute agreement, two-way mixed); all dfs= 35. 3.2.3. Cross-format agreement for categorical indices A four-by-four McNemar–Bowker test showed no systematic shift in dominance categories between versions (χ²(3) = 2.00, p = .572). Agreement statistics are summarized in Table 2. Thirty-two of the 36 participants (88.9%) received the same label in PP-CT and OL-CT; the four discrepancies were evenly distributed across off-diagonal cells. The same pattern was observed for temporal-relatedness classifications (χ²(3) = 4.00, p = .261). Again, 88.9% of the cases were concordant, and the few mismatches showed no directional bias. The absence of significant asymmetry and the high exact match rate confirm that the OL-CT faithfully reproduces the qualitative profile structure of the PP-CT, extending the metric equivalence of continuous indices to categorical outcomes. Table 2 Cross-Format Agreement for Categorical Indices Categorical Index Agreement McNemar–Bowker df p Mismatches Temporal Dominance 88.9% 2.00 3 .572 4 Temporal Relatedness 88.9% 4.00 3 .261 4 Note. N = 36. Agreement (%) refers to the percentage of participants whose classification labels (e.g., Past Dominance, Balanced) were identical across both formats. The McNemar–Bowker test evaluates the symmetry of the disagreement; a non-significant p -value indicates no systematic bias in classification changes between formats. 3.3. Objective 1b — Clinical validity with depressive symptoms 3.3.1. Clinical validity of absolute circle areas for depressive symptoms Hierarchical regression results (Table 3) indicated that Step 1 variables (context and total area) did not significantly predict depressive symptoms (R 2 = .039, F (2, 33) = 0.67, p = .519). However, the addition of log past and log future areas in Step 2 significantly improved the model fit (ΔR 2 = .387, p = .001), with the final model accounting for 42.6% of the variance in J-PHQ-9 scores (R 2 = .426, F (4, 31) = 5.74, p = .001). Specifically, log past area emerged as a significant positive predictor (B = 4.12, p = .030), indicating that individuals drawing larger absolute past circles reported higher depression levels independent of total drawing size3. Log future area showed a non-significant negative association with depression (B = -2.46, p = .131). Table 3 Hierarchical Regression Predicting PHQ-9 Scores from Absolute Circle Areas Predictor B SE β t p 95%CI Step 1 Constant 25.83 16.71 1.55 .132 [-8.16,59.81] Context -0.71 1.57 -.08 -0.45 .656 [-3.90,2.49] Log Total Area -2.02 1.88 -.18 -1.08 .290 [-5.84,1.80] R 2 .039 F 0.67 Step 2 Constant -8.58 15.32 -0.56 .579 [-39.82, 22.65] Context -0.31 1.26 -.03 -0.25 .806 [-2.88, 2.25] Log Total Area 0.39 3.13 .04 0.12 .902 [-5.98, 6.76] Log Past Area 4.12 1.81 .42 2.28 .030 [0.43, 7.81] Log Future Area -2.46 1.59 -.43 -1.55 .131 [-5.71, 0.78] Total R 2 .426 Model F 5.74 ** ΔR 2 .387 ** Note. N = 36. Context coding: 0 = Laboratory, 1 = Home. Dependent Variable: J-PHQ-9 Total Score. * p < .05, ** p < .01. 3.3.2. Clinical validity of proportional circle areas for depressive symptoms A second hierarchical regression analysis was used to assess the predictive value of the proportional areas. This metric inherently controls for individual differences in the drawing scale. As the sum of the three proportions was constant, the Present proportion was excluded to avoid compositional multicollinearity, serving as the reference category. Step 1 context was not significant (R 2 = .005, p = .674). In Step 2, the addition of Past and Future proportions significantly increased the explained variance (ΔR 2 = .409, p < .001), resulting in a robust final model (R 2 = .414, F (3, 32) = 7.53, p < .001). The collinearity statistics were within acceptable limits (VIFs < 2.0). Consistent with the log-transformed absolute area findings, the past proportion was a significant positive predictor of depressive symptoms (B = 14.11, p = .014). Specifically, a 10% increase in the proportion of the canvas allocated to the past was associated with an approximately 1.4-point increase in the PHQ-9 scores. The Future proportion was negatively associated with depression scores (B = -4.77), but this effect was not statistically significant ( p = .345). The results are summarized in Table 4. Table 4 Hierarchical Regression Predicting PHQ-9 Scores from Proportional Circle Areas Predictor B SE β t p 95%CI Step 1 Constant 7.89 1.11 7.09 <.001 [5.63, 10.15] Context -0.67 1.57 -.07 -0.42 .674 [-3.86, 2.53] R 2 0.05 F 0.18 Step 2 Constant 4.64 3.34 1.39 .174 [-2.16, 11.43] Context -0.69 1.24 -.08 -0.56 .583 [-3.23, 1.85] Past Proportion 14.11 5.41 .50 2.61 .014 [3.09, 25.12] Future Proportion -4.77 4.98 -.18 -0.96 .345 [-14.91, 5.37] Total R 2 .414 Model F 7.53 ** ΔR 2 .409 ** Note. N = 36. Context coding: 0 = Laboratory, 1 = Home. Dependent Variable: J-PHQ-9 Total Score. * p < .05, ** p < .01. 3.4. Objective 2a — One-week test–retest reliability 3.4.1. Test–retest reliability of continuous indices Table 5 reports the pre-specified ICC(3,1) estimates (95% CIs) indexing one-week test–retest reliability for OL-CT continuous indices, separately for (a) log-transformed absolute circle areas and (b) proportional scores. Table 5 Week-to-week intraclass correlations for log-transformed absolute area and proportional scores Measure Time-zone ICC(3,1) 95 % CI F p Log-transformed absolute area Past .79 .62 – .89 8.47 < .001 Present .69 .47 – .83 5.38 < .001 Future .89 .79 – .94 16.69 < .001 Proportion Past .95 .91 – .98 40.67 < .001 Present .93 .87 – .96 27.99 < .001 Future .94 .89 – .97 34.71 < .001 Note . All values refer to the stability between Time 1 and Time 2. A clear pattern emerged in that the log-transformed absolute circle areas showed only moderate consistency, whereas the proportional areas displayed excellent reliability. More specifically, ICCs for the log-transformed absolute metrics ranged from .69 to .89, indicating that participants tended to redraw their circles at a somewhat different absolute scale after 7-9 days. In contrast, all proportional indices exceeded .90, with correspondingly large F ratios, underscoring that the relative psychological space allotted to the past, present, and future was virtually unchanged at retest. This dissociation replicates the cross-format findings from Visit 1 and confirms that proportional scores constitute the most stable and, therefore, the most informative continuous outcome of the OL-CT. 3.4.2. Test–retest stability of categorical indices To assess whether participants retained the same qualitative time-orientation pattern after one week, we cross-tabulated temporal dominance and temporal relatedness labels obtained at Visit 1 with those obtained at Visit 2. As in Visit 1, symmetry was tested using a four-by-four McNemar–Bowker procedure, and overall agreement was indexed using weighted Cohen’s κ. For temporal dominance, 33 of the 36 cases (91.7 %) fell on the principal diagonal, indicating perfect label replication. Agreement was excellent (κ = .88, p < .001), and no systematic reclassification occurred (χ²(3) = 0.33, p = .56). In other words, whether a participant was Past, Present, Future, or Balanced-dominant at baseline, the same classification was overwhelmingly reproduced at retest. A similarly robust pattern emerged for temporal relatedness. 86% of the sample received identical labels at both time points (κ = .76, p < .001). The McNemar–Bowker statistic again indicated symmetry (χ²(3) = 4.00, p = .41), signifying that no particular profile was disproportionately likely to change. Integration/Projection remained the modal category, while Atomicity, Continuity, and Other patterns were comparatively rare and stable. 3.5. Objective 2b — Contextual stability across laboratory and home testing 3.5.1. Contextual effects on continuous indices To examine whether the environment in which the task was performed influenced the continuous indices, we compared the results of participants who completed the second session (T2) at home ( n = 18) with those who completed it in the laboratory ( n = 18). Table 6 presents the descriptive statistics and 90% confidence intervals (CIs) for both the log-transformed absolute areas and proportions across the two environments. The analysis revealed that the 90% CIs for all log-transformed absolute area indices (Past, Present, Future, and Total) showed substantial overlap between the home and laboratory settings, with remarkably similar mean values. For instance, the log-transformed total area at home was 8.86 (90% CI 8.61, 9.12), whereas it was 8.88 (90% CI 8.72, 9.05) in the laboratory. More importantly, the proportional areas of the three circles were almost identical between the two groups, with their 90% CIs being nearly indistinguishable. Specifically, the mean proportion for the Future circle was 0.329 (90% CI 0.241, 0.417) in the home environment and 0.329 (90% CI 0.237, 0.421) in the laboratory. These results demonstrate that the physical environment did not exert any systematic effect on the continuous metrics of the OL-CT, further supporting the tool’s contextual stability and robustness for its remote administration. Table 6 Descriptive Statistics and 90% Confidence Intervals for Continuous Indices by Environment at T2 Variable Home Mean (SD) Home (90% CI) Lab Mean (SD) Lab (90% CI) Log-transformed absolute area Past 7.63 (0.55) (7.41, 7.85) 7.71 (0.41) (7.54, 7.88) Present 7.64 (0.79) (7.32, 7.96) 7.71 (0.48) (7.51, 7.91) Future 7.61 (0.89) (7.24, 7.98) 7.61 (0.77) (7.29, 7.93) Total 8.86 (0.51) (8.65, 9.07) 8.88 (0.33) (8.75, 9.01) Proportion Past 0.342 (0.147) (0.267,0.419) 0.343 (0.184) (0.282,0.402) Present 0.330 (0.116) (0.267,0.391) 0.329 (0.150) (0.282,0.378) Future 0.329 (0.185) (0.256,0.402) 0.329 (0.177) (0.253,0.405) Note. All values refer to the stability between Time 1 and Time 2. 3.5.2. Contextual effects on categorical indices To examine whether the testing environment influenced the categorical outcomes of the OL-CT, we compared the frequency distributions of the Temporal Dominance and Temporal Relatedness types between the home ( n =18) and laboratory ( n =18) settings. Chi-square tests of independence were conducted to assess the association between the testing environment and categorical indices. For Temporal Dominance, the distribution of dominance types (Past, Present, Future, and Balanced) did not differ significantly between the home and laboratory conditions (χ 2 (3) = 0.34, p = .95). Similarly, for Temporal Relatedness, no significant difference was found in the distribution of relatedness patterns (Atomicity, Continuity, Integration/Projection, and Others) across the two environments (χ 2 (3) = 0.67, p = .88). The contingency tables showed highly consistent classification patterns across contexts. These results suggest that the categorical classifications yielded by OL-CT are robust to contextual variations and remain stable, whether the test is administered in a controlled laboratory setting or remotely at home. Discussion The present study provides comprehensive psychometric evidence supporting the online Circle Test (OL-CT) as a valid and reliable digital adaptation of Cottle’s Circle Test (Cottle, 1967). Across two visits, we demonstrated cross-format equivalence with the paper-and-pencil Circle Test (PP-CT), strong one-week stability, and robust performance across laboratory and home environments, while also showing a theoretically meaningful association between time perspective indices and depressive symptoms. Psychometric properties: reliability, stability, and format equivalence A central finding is that continuous indices derived from proportional (relative) circle areas show excellent