Enhancing wellness: a systematic review of biofeedback interventions for healthcare professionals
Elisa Cantone, Antonio Urban, Alessandra Perra, Giulia Cossu, Massimo Tusconi, Serdar M Dursun, Mauro Giovanni Carta
TL;DR
This review examines how biofeedback interventions can help healthcare professionals manage stress and improve their well-being.
Contribution
The study systematically evaluates biofeedback's effectiveness in improving stress and physiological outcomes among healthcare workers.
Findings
Biofeedback improved perceived stress, anxiety, and resilience in healthcare workers.
Physiological benefits included increased heart rate variability and reduced sympathetic arousal.
Interventions with breathing or mindfulness techniques showed the strongest effects.
Abstract
Healthcare professionals are routinely exposed to high psychosocial and physiological demands, placing them at elevated risk for stress-related disorders, including burnout, anxiety, and impaired autonomic regulation. Biofeedback has emerged as a promising non-pharmacological approach to enhance self-regulation and resilience. This systematic review aimed to evaluate the effectiveness of biofeedback-based interventions in improving psychological and physiological outcomes among healthcare workers. A systematic search was conducted across PubMed, Cochrane, Embase, PsycINFO, and grey literature (December 2023–January 2024), following PRISMA guidelines and a registered PROSPERO protocol (CRD42024544687). Eligible studies included randomized controlled trials, quasi-experimental designs, and pre-post studies involving adult healthcare workers exposed to work-related stress. Primary…
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| Autor, country, year | Aim of study | Sample | Health workers | Study design | N group (IG: experimental and CG: control group) | Biofeedback intervention type | Measuring instruments | Main results |
|---|---|---|---|---|---|---|---|---|
| Munafò ( | Assess the effectiveness of a short Training RSA-BF versus a standard stress management intervention in improving psychological and physiological well-being. | 31 managers | Managers and middle managers from the health care | RCT | IG = 16 | Training RSA-BF | RSA-BF training significantly improved autonomic regulation, with increased cardiac variability (lnRSA; p = .04) and decreased SCL (p <.001), indicating reduced sympathetic arousal. In addition, only the RSA-BF group showed a significant reduction in work limitations due to emotional problems, alongside general improvements in perceived health and vitality. | |
| Seiça ( | Evaluate whether the biofeedback intervention of Qigong can be used as a complementary therapy for emotional exhaustion among nurses. The aim was to improve emotional regulation and decrease sympathetic nervous system activation. | 44 nurses | Nurses from the hospital center | RCT | IG = 22 | “white ball” Qigong system (Biofeedback). | The Qigong group showed a significant reduction in emotional exhaustion (EE) scores compared with the control group. The dimensions of depersonalization and personal accomplishment did not show significant changes ( | |
| Lemaire ( | Determine whether the use of a biofeedback-based stress management tool combining rhythmic breathing, self-generated positive emotions, and feedback helps to reduce physician stress. | 40 | staff physicians (1 from primary care, 30 from a medical specialty and 9 from a surgical specialty) from urban tertiary care centers | RCT | IG = 21 | (Rhythmic breathing with positive emotions) Biofeedback. | PSS: | |
| Macedo ( | Evaluate whether cardiovascular biofeedback (CBKF) training could increase HRV, improve autonomic nervous system regulation, and reduce stress in nurses. | 115 | Nursing professionals (nurse; technician; assistant) with stress symptoms, from a university hospital | RCT | IG = 58 | Cardiovascular biofeedback (CBKF) training | Significant increases in SDNN (p = 0.016) and LF/HF ratio (p < 0.001) were observed in the intervention group, reflecting improved autonomic regulation and stress adaptability following cardiovascular biofeedback training. | |
| Hsieh ( | Compare the effectiveness of Biofeedback training BT and a SDBT (SDBT) on work stress, depressive symptoms, resilience, heart rate variability HRV, and respiration rate among psychiatric nurses who experienced workplace violence. | 135 | Psychiatric nurses exposed to workplace violence from three hospitals | Quasi-experimental study with randomized cluster sampling | IG = 49 BT | Biofeedback | Mental Health and Psychological outcomes: | |
