Effectiveness of a fully immersive virtual reality-based therapeutic exercise programme with altered visual feedback in patients with fibromyalgia: A study protocol for a randomised controlled trial.

Effectiveness of a fully immersive virtual reality-based therapeutic exercise programme with altered visual feedback in patients with fibromyalgia: A study protocol for a randomised controlled trial - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice PLoS One . 2026 Jun 4;21(6):e0348346. doi: 10.1371/journal.pone.0348346 Effectiveness of a fully immersive virtual reality-based therapeutic exercise programme with altered visual feedback in patients with fibromyalgia: A study protocol for a randomised controlled trial Carlos Salvador-Huerta 1 , Jaime Jordán-López 1 , Pedro Azanon-Nogueira 1 , Celia García-Lucas 1 , Juan J Amer-Cuenca 2, * , Juan Francisco Lisón 1, 3 Editor: Hui-Juan Cao 4 Author information Article notes Copyright and License information 1 Department of Biomedical Sciences, School of Health Sciences, Universidad Cardenal Herrera-CEU, CEU Universities, Alfara del Patriarca, Valencia, Spain 2 Department of Physiotherapy, School of Health Sciences, Universidad Cardenal Herrera-CEU, CEU Universities, Alfara del Patriarca, Valencia, Spain 3 CIBER of Physiopathology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain 4 Beijing University of Chinese Medicine, CHINA Competing Interests: The authors have declared that no competing interests exist. ✉ * E-mail: juanjoamer@uchceu.es Roles Carlos Salvador-Huerta : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing Jaime Jordán-López : Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Writing – review & editing Pedro Azanon-Nogueira : Investigation, Writing – review & editing Celia García-Lucas : Investigation, Writing – review & editing Juan J Amer-Cuenca : Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing Juan Francisco Lisón : Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing Hui-Juan Cao : Editor Received 2025 Nov 21; Accepted 2026 Apr 7; Collection date 2026. © 2026 Salvador-Huerta et al This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice PMCID: PMC13235889  PMID: 42241401 Abstract Fibromyalgia (FM) is a chronic condition characterized by widespread pain, fatigue, and diverse physical and psychological symptoms that significantly impair daily functioning. Latest guidelines advocate for a comprehensive approach to FM management, emphasizing patient education, therapeutic exercise, pharmacological treatments, and psychotherapy. Although exercise remains the primary non-pharmacological strategy supported by robust evidence, its clinical implementation faces several limitations, including poor adherence, fear of movement, pain catastrophizing and proprioceptive deficits. Immersive virtual reality (IVR) has recently emerged as a promising adjunctive tool that not only addresses these limitations but also provides additional therapeutic benefits. Specifically, preliminary studies suggest that incorporating visual feedback manipulation through IVR can effectively modify movement perception, potentially enhancing clinical outcomes. This randomised controlled trial aims to assess the efficacy of a fully immersive virtual reality (FIVR)-based therapeutic exercise programme designed to induce implicit visual illusions, making participants perceive less movement than is performed during structured resistance exercises. Eighty participants diagnosed with FM will be recruited and randomly allocated to either the experimental group, which will engage in a structured resistance exercise regimen combined with FIVR, or the control group, performing identical exercises without FIVR. The primary measure will be the impact of FM on daily life, evaluated using the Revised Fibromyalgia Impact Questionnaire. Secondary outcomes include fatigue, sleep quality, FM symptom severity, health-related quality of life, psychological factors, central sensitization, body perception distortion, lower limb strength and handgrip strength, functional mobility, lumbar range of motion, influence of modified visual feedback, behavioural regulation during exercise and overall physical activity levels. This study aims to provide robust evidence regarding the potential benefits of integrating FIVR with therapeutic exercise. Findings could support FIVR as a valuable clinical innovation, potentially yielding superior improvements in daily functioning, physiological outcomes, and psychological well-being compared to traditional exercise interventions alone. ClinicalTrials.gov ( NCT06948500 ). URL: https://clinicaltrials.gov/ct2/show/NCT06948500 . Introduction Fibromyalgia (FM) is a condition of unknown etiology, primarily characterized by the presence of chronic (>3 months) and widespread pain affecting various regions of the body [ 1 ]. Although widespread pain is the most distinctive clinical feature, FM is a complex, polysymptomatic disorder that includes other core symptoms such as fatigue and sleep disturbances [ 2 ]. In addition to these cardinal symptoms, individuals often experience a range of other symptoms and/or dysfunctions, including cognitive impairments [ 2 ], regional pain syndromes, autonomic dysfunction [ 3 ], psychiatric symptoms and hypersensitivity to external stimuli [ 1 – 3 ]. FM prevalence significantly varies by diagnostic criteria, remaining underrecognized clinically [ 4 ]. Despite this, FM is the second most common rheumatic condition after osteoarthritis [ 1 , 4 ], with a global prevalence of 2%–8% [ 1 , 5 ]. FM substantially impairs quality of life and functional capacity, significantly increasing healthcare demand and costs [ 6 ]. Annual costs associated with FM have been estimated at €12,993 million in Spain [ 5 ]. In the United States, an estimated economic burden of over US$20 billion annually has been reported [ 7 ]. Indirect societal costs, primarily from lost productivity, are also considerable; approximately 24.3% of patients stop working within five years