psychometric performance, whereas absolute areas (even after log transformation) are comparatively less interchangeable across measurement occasions and formats. Specifically, the cross-format agreement between PP-CT and OL-CT for log-transformed absolute areas was moderate (ICCs = .73–.77), whereas the proportional indices showed excellent agreement (ICCs = .89–.90). Consistent with established guidelines for interpreting ICCs [19], this pattern suggests that the OL-CT preserves the relative allocation of psychological “time space” (past/present/future) particularly well, even if the absolute drawing scale varies across modalities. The same dissociation emerged for one-week stability: log-transformed absolute areas showed moderate-to-good test–retest reliability (ICCs = .69–.89), while proportional indices showed excellent stability (ICCs = .93–.95). Taken together, these results support a clear methodological recommendation: when the goal is comparability across formats, contexts, or time, proportional indices should be treated as the primary continuous outcomes, and absolute area metrics may remain useful when the research question explicitly concerns overall drawing magnitude or when measurement conditions are tightly standardized. From a measurement perspective, the weaker stability/interchangeability of the absolute areas is not unexpected. The absolute area is inherently sensitive to motor constraints (mouse vs. pen), device ergonomics, and implicit scaling strategies, all of which can shift without necessarily altering the underlying construct (time perspective). In contrast, proportional indices normalize much of this variance, functioning as a within-person compositional representation of the temporal salience. This interpretation is reinforced by the observation that order effects were limited and primarily apparent in absolute indices, whereas proportional indices were comparatively unaffected by the administration order. Categorical indices: preservation of qualitative profiles Beyond continuous scores, the OL-CT also reproduced the qualitative profile structure of the Circle Test. Cross-format agreement for Temporal Dominance and Temporal Relatedness classifications was high (both 88.9%), and no systematic asymmetry was detected. These findings are particularly important because categorical classifications are often used to describe “types” of time perspectives in applied settings [7]. The high concordance suggests that OL-CT preserves the interpretive framework of the classical task, extending equivalence from metric indices to clinically interpretable profiles. Test–retest results converged with this conclusion: Temporal Dominance showed 91.7% exact agreement and excellent weighted kappa, again without evidence of systematic reclassification over time. In practical terms, OL-CT appears capable of capturing stable individual differences in qualitative time-orientation patterns, supporting its use in longitudinal designs and individual-difference research. Contextual stability: robustness across testing contexts A further contribution of this study is the direct evidence that the testing environment did not systematically influence OL-CT outcomes. Neither continuous indices nor categorical classifications differed between the laboratory and home administrations. This supports the contextual stability of the OL-CT and strengthens the case for remote assessment, an especially relevant advantage for large-scale data collection and contexts where in-person testing is impractical. These results align with broader evidence that well-designed web-based cognitive and behavioral tasks can yield reliable data, provided that the sampling and procedural controls are adequate [20]. Clinical correlates with depressive symptoms: theoretical interpretation The most clinically informative result was that greater past salience predicted more severe depression symptoms. In the proportional model, the past proportion was a significant positive predictor of PHQ-9 scores, such that a 10% increase in past allocation corresponded to an approximately 1.4-point increase in the PHQ-9 score. This pattern was consistent with the absolute-area model, where log past area significantly predicted PHQ-9 scores. Importantly, context (home vs. laboratory) did not predict depressive symptoms, indicating that the observed association was unlikely to be an artifact of the testing environment. This finding is conceptually coherent with classical and contemporary accounts of depression, which involve a constricted or distorted time experience characterized by increased past-oriented cognition and diminished flexibility in shifting temporal focus [21, 22]. A larger “past” representation in the Circle Test may index not only temporal attention but also the emotional and mnemonic weight of past experiences, consistent with evidence that depression is associated with biased processing of negative memories and difficulties disengaging from them [18]. Simultaneously, the future proportion was negatively, but not significantly, associated with depressive symptoms. One plausible explanation is that in a non-clinical undergraduate sample, future-oriented variance may be constrained by normative developmental and situational factors (e.g., academic planning), potentially attenuating the associations with symptom severity. In clinically depressed samples, future-directed cognition is often markedly disrupted, particularly by the reduced generation of specific and positive future events and elevated hopelessness. Therefore, associations with future representations may become clearer when variability is greater [23–25]. Accordingly, future studies should replicate the present analyses in diagnostically confirmed clinical samples (e.g., major depressive disorder) and examine whether future indices are more strongly related to symptom severity and hopelessness. Another possibility is that the Circle Test’s quantitative future allocation may not cleanly distinguish adaptive future orientation (goal-directed planning) from maladaptive future cognition (worry and threat anticipation), both of which can occur in depression cases. This nuance is increasingly emphasized in contemporary models of “psychological time” in depression, which highlight the heterogeneity in how future thinking is disrupted and how it might be targeted by interventions [26]. From the perspective of time perspective theory, the observed past–depression association is also consistent with evidence that deviations from a balanced time perspective are linked to poorer mental health [1, 2]. Therefore, OL-CT may provide a rapid behavioral indicator of temporal imbalance that complements questionnaire-based approaches. Broader implications for digital assessment The OL-CT’s automation and scalability matter not only for convenience but also for measurement innovation. Digital assessment can enable standardized scoring pipelines, reduce the examiner’s burden, and make large-scale screening feasible. Simultaneously, digital formats create opportunities to capture process-level data (e.g., drawing order, latency, corrections, and cursor trajectories), which may provide additional psychological information beyond final outcomes [27]. Although the current study focused on classical CT indices (areas and categorical types), future studies could leverage process data to better understand how depressive symptoms relate to temporal representation dynamics (e.g., hesitation when drawing the future, repeated revisions of the past). For example, the latency to initiate each circle, cumulative pause time, and revision frequency can be examined as candidate markers of reduced approach motivation and avoidance toward future-oriented cognition in depression [28]. These kinematic features are also associated with psychomotor retardation, which has been documented through kinematic analyses showing slower and delayed handwriting or drawing movements in depressed patients [29, 30]. In particular, prolonged latency or increased pausing when initiating the future circle may reflect reduced positive episodic future thinking and cognitive avoidance of goal-directed simulation [31, 32], offering a testable bridge between temporal representation and motor-behavioral signatures. Another practical advantage is that a performance-based measure, such as the Circle Test, may be comparatively less vulnerable to certain response-style distortions that can affect self-reports, including social desirability pressure [6]. This does not imply immunity to bias, but it strengthens the rationale for using OL-CT as a complementary tool in research and early stage screening contexts, particularly when paired with validated symptom measures. Limitations and future directions Several limitations should guide the interpretation and motivate follow-up research. First, the sample size was modest and restricted to Japanese undergraduates, which may limit its generalizability to clinical populations and broader age ranges. Second, while one-week stability was strong, especially for proportional indices, longer retest intervals are needed to evaluate trait-like stability versus state sensitivity. Third, despite the null findings for environmental effects, future studies should systematically examine device variability (e.g., trackpad vs. mouse; screen size differences) and its impact on absolute versus proportional indices. Finally, clinical utility should be tested more directly by including diagnostically characterized samples and evaluating incremental validity (e.g., whether OL-CT indices improve predictions beyond symptom questionnaires). Conclusion Overall, the OL-CT appears to be a psychometrically sound digital adaptation of the classical Circle Test, particularly when proportional indices are used as the primary outcomes. The tool demonstrates strong cross-format agreement, excellent week-to-week reliability for proportional scores, robust performance across laboratory and home contexts, and a theoretically meaningful association between past salience and depressive symptoms. These findings support the OL-CT as a scalable behavioral measure of time perspective for both research applications and potential clinical screening contexts, while also highlighting proportional indices as the most stable and format-robust representation of temporal orientation. Abbreviations OL-CT：Online Circle Test PP-CT：Paper-and-pencil Circle Test J-PHQ-9: Japanese version of the Patient Health Questionnaire-9 Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Chuo University Research Ethics Committee (trial number 2025-020, date of approval 16/04/2025). Consent for publication All participants provided written informed consent. They could withdraw at any time without any consequence or penalty. Data were collected and analysed anonymously. Availability of data and materials The Online Circle Test（OL-CT） and datasets analysed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding Not applicable Authors’ contributions Author contributions HH conducted the experiments, performed data collection and data analysis, and drafted the manuscript. AM supervised the overall revision of the manuscript, including its structure, data analysis, and the preparation of tables, and was responsible for the writing and checking of all sections. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank YAYOI SHIGEMUNE for her valuable suggestions regarding the design of the Online Circle Test (OL-CT). 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The measurement of pessimism: The Hopelessness Scale. Journal of Consulting and Clinical Psychology. 