| Orlando ( | To examine the effectiveness of a self-regulation training program with Biofeedback in reducing perceived stress and improving job satisfaction among hospital-based primary care professionals | 18 | Physicians, nurse practitioners, and nurses from two family medicine | Pilot study | IG = 9 | (Self-Regulation) Biofeedback | A significant initial increase in perceived stress was observed in both groups, more pronounced in the treatment group (p = 0.03). Participants in both groups reported independent use of self-regulation techniques, although many described difficulties related to time constraints, feelings of overload, and challenges in maintaining regular practice. | |
| Balk ( | To evaluate whether relaxation training reduces stress levels and has a physiological and Mental Health and Psychological impact | 90 | - operating room (OR) nurses and staff (mean age 42 ± 9.5); | Pre-post intervention study with group comparisons (non-randomized, no control group). | GI: | Biofeedback | All groups showed significant reductions in perceived stress (PSS) and increases in skin temperature, indicating decreased sympathetic activity (p <.05). | |
| Allen ( | To measure the impact of meditation with neurofeedback on brainwave regulation, physiological stress (cortisol), and subjective well-being in O&G doctors. | 12 | obstetrics and gynaecology (O&G) doctors and labour ward teams in metropolitan hospitals. | Mixed-methods exploratory study, pre-post design without a control group. | GI: 12 | Neurofeedback | Mental Health and Psychological outcomes: | |
| Cutshall ( | To evaluate the effectiveness of a self-directed, computer-guided meditation training program using biofeedback in reducing stress among hospital nurses. | 11 (8 completed the | nurses in Mayo Clinic hospital | Pilot prospective pre-post study without a control group | GI= 8 | Biofeedback | The intervention group showed significant reductions in anxiety (p <.03) and stress levels (p <.01), along with improvements in vitality (p <.04). | |
| Mensinger ( | To examine the feasibility, acceptability, and preliminary efficacy of a rate variability biofeedback (HRVB) smartphone app for improving well-being in HCWs during the COVID-19 pandemic. | 28 | Nurses (79%), Physicians, EMTs (and Chaplains) in to | Pilot single-arm, non-randomized feasibility study with pre-, mid-, and post-assessment. | GI= 28 | Heart Rate Variability Biofeedback (HRV-B) smartphone app | Participants reported that HRV biofeedback (HRVB) promoted relaxation and improved stress management, as well as greater body awareness, intuitive eating, self-care, and resilience. | |
| Ribeiro ( | Assess the effectiveness of HRV biofeedback (HRV-BF) protocol in mitigating mental health symptoms in a sample of frontline HCWs (HCWs) during COVID-19 pandemic. | 21 | Clinicians 6 (28.6%) | Single-group pre-post experimental study using two psychological assessment approaches. | GI= 21 | Heart Rate Variability Biofeedback (HRV-BF) | HRV-BF training significantly reduced chronic stress, anxiety, and PTSD-related symptoms, accompanied by increased HRV (SDNN, LF power) and decreased respiratory rate, consistent with parasympathetic activation. Participants showed high engagement and adherence. Phasic EDA indicated transient sympathetic arousal during sessions, suggesting adaptive autonomic recalibration. | |
| Park ( | To examine the effects of a wearable biofeedback postural training device on pain and posture in workers with pre-existing low back pain caused by prolonged static posture (e.g., sitting). | 31 | Hospital administrative staff and laboratory workers with chronic low back pain due to static posture. | Randomized Controlled Trial (RCT) | IG= 15 | Wearable posture biofeedback | Wearable postural biofeedback significantly reduced low back pain (VAS; p <.05) and improved postural alignment, as indicated by decreased trunk and head angles. The intervention was well-tolerated and accepted, confirming its effectiveness in enhancing musculoskeletal well-being among sedentary HCWs. |
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Taxonomy
TopicsHealthcare professionals’ stress and burnout · Heart Rate Variability and Autonomic Control · Workplace Health and Well-being
Introduction
Healthcare Workers (HCWs) frequently face substantial stressors, including extended working hours, low staff turnover, persistent personnel shortages, and the emotional burden of patient care. Like any large organization, the healthcare system is influenced by several factors that the European Agency for Safety and Health at Work (EU-OSHA, 2014) (1, 2) calls “risk factors”: conflicting demands and unclear roles; limited involvement in decision-making processes affecting workers; little control over task execution; poorly managed organizational changes; job insecurity; ineffective communication; inadequate support from managers or colleagues; psychological and sexual harassment; and, finally, demanding clients, patients, or students (1).