post-diagnosis [ 8 ]. Given FM’s clinical complexity and multisystem involvement, a multidisciplinary approach is essential. The latest European League Against Rheumatism (EULAR) guidelines recommend four main pillars for its management: patient education, therapeutic exercise, pharmacological treatment, and psychotherapy [ 9 ]. Non-pharmacological strategies should be prioritized, particularly therapeutic exercise remains the first-line intervention, with strong evidence supporting its benefits [ 9 ]. High-frequency, high-intensity transcutaneous electrical nerve stimulation (TENS) has also shown its efficacy in pain reduction being an option for pain relief [ 10 ]. Although therapeutic exercise has proven to be an effective intervention in the management of FM, its clinical implementation presents significant limitations. One of the main barriers is poor adherence due to low motivation and high dropout rates, largely attributable to the lack of individualized programmes and the monotony of prescribed routines [ 11 ]. In addition, in patients with FM and other disorders characterized by central sensitization, further limiting factors such as kinesiophobia and pain catastrophizing have been identified [ 12 ]. Fear of movement, driven by the belief that physical activity will exacerbate pain, leads to avoidance behaviors that contribute to both the maintenance and progression of disability [ 13 ]. In this context, kinesiophobia functions not merely as a consequence of pain but as an active perpetuating factor. Maladaptive beliefs about pain, particularly when combined with high levels of kinesiophobia, play a critical role in symptom exacerbation [ 14 ]. Recent evidence in individuals with chronic low back pain has shown a similar pattern, with higher pain intensity correlating positively with kinesiophobia, catastrophising and anxiety, and catastrophising emerging as an independent predictor of kinesiophobia [ 15 ]. Considering these challenges, innovative therapeutic approaches such as virtual reality (VR) have gained increasing attention. When integrated with therapeutic exercise, VR has shown promise in the management of chronic pain [ 16 ]. Recent studies suggest that VR may modulate both pain perception and kinesiophobia through mechanisms such as sensory immersion, cognitive distraction, and multisensory stimulation, positioning it as a valuable tool for pain management [ 17 , 18 ]. VR technologies can be classified into three categories according to their level of immersion: non-immersive, semi-immersive, and fully immersive systems [ 19 ]. The degree of immersion directly influences both the user’s perceptual and psychological experience, as well as their sense of embodiment, that is, the perceived integration of the self with a virtual avatar within the simulated environment [ 20 ]. To date, most VR interventions for FM treatment have employed non-immersive systems [ 21 ]. Although offering limited immersion, these platforms demonstrate promising effects in reducing FM’s impact on daily life by improving quality of life, symptom burden (fatigue, anxiety, depression), functional capacity and improving adherence [ 21 , 22 ]. In contrast, Immersive virtual reality (IVR) has demonstrated therapeutic advantages over non-immersive systems, primarily due to its capacity to enhance embodiment and deliver real-time multisensory feedback [ 19 , 23 ]. These mechanisms appear to play a pivotal role in clinical outcomes, particularly in pain modulation. Higher levels of immersion are associated with greater attentional distraction and significant reductions in perceived pain [ 24 ]. Although limited, existing evidence in FM patients is promising; a recent study integrating IVR with exercise reported significant improvements in pain, kinesiophobia, fatigue, physical activity, and mental quality of life [ 25 ]. These findings suggest that increased embodiment and immersion may potentiate the effects of exercise by enhancing cognitive engagement and attenuating central sensitization. Moreover, visual manipulations in IVR have shown promise in promoting greater movement without increasing perceived effort or pain. Chen et al. [ 26 ] demonstrated that patients with chronic neck pain, when exposed to altered visual feedback suggesting they were moving less than they actually were, significantly increased their range of motion (ROM), resulting in clinical improvement. Furthermore, these findings are supported by previous studies, in which the use of fully immersive virtual reality (FIVR) with manipulated visual feedback led to significant reductions in pain, disability, and kinesiophobia in patients with chronic low back pain [ 27 , 28 ]. Therefore, FIVR emerges as an innovative therapeutic approach, not only for modulating pain perception and related FM symptoms but also for improving patients’ functionality [ 25 ]. Although previous research has extensively investigated VR in non-immersive contexts, mainly through exergames and post-exercise interventions, the therapeutic impact of simultaneously integrating FIVR with exercise remains underexplored. Moreover, the clinical effects of modifying visual feedback during FIVR-based therapeutic exercise programmes for FM patients have yet to be thoroughly examined. To our knowledge, no previous studies have evaluated the impact of a therapeutic exercise programme based on FIVR that incorporates altered visual feedback on clinical outcomes in patients with FM. In this context, the present protocol describes a research project aimed at assessing the efficacy of an FIVR system that modulates visual input to create the illusion of reduced bodily movement relative to actual motion during exercise training. The experimental group (EG) will engage in an exercise regimen delivered within an immersive virtual environment featuring visual manipulations intended to induce the perception of underperformance. This approach has been shown to promote a greater ROM resulting in clinical improvement. The control group (CG) will