1974;42:861–5. https://doi.org/10.1037/h0037562. Hallford DJ, Austin DW, Takano K, Raes F. Psychopathology and episodic future thinking: A systematic review and meta-analysis of specificity and episodic detail. Behav Res Ther. 2018;102:42–51. https://doi.org/10.1016/j.brat.2018.01.003. MacLeod AK, Rose GS, Williams JMG. Components of hopelessness about the future in parasuicide. Cogn Ther Res. 1993;17:441–55. https://doi.org/10.1007/BF01173056. Ren H, Zhang Q, Ren Y, Zhou Q, Fang Y, Huang L, et al. Characteristics of psychological time in patients with depression and potential intervention strategies. Front Psychiatry. 2023;14:1173535. https://doi.org/10.3389/fpsyt.2023.1173535. Goldhammer F, Zehner F. What to Make Of and How to Interpret Process Data. Measurement: Interdisciplinary Research and Perspectives. 2017;15:128–32. https://doi.org/10.1080/15366367.2017.1411651. Trew JL. 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Reducing Anhedonia in Major Depressive Disorder with Future Event Specificity Training (FEST): A Randomized Controlled Trial. Cognitive Therapy and Research. 2022;47. https://doi.org/10.1007/s10608-022-10330-z. Additional Declarations No competing interests reported. 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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-8514018","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582278911,"identity":"dd060507-5755-49fc-8d53-e6b498b80747","order_by":0,"name":"HAILONG HAN","email":"data:image/png;base64,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","orcid":"","institution":"Chuo University","correspondingAuthor":true,"prefix":"","firstName":"HAILONG","middleName":"","lastName":"HAN","suffix":""},{"id":582278912,"identity":"d1e18c82-f6a4-4af4-a151-dec0f591f9e5","order_by":1,"name":"AKIRA MIDORIKAWA","email":"","orcid":"","institution":"Chuo University","correspondingAuthor":false,"prefix":"","firstName":"AKIRA","middleName":"","lastName":"MIDORIKAWA","suffix":""}],"badges":[],"createdAt":"2026-01-04 15:08:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8514018/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8514018/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102426935,"identity":"293dbf54-7e33-4d36-b48c-d491756255a9","added_by":"auto","created_at":"2026-02-11 14:43:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":155580,"visible":true,"origin":"","legend":"\u003cp\u003eUser interface of the Online Circle Test (OL-CT) showing the drawing canvas and Japanese instructions.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8514018/v1/84a46ea62b09351141067c9a.png"},{"id":102745603,"identity":"35f5e699-080b-4dcb-8b6c-6916a31d6df4","added_by":"auto","created_at":"2026-02-16 08:52:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1369719,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8514018/v1/2519eab4-687e-44e9-964f-cb7e676644ea.pdf"},{"id":102426934,"identity":"c0c1bda2-7dd3-46af-ba1e-b4d380c46b28","added_by":"auto","created_at":"2026-02-11 14:43:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15752,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8514018/v1/83a4203c54ca2ebdc3ca0d05.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validating the Online Circle Test (OL-CT): Cross-Format Equivalence, One-Week Reliability, and Depressive Symptom Correlates in Japanese Undergraduates","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTime perspectives describe individuals\u0026rsquo; views on the past, present, and future, which are relatively stable individual characteristics [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recent reviews suggest that a balanced time perspective is broadly associated with higher subjective well-being, adaptive health behaviors, academic achievement, and lower psychopathology vulnerability [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, a variety of methods have been developed to measure time perspective.\u003c/p\u003e \u003cp\u003eTime perspective has traditionally been assessed using questionnaire-based (self-report) and performance-based (projective) measures. Self-report instruments such as the Zimbardo Time Perspective Inventory (ZTPI)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and the Temporal Focus Scale [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] are easy to administer and psychometrically established. However, questionnaire methods are influenced by introspective ability and social desirability, which limits their applicability in clinical or screening contexts [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Performance-based measures have the potential to address these limitations; however, they are time-consuming to administer and are not well-suited for large-scale surveys.\u003c/p\u003e \u003cp\u003eA widely used performance-based measure is Cottle\u0026rsquo;s circle test [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], in which participants were asked to draw three circles representing the past, present, and future respectively. Traditionally, there are two indexes based on drawings namely temporal dominance, and temporal relatedness. Temporal dominance refers to the relative preeminence of one temporal zone, as indicated by the circle size. Temporal relatedness refers to the degree of perceived linkage among the three temporal zones. Because the Circle Test explores implicit aspects of time perspective [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], its utility has been demonstrated across diverse populations and outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, because the original scoring methods require considerable effort, they are unsuitable for large-scale surveys.\u003c/p\u003e \u003cp\u003eThe use of digital technologies can be a useful way to address these challenges. Web-based online versions of the circle test allow participants to complete the task and automatically record geometric information, such as circle centers, radii, and final layouts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], while other versions can also compute the absolute area [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, these studies rely on manual judgment for scoring of temporal dominance and temporal relatedness, therefore scoring still requires considerable effort. In addition, these studies have not confirmed the equivalence between the original paper-based and online versions, test-retest reliability, and validity for clinically relevant outcomes.\u003c/p\u003e \u003cp\u003eTo overcome these challenges, we newly developed a web-based online circle test (OL-CT) that automatically scores temporal dominance and temporal relatedness. In addition, the system enables automated capture of both end-state geometric features of the three circles and process-level drawing data (e.g., latencies and kinematic traces); however, the present study focuses on the psychometric evaluation of the end-state indices. The OL-CT has at least three advantages. First, the administration and scoring can be conducted automatically. It requires no rater training and can relieve heavy burdens on human raters. Second, uniform instructions are provided by the browser and therefore reduce the variability caused by inter-rater differences and environmental inconsistencies. Third, OL-CT scores can be provided and used directly for the interpretation of results and further data analyses. Thus, the OL-CT shows great feasibility in large-scale cohort studies and also remote research contexts. However, the utility of the OL-CT is constrained owing to the unknown psychometric properties of the target population. Drawing inspiration from contemporary digital validation frameworks [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]), we prespecified two objectives with a strict analysis\u0026ndash;reporting correspondence: Objective 1 (Validity)\u0026mdash;(a) evaluate cross-format equivalence between the paper-and-pencil Circle Test (PP-CT) and the OL-CT, and (b) evaluate clinical validity via associations with depressive symptoms; Objective 2 (Reliability)\u0026mdash;(a) estimate one-week test\u0026ndash;retest reliability of the OL-CT, and (b) assess contextual stability across laboratory versus home administration.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study consisted of two visits. In visit 1, participants completed both the paper-and-pencil Circle Test (PP-CT) and the online Circle Test (OL-CT). They were randomly allocated either OL-CT-first or PP-CT-first. After finishing the circle tests, the Japanese version of the Patient Health Questionnaire-9 (J-PHQ-9)[14] was administered. Participants returned for Visit 2, 7\u0026ndash;9 days after the first visit, to repeat the OL-CT to provide data for test\u0026ndash;retest reliability analyses. At the same time, to examine test-retest reliability, they were block randomized, considering sex and age, to complete the retest in the laboratory or at home under standardized instructions, and the J-PHQ-9 was again administered. All other procedures (instructions, timing, and scoring pipelines) were identical across visits and order groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were recruited over a two-week period in April 2025 through in-class announcements in psychology and general education courses, as well as the distribution of flyers on campus (convenience sampling). Interested individuals scheduled an appointment via an online registration form or email for Visit 1 and a second session approximately 7\u0026ndash;9 days later (Visit 2). The second session was conducted either in the laboratory or at home, depending on the assigned context. After completing Visit 1, each participant received a snack valued at approximately \u0026yen;500 as an incentive. Eligibility criteria included: (1) being 18 years or older, (2) providing written informed consent, (3) understanding Japanese and being able to follow task instructions, and (4) no motor or visual limitations (corrected vision permitted) that would preclude drawing the circles, operating a mouse/trackpad, or viewing the on-screen materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePP-CT Task (Visit 1 only)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PP-CT followed Cottle\u0026rsquo;s procedure [7] and administered on A4 landscape paper and the subjects were asked to draw three circles representing past, present, and future. The left half of the sheet printed the Japanese instruction text identical in content to the online version, and, beneath it, a demographic panel with fields for sex, age, birth month/day (MMDD), and the last three digits of the mobile number was provided. From these entries, an anonymous participant ID (MMDD-XXX) was formed and written on the sheet. The right half contained a 120*120 mm blank drawing frame printed in thin black lines, centered vertically with margins \u0026gt;\u0026nbsp;10 mm to the page edges.