In the literature, we find that several systematic reviews have already shown that healthcare professionals experience high levels of psychological distress, including burnout, anxiety, and depression (3, 4). Several other studies also highlight that dissatisfaction with limited organizational resources and insufficient institutional support significantly contributes to stress and reduces well-being among professionals (5, 6). During and after the COVID-19 pandemic, these conditions worsened, with a notable increase in reports of burnout, distress, anxiety, and depression, mainly due to resource shortages, work overload, and organizational pressures (7, 8). Overall, the evidence suggests that although stress and burnout were common in healthcare before COVID-19, the pandemic greatly intensified these issues, worsening the psychological burden associated with resource limitations and high emotional demands (9, 10).
Currently, the structure of healthcare delivery is becoming more complex, exposing HCWs to more extended periods of intense stress and increasing the risk of adopting dysfunctional and maladaptive coping strategies (11). Under these conditions of high workload, the threshold for well-being is lowered, and symptoms such as anxiety, loss of motivation, mental and physical fatigue, and both personal and professional dissatisfaction may appear, often accompanied by feelings of helplessness and frustration (12, 13). High levels of frustration, particularly, play a key role in the development of burnout, especially among workers who receive limited support from their organization (13).
When these resources are seen as insufficient, HCWs become more susceptible to psychological and physical distress. This distress raises the chances of interpersonal difficulties with colleagues, undermines the ability to maintain assertive and collaborative behavior, and compromises the quality of professional interactions (13, 14). Workers in this situation often feel mistreated and unsupported by their organization, with leadership also viewed as lacking (12, 15). As a result, the organizational climate is experienced as hostile, marked by high emotional stress and professional demands that surpass available personal resources (15).
When the relationship between hospital staff and patients becomes ineffective, patients may lose trust, which in turn can reduce adherence to recommended treatments (16). For healthcare organizations, this may result in increased absenteeism or, conversely, presenteeism, where employees attend work despite being ill and unable to perform effectively (17). Even in structurally well-organized healthcare environments, conditions of distress may emerge if insufficient attention is given to emotional well-being (18). This condition compromises both physical and mental health, with severe consequences for the quality of life (QoL) of HCWs and their patients. Beyond mental health issues such as burnout, anxiety, depression, and even suicidal ideation, prolonged occupational stress also increases the risk of severe physical health conditions, including cardiovascular diseases and stroke (19). Chronic stress hyperactivates the sympathetic nervous system, triggering primitive physiological responses designed to ensure survival. Heart rate and blood pressure rise, and the body prepares to respond through fight-or-flight or freeze responses (20–22). At the same time, parasympathetic activity is inhibited, leading to progressive physiological wear and tear. According to polyvagal theory, sustained sympathetic dominance and vagal withdrawal impair emotional regulation, social engagement, and physical health (20, 23).
Implementing the European Framework Agreement of October 8, 2004, on work-related stress (14) requires employers to assess and manage stress in the workplace. Employers are legally responsible for ensuring that occupational risks are correctly evaluated and effectively mitigated. This regulatory framework underscores the importance of adopting a structured and preventive approach to addressing psychological and physical distress in professional settings. In line with this, the EU Strategic Framework on Health and Safety at Work 2021–2027 (24) further emphasizes the need to address psychosocial risks and promote mental well-being at work, underscoring the importance of preventive, evidence-based approaches to occupational stress management. Recent literature has identified several strategies as effective for managing occupational stress in such settings (25, 26).
Among these, Biofeedback has emerged as a promising physiological self-regulation technique capable of modulating heart rate variability (HRV) and supporting balance of the autonomic nervous system (14, 27). This approach is grounded in the contemporary psychophysiological framework of self-regulation, which integrates neurovisceral and biopsychosocial models of stress and adaptation (14, 23, 26, 28). Biofeedback enhances physiological regulation, bodily awareness, resilience, and cognitive performance, especially with breathing or mindfulness techniques (23, 29). It is a user-friendly, non-invasive, non-pharmacological intervention used for neuropsychological conditions like anxiety, stress, and emotional dysregulation. With no reported side effects, it helps individuals actively maintain health and develop adaptive coping strategies, improving energy and cognitive performance (14, 26, 28, 30). By providing real-time monitoring and feedback of key biomedical signals, such as electromyographic activity (EMG), skin temperature (ST), electrodermal activity (EDA), respiratory rate (RR), heart rate (HR), heart rate variability (HRV), blood pressure (BP), electroencephalographic activity (EEG), and peripheral blood flow (PBF), biofeedback provides real-time sensory feedback (visual, auditory, tactile), increasing awareness and voluntary control of autonomic functions, thus enhancing emotional regulation and physiological balance (31, 32). Various forms of biofeedback, often combined with rehabilitative or behavioral approaches, target medical and psychological conditions (33). Key types of particular relevance to this review include Heart Rate Variability Biofeedback (HRV-BF), Respiratory Sinus Arrhythmia Biofeedback (RSA-BF), Electromyographic Biofeedback (EMG-BF), Neurofeedback, an electroencephalographic biofeedback (EEG-BF) technique, Cardiovascular Biofeedback (CBKF), and wearable postural feedback systems. Despite differences, they all aim to promote psychophysiological balance through feedback learning (26). Biofeedback is effective in reducing chronic fatigue, anxiety, depression, and pain (34).