perform the same exercise protocol without the integration of FIVR. This controlled, randomised, parallel clinical trial study will provide evidence regarding the efficacy of FIVR and visual feedback manipulation as an innovative strategy in the management of FM, highlighting their potential to improve the condition’s impact on patients’ daily lives. Additionally, it will investigate potential improvements in physiological, psychological, and physical outcomes. Materials and methods Study design This is a randomised, controlled, two-arm, parallel-group, superiority clinical trial with 1:1 allocation. The protocol follows SPIRIT 2025 and SPIRIT-Outcomes guidance [ 29 ]. The participant timeline is shown in Fig 1 . Fig 1. SPIRIT schedule of enrolment, interventions, and assessments (t0 = pre-intervention baseline; t1 = immediately after the 6-week programme; Sessions 1–12 = two sessions per week for six weeks). Open in a new tab Ethical approval and registration This study complies with the Declaration of Helsinki. The protocol was approved by the Research Ethics Committee of CEU Cardenal Herrera University (CEEI25/643). Participation is voluntary, and written informed consent will be obtained before any study procedure. Participants may withdraw from the study at any time without providing a reason and without any penalty or loss of access to current or future healthcare services at the “Asociación Valenciana de Afectados de Fibromialgia” (AVAFI) or any other healthcare facility. Eligible patients will be informed of objectives, procedures, potential risks, and expected benefits before starting the exercise programme. The protocol is registered at ClinicalTrials.gov ( NCT06948500 ). Protocol version v1.0 (18 September 2025. Personal data will be collected by clinical research coordinators and stored in a secure, password-protected database with access limited to authorised personnel. Participants: Recruitment and eligibility criteria This study will enrol 80 participants. Inclusion criteria will consist of adult individuals diagnosed with FM according to any of the classification criteria established by the American College of Rheumatology (ACR), including the 1990, 2010, 2011 or 2016 criteria [ 30 – 33 ]. Additional requirements for meeting the inclusion criteria include the ability to communicate effectively with the research staff, provision of written informed consent demonstrating a clear willingness to participate, and a self-reported pain intensity score of ≥3 on an 11-point Numerical Pain Rating Scale (NPRS-11). Participants will be excluded if they meet any of the following criteria: (1) presence of comorbidities and/or symptoms that constitute a contraindication for FIVR and exercise-based interventions, including: (i) FIVR-related: history of epilepsy or photosensitive seizures; severe vestibular disorders/vertigo or marked susceptibility to motion sickness/cybersickness; acute symptoms increasing the risk of adverse VR effects at the time of a session (e.g., severe headache/migraine, pronounced dizziness, nausea/vomiting, or disorientation); or severe uncorrected visual impairment preventing safe headset use; and (ii) exercise-related: any unstable or acute cardiopulmonary condition (e.g., unstable angina, uncontrolled arrhythmias, decompensated heart failure, acute myocarditis/pericarditis, acute pulmonary embolism), uncontrolled severe hypertension, or acute systemic illness/fever; (2) presence of medical conditions likely to interfere with outcome assessment or interpretation, such as severe auditory, perceptual, or sensory disorders, or concurrent rheumatological diseases (e.g., rheumatoid arthritis or osteoarthritis); (3) use of medications that are likely to materially interfere with the assessment or interpretation of study outcomes; could potentially affect study results; (4) (i) engagement in another therapeutic physical activity programme during the intervention period; and/or (ii) initiation of new therapies or substantial modifications to existing therapies (including medication or physical therapy) during the study period, which could materially affect study outcomes throughout the study; (5) inability to attend the centre where the interventions will take place. Participant recruitment strategies will include community outreach initiatives, partnerships with primary care centres, and targeted digital promotion through institutional social media platforms, aimed at ensuring timely and adequate enrolment to achieve the planned sample size. Recruitment began on 5 May 2025. As of the time of submission, the study is currently in the recruitment phase. We anticipate the following timeline: A) Participant recruitment will be completed by 10 February 2027; B) Data collection will be completed by December 2027; and C) Results are expected to be published in 2028. Randomisation and blinding Participant recruitment and written informed consent will be obtained by physicians from the “Lifestyle and Health” research group at CEU Cardenal Herrera University. Randomisation will be conducted by an independent researcher using a computer-generated 1:1 sequence with stratified permuted blocks (block size = 2) within strata defined by sex (male/female) and baseline FM severity level (two FIQR-based severity levels derived from clusters 1–2 vs clusters 3–4) [ 34 ]. Baseline FIQR will be completed prior to randomisation and scored immediately using item-level responses. FIQR severity level will be assigned at baseline according to Pérez-Aranda et al. using a prespecified scoring tool (e.g., a pre-programmed spreadsheet/REDCap instrument) to enable real-time classification. Stratification by sex was chosen because FM is substantially more prevalent in women than in men and, given the planned sample size, chance imbalances could otherwise occur. Stratification by FM severity was prioritised to ensure comparability in baseline symptom burden, an important prognostic factor, and to minimise potential shifts in severity distribution arising from