\u003c/p\u003e\n\u003cp\u003eCompleted sheets were digitized using a flatbed scanner at 600 dpi, and images were processed with ImageJ (v1.54) [15] to extract calibrated radii, areas, centroids, and pairwise spatial indices (center-to-center distances and overlap areas) of the three circles. From these geometric features, we derived (a) continuous indices: absolute areas (mm\u0026sup2;) and proportional areas (each circle\u0026rsquo;s area divided by the sum of the three), and (b) two qualitative typologies followed Cottle\u0026rsquo;s procedure [7]: temporal dominance and temporal relatedness. Temporal dominance was assigned by comparing proportional areas: cases were labeled Past-, Present-, or Future-dominant when one time zone\u0026rsquo;s proportional area clearly exceeded the other two by a pre-specified margin. Drawings were labeled Balanced when all three shares were within the pre-specified tolerance of an equal one-third split (i.e., each circle\u0026rsquo;s radius deviated by less than 10% from the mean radius of the three circles). Temporal relatedness was coded based on the pattern and magnitude of pairwise overlaps: Atomicity (no meaningful overlap among any pair), Continuity (exactly one substantive pairwise overlap forming a \u0026ldquo;chain\u0026rdquo; with the third circle separate), Integration/Projection (two or more substantive overlaps, including triple-overlap configurations or instances where one circle is substantially projected into another), and Other for atypical or ambiguous arrangements (e.g., tangencies, severe distortions, or labeling anomalies). Two independent, trained raters blinded to participant identity and any online outputs coded all PP-CT drawings for both typologies. Discrepancies were resolved by consensus after computing inter-rater agreement, which was 88.9% for dominance (\u0026kappa; = .84) and 100% for relatedness (\u0026kappa; = 1.00).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOL-CT Task (Visit 1 and Visit 2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants completed the OL-CT independently in a browser-based application on laboratory-standardized laptops (Lenovo IdeaPad 16 Pro) with external mice. The program (https://reurl.cc/4LZNyK) proceeded as follows: (1) a full-screen 800*800px canvas with high-precision event logging; (2) demographic entry and automatic anonymous ID (MMDD-XXX); (3) two-point screen calibration (200/250px) using a ruler to compute pixel-to-mm, with \u0026gt;\u0026plusmn;5% ratio error triggering repeat; (4) brief guided practice (create/move/resize/toggle/delete); and (5) asked to draw three circles for Past, Present, and Future and labels each. The system records geometry, overlaps/proportions, dominance/relatedness, and kinematics (Fig. 1). (6) automatic export (static CSV, dynamic CSV, JPEG) and secure upload with ZIP fallback (A step-by-step description of the OL-CT program flow is provided in Appendix 1).\u003c/p\u003e\n\u003cp\u003eThe OL-CT platform generated the same geometric feature set (radii, areas, centroids, and pairwise overlap areas), from which identical dominance and relatedness rules were algorithmically applied to assign categorical labels. For quality control, one trained human rater\u0026mdash;blinded to the algorithm\u0026rsquo;s output\u0026mdash;independently coded each OL-CT image using the same codebook. Human\u0026ndash;algorithm concordance was perfect (\u0026kappa; = 1.00 for both dominance and relatedness), confirming the functional equivalence of the automated scoring to expert judgment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuestionnaire\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the Japanese version of the Patient Health Questionnaire-9 (J-PHQ-9) as a brief self-reported measure of depressive symptom severity. The instrument comprises nine items reflecting DSM-consistent symptoms over the past two weeks, each rated on a 0\u0026ndash;3 scale (\u0026ldquo;not at all\u0026rdquo; to \u0026ldquo;nearly every day\u0026rdquo;), yielding a total score of 0\u0026ndash;27 points. Scoring followed the standard sum of the item responses. The J-PHQ-9 was administered in both visits. In both visits, participants completed the questionnaire digitally immediately after finishing the OL-CT session, using the same secure browser interface as the online task.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses were organized to mirror the prespecified objectives: Objective 1(Validity) (a) evaluate cross-format equivalence between the paper and pencil circle test (PP-CT) and the online circle test (OL-CT), and (b) evaluate clinical validity with depressive symptom using the J-PHQ-9; Objective 2 (Reliability) (a) one week test\u0026ndash;retest reliability of the OL-CT, and (b) assess contextual stability across laboratory vs. home administration. Although the OL-CT logs process-level variables, these data were not analyzed in the present report and are retained for future work.\u003c/p\u003e\n\u003cp\u003eFor all analysis, a significant level of \u0026alpha; = .05 (two-tailed) was applied, and 95% confidence intervals (CIs) were reported where appropriate. In regard to quantitative analysis of the circle test, data preparation followed a two-pronged approach. First, the absolute areas (mm\u0026sup2;) of the Past, Present, and Future circles, as well as their total area were extracted, and natural logarithm transformations were applied to each area (log-Past, log-Present, log-Future) to reduce positive skewness and accommodate multiplicative scale differences, following common recommendations for log-transforming skewed psychometric variables [16, 17]; the log-transformed total area was used to index each participant\u0026rsquo;s overall drawing scale. Second, proportional areas for Past, Present, and Future were calculated dividing each circle\u0026apos;s area by the total three areas. In regard to qualitative analysis of the circle test, based on Cottle\u0026rsquo;s original distinction [7]; temporal dominance profiles and temporal-relatedness configurations of the three circles were coded as categorical indices based on the spatial arrangement of the circles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe statistical analyses in this study encompassed both continuous and categorical dimensions. For quantitative indices, including log-transformed absolute and proportional areas, intraclass correlation coefficients (ICC 3,1) were employed to evaluate cross-format equivalence and one-week test\u0026ndash;retest reliability. Categorical indices\u0026mdash;temporal dominance and relatedness\u0026mdash;were summarized using frequency distributions and assessed for within-person stability across formats and time using McNemar\u0026ndash;Bowker tests. To establish clinical validity, hierarchical multiple regression models were used to examine the predictive utility of the circle indices for J-PHQ-9 depressive symptom severity. Additionally, independent-samples t-tests served as randomization checks for the counterbalanced administration order, while contextual stability between the laboratory and home settings was evaluated through mean comparisons with 90% confidence intervals and chi-square tests of independence. In order to evaluate the validity and reliability of the Circle Test, we conducted the following analyses;\u003c/p\u003e\n\u003cp\u003eObjective 1(a): To examine the validity of cross format equivalence between the paper and online version for quantitative outcomes (absolute and proportional areas; within subjects), data obtained from Visit 1 were utilized. Intraclass correlation coefficients (ICCs) were calculated using ICC (3,1) (two-way mixed effects, absolute agreement) with 95% confidence intervals. For categorical comparability (temporal dominance and relatedness), PP-CT classifications were cross-tabulated with OL-CT classifications and evaluated using the McNemar\u0026ndash;Bowker test of symmetry for paired multi-category data.\u003c/p\u003e\n\u003cp\u003eObjective 1(b): To evaluate the association between depressive symptoms and circular indices using data collected during Visit 2, we employed a consistent hierarchical multiple regression framework for both the absolute and proportional metrics. First, for log-transformed absolute areas, we assessed whether the specific sizes of the Past and Future circles predicted J-PHQ-9 scores after strictly controlling for the overall drawing size. In Step 1, the administration context (laboratory vs. home) and log-transformed Total Area were entered as control variables. The former was included to account for any potential environmental variance, and the latter served to control for individual differences in the overall drawing scale, thereby isolating the unique predictive power of specific temporal zones, independent of total figure size. In Step 2, the log-transformed areas of the Past and Future circles were added to the model. The Present circle area was excluded from this analysis for two primary reasons. First, statistically, preliminary diagnostics indicated that including all three temporal zones alongside the total area introduced severe multicollinearity (Variance Inflation Factors \u0026gt; 10), whereas excluding the present dimension reduced collinearity to acceptable levels. Second, theoretically, depressive symptomatology is most strongly linked to distortions in past (e.g., rumination) and future (e.g., hopelessness) perspectives [18]. Second, for proportional areas, a similar hierarchical approach was used to predict the J-PHQ-9 scores.\u003c/p\u003e\n\u003cp\u003eObjective 2(a) Test\u0026ndash;retest reliability (within subjects) between Visit 1 and Visit 2 for quantitative indices was calculated using ICC(3,1) with 95% confidence intervals, and test\u0026ndash;retest stability for categorical dominance and relatedness classifications was examined using McNemar\u0026ndash;Bowker tests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eObjective 2(b): To assess the contextual stability of the OL-CT (between subjects), we evaluated whether the administration environment influenced the outcomes by comparing the mean values and 90% confidence intervals (CIs) of continuous indices (absolute and proportional areas) between the laboratory and home settings at Visit 2. In accordance with equivalence testing standards (\u0026alpha; = 0.05), overlapping 90% CIs and the absence of substantial mean differences were interpreted as evidence that the physical environment did not exert a systematic effect on the metrics. For categorical indices (dominance and relatedness), we employed chi-square tests of independence to compare the frequency distributions of classifications across the two contexts.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Sample description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 36 Japanese undergraduates participated (23 women, 13 men; \u003cem\u003eM\u003c/em\u003e age = 20.78, \u003cem\u003eSD\u003c/em\u003e = 2.15) and completed both visits without attrition. In Visit 1 (laboratory), participants were randomized to two order groups: OL-CT-first (PP-CT-second) (\u003cem\u003en\u003c/em\u003e = 18; 9 women, 9 men; \u003cem\u003eM\u003c/em\u003e age = 21.56, \u003cem\u003eSD\u003c/em\u003e = 2.55) and PP-CT-first (OL-CT-second) (\u003cem\u003en\u003c/em\u003e = 18; 14 women, 4 men; \u003cem\u003eM\u003c/em\u003e age = 20.00, \u003cem\u003eSD\u003c/em\u003e = 1.33). In Visit 2 (7\u0026ndash;9 days later), the same participants were block-randomized by sex and age into laboratory (\u003cem\u003en\u003c/em\u003e = 18; 11 women; \u003cem\u003eM\u003c/em\u003e age = 20.61, \u003cem\u003eSD\u003c/em\u003e = 2.17) and home (\u003cem\u003en\u003c/em\u003e = 18; 12 women; \u003cem\u003eM\u003c/em\u003e age = 20.94, \u003cem\u003eSD\u003c/em\u003e = 2.18) retest contexts. All datasets met the prespecified quality-control criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Objective 1a \u0026mdash; Cross-format equivalence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1. Order randomization checks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate whether the counterbalanced administration order introduced systematic differences in OL-CT performance, independent-samples t tests compared Visit 1 OL-CT indices between participants who completed PP-CT first (PP-first; \u003cem\u003en\u003c/em\u003e = 18) and those who completed OL-CT first (OL-first; \u003cem\u003en\u003c/em\u003e = 18). For log-transformed absolute areas, there were no significant order effects for the Past or Future circles, nor for the total OL-CT area (Past: \u003cem\u003et\u003c/em\u003e(34) = \u0026ndash;0.28, \u003cem\u003ep\u003c/em\u003e = .78; Future: \u003cem\u003et\u003c/em\u003e(34) = \u0026ndash;0.40, \u003cem\u003ep\u003c/em\u003e = .69; total: \u003cem\u003et\u003c/em\u003e(34) = \u0026ndash;1.25, \u003cem\u003ep\u003c/em\u003e = .22). The log-transformed Present circle area was larger when OL-CT was administered first than when it followed PP-CT, \u003cem\u003et\u003c/em\u003e(34) = \u0026ndash;2.28, \u003cem\u003ep\u003c/em\u003e = .03. For proportional indices, no order differences reached conventional significance (Past: \u003cem\u003et\u003c/em\u003e(34) = 0.75, \u003cem\u003ep\u003c/em\u003e = .46; Present: \u003cem\u003et\u003c/em\u003e(34) = \u0026ndash;1.71, \u003cem\u003ep\u003c/em\u003e = .10; Future: \u003cem\u003et\u003c/em\u003e(34) = 0.45, \u003cem\u003ep\u003c/em\u003e = .65).