Beyond its use in clinical settings, biofeedback has been explored with non-clinical groups, including students, athletes, and workers. It has been found to support psychological well-being, resilience, and cognitive efficiency (14, 28, 35). These results emphasize that biofeedback not only helps reduce stress-related symptoms but also encourages optimal functioning, aligning with the idea of psychophysiological flourishing. By improving self-regulation and interoceptive awareness, biofeedback aids in maintaining focus, emotional stability, and energy balance, key factors in job performance and engagement (28, 36). This proactive and empowering role is especially significant for HCWs, who, although not in clinical settings, face ongoing interpersonal challenges and emotional strain. Enhancing their self-regulation through biofeedback training could boost their well-being and job performance while decreasing the risk of stress and burnout.
However, despite the growing body of evidence supporting its use in the general population, no systematic review to date has specifically examined its application among HCWs (32, 33). This gap in the literature provides the rationale for the present review, which aims to systematically evaluate the effectiveness of biofeedback-based interventions in healthcare settings. Accordingly, the present systematic review aimed to address the following research question, structured according to the PICOS framework: among adult healthcare professionals exposed to work-related stress (Population), do biofeedback-based interventions, including HRV-biofeedback, respiratory, electromyographic, or neurofeedback approaches (Intervention), compared with no intervention or alternative stress-management strategies (Comparison), improve mental health and psychological outcomes, such as stress, burnout, anxiety, depression, and resilience, and physiological indicators of autonomic regulation (Outcomes), across experimental and pre-post study designs (Study design).
The review focuses on their impact on psychological and mental health outcomes and physical well-being, as well as on the quality of life of HCWs. It is anticipated that effective biofeedback use may alleviate work-related stress and burnout symptoms, and that this review will offer an overview of the most promising clinical applications for this population.
Methods
Protocol and registration
The systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO; https://www.crd.york.ac.uk/prospero) under protocol number CRD42024544687. The study was conducted in accordance with PRISMA guidelines and the PICOS framework (37).
Eligibility criteria
The eligibility criteria followed the PICOS model (Population, Intervention, Comparison, Outcome, Study design): Adults (≥18 years, any gender) employed in the healthcare sector, including healthcare managers with high-level responsibilities, healthcare assistants, nurses, and physicians. Intervention: Biofeedback training (e.g., HRV-BF, EMG, Neurofeedback). Studies combining biofeedback with other interventions were included if the specific effect of biofeedback could be isolated. Interventions not based on biofeedback were excluded; Comparison: No restrictions applied; Outcomes: Mental Health and Psychological outcomes (stress, burnout, anxiety, depression, psychosomatic symptoms, pain, work well-being, efficiency, job satisfaction, resilience) and physiological outcomes (HRV, heart rate, blood pressure, skin conductance, salivary cortisol); Study design: Pre–post studies, feasibility studies, quasi-experimental studies, controlled studies, and randomized controlled trials. Only published studies with accessible full texts were considered.
Exclusion criteria included: participants who were not healthcare professionals or students, interventions other than biofeedback, duplicate records, irrelevant studies (i.e., not addressing stress-related or psychosomatic symptoms), and studies unavailable despite attempts to contact the corresponding author. No restrictions were applied regarding the time frame or language of publication.
Information sources
The literature search was conducted between December 2023 and January 2024 across the following databases: PubMed/MEDLINE, Cochrane Library, Embase, and PsycINFO.
Gray literature was searched using ProQuest (38). References of retrieved articles, relevant studies, and systematic reviews were examined to identify additional potentially eligible studies. Five additional studies were identified through other sources, of which two were deemed eligible.
When studies were not available, the corresponding authors were contacted directly. The last search was in January 2024 (Table 1).
