Advanced Computational and Biotechnological Approaches to Systemic Family Therapy: Predicting Marital Satisfaction and Emotional Wellbeing in Couples

Abstract

This quantitative, cross-sectional, case-study–based research addressed the problem that systemic couple distress is often assessed using subjective impressions without an integrated, data-driven model that jointly explains marital satisfaction and emotional wellbeing using measurable systemic interaction processes and biotech-informed regulation indicators. The purpose was to develop and test a predictive framework, grounded in systemic family therapy, that estimates marital satisfaction and emotional wellbeing from modifiable relationship-process variables plus stress-recovery indicators. Using a purposive, case-based sample of 180 couple cases within a bounded context, participants completed Likert 5-point composite measures for Communication Quality (CQ), Conflict Regulation (CR), Emotional Responsiveness (ER), Repair Capacity (RC), Stress Regulation Indicator (SRI), Sleep Quality Indicator (SQI), and outcomes Marital Satisfaction (MS) and Emotional Wellbeing (EWB). The analysis plan applied descriptive statistics, reliability testing, Pearson correlations, and hierarchical multiple regression for MS and EWB, followed by System Dynamics Index profiling (SDI = mean of CQ, CR, ER, RC) and prediction risk-banding. Descriptively, CQ (M = 3.62, SD = 0.71), ER (M = 3.69, SD = 0.68), MS (M = 3.58, SD = 0.74), and EWB (M = 3.46, SD = 0.73) were moderately high, with strong reliabilities (α = 0.82–0.90). Correlations showed robust systemic links to outcomes, especially ER with MS (r = .64) and EWB (r = .57), and CQ with MS (r = .61). In regression, systemic predictors explained substantial variance in MS (R² = .52), improved to R² = .56 when biotech indicators were added (ΔR² = .04, p = .012); ER remained the strongest MS predictor (β = .31). For EWB, systemic predictors explained R² = .44, rising to R² = .55 after adding SRI and SQI (ΔR² = .11, p < .001), with SRI (β = .27) and SQI (β = .19) significant. SDI profiling showed clear gradients: High SDI cases reported higher MS (M = 4.01) and EWB (M = 3.89) than Low SDI cases (MS M = 2.97; EWB M = 2.91). Implications indicate that therapy assessment can prioritize responsiveness, repair, and conflict regulation while adding brief stress and sleep screening to better identify wellbeing risk and tailor intervention intensity.