\u003c/p\u003e\n\u003cp\u003eTo account for individual differences in baseline drawing style, we also computed within-person format-difference scores for the Present circle, defined as the log-transformed difference between OL-CT and PP-CT areas. An independent-samples t test on these change scores revealed no significant order effect, \u003cem\u003et\u003c/em\u003e(34) = \u0026ndash;0.66, \u003cem\u003ep\u003c/em\u003e = .52, indicating that the apparent raw group difference in OL-CT Present area was largely attributable to pre-existing between-group variability rather than a systematic administration-order bias.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCross-format agreement for continuous indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults for cross-format agreement are summarized in Table 1. Across log-transformed absolute areas, the ICCs (.73\u0026ndash;.77) indicate moderate cross-version agreement: each significantly exceeded zero yet remained below the .75 benchmark typically considered \u0026ldquo;good\u0026rdquo; for single observations. When areas were expressed as proportions of total drawing space, agreement rose sharply; ICCs for Past (.89), Present (.90), and Future (.89) all surpassed the \u0026ldquo;good\u0026rdquo; threshold and approached the .90 criterion for excellence, with narrow confidence intervals entirely above .75. Hence, while log-transformed absolute area metrics are reasonably consistent, proportional scores exhibit excellent reliability and may be regarded as fully interchangeable between PP and OL administrations. Therefore, subsequent analyses relied on proportional indices as the primary continuous outcomes.\u003c/p\u003e\n\u003cp\u003eTable 1 Cross-Version Intraclass Correlations for Log-transformed absolute area and Proportion Scores\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eMeasure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eTime-zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eICC(3,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e95 % CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e(35, 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLog-transformed absolute area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ePast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.60 \u0026ndash; .88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.57 \u0026ndash; .87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eFuture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.53 \u0026ndash; .85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 123px;\"\u003e\n \u003cp\u003eProportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ePast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.80 \u0026ndash; .94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e17.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.81 \u0026ndash; .95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e17.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eFuture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.80 \u0026ndash; .94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e17.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. ICC= intraclass correlation coefficient (single-measure, absolute agreement, two-way mixed); all dfs= 35.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCross-format agreement for categorical indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA four-by-four McNemar\u0026ndash;Bowker test showed no systematic shift in dominance categories between versions (\u0026chi;\u0026sup2;(3) = 2.00, \u003cem\u003ep\u003c/em\u003e = .572). Agreement statistics are summarized in Table 2. Thirty-two of the 36 participants (88.9%) received the same label in PP-CT and OL-CT; the four discrepancies were evenly distributed across off-diagonal cells. The same pattern was observed for temporal-relatedness classifications (\u0026chi;\u0026sup2;(3) = 4.00, \u003cem\u003ep\u003c/em\u003e = .261). Again, 88.9% of the cases were concordant, and the few mismatches showed no directional bias. The absence of significant asymmetry and the high exact match rate confirm that the OL-CT faithfully reproduces the qualitative profile structure of the PP-CT, extending the metric equivalence of continuous indices to categorical outcomes.\u003c/p\u003e\n\u003cp\u003eTable 2 Cross-Format Agreement for Categorical Indices\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eCategorical Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eAgreement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eMcNemar\u0026ndash;Bowker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMismatches\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eTemporal Dominance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e88.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eTemporal Relatedness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e88.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u003c/em\u003e= 36. Agreement (%) refers to the percentage of participants whose classification labels (e.g., Past Dominance, Balanced) were identical across both formats. The McNemar\u0026ndash;Bowker test evaluates the symmetry of the disagreement; a non-significant \u003cem\u003ep\u003c/em\u003e-value indicates no systematic bias in classification changes between formats.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Objective 1b \u0026mdash; Clinical validity with depressive symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eClinical\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003evalidity of absolute circle areas for depressive symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHierarchical regression results (Table 3) indicated that Step 1 variables (context and total area) did not significantly predict depressive symptoms (R\u003csup\u003e2\u003c/sup\u003e = .039, \u003cem\u003eF\u003c/em\u003e(2, 33) = 0.67, \u003cem\u003ep\u003c/em\u003e = .519). However, the addition of log past and log future areas in Step 2 significantly improved the model fit (\u0026Delta;R\u003csup\u003e2\u003c/sup\u003e = .387, \u003cem\u003ep\u003c/em\u003e = .001), with the final model accounting for 42.6% of the variance in J-PHQ-9 scores (R\u003csup\u003e2\u003c/sup\u003e = .426, \u003cem\u003eF\u003c/em\u003e(4, 31) = 5.74, \u003cem\u003ep\u003c/em\u003e = .001). Specifically, log past area emerged as a significant positive predictor (B = 4.12, \u003cem\u003ep\u003c/em\u003e = .030), indicating that individuals drawing larger absolute past circles reported higher depression levels independent of total drawing size3. Log future area showed a non-significant negative association with depression (B = -2.46, \u003cem\u003ep\u003c/em\u003e = .131).\u003c/p\u003e\n\u003cp\u003eTable 3 Hierarchical Regression Predicting PHQ-9 Scores from Absolute Circle Areas\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStep 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e25.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e16.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[-8.16,59.81]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eContext\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[-3.90,2.49]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLog Total Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[-5.84,1.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStep 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-8.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e15.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[-39.82, 22.65]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eContext\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[-2.88, 2.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLog Total Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[-5.98, 6.76]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLog Past Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[0.43, 7.81]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLog Future Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[-5.71, 0.78]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eTotal R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eModel \u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5.74\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026Delta;R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e.387\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u003c/em\u003e = 36. Context coding: 0 = Laboratory, 1 = Home. Dependent Variable: J-PHQ-9 Total Score. \u003csup\u003e*\u003c/sup\u003ep \u0026lt; .05, \u003csup\u003e**\u003c/sup\u003ep \u0026lt; .01.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eClinical validity of proportional circle areas for depressive symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA second hierarchical regression analysis was used to assess the predictive value of the proportional areas. This metric inherently controls for individual differences in the drawing scale. As the sum of the three proportions was constant, the Present proportion was excluded to avoid compositional multicollinearity, serving as the reference category.\u003c/p\u003e\n\u003cp\u003eStep 1 context was not significant (R\u003csup\u003e2\u003c/sup\u003e = .005, \u003cem\u003ep\u003c/em\u003e = .674). In Step 2, the addition of Past and Future proportions significantly increased the explained variance (\u0026Delta;R\u003csup\u003e2\u003c/sup\u003e = .409, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), resulting in a robust final model (R\u003csup\u003e2\u003c/sup\u003e = .414, \u003cem\u003eF\u003c/em\u003e(3, 32) = 7.53, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). The collinearity statistics were within acceptable limits (VIFs \u0026lt; 2.0). Consistent with the log-transformed absolute area findings, the past proportion was a significant positive predictor of depressive symptoms (B = 14.11, \u003cem\u003ep\u003c/em\u003e = .014). Specifically, a 10% increase in the proportion of the canvas allocated to the past was associated with an approximately 1.4-point increase in the PHQ-9 scores. The Future proportion was negatively associated with depression scores (B = -4.77), but this effect was not statistically significant (\u003cem\u003ep\u003c/em\u003e = .345). The results are summarized in Table 4.\u003c/p\u003e\n\u003cp\u003eTable 4 Hierarchical Regression Predicting PHQ-9 Scores from Proportional Circle Areas\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStep 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e7.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e7.