Search strategy
The search strategy included the following keywords: Biofeedback, Self-regulation, Well-being, Work-related stress, Healthcare professionals, job stress, burnout, occupational health, workload, job satisfaction, occupational. These were combined to construct specific search strings. The search strategy combined controlled vocabulary terms and free-text keywords related to biofeedback, occupational stress, and healthcare professionals using Boolean operators (AND/OR). Search strings were adapted to the specific syntax and indexing systems of each database (e.g., MeSH terms for PubMed, Emtree terms for Embase), while maintaining conceptual consistency across sources. A complete example of the search strategy is provided in the Supplementary Material. In the supplementary document, we report an example of a complete string used in PubMed, one of the main search engines (as shown in Supplementary Table S1).
Study selection
Two reviewers (EC and AU) independently screened titles and abstracts using a standardized Excel form. Duplicate records were removed before screening. Potentially eligible studies were retrieved in full text and assessed independently by the same reviewers.
Discrepancies were resolved through discussion; when consensus was not reached, a third reviewer (GC) adjudicated.
The selection procedure is detailed in the PRISMA 2020 flow diagram (Supplementary Figure S1).
Data collection process
Data extraction was performed independently by two reviewers (EC and AU) using a piloted and standardized extraction sheet. Extracted data included: study characteristics (authors, year, country, study design, aim); population characteristics (type of healthcare workers, age, sample size); intervention characteristics (e.g., EMG-BF, HRV-BF, neurofeedback; duration, frequency, comparators); outcome domains, including: mental health and psychological outcomes (stress, burnout, anxiety, depression, psychosomatic symptoms, pain, job satisfaction, resilience, well-being, work efficiency); physiological outcomes (HRV parameters, heart rate, blood pressure, skin conductance, salivary cortisol); measurement instruments; main results (direction and magnitude of effects, p-values, effect sizes).
Any discrepancies in the extracted data were discussed among reviewers and, when needed, verified by the supervisor (GC). Authors were not contacted for missing data, and no automation tools were used for the extraction process. Information on funding sources or conflicts of interest of the primary studies was not collected. Assumptions were minimized and made only when judged methodologically reasonable based on the available information.
The complete list of extracted data items is reported in Supplementary Table S1.
Risk of bias assessment
The risk of bias was evaluated at the study level using established and widely accepted methodological guidance for assessing internal validity, which were chosen beforehand based on the study design (44). Randomized controlled trials (RCTs) (32, 33, 39, 40, 43) were evaluated using a domain-based risk of bias approach, whereas non-randomized and pre-post studies (27, 29–31, 34, 41, 42) were evaluated using the ROBINS-I tool (45). This tool examines biases from confounding factors, participant selection, intervention classification, deviations from planned interventions, missing data, outcome measurement, and selective reporting.
For RCTs, risk-of-bias assessments across domains were visually summarized with the robvis package in R (version 3.0), used solely for graphical display (see Supplementary Materials). The overall risk of bias was classified as low, moderate (some concerns), or high, depending on the number and severity of domains rated as at risk.
Of the 12 included studies, 5 RCTs (32, 33, 39, 40, 43) exhibited a moderate risk of bias. Conversely, all non-randomized and pre–post studies (27, 29–31, 34, 41, 42) raised significant methodological issues, mainly due to uncontrolled confounding factors, reliance on self-reported outcomes, and inadequate handling of missing data.
Summary measures
Principal summary measures included mean differences, percentage improvements, p-values, standardized effect sizes (e.g., Cohen’s d), and physiological indicators such as HRV and salivary cortisol.
Synthesis of results
A synthesis table (Supplementary Table S2) was created that includes study characteristics, interventions, outcomes, and main results. Due to heterogeneity in design, outcome measures, and reported data, a meta-analysis was not feasible. Therefore, results were synthesized qualitatively.
Several methodological weaknesses across the included studies warrant explicit consideration, including small sample sizes, uncontrolled pre–post designs, and the frequent absence of active comparators. In addition, some studies reported null or discordant findings, particularly with respect to subjective stress measures or selected physiological outcomes, underscoring the inconsistency of effects across domains.
Comparators varied substantially, ranging from no intervention to stress diaries, relaxation exercises, or alternative behavioral activities, further contributing to heterogeneity and limiting cross-study comparability.