recruitment through a single patient association. Given the target sample size (N = 80) and the expected predominance of women in fibromyalgia, we anticipate that most participants will fall within the female strata, while male strata may be comparatively small. We do not apply a minimum-size rule for strata; if recruitment within a stratum is sparse, the prespecified sequence will still be applied, and final allocation counts by arm overall and within strata will be reported. Randomisation performance will be monitored descriptively by periodically summarising allocation counts by arm overall and within strata (without examining outcomes), to identify any unexpected allocation irregularities. Allocation will be concealed with sequentially numbered, opaque, sealed envelopes (SNOSE) prepared off-site with tamper-evident seals and carbonless copies. After baseline assessment, site staff will open the next envelope in numerical order to assign the EG or CG. The randomisation list will be held by an independent data manager with no role in enrolment or assessment. Outcome assessors and the trial statistician will be blinded to group allocation. Participants and intervention providers cannot be blinded due to the nature of FIVR and will be instructed not to disclose allocation during assessments. Assessments will be performed by personnel without access to scheduling or allocation logs; groups will be labelled A/B until database lock. Unblinding will occur only to address serious safety concerns or protocol deviations with safety implications, following written authorisation from the Principal Investigator; all unblinding events will be logged. The randomisation list will remain with the independent data manager. Intervention Participants will complete a six-week therapeutic exercise programme. The only between-group difference is the use of FIVR-based altered visual feedback. All sessions will take place at AVAFI (Valencia, Spain). Frequency: twice weekly, 60 min. Rooms will be climate-controlled, private, and equipped with HTC Vive Pro and exercise implements. All sessions will be delivered by a single licensed, experienced physiotherapist trained in the intervention manual and FIVR safety. Each session comprises: Warm-up (5 min): low-intensity mobility and breathing exercises for cervical, lumbopelvic, and peripheral regions, without external load. Main phase (50 min): multi-joint exercises for trunk, upper, and lower limbs in standing, seated, supine, and prone positions. Movements synchronised with breathing to enhance motor control. Intensity regulated by the Borg 6–20 scale targeting RPE 13–17. Sessions 1–5: bodyweight only; from session 6: progressive external loads. Per-exercise volume adjusted to rate of perceived exertion (RPE). At each session we will record NPRS-11, RPE, repetitions, and FIVR-related well-being. These session-level variables will be recorded solely to monitor session delivery and participant response and will not be included in the statistical analysis plan. Cool-down (5 min): static stretching of lumbar spine and lower limbs plus diaphragmatic breathing in supine. The EG follows the same 6-week, twice-weekly, 60-min programme using an FIVR (HTC Vive Pro) that alters visual/proprioceptive input to simulate reduced movement amplitude. The FIVR system applies two visual-proprioceptive illusions: ≈ 20% arm-shortening during flexion tasks and ≈10% underestimation of extension via a virtual bar, as detailed in S3 Appendix [ 27 , 28 ]. The CG follows the identical protocol without FIVR. Full operational details, session scripts, figures, and videos are provided in S3 Appendix . Intervention manual to enable replication. Concomitant care and interventions Participants will maintain usual care during the 6-week intervention. Permitted: stable pharmacotherapy for FM/comorbidities (≥4 weeks pre-baseline, unchanged until t1); over-the-counter analgesics as rescue per usual care; sleep-hygiene and self-management advice; normal daily physical activity not amounting to a structured programme. Prohibited (from 4 weeks pre-baseline to t1): initiation or dose changes of regular (non-rescue) prescribed analgesics, antidepressants, or anticonvulsants; new physiotherapy, TENS, acupuncture, massage, or other manual therapies; enrolment in structured exercise or VR programmes outside the trial. Participants will avoid rescue analgesics within 24 h before outcome assessments when clinically feasible. Medication use, including rescue analgesic intake and the timing of the last dose within the preceding 24 h, will be recorded at each assessment timepoint and summarised descriptively by group and timepoint. Harms and safety reporting Adverse events (AEs) will be screened at each session and summarised monthly. AEs will be assessed by the blinded outcome assessor and reviewed by the Principal Investigator monthly and ad hoc. Sessions will be paused or modified for moderate-to-severe cybersickness, presyncope, acute illness, any clinically significant AEs, or at investigator judgement. Participants may be discontinued for safety or major protocol violations. Any serious AEs will be reported to the Research Ethics Committee within 24 hours and will trigger an extraordinary safety review. No data monitoring committee is planned given the minimal-risk intervention; no interim analyses or early stopping rules are planned. All AEs will be systematically monitored, documented, and reported in the final dissemination of findings. Study-related AEs will be managed according to institutional procedures; no additional compensation is planned beyond applicable regulations. Outcomes and measurements Sociodemographic data (including participant sex, age, height, weight, BMI, educational level, physical activity level, employment situation, years since FM diagnosis, smoking status and medication intake) will be collected prior to the implementation of the intervention programme. Primary outcome. The primary outcome, FM-related functional impact, will be