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Perceived stress is prevalent in industrial societies, negatively impacting mental health. Smartphone-based stress management interventions provide accessible alternatives to traditional methods, but their efficacy remains modest, potentially due to limited integration of smartphone sensor technology. The primary aim of this study was to evaluate the efficacy of an 18-day smartphone-based stress management intervention, MT-StressLess with integrated heart rate (HR)-based biofeedback using built-in accelerometer sensors, compared to a waitlist control (WLC) condition. Secondary outcomes included emotion regulation skills, depressive symptoms, overall well-being, usbiality and usage data. As exploratory aims, we investigated whether the MT-StressLess version without HR-based biofeedback was also superior to the WLC condition, and whether the version with HR-based biofeedback provided additional benefits compared to the version without. In a three-arm randomized controlled trial, 166 participants were assigned to MT-StressLess with HR-based biofeedback, MT-StressLess, or the WLC condition. Linear mixed-effects models were used to analyze intervention effects over time (baseline, postintervention, and 1-month follow-up). At postintervention, MT-StressLess with HR-based biofeedback showed significantly greater reductions in perceived stress compared to the WLC condition (d = 0.41, 95% CI [0.03, 0.79]), whereas the version without biofeedback did not differ significantly (d = 0.14, 95% CI [-0.24, 0.51]). No significant differences were observed between the two active conditions (d = 0.29, 95% CI [-0.08, 0.66]). Both active conditions, however, led to significant improvements in the secondary outcomes of emotion regulation skills and well-being compared to the WLC (all ds = -0.58 to -0.27). These patterns persisted at the 1-month follow-up. Usability ratings were high, but overall adherence was moderate. The findings in the main comparison may reflect increased interoceptive awareness and self-regulation. Yet, the limited effects of the core intervention and the biofeedback component also suggest the influence of non-specific factors, such as placebo effects, outcome expectancy and user engagement, which highlights the need to better understand optimal intervention duration, motivation, reinforcement, and more individualized approaches to stress reactivity. Overall, the findings provide preliminary support for the potential of a smartphone-based intervention that includes HR-based biofeedback to reduce perceived stress compared to no intervention. As these interventions are still in their early stages, future research should explore how personalization driven by artificial intelligence and real-time physiological tracking can enhance engagement and efficacy.