This methodological diversity posed significant challenges for synthesis, requiring a structured narrative approach that prioritized critical appraisal and contextual interpretation over formal aggregation. The synthesis process therefore involved balancing heterogeneous designs, outcomes, and risk-of-bias profiles to provide an accurate and cautious representation of the current evidence base.
Patterns of consistency and variation among interventions and outcomes were described narratively. No subgroup or sensitivity analyses were conducted, and no missing data were imputed.
Results
Descriptions of studies
Initially, 3,564 records were identified via electronic database searches, and 5 more via citation searches. After removing 207 duplicates and excluding 3,328 non-matching records during screening, four full texts couldn’t be retrieved, and three more articles failed to meet the criteria. In total, 12 studies were included (Supplementary Figure S1).
The analysis of the studies showed that the Biofeedback interventions examined were diverse: HRV-BF (29, 30); RSA-BF (39); CBKF (33); EEG-BF (42); Biofeedback using Smartphone (SDBT) (27); Biofeedback through the Healing Rhythms meditation program (34); Wearable posture Biofeedback (43); and finally, Biofeedback combined with other techniques (30–32, 40, 41). All studies aimed to improve HCWs health and reduce stress symptoms through structured programs. Five studies (32, 33, 39, 40, 44) conducted randomized controlled trials (RCTs), comparing an experimental group with a control group; two (27, 31) were quasi-experimental studies, one of which was a pilot study; and five (29, 30, 34, 41, 42) were pre-post intervention studies with or without a control group. All studies aimed to improve HCWs’ health and reduce stress symptoms through structured programs. They mainly involved workers experiencing stress-related symptoms. Methodological quality was mostly moderate to low, due to small sample sizes and lack of blinding.
Mental health and psychological measures
Stress, Job Satisfaction, and Burnout: Eight studies (29–34, 41, 42) used various tools to assess perceived stress, including the Perceived Stress Scale (PSS) (29–32, 41), the Stress Symptoms Scale (SSS) (33), the Depression Anxiety Stress Scale (DASS-21) (42), the Visual Stress Scale (VSS) (42), and the Linear Analogue Self-Assessment (LASA) (34).
Across these studies, significant reductions in stress were consistently observed following biofeedback interventions (p <.05) (30, 32, 34, 41, 42). One study (33) reported no change in self-perceived stress but a clear physiological improvement in HRV. The other (27) found that smartphone-delivered biofeedback (SDBT) produced stronger reductions in occupational stress (p = .013) than both traditional biofeedback and the control condition. One study (40) observed that Qigong combined with biofeedback significantly reduced emotional exhaustion among nurses, though other burnout dimensions remained unchanged. Job satisfaction was examined in a few studies: one (31) noted near-significant post-intervention improvements (p = .06–.07).
Anxiety, Depression, and Well-Being: The studies evaluated anxiety using the State-Trait Anxiety Inventory (STAI-Y) (34, 39) or the DASS-21 (42). Significant decreases were observed in two studies (34, 39) for trait anxiety (p = .004) and for state and trait anxiety (p = .03). One author (42) reported lower anxiety and stress scores following neurofeedback training, indicating improved mood and emotional regulation.
Depression was assessed with the Centre for Epidemiological Studies Depression (CES-D), the PHQ-9, or the DASS-21 in three studies (27, 30, 42), all of which reported significant post-intervention reductions.
Combined programs, biofeedback plus breathing, mindfulness, or relaxation, showed the strongest and most consistent psychological improvements (27, 30, 34).
Two studies using the 36-Item Short Form Health Survey (SF-36) (34, 39) reported increases in vitality, social functioning, and perceived health. At the same time, another author (29) found enhanced resilience, interoceptive awareness, and self-care using an HRV biofeedback smartphone app.
Collectively, these results highlight biofeedback’s role in reducing stress, anxiety, and depressive symptoms while fostering emotional balance and well-being.
Pain and Physical Functioning: The RCT (43) demonstrated that wearable postural biofeedback significantly reduced low-back pain and improved spinal alignment (p <.01), suggesting promising applications for musculoskeletal and occupational health in sedentary healthcare staff.