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[5.63, 10.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eContext\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[-3.86, 2.53]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStep 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[-2.16, 11.43]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eContext\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[-3.23, 1.85]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePast Proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e14.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[3.09, 25.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eFuture Proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e[-14.91, 5.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eTotal R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eModel \u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e7.53\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026Delta;R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e.409\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u003c/em\u003e = 36. Context coding: 0 = Laboratory, 1 = Home. Dependent Variable: J-PHQ-9 Total Score. \u003csup\u003e*\u003c/sup\u003ep \u0026lt; .05, \u003csup\u003e**\u003c/sup\u003ep \u0026lt; .01.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Objective 2a \u0026mdash; One-week test\u0026ndash;retest reliability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTest\u0026ndash;retest reliability of continuous indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 5 reports the pre-specified ICC(3,1) estimates (95% CIs) indexing one-week test\u0026ndash;retest reliability for OL-CT continuous indices, separately for (a) log-transformed absolute circle areas and (b) proportional scores.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5 Week-to-week intraclass correlations for log-transformed absolute area and proportional scores\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMeasure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eTime-zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eICC(3,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e95 % CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 95px;\"\u003e\n \u003cp\u003eLog-transformed absolute area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e.62 \u0026ndash; .89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e8.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e.47 \u0026ndash; .83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eFuture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e.79 \u0026ndash; .94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e16.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 95px;\"\u003e\n \u003cp\u003eProportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e.91 \u0026ndash; .98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e40.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e.87 \u0026ndash; .96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e27.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eFuture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e.89 \u0026ndash; .97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e34.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. All values refer to the stability between Time 1 and Time 2.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA clear pattern emerged in that the log-transformed absolute circle areas showed only moderate consistency, whereas the proportional areas displayed excellent reliability. More specifically, ICCs for the log-transformed absolute metrics ranged from .69 to .89, indicating that participants tended to redraw their circles at a somewhat different absolute scale after 7-9 days. In contrast, all proportional indices exceeded .90, with correspondingly large F ratios, underscoring that the relative psychological space allotted to the past, present, and future was virtually unchanged at retest. This dissociation replicates the cross-format findings from Visit 1 and confirms that proportional scores constitute the most stable and, therefore, the most informative continuous outcome of the OL-CT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.2. Test\u0026ndash;retest stability of categorical indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess whether participants retained the same qualitative time-orientation pattern after one week, we cross-tabulated temporal dominance and temporal relatedness labels obtained at Visit 1 with those obtained at Visit 2. As in Visit 1, symmetry was tested using a four-by-four McNemar\u0026ndash;Bowker procedure, and overall agreement was indexed using weighted Cohen\u0026rsquo;s \u0026kappa;.\u003c/p\u003e\n\u003cp\u003eFor temporal dominance, 33 of the 36 cases (91.7 %) fell on the principal diagonal, indicating perfect label replication. Agreement was excellent (\u0026kappa; = .88, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and no systematic reclassification occurred (\u0026chi;\u0026sup2;(3) = 0.33, \u003cem\u003ep\u003c/em\u003e = .56). In other words, whether a participant was Past, Present, Future, or Balanced-dominant at baseline, the same classification was overwhelmingly reproduced at retest.\u003c/p\u003e\n\u003cp\u003eA similarly robust pattern emerged for temporal relatedness. 86% of the sample received identical labels at both time points (\u0026kappa; = .76, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). The McNemar\u0026ndash;Bowker statistic again indicated symmetry (\u0026chi;\u0026sup2;(3) = 4.00, \u003cem\u003ep\u003c/em\u003e = .41), signifying that no particular profile was disproportionately likely to change. Integration/Projection remained the modal category, while Atomicity, Continuity, and Other patterns were comparatively rare and stable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. Objective 2b \u0026mdash; Contextual stability across laboratory and home testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.1. Contextual effects on continuous indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine whether the environment in which the task was performed influenced the continuous indices, we compared the results of participants who completed the second session (T2) at home (\u003cem\u003en\u003c/em\u003e = 18) with those who completed it in the laboratory (\u003cem\u003en\u003c/em\u003e = 18). Table 6 presents the descriptive statistics and 90% confidence intervals (CIs) for both the log-transformed absolute areas and proportions across the two environments.\u003c/p\u003e\n\u003cp\u003eThe analysis revealed that the 90% CIs for all log-transformed absolute area indices (Past, Present, Future, and Total) showed substantial overlap between the home and laboratory settings, with remarkably similar mean values. For instance, the log-transformed total area at home was 8.86 (90% CI 8.61, 9.12), whereas it was 8.88 (90% CI 8.72, 9.05) in the laboratory. More importantly, the proportional areas of the three circles were almost identical between the two groups, with their 90% CIs being nearly indistinguishable. Specifically, the mean proportion for the Future circle was 0.329 (90% CI 0.241, 0.417) in the home environment and 0.329 (90% CI 0.237, 0.421) in the laboratory. These results demonstrate that the physical environment did not exert any systematic effect on the continuous metrics of the OL-CT, further supporting the tool\u0026rsquo;s contextual stability and robustness for its remote administration.\u003c/p\u003e\n\u003cp\u003eTable 6 Descriptive Statistics and 90% Confidence Intervals for Continuous Indices by Environment at T2\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eHome Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eHome (90% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLab Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003eLab (90% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog-transformed absolute area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003ePast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003e7.63 (0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e(7.41, 7.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7.71 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(7.54, 7.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003e7.64 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e(7.32, 7.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7.71 (0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(7.51, 7.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003eFuture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003e7.61 (0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e(7.24, 7.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7.61 (0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(7.29, 7.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003e8.86 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e(8.65, 9.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e8.88 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(8.75, 9.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003ePast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.342 (0.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e(0.267,0.419)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.343 (0.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.282,0.402)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.330 (0.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e(0.267,0.391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.329 (0.150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.282,0.378)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003eFuture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.329 (0.185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e(0.256,0.402)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.329 (0.177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.253,0.405)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 568px;\"\u003e\n \u003cp\u003eNote. All values refer to the stability between Time 1 and Time 2.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.2. Contextual effects on categorical indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine whether the testing environment influenced the categorical outcomes of the OL-CT, we compared the frequency distributions of the Temporal Dominance and Temporal Relatedness types between the home (\u003cem\u003en\u003c/em\u003e=18) and laboratory (\u003cem\u003en\u003c/em\u003e=18) settings. Chi-square tests of independence were conducted to assess the association between the testing environment and categorical indices. For Temporal Dominance, the distribution of dominance types (Past, Present, Future, and Balanced) did not differ significantly between the home and laboratory conditions (\u0026chi;\u003csup\u003e2\u003c/sup\u003e(3) = 0.34, \u003cem\u003ep\u003c/em\u003e = .95). Similarly, for Temporal Relatedness, no significant difference was found in the distribution of relatedness patterns (Atomicity, Continuity, Integration/Projection, and Others) across the two environments (\u0026chi;\u003csup\u003e2\u003c/sup\u003e(3) = 0.67, \u003cem\u003ep\u003c/em\u003e = .88). The contingency tables showed highly consistent classification patterns across contexts. These results suggest that the categorical classifications yielded by OL-CT are robust to contextual variations and remain stable, whether the test is administered in a controlled laboratory setting or remotely at home.