assessed using the FIQR [ 35 ]. This 21-item instrument evaluates physical function (0–30), overall impact (0–20), and symptom severity (0–50), yielding a total score from 0 to 100, with higher scores indicating greater impairment. The Spanish version demonstrates high internal consistency (α = 0.91–0.95), test–retest reliability (r = 0.82), and strong construct validity [ 35 , 36 ]. Widely considered the gold standard for evaluating FM-related disability [ 34 ], the FIQR has also been used to identify clinically meaningful subgroups through cluster analysis, supporting disease stratification and tailored interventions [ 34 ]. A change of ≈14% in the total FIQR score has been established as the minimum clinically important difference (MCID) for detecting meaningful improvement [ 37 ]. Secondary outcomes. Fatigue will be assessed using the Spanish version of the Multidimensional Fatigue Inventory (MFI-20) [ 38 ], a validated 20-item self-report instrument measuring five dimensions: general and physical fatigue, reduced activity and motivation, and mental fatigue. Items are scored on a 5-point Likert scale, yielding total scores from 20 to 100, with higher scores indicating greater fatigue. The MFI-20 has shown strong test–retest reliability and validity in Spanish populations, particularly in FM, supporting its clinical and research utility [ 38 ]. Sleep quality will be assessed using the validated Spanish version of the Pittsburgh Sleep Quality Index (PSQI), a 19-item self-report measure evaluating sleep over the past month across seven components [ 39 ]. Total scores range from 0 to 21, with higher scores indicating poorer sleep. The Spanish PSQI demonstrates strong reliability (α = 0.805; test–retest r = 0.773, p < 0.001). It is widely used in FM research as a reliable measure of sleep dysfunction [ 40 , 41 ]. FM symptoms will be assessed using the 6-item Fibromyalgia Survey Diagnostic Criteria (FSDC), aligned with the 2016 ACR revision [ 33 ]. It includes the Widespread Pain Index (WPI; 0–19) and the Symptom Severity Scale (SSS; 0–12), evaluating pain distribution and core symptoms (fatigue, nonrestorative sleep, cognitive issues), plus additional somatic symptoms. The total score (0–31) reflects FM symptom burden, with higher scores indicating greater severity. The FSDC has been used in RCTs with Spanish populations, supporting its validity in this context [ 42 ]. Health-related quality of life will be assessed using the Spanish EQ-5D-5L, a validated self-reported tool measuring five dimensions on five severity levels [ 43 ]. Responses yield an index score (−0.5 to 1.0), with lower values indicating worse health, calculated using the Spanish value set [ 43 ]. In addition, patients will rate their perceived health-related quality of life using the EQ-5D-5L visual analogue scale (EQ VAS, 0–100), where 0 represents the worst and 100 the best imaginable health state. Widely used in FM research [ 44 ], a change of 0.03–0.07 in the index is generally considered clinically meaningful [ 45 ]. Anxiety and depression will be assessed using the Spanish version of the Hospital Anxiety and Depression Scale (HADS) [ 46 ], a 14-item self-report tool comprising two subscales, HADS-Anxiety and HADS-Depression, each scored 0–21. Items are rated on a 4-point Likert scale (0–3), with higher scores indicating greater severity. Scores of 0–7 are normal, 8–10 suggest possible cases, and ≥11 indicate clinically significant symptoms [ 47 ]. The scale has shown high internal consistency and strong validity in both general and FM populations [ 48 ]. Pain catastrophizing will be assessed using the Spanish version of the 13-item Pain Catastrophizing Scale (PCS) [ 49 , 50 ], which evaluates the frequency of catastrophic thoughts and feelings related to pain across three subscales: rumination, magnification, and helplessness. Items are rated from 0 to 4, yielding a total score of 0–52, where higher scores indicate greater catastrophizing. The PCS has shown good psychometric properties and sensitivity to change [ 50 ]. A total score of >30 will be considered clinically significant [ 51 ]. Fear-avoidance beliefs will be assessed using the Spanish version of the 16-item Fear-Avoidance Beliefs Questionnaire (FABQ) [ 52 , 53 ], scored on a 7-point Likert scale (0–6), where higher scores indicate stronger avoidance beliefs. FABQ comprises two subscales: Work (FABQ-W; range 0–42) and Physical Activity (FABQ-PA; range 0–24), evaluating the perceived influence of occupational and physical activity on pain. The Spanish version has demonstrated excellent psychometric properties [ 53 ].The MCID for the FABQ-PA subscale is estimated at 6 points [ 54 ]. Kinesiophobia will be assessed using the Spanish version of the Tampa Scale for Kinesiophobia (TSK-11), an 11-item self-report measure of fear of movement and (re)injury [ 55 ]. Items are scored on a 4-point Likert scale (1–4), with total scores ranging from 11 to 44; higher scores indicate greater kinesiophobia. The TSK-11 has demonstrated strong psychometric properties and validity in Spanish chronic pain populations, including FM [ 55 , 56 ]. Central sensitization will be assessed using the Central Sensitization Inventory (CSI), a validated 25-item self-report measure [ 57 ]. Scores range from 0 to 100, with values ≥40 indicating the presence of a central sensitization syndrome (CSS), including FM [ 58 ]. Severity levels include subclinical (0–29), mild (30–39), moderate (40–49), severe (50–59), and extreme (60–100) [ 59 ]. The Spanish version demonstrates excellent test–retest reliability (ICC = 0.82–0.91) and is widely used in chronic pain research [ 60 , 61 ]. Quantitative sensory testing (QST) will be used to characterise central nociceptive processing through pressure pain threshold (PPT), temporal summation (TS) and conditioned pain modulation (CPM). Baseline PPTs will be assessed by algometry at two sites: the dorsal aspect of the distal phalanx of the index finger and a point 5 cm to the right of the L3 spinous process. PPT