Physiological measures
The HRV emerged as the primary indicator of parasympathetic regulation. Several studies reported a significant increase in HRV after biofeedback training (29, 30, 33, 39), signaling improved autonomic balance and cardiorespiratory coherence. These results align with enhanced self-regulation and relaxation abilities, often achieved through slow, diaphragmatic breathing techniques (27, 30, 33, 39). Findings regarding HR, BP, and salivary cortisol were less consistent: multiple studies showed no significant changes (33, 39, 42), although one reported a significant decrease in cortisol levels in the intervention group (32). Similarly, EEG alpha activity showed no notable differences (42). Conversely, some studies found a significant decrease in skin conductance level (SCL) and an increase in skin temperature, indicating reduced sympathetic arousal (39, 41). Discrepancies appeared between physiological and subjective measures: for example, certain studies reported improvements in HRV without corresponding reductions in perceived stress (33), while others noted psychological benefits without changes in biomarkers (42). Lastly, one study demonstrated that wearable postural biofeedback notably improved posture and alleviated low back pain (43), highlighting the potential of this technology for musculoskeletal and postural modulation.
In summary, the physiological findings reveal a consistent pattern across studies: increased HRV, decreased SCL and respiratory rate, and improved autonomic balance, all of which support the psychological benefits associated with biofeedback interventions.
Quality assessment
The RCTs showed a moderate-to-low risk of bias, with appropriate randomization procedures and outcome assessments supporting the reliability of their findings (32, 33, 39, 40, 43). In contrast, pre-post and non-randomized studies were at serious risk of bias, primarily due to uncontrolled confounding, reliance on self-reported measures, and inadequate handling of missing data (27, 29–31, 34, 41, 42). The absence of control groups and formal randomization further limits causal inferences, while the use of subjective outcomes without blinding may have increased response bias. Additionally, incomplete reporting on attrition and data management raises concerns about the generalizability of results. Despite these methodological weaknesses, most studies provided clear descriptions of interventions, reported outcomes transparently, and offered preliminary but promising evidence that biofeedback can enhance well-being and stress regulation among HCWs.
Discussion
This systematic review shows that biofeedback interventions, especially HRV-BF and RSA-BF, have potential in supporting healthcare professionals’ psychophysiological well-being. In the included studies, these interventions were linked to improved parasympathetic regulation and reductions in perceived stress, anxiety, and depressive symptoms, helping achieve greater emotional balance (31–33, 39, 41–43).Although effect sizes and outcome measures varied, the direction of findings was consistent with previous evidence showing that biofeedback may help mitigate chronic stress and enhance resilience across both clinical and non-clinical populations.
Furthermore, interventions that integrated biofeedback with slow-breathing, mindfulness, or relaxation techniques tended to produce more stable and lasting benefits than standalone biofeedback protocols (27, 30, 33, 39), as reported by the respective study authors.
The observed improvements can be interpreted within contemporary psychophysiological models, in which enhanced HRV and respiratory synchrony promote greater vagal activation, reduced sympathetic arousal, and improved emotional self-regulation.
Taken together, these converging psychological and physiological effects suggest that biofeedback may represent a promising avenue for strengthening resilience and mitigating the burden of occupational stress in healthcare environments.
Quality and limitations of evidence
Although the results are promising, the quality of the available evidence remains limited. Only five out of twelve studies were randomized controlled trials (RCTs), while the remainder employed pre–post or quasi-experimental designs without control groups. Sample sizes were generally small and mainly composed of nursing professionals, limiting representativeness. Mental Health and Psychological outcomes were measured with heterogeneous tools, such as the PSS, the DASS-21, and the VSS, which reduces comparability across studies. Importantly, the majority of included studies were characterized by small sample sizes and uncontrolled pre-post designs, which substantially limit internal validity and preclude robust causal inference. While the direction of findings appears broadly consistent, the current evidence should be interpreted as preliminary and hypothesis-generating rather than confirmatory. Accordingly, biofeedback interventions cannot yet be considered as having established efficacy in this population, underscoring the need for adequately powered randomized controlled trials. Furthermore, some studies lacked follow-up evaluations or objective physiological measures, while others relied solely on self-reported outcomes (29, 42, 43), increasing the risk of bias. A further limitation of this review is the absence of a formal assessment of the certainty of evidence, such as the GRADE approach. Given the substantial heterogeneity in study designs, interventions, and outcome measures, applying a standardized certainty framework was not methodologically appropriate. Consequently, conclusions should be interpreted as indicative rather than definitive, reinforcing the need for larger, well-designed randomized trials with standardized outcomes. The generalizability of the present findings is further constrained by the predominance of nursing populations and the limited representation of other healthcare professionals, such as physicians, allied health workers, and administrative staff. Moreover, the scarcity of multicenter studies restricts the external validity of the evidence across diverse healthcare systems and organizational contexts. It should be acknowledged that the limited number of methodologically acceptable studies and their pronounced heterogeneity required a narrative systematic review approach rather than a fully standardized systematic synthesis. Although this methodological framing is not explicitly reflected in the title, it was deliberately adopted to ensure coherence with the scope and structure of the available evidence. This choice prioritizes transparency and critical appraisal over formal aggregation, allowing a balanced interpretation of findings while avoiding overstatement of evidentiary strength. Future research should prioritize multicenter designs and more heterogeneous professional samples to enhance the applicability of biofeedback interventions in occupational health settings.
