Exploring the Efficacy of Application Managing Somatic Symptoms Disorder & Medically Unexplained Symptoms (MUS) : A Pilot Study

Objectives The primary objective of this study is to evaluate the effectiveness of an eight-week self-management mobile application for adults experiencing somatic symptom disorder (SSD) or medically unexplained symptoms (MUS). Improvement will be assessed using the PHQ-15. The secondary objective is to examine changes in clinical symptoms and physiological indicators following app use, as well as to assess the acceptability and usability of the intervention.

Scientific Rationale Somatic symptoms without clear medical explanation account for 15-30% of primary care visits (Peveler et al., 1997; Fink et al., 1999; Ko et al., 2011). Diagnostic terminology has evolved from somatoform disorders in DSM-IV and MUS to SSD in DSM-5, which emphasizes distressing symptoms accompanied by maladaptive thoughts, emotions, or behaviors (Scott et al., 2022). SSD is frequently associated with alexithymia, emotional suppression, and difficulty regulating anger, consistent with theories of somatization as the bodily expression of unprocessed affect (Anuk & Bahadir, 2017; Liu et al., 2011). These difficulties contribute to interpersonal impairment and excessive health care use, often resulting in frustration for clinicians (Orzechowska et al., 2020).

Psychological treatments, including CBT, MBSR, MBCT, and ACT, have demonstrated efficacy in reducing symptom burden across conditions such as IBS, fibromyalgia, tinnitus, and chronic fatigue (Hauge et al., 2015; Kikuchi et al., 2020; Roland et al., 2015). These therapies share mechanisms such as enhancing mind-body awareness, reducing physiological hyperarousal, and promoting adaptive coping and acceptance (Aktas et al., 2019; Jing et al., 2019). However, barriers to dissemination remain, as patients with SSD often reject psychological explanations, engage in medical shopping, and maintain strong somatic attributions (Brown, 2007; Harris et al., 2009).

Digital interventions offer a scalable solution by providing psychoeducation on mind-body connections, CBT- and mindfulness-based strategies, stress management tools, and behavioral monitoring in an accessible, daily-life format. Evidence suggests that internet- and app-based CBT programs can improve somatic distress and related outcomes, while also reducing treatment costs and improving adherence (Van et al., 2022).

Physiological measures such as heart rate variability (HRV) provide an objective biomarker of stress regulation. Lower HRV reflects reduced resilience and greater emotional dysregulation, while higher HRV indicates excessive arousal or impaired recovery. Despite theoretical links between autonomic regulation and somatization, few studies have examined HRV responses in SSD populations alongside psychological interventions. This study addresses that gap.

Study Design A randomized controlled trial will recruit 110 adults aged ≥19 years who report significant somatic distress. After screening and informed consent, participants will be randomly assigned (1:1) to intervention (n=55) or control (n=55) using an R-generated allocation table managed independently of investigators.

Baseline assessments include standardized questionnaires and HRV measurement. The intervention group will use the mobile application for eight weeks, while the control group will continue treatment as usual. After eight weeks, both groups will complete follow-up questionnaires and HRV measurement. Subsequently, the intervention group will discontinue app use, and the control group will be offered app access. An additional follow-up survey will be conducted online for both groups at 16 weeks.

Expected Impact This study will provide empirical evidence on the efficacy, usability, and acceptability of a digital intervention for SSD and MUS. By integrating self-management strategies with physiological monitoring, it aims to advance scalable, evidence-based approaches for a population that has historically been difficult to treat within traditional medical models.