will be defined as the lowest pressure that elicits the first sensation of pain under standardised conditions and will be measured with an analogue Wagner algometer (Wagner Instruments, Greenwich, CT, USA) with a 1 cm² probe, applying pressure at 1 kg/cm²/s; three consecutive PPT values will be obtained at the index finger at 30-second intervals and their mean will be used as the test stimulus in the TS and CPM protocols [ 62 , 63 ]. After a 2-minute rest, TS will be evaluated by delivering 10 pressure pulses at the individual index-finger PPT (≈2 kg/s), while participants rate the pain intensity of the first and tenth pulses on the NPRS-11; TS will be calculated as the difference between these ratings, with positive values indicating increased pain facilitation [ 15 , 63 , 64 ]. Following a further 5-minute interval, CPM will be assessed by repeating the TS procedure in the presence of a conditioning stimulus induced by an occlusion cuff on the left arm, inflated at 20 mmHg/s until the first sensation of pain is reported, maintained for 30 seconds and then adjusted to a pain intensity of 3/10 on the NPRS; CPM will be defined as the difference between PPT during conditioning and baseline PPT at the index finger (CPM = PPT_post – PPT_pre), with positive values reflecting effective endogenous inhibition and lower or negative values indicating reduced inhibitory capacity [ 63 , 65 ]. Body perception distortion will be assessed using the Spanish FreBAQ-S [ 66 ], a validated 9-item scale (0–36) originally designed to evaluate altered body image and tactile awareness in chronic musculoskeletal pain [ 67 ]. A slightly adapted version replacing “back” with “body” will be used, as in prior FM studies [ 68 ]. FreBAQ-S shows solid psychometrics: unidimensional structure (CFI = 0.97; TLI = 0.96; RMSEA = 0.06), high internal consistency (α = 0.82), test–retest reliability (ICC = 0.78), and convergent validity with pain, disability, kinesiophobia, catastrophizing, and CS [ 66 ]. Lower limb strength and endurance will be assessed using the 30-Second Sit-to-Stand Test (STS-30), a functional test that evaluates an individual’s ability to repeatedly rise from a chair without using their arms over a 30-second period. The total number of repetitions performed within the time frame serves as an indicator of functional capacity, with higher scores reflecting better performance. The test has demonstrated high test–retest reliability (ICC = 0.84–0.96), and it has been validated in studies involving populations with FM and older adults [ 69 – 71 ]. Functional mobility will be assessed using the Timed Up and Go (TUG) Test, a widely used clinical tool for evaluating gait speed, dynamic balance, and overall functional capacity [ 72 ]. The test involves timing how long it takes the participant to rise from a chair, walk three metres, turn 180 degrees, return, and sit down again. It has demonstrated high test–retest reliability, with intraclass correlation coefficients (ICCs) of 0.935 using a manual stopwatch and 0.955 with an automated timer. Isometric quadriceps strength will be assessed using handheld dynamometry, a widely applied technique for quantifying neuromuscular function in individuals with FM [ 71 ]. A portable dynamometer will be positioned at the level of the ankle with the knee flexed at 90°, to measure the participant’s maximal voluntary isometric contraction (MVIC) of the quadriceps muscle [ 73 ]. Assessing quadriceps strength serves as a key indicator of lower-limb functionality and the risk of disability in this population. The test–retest reliability has been reported to range between 0.85 and 0.96 (ICC), and its use has been validated in previous studies, providing an objective measure of muscular response to exercise-based therapeutic interventions [ 73 ]. Handgrip strength will be measured with a Jamar hydraulic dynamometer following Hamilton et al. protocol [ 74 ]. Participants will sit with feet flat, shoulder adducted/neutrally rotated, elbow at 90°, and wrist/forearm neutral. Three maximal trials with verbal encouragement and ≥4-minute rests will be averaged. This reliable method is widely used in FM [ 70 , 71 ]. Lumbar flexion and extension ROM and the influence of modified visual feedback will be assessed under three experimental conditions. Each participant will perform three repetitions of each movement in: (i) a control condition without VR, (ii) an understated condition (E−), in which VR feedback portrays the participant’s movement as reduced relative to actual performance, and (iii) an overstated condition (E+), in which VR feedback presents movement as amplified compared with reality. Lumbar motion, including the flexion-evoked pain threshold (defined as the angle at which the participant first reports pain during forward bending), will be recorded in degrees using a 3-Space Fastrack electrogoniometer connected to a Windows 10-based computer. For each condition, the mean ROM across the three repetitions (absolute data) will be calculated. To account for inter-individual variability, ROM values in the E+ and E− conditions will also be expressed as a percentage of the control condition (relative data). The 3-Space Fastrack system is a validated electro-goniometer for assessing lumbar mobility in patients with low back pain; it uses two motion sensors placed over the T12 and S1 spinous processes and has demonstrated high reliability [ 75 , 76 ]. Behavioural regulation in exercise will be assessed with the Behavioural Regulation in Exercise Questionnaire-3 (BREQ-3), a 24-item self-report tool based on Self-Determination Theory [ 77 ]. It measures six motivation types and overall self-determination. The validated Spanish version shows good reliability (α = 0.81 for intrinsic, 0.70 for amotivation) and strong factorial validity [ 78 ]. In FM, motivation is key for exercise adherence, impacting function and quality of life, making BREQ-3 essential for evaluating motivational profiles in therapeutic contexts. Participants’ experience with FIVR will