Strengths and methodological considerations
Although a meta-analysis was not possible due to data heterogeneity and small sample sizes, the narrative synthesis provided an integrated understanding of both Mental Health and Psychological and physiological outcomes. A major strength of this review is that it represents the first systematic synthesis specifically focused on the application of biofeedback interventions among healthcare professionals, offering a comprehensive overview of both psychological and physiological outcomes.
A key strength is the inclusion of various biofeedback modalities, offering a comprehensive overview of emerging applications, from HRV and respiration-based protocols to neurofeedback and wearable devices. However, the lack of long-term follow-up data limits the ability to determine whether participants maintained their self-regulation skills over time.
Implications for clinical practice and occupational health
The interpretation of the findings is limited by substantial heterogeneity across study designs, biofeedback modalities, outcome measures, small sample sizes, and the predominance of nursing staff, which restricts the generalizability of the results. Despite these limitations, the evidence suggests that biofeedback is a valuable, non-invasive, and feasible strategy for managing work-related stress and promoting resilience among HCWs. Its integration into psychoeducational and training programs could reduce burnout, improve job satisfaction, and enhance the quality of patient care. The emergence of portable and smartphone-based biofeedback systems expands accessibility and practicality in dynamic healthcare environments, where traditional psychological interventions are challenging to implement. Such interventions could be incorporated into occupational health and psychosocial risk prevention programs, in line with European directives and the Italian Legislative Decree 81/2008 on workplace safety. Future research should adopt a more standardized, integrative approach, combining Mental Health and Psychological, physiological, and occupational measures to capture the multidimensional impact of biofeedback fully. Large, multicenter randomized controlled trials are needed to verify the stability of results over time and to compare the effectiveness of different biofeedback modalities, such as HRV-BF, RSA-BF, neurofeedback, and postural feedback.
Although the methods and results of this review are coherent and internally consistent, the overall level of evidence supporting biofeedback interventions in healthcare professionals remains limited. The predominance of small, heterogeneous, and non-controlled studies necessitates a conservative interpretation, in which observed benefits should be viewed as indicative signals rather than definitive evidence of effectiveness. Accordingly, conclusions are framed to emphasize uncertainty and the need for more rigorous, adequately powered trials.
An important practical consideration emerging from this review concerns the context in which biofeedback interventions were delivered. Most included studies implemented training protocols outside regular working hours or in controlled settings, often as scheduled sessions or self-directed home practice. Only a limited number of interventions approximated real-life workplace application, such as brief sessions integrated into the workday or the use of portable or wearable biofeedback devices; this distinction is relevant for interpreting feasibility and ecological validity, as interventions delivered outside working hours may introduce selection bias and limit scalability. Future research should prioritize the evaluation of biofeedback methods embedded within routine clinical workflows to better reflect real-world occupational settings.
Equally important is developing transparent, replicable intervention frameworks, ensuring that methodologies, training protocols, and adherence-monitoring procedures are clearly documented. This will enable future studies to refine biofeedback applications and establish evidence-based programs suitable for clinical and occupational health settings. Such improvements are crucial to establishing a gold-standard biofeedback protocol for healthcare professionals, providing evidence-based strategies to prevent burnout and improve overall well-being in clinical and occupational environments.
Conclusion
Evaluating the effectiveness of Biofeedback interventions is essential for developing standardized programs to manage work-related stress and burnout, ultimately enhancing organizational climate and HCWs’ confidence in managing their own health. A structured intervention framework is required to promote positive perceptions of care, support self-satisfaction in the work environment, and enable individuals to function as competent and efficient professionals.
Despite encouraging findings, the evidence remains preliminary due to methodological heterogeneity and the lack of standardized protocols or long-term follow-up. Large, multicenter randomized controlled trials are urgently needed to establish robust, reproducible intervention models for implementation in occupational health settings.
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