Abstract

Stress in its various forms—including acute, physical, cognitive, socio‑evaluative and perceived—undermines mental and cardiovascular health. Wearable devices, mHealth and telemedicine offer real‑time stress monitoring, yet evidence is limited by small samples, short durations and heterogeneous outcomes. In this scoping review of 42 PubMed and IEEE Xplore studies (to September 10, 2024), heart rate variability dominated, while cortisol and telemedicine interventions were under‑utilized. We call for standardized protocols, long‑term effectiveness trials and scalable solutions across diverse populations.

1 Introduction

Stress—whether acute, physical, cognitive, socio‑evaluative or perceived—adversely affects mental and physical health, contributing to anxiety, cardiovascular disease and immune dysfunction [Thoits 2013; nu12082428; ROMEO 2017; Wen 2022]. Traditional assessments rely on self‑reports and episodic clinical measures [Lemyre 2009; Lesage 2012], but advances in wearable sensors, mHealth apps and telemedicine enable continuous, real‑time monitoring of physiological and psychological stress markers [Susanna 2018; Hanna 2024]. This scoping review maps the current evidence on these digital interventions, identifies methodological gaps and proposes directions for future research and clinical implementation.

2 Methods

We followed PRISMA‑ScR [Tricco 2018] and Arksey & O’Malley’s framework [Arksey 2005; Levac 2010].

2.1 Data Sources

  • Databases: PubMed, IEEE Xplore (searched to September 10, 2024)
  • Supplementary: Reference lists, WHO & APA digital health sites

2.2 Search Strategy

  • Key terms: psychological stress, “wearable sensors,” mHealth, telemedicine
  • Boolean logic combined stress and technology terms
  • Filters: English, human subjects, 2019–2024

2.3 Inclusion Criteria

  1. Human studies on acute, physical, cognitive, socio‑evaluative or perceived stress
  2. Use of wearable or mHealth technologies for stress assessment/management
  3. Empirical designs: RCTs, cohort, cross‑sectional, observational, feasibility
  4. Outcome measures: physiological markers (HRV, cortisol) or validated scales (PSS)

Review articles, protocols and commentaries were excluded.

2.4 Study Selection & Data Extraction

  • Screening: Two reviewers independently screened titles, abstracts and full texts; conflicts resolved by discussion or third reviewer.
  • Extraction: Standard form captured design, sample size, technology, biomarkers, self‑report scales and outcomes.
  • Mapping: Iterative thematic analysis per JBI guidance [Peters 2020].

3 Results

PRISMA 2020 flow diagram of article selection process

3.1 Study Selection

  • Records identified: 347
  • After duplicates & screening: 42 studies included
  • Common exclusions: no direct stress measure or irrelevant technology

3.2 Study Characteristics

  • Sample sizes: 14–2,102 participants
  • Wearables: HRV, EDA, SpO₂, skin temperature; cortisol sensors in 5 studies
  • mHealth apps: Perceived stress tracking, ecological momentary assessment (EMA)
  • Stress markers:
    • HRV (most frequent)
    • Cortisol (5 studies)
    • Perceived Stress Scale (26 studies)

3.3 Key Trends

  • HRV is a reliable autonomic stress indicator.
  • Self‑reports add context but vary widely.
  • Telemedicine interventions are under‑explored.

3.4 Gaps

  • Sample size & duration: Predominantly small pilots, short follow‑ups (< 3 months).
  • Outcome heterogeneity: Inconsistent biomarkers and scales.
  • Population diversity: Underrepresentation of varied ages, genders and socioeconomic backgrounds.

4 Discussion

4.1 Principal Findings

Wearable and mHealth solutions capture stress physiology effectively—especially via HRV—while combining hormonal and subjective data enriches insights but remains infrequent.

4.2 Strengths & Limitations

  • Strengths: Continuous, objective data; potential for early detection and personalized feedback.
  • Limitations: Pilot‑scale studies; disparate protocols; absence of standardized assessment frameworks.

4.3 Future Directions

  • Larger, longitudinal trials with standardized measures.
  • Diverse cohorts including underserved populations.
  • Consensus guidelines for digital stress monitoring.
  • Implementation research on long‑term engagement and clinical integration.

5 Conclusion

Wearable sensors and mHealth apps—particularly HRV‑based—offer scalable, personalized stress monitoring and management. To unlock their full potential, future work must emphasize robust designs, harmonized outcome measures and equitable deployment across diverse populations.