be assessed using NPRS-11for both cybersickness intensity and satisfaction, in order to monitor the tolerability and acceptance of the intervention. FIVR has shown promise as an effective tool for managing chronic pain, including FM, and its application in the present study will be closely monitored to ensure participant comfort. Additionally, NPRS-11 will be used to assess pain intensity immediately before and after each session, providing an objective measure of the intervention’s short-term impact on perceived pain. Physical activity will be measured using the IPAQ-E, the Spanish short-form version of the International Physical Activity Questionnaire [ 79 ]. This validated tool includes seven items assessing sedentary behaviour, walking, and moderate-to-vigorous activities over the past week. Total physical activity is expressed in MET-min/week, categorizing individuals into low, moderate, or high activity levels [ 79 ]. All AEs that may occur during the study will be systematically monitored, documented, and reported in the final dissemination of findings. During each session, the number of repetitions in every exercise set, perceived exertion (Borg scale), and pain level (pre- and post-session) will be recorded. The study design and progression of patients through the study protocol are outlined in Fig 2 . Fig 2. Protocol timeline implemented for the study. Open in a new tab Statistical methods Sample size was calculated using G*Power 3.1.9.2 based on standard methodological guidelines. The primary outcome is the impact of FM on patients’ lives, assessed with FIQR. The minimum clinically important difference between groups was set at ≈14% based on previous research [ 37 ], which corresponds to an absolute between-group difference of 9.1 FIQR points assuming a baseline mean of 65. With α = 0.05 (two-sided), 80% power, and an assumed SD of 20, the trial was powered to detect a baseline-adjusted post-intervention between-group difference using an ANCOVA framework. GPower inputs were: test family = F tests; statistical test = ANCOVA: fixed effects, main effects and interactions; number of groups = 2; number of covariates = 1 (baseline FIQR); effect size f = 0.23 (equivalent to d ≈ 0.46). This yielded 32 participants per group (64 total), which was increased to 80 participants to account for a potential 20% dropout rate. This calculation aligns with the post-intervention between-group contrast estimated from the prespecified two-time point LMM. Analyses will follow an intention-to-treat (ITT) approach, including all randomised participants in the groups to which they were allocated. Primary and secondary outcomes will be analysed using linear mixed-effects models (LMMs), including fixed effects for group, time, and the group × time interaction, with a random intercept for participants to account for within-subject correlation. Outcomes are assessed at baseline and post-intervention only. Models will be adjusted for prespecified covariates (sex and FM severity level), with covariate adjustment intentionally restricted to this small prespecified set to minimise overfitting and preserve model stability given the planned sample size. Baseline outcome values are accommodated within the two-time point longitudinal model, rather than entered as separate baseline covariates. Other baseline variables (e.g., medication use and other clinical characteristics) will be summarised descriptively and explored only in prespecified sensitivity analyses if substantial baseline imbalance is observed; covariates will not be selected data-driven based on statistical significance. The primary estimand will be the between-group treatment effect from baseline to post-intervention for FIQR. For the two-time point setting, we will use a constrained longitudinal data analysis (cLDA) parametrisation, in which baseline means are constrained to be equal and the treatment effect (difference in change) is expressed as the post-intervention between-group contrast; this aligns with the baseline-adjusted post-intervention treatment effect used for the sample size calculation. In simpler terms, the cLDA approach assumes that both groups start from the same baseline, which is guaranteed by the randomization process, and focuses on comparing the groups at the end of the 6-week program. This allows for a more intuitive assessment of whether the integrated FIVR (EG) provides a specific therapeutic benefit over the same exercises performed without the virtual reality headsets (CG). Given the large number of secondary outcomes, all secondary outcomes will be treated as exploratory and will be reported with effect estimates and 95% confidence intervals, without confirmatory inferential claims. When feasible, standardised effect sizes (Hedges’ g) or partial eta-squared (ηp²) will be reported. Medication use and rescue analgesic intake will be recorded at each assessment and explored in sensitivity analyses if substantial baseline imbalances are observed. Missing data will be handled primarily via maximum likelihood estimation within the LMM framework under the missing-at-random (MAR) assumption, utilizing all available observations. To support this assumption, we will document reasons for missingness and compare baseline characteristics between participants with complete and incomplete follow-up. The MAR assumption is considered plausible in this clinical context because dropout in fibromyalgia exercise trials is often related to baseline factors already accounted for in our model, such as initial pain intensity or functional impairment. However, a limitation of this assumption is the possibility of Missing Not At Random (MNAR) data, where a participant might withdraw due to factors not captured by baseline variables, such as a lack of perceived improvement or a specific individual intolerance to the FIVR technology. As a sensitivity analysis, we will perform multiple imputations (FCS/MICE, m = 30), incorporating outcomes at both time points, baseline values, prespecified covariates (sex and FM severity), time indicators, and relevant auxiliary variables predictive of missingness or outcomes. Quantitative sensory testing (QST) outcomes, including PPT, TS, and CPM, will be analysed as continuous variables using the prespecified LMM. QST will be obtained unilaterally on the dominant side at the hand and lumbar region. PPT will be summarised per site as the mean of three trials. TS and CPM will be derived from pain ratings collected at the first and last repetition (computed as last minus first). If a participant declines testing or a measure cannot be completed, the outcome will be treated as missing. If the maximum limit of the algometer is reached, the value will be recorded at the device maximum and flagged to assess the impact of these ceiling values. Model diagnostics will be inspected, and appropriate transformations (e.g., log-transformation for PPT) or sensitivity analyses excluding extreme values (>3 SD from the mean) will be conducted if assumptions are not met. In addition to the ITT analyses, a prespecified per-protocol (PP) analysis will be reported as a sensitivity analysis. The PP set will include participants who attend at least 75% of the scheduled sessions (9 of 12 sessions over 6 weeks) without major protocol deviations, using the same LMM approach as the primary analysis. Baseline characteristics will be summarised descriptively by group, and intervention effects will be reported with 95% confidence intervals. Statistical significance for the primary outcome (FIQR) will be assessed at p < 0.05 (two-sided). Adverse events will be summarised by group (number of participants with ≥1 event, total events, severity, and relatedness). Between-group differences in incidence proportions will be estimated using risk ratios and risk differences with 95% confidence intervals; where appropriate, Fisher’s exact test will be used to obtain p-values given expected small counts. Data management Participant data will be managed using anonymised identification codes, with all entries stored in a secure, password-protected database. Data access will be limited exclusively to an independent data manager with no conflicts of interest or involvement in other aspects of the study. Given the low-risk nature of the research, the establishment of a Data Monitoring Committee has been deemed unnecessary. Any relevant amendments to the study protocol occurring throughout the course of the trial will be duly reported to both the trial registry and the journal where the protocol is published. Patient and public involvement Patients or the public were not involved in the design, conduct, or reporting of this trial. After completion, participants will receive a plain-language results summary, and a public summary will be posted on ClinicalTrials.gov and shared via AVAFI channels. Discussion To the best of our knowledge, this will be the first randomised clinical trial to evaluate the efficacy of a FIVR intervention incorporating real‑time feedback modulation during a therapeutic exercise programme for individuals with FM. This protocol addresses a relevant clinical gap in FM management, where exercise is a first‑line, evidence‑based intervention yet implementation is hindered by poor adherence and limited effectiveness for pain and fatigue. Patients often struggle to engage in conventional programmes due to fear of symptom exacerbation, low motivation, and poor tolerance, underscoring the need for approaches that enhance acceptability and long‑term adherence. VR integration into exercise prescription has shown promise [ 80 , 81 ]. The FM literature has predominantly used non‑immersive VR [ 21 ], with encouraging results [ 21 , 22 , 82 ]. Although FIVR in FM remains largely unexplored, evidence indicates FIVR outperforms non‑immersive VR for chronic pain [ 83 – 85 ]. FIVR can provide analgesia, increase engagement, and enable customisable experiences; in FM it has improved pain, kinesiophobia, fatigue, physical activity, and HRQoL‑mental vs controls [ 25 ]. However, prior work has key methodological limits. Gulsen et al. (2020) had a small sample (n = 20), delivered FIVR after exercise, and limited exposure to 20 min/session, restricting generalisability and dose–response inference and possibly influencing outcomes. This protocol tests a 60‑min FIVR intervention with manipulated visual feedback delivered concurrently with a structured TEP. The system dynamically underrepresents actual movement amplitude, encouraging movement beyond perceived limits and promoting greater ROM and functional gains. This perceptual manipulation seeks to enhance motor learning and symptom modulation without increasing perceived exertion or pain, while sustained in‑session engagement may support adherence and clinical effectiveness. Supporting information S1 Checklist. SPIRIT 2025 checklist. (DOCX) pone.0348346.s001.docx (35KB, docx) S1 Protocol. Spanish protocol. (DOCX) pone.0348346.s002.docx (5MB, docx) S2 Protocol. English protocol. (DOCX) pone.0348346.s003.docx (4.9MB, docx) S3 Appendix. Intervention. (DOCX) pone.0348346.s004.docx (7.7MB, docx) S4 Document. Ethics approval (EN). (PDF) pone.0348346.s005.pdf (62.5KB, pdf) S5 Document. Ethics approval (ES). (PDF) pone.0348346.s006.pdf (71.8KB, pdf) S6 Checklist. Human participants research checklist. (DOCX) pone.0348346.s007.docx (57.4KB, docx) Acknowledgments We thank all participants and the Asociación Valenciana de Afectados de Fibromialgia (AVAFI) for recruitment support and access to facilities. We also thank the staff of CEU Cardenal Herrera University for administrative support. Data Availability No datasets were generated or analysed during the current study, as this manuscript reports a study protocol only. Upon trial completion, the de-identified dataset, accompanying data dictionary, and analysis code will be deposited in Zenodo and made publicly available under an open licence; DOIs will be provided upon release. 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PMC Copyright notice 24 Feb 2026 -