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study provides comprehensive psychometric evidence supporting the online Circle Test (OL-CT) as a valid and reliable digital adaptation of Cottle\u0026rsquo;s Circle Test (Cottle, 1967). Across two visits, we demonstrated cross-format equivalence with the paper-and-pencil Circle Test (PP-CT), strong one-week stability, and robust performance across laboratory and home environments, while also showing a theoretically meaningful association between time perspective indices and depressive symptoms. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePsychometric properties: reliability, stability, and format equivalence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA central finding is that continuous indices derived from proportional (relative) circle areas show excellent psychometric performance, whereas absolute areas (even after log transformation) are comparatively less interchangeable across measurement occasions and formats. Specifically, the cross-format agreement between PP-CT and OL-CT for log-transformed absolute areas was moderate (ICCs = .73\u0026ndash;.77), whereas the proportional indices showed excellent agreement (ICCs = .89\u0026ndash;.90). Consistent with established guidelines for interpreting ICCs [19], this pattern suggests that the OL-CT preserves the relative allocation of psychological \u0026ldquo;time space\u0026rdquo; (past/present/future) particularly well, even if the absolute drawing scale varies across modalities.\u003c/p\u003e\n\u003cp\u003eThe same dissociation emerged for one-week stability: log-transformed absolute areas showed moderate-to-good test\u0026ndash;retest reliability (ICCs = .69\u0026ndash;.89), while proportional indices showed excellent stability (ICCs = .93\u0026ndash;.95). Taken together, these results support a clear methodological recommendation: when the goal is comparability across formats, contexts, or time, proportional indices should be treated as the primary continuous outcomes, and absolute area metrics may remain useful when the research question explicitly concerns overall drawing magnitude or when measurement conditions are tightly standardized.\u003c/p\u003e\n\u003cp\u003eFrom a measurement perspective, the weaker stability/interchangeability of the absolute areas is not unexpected. The absolute area is inherently sensitive to motor constraints (mouse vs. pen), device ergonomics, and implicit scaling strategies, all of which can shift without necessarily altering the underlying construct (time perspective). In contrast, proportional indices normalize much of this variance, functioning as a within-person compositional representation of the temporal salience. This interpretation is reinforced by the observation that order effects were limited and primarily apparent in absolute indices, whereas proportional indices were comparatively unaffected by the administration order.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCategorical indices: preservation of qualitative profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeyond continuous scores, the OL-CT also reproduced the qualitative profile structure of the Circle Test. Cross-format agreement for Temporal Dominance and Temporal Relatedness classifications was high (both 88.9%), and no systematic asymmetry was detected. These findings are particularly important because categorical classifications are often used to describe \u0026ldquo;types\u0026rdquo; of time perspectives in applied settings [7]. The high concordance suggests that OL-CT preserves the interpretive framework of the classical task, extending equivalence from metric indices to clinically interpretable profiles.\u003c/p\u003e\n\u003cp\u003eTest\u0026ndash;retest results converged with this conclusion: Temporal Dominance showed 91.7% exact agreement and excellent weighted kappa, again without evidence of systematic reclassification over time. In practical terms, OL-CT appears capable of capturing stable individual differences in qualitative time-orientation patterns, supporting its use in longitudinal designs and individual-difference research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContextual stability: robustness across testing contexts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA further contribution of this study is the direct evidence that the testing environment did not systematically influence OL-CT outcomes. Neither continuous indices nor categorical classifications differed between the laboratory and home administrations. This supports the contextual stability of the OL-CT and strengthens the case for remote assessment, an especially relevant advantage for large-scale data collection and contexts where in-person testing is impractical. These results align with broader evidence that well-designed web-based cognitive and behavioral tasks can yield reliable data, provided that the sampling and procedural controls are adequate [20].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical correlates with depressive symptoms: theoretical interpretation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe most clinically informative result was that greater past salience predicted more severe depression symptoms. In the proportional model, the past proportion was a significant positive predictor of PHQ-9 scores, such that a 10% increase in past allocation corresponded to an approximately 1.4-point increase in the PHQ-9 score. This pattern was consistent with the absolute-area model, where log past area significantly predicted PHQ-9 scores. Importantly, context (home vs. laboratory) did not predict depressive symptoms, indicating that the observed association was unlikely to be an artifact of the testing environment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis finding is conceptually coherent with classical and contemporary accounts of depression, which involve a constricted or distorted time experience characterized by increased past-oriented cognition and diminished flexibility in shifting temporal focus [21, 22]. A larger \u0026ldquo;past\u0026rdquo; representation in the Circle Test may index not only temporal attention but also the emotional and mnemonic weight of past experiences, consistent with evidence that depression is associated with biased processing of negative memories and difficulties disengaging from them [18].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimultaneously, the future proportion was negatively, but not significantly, associated with depressive symptoms. One plausible explanation is that in a non-clinical undergraduate sample, future-oriented variance may be constrained by normative developmental and situational factors (e.g., academic planning), potentially attenuating the associations with symptom severity. In clinically depressed samples, future-directed cognition is often markedly disrupted, particularly by the reduced generation of specific and positive future events and elevated hopelessness. Therefore, associations with future representations may become clearer when variability is greater [23\u0026ndash;25]. Accordingly, future studies should replicate the present analyses in diagnostically confirmed clinical samples (e.g., major depressive disorder) and examine whether future indices are more strongly related to symptom severity and hopelessness. Another possibility is that the Circle Test\u0026rsquo;s quantitative future allocation may not cleanly distinguish adaptive future orientation (goal-directed planning) from maladaptive future cognition (worry and threat anticipation), both of which can occur in depression cases. This nuance is increasingly emphasized in contemporary models of \u0026ldquo;psychological time\u0026rdquo; in depression, which highlight the heterogeneity in how future thinking is disrupted and how it might be targeted by interventions\u0026nbsp;[26].\u003c/p\u003e\n\u003cp\u003eFrom the perspective of time perspective theory, the observed past\u0026ndash;depression association is also consistent with evidence that deviations from a balanced time perspective are linked to poorer mental health [1, 2]. Therefore, OL-CT may provide a rapid behavioral indicator of temporal imbalance that complements questionnaire-based approaches.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBroader implications for digital assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe OL-CT\u0026rsquo;s automation and scalability matter not only for convenience but also for measurement innovation. Digital assessment can enable standardized scoring pipelines, reduce the examiner\u0026rsquo;s burden, and make large-scale screening feasible. Simultaneously, digital formats create opportunities to capture process-level data (e.g., drawing order, latency, corrections, and cursor trajectories), which may provide additional psychological information beyond final outcomes [27]. Although the current study focused on classical CT indices (areas and categorical types), future studies could leverage process data to better understand how depressive symptoms relate to temporal representation dynamics (e.g., hesitation when drawing the future, repeated revisions of the past). For example, the latency to initiate each circle, cumulative pause time, and revision frequency can be examined as candidate markers of reduced approach motivation and avoidance toward future-oriented cognition in depression [28]. These kinematic features are also associated with psychomotor retardation, which has been documented through kinematic analyses showing slower and delayed handwriting or drawing movements in depressed patients [29, 30]. In particular, prolonged latency or increased pausing when initiating the future circle may reflect reduced positive episodic future thinking and cognitive avoidance of goal-directed simulation [31, 32], offering a testable bridge between temporal representation and motor-behavioral signatures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother practical advantage is that a performance-based measure, such as the Circle Test, may be comparatively less vulnerable to certain response-style distortions that can affect self-reports, including social desirability pressure [6]. This does not imply immunity to bias, but it strengthens the rationale for using OL-CT as a complementary tool in research and early stage screening contexts, particularly when paired with validated symptom measures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and future directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations should guide the interpretation and motivate follow-up research. First, the sample size was modest and restricted to Japanese undergraduates, which may limit its generalizability to clinical populations and broader age ranges. Second, while one-week stability was strong, especially for proportional indices, longer retest intervals are needed to evaluate trait-like stability versus state sensitivity. Third, despite the null findings for environmental effects, future studies should systematically examine device variability (e.g., trackpad vs. mouse; screen size differences) and its impact on absolute versus proportional indices. Finally, clinical utility should be tested more directly by including diagnostically characterized samples and evaluating incremental validity (e.g., whether OL-CT indices improve predictions beyond symptom questionnaires).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, the OL-CT appears to be a psychometrically sound digital adaptation of the classical Circle Test, particularly when proportional indices are used as the primary outcomes. The tool demonstrates strong cross-format agreement, excellent week-to-week reliability for proportional scores, robust performance across laboratory and home contexts, and a theoretically meaningful association between past salience and depressive symptoms. These findings support the OL-CT as a scalable behavioral measure of time perspective for both research applications and potential clinical screening contexts, while also highlighting proportional indices as the most stable and format-robust representation of temporal orientation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eOL-CT：Online Circle Test\u003c/p\u003e\n\u003cp\u003ePP-CT：Paper-and-pencil Circle Test\u003c/p\u003e\n\u003cp\u003eJ-PHQ-9: Japanese version of the Patient Health Questionnaire-9\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for\u0026nbsp;this study was obtained from the Chuo University Research Ethics Committee (trial number 2025-020, date of approval 16/04/2025).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent. They could withdraw at any time without any consequence or penalty. Data were collected and analysed anonymously.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Online Circle Test（OL-CT）\u0026nbsp;and datasets analysed during the current study are available from the corresponding author upon reasonable request.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor contributions HH conducted the experiments, performed data collection and data analysis, and drafted the manuscript. AM supervised the overall revision of the manuscript, including its structure, data analysis, and the preparation of tables, and was responsible for the writing and checking of all sections. All authors read and approved the final manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank YAYOI SHIGEMUNE for her valuable suggestions regarding the design of the Online Circle Test (OL-CT). We also extend our gratitude to RYUTA OCHI for his insightful advice on the data analysis of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStolarski M, Zajenkowski M, Jankowski KS, Szymaniak K. Deviation from the balanced time perspective: A systematic review of empirical relationships with psychological variables. Personality and Individual Differences. 2020;156. https://doi.org/10.1016/j.paid.2019.109772.\u003c/li\u003e\n\u003cli\u003eStolarski M, Czajkowska-Łukasiewicz K, Styła R, Zajenkowska A. Time matters for mental health: a systematic review of quantitative studies on time perspective in psychiatric populations. Curr Opin Psychiatry. 2024;37:309\u0026ndash;19. https://doi.org/10.1097/YCO.0000000000000942.\u003c/li\u003e\n\u003cli\u003ePyszkowska A, \u0026Aring;str\u0026ouml;m E, R\u0026ouml;nnlund M. Deviations from the balanced time perspective, cognitive fusion, and self-compassion in individuals with or without a depression diagnosis: different mean profiles but common links to depressive symptoms. Front Psychol. 2024;14. https://doi.org/10.3389/fpsyg.2023.1290676.\u003c/li\u003e\n\u003cli\u003eZimbardo PG, Boyd JN. Putting time in perspective: A valid, reliable individual-differences metric. 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Nat Methods. 2012;9:671\u0026ndash;5. https://doi.org/10.1038/nmeth.2089.\u003c/li\u003e\n\u003cli\u003eStanczak DE, Triplett G. Psychometric properties of the Mid-Range Expanded Trail Making Test. An examination of learning-disabled and non-learning-disabled children. Arch Clin Neuropsychol. 2003;18:107\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eWest RM. Best practice in statistics: The use of log transformation. Ann Clin Biochem. 2022;59:162\u0026ndash;5. https://doi.org/10.1177/00045632211050531.\u003c/li\u003e\n\u003cli\u003eEveraert J, Vrijsen JN, Martin-Willett R, van de Kraats L, Joormann J. A meta-analytic review of the relationship between explicit memory bias and depression: Depression features an explicit memory bias that persists beyond a depressive episode. Psychological Bulletin. 2022;148:435\u0026ndash;63. https://doi.org/10.1037/bul0000367.\u003c/li\u003e\n\u003cli\u003eKoo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med. 2016;15:155\u0026ndash;63. https://doi.org/10.1016/j.jcm.2016.02.012.\u003c/li\u003e\n\u003cli\u003eUittenhove K, Jeanneret S, Vergauwe E. From Lab-Testing to Web-Testing in Cognitive Research: Who You Test is More Important than how You Test. J Cogn. 2023;6:13. https://doi.org/10.5334/joc.259.\u003c/li\u003e\n\u003cli\u003eLewin K. Time perspective and morale. In: Civilian morale. Oxford, England: Houghton Mifflin; 1942. p. 48\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eMoskalewicz M, Schwartz MA. Temporal experience as a core quality in mental disorders. Phenom Cogn Sci. 2020;19:207\u0026ndash;16. https://doi.org/10.1007/s11097-020-09665-3.\u003c/li\u003e\n\u003cli\u003eBeck AT, Weissman A, Lester D, Trexler L. The measurement of pessimism: The Hopelessness Scale. Journal of Consulting and Clinical Psychology. 1974;42:861\u0026ndash;5. https://doi.org/10.1037/h0037562.\u003c/li\u003e\n\u003cli\u003eHallford DJ, Austin DW, Takano K, Raes F. Psychopathology and episodic future thinking: A systematic review and meta-analysis of specificity and episodic detail. Behav Res Ther. 2018;102:42\u0026ndash;51. https://doi.org/10.1016/j.brat.2018.01.003.\u003c/li\u003e\n\u003cli\u003eMacLeod AK, Rose GS, Williams JMG. Components of hopelessness about the future in parasuicide. Cogn Ther Res. 1993;17:441\u0026ndash;55. https://doi.org/10.1007/BF01173056.\u003c/li\u003e\n\u003cli\u003eRen H, Zhang Q, Ren Y, Zhou Q, Fang Y, Huang L, et al. Characteristics of psychological time in patients with depression and potential intervention strategies. Front Psychiatry. 2023;14:1173535. https://doi.org/10.3389/fpsyt.2023.1173535.\u003c/li\u003e\n\u003cli\u003eGoldhammer F, Zehner F. What to Make Of and How to Interpret Process Data. Measurement: Interdisciplinary Research and Perspectives. 2017;15:128\u0026ndash;32. https://doi.org/10.1080/15366367.2017.1411651.\u003c/li\u003e\n\u003cli\u003eTrew JL. Exploring the roles of approach and avoidance in depression: an integrative model. Clin Psychol Rev. 2011;31:1156\u0026ndash;68. https://doi.org/10.1016/j.cpr.2011.07.007.\u003c/li\u003e\n\u003cli\u003eMergl R, Juckel G, Rihl J, Henkel V, Karner M, Tigges P, et al. Kinematical analysis of handwriting movements in depressed patients. Acta Psychiatrica Scandinavica. 2004;109:383\u0026ndash;91. https://doi.org/10.1046/j.1600-0447.2003.00262.x.\u003c/li\u003e\n\u003cli\u003eSabbe B, Hulstijn W, Van Hoof J, Zitman F. Fine motor retardation and depression. Journal of Psychiatric Research. 1996;30:295\u0026ndash;306. https://doi.org/10.1016/0022-3956(96)00014-3.\u003c/li\u003e\n\u003cli\u003eHallford DJ, Barry TJ, Austin DW, Raes F, Takano K, Klein B. Impairments in episodic future thinking for positive events and anticipatory pleasure in major depression. J Affect Disord. 2020;260:536\u0026ndash;43. https://doi.org/10.1016/j.jad.2019.09.039.\u003c/li\u003e\n\u003cli\u003eHallford D, Rusanov D, Yeow J, Austin D, D\u0026rsquo;Argembeau A, Fuller-Tyszkiewicz M, et al. Reducing Anhedonia in Major Depressive Disorder with Future Event Specificity Training (FEST): A Randomized Controlled Trial. Cognitive Therapy and Research. 2022;47. https://doi.org/10.1007/s10608-022-10330-z.\u003c/li\u003e\n\u003c/ol\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-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cottle’s Circle Test, time perspective, online assessment, psychometric validation, test–retest reliability, depressive symptoms, digital phenotyping","lastPublishedDoi":"10.21203/rs.3.rs-8514018/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8514018/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe Circle Test is a drawing-based task for assessing time perspective, and its indices have been linked to psychological well-being, including depression symptoms. However, conventional paper-and-pencil administration requires substantial human resources for scoring and quantification, limiting its scalability for large or remote surveys to be conducted. We developed an online web-based circle test with automated scoring and evaluated its validity and reliability.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThirty-six Japanese undergraduates completed both the Online Circle Test (OL-CT) and paper-and-pencil Circle Test (PP-CT) in a counterbalanced crossover design (Visit 1) and repeated the OL-CT after 7\u0026ndash;9 days either in the laboratory or at home (Visit 2). Continuous indices included log-transformed absolute and proportional areas, and categorical indices included temporal dominance and temporal relatedness. Validity analyses evaluated (a) cross-format equivalence between PP-CT and OL-CT and (b) clinical validity through associations with depressive symptoms measured using the Japanese version of the Patient Health Questionnaire-9 (J-PHQ-9). Reliability analyses evaluated (a) the one-week test-retest reliability of the OL-CT and (b) the contextual stability across laboratory versus home administration.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCross-format equivalence was moderate for log-transformed absolute areas (ICC\u0026thinsp;=\u0026thinsp;.73-.77) but excellent for proportional areas (ICC\u0026thinsp;=\u0026thinsp;.89-.90). The categorical agreement was high (88.9%), with no systematic category shift. For clinical validity, higher J-PHQ-9 scores were associated with a greater representational emphasis on the past, whereas future indices were not significant predictors in this non-clinical sample. One-week test-retest reliability was moderate-to-excellent for absolute areas (ICC\u0026thinsp;=\u0026thinsp;.69-.89) and excellent for proportional areas (ICC\u0026thinsp;=\u0026thinsp;.93-.95). Categorical stability was also high (temporal dominance: 91.7%, κ\u0026thinsp;=\u0026thinsp;.88; temporal relatedness: 86.0%, κ\u0026thinsp;=\u0026thinsp;.76). The OL-CT indices did not differ between the laboratory and home retesting contexts.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe OL-CT demonstrates strong validity and reliability as a scalable digital time-perspective assessment, with particularly robust proportional indices and stable categorical profiles across formats, time, and testing contexts.\u003c/p\u003e","manuscriptTitle":"Validating the Online Circle Test (OL-CT): Cross-Format Equivalence, One-Week Reliability, and Depressive Symptom Correlates in Japanese Undergraduates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 14:43:29","doi":"10.21203/rs.3.rs-8514018/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-09T15:12:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-27T19:39:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117617092807753366237714074740198233135","date":"2026-02-25T23:24:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T12:32:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117350392952946764955066002109607034637","date":"2026-02-24T12:10:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T08:10:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-14T10:56:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-12T10:45:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-12T10:41:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-01-04T15:05:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b94d3a39-adf4-4f7f-97cd-8b378956c866","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T09:09:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 14:43:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8514018","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8514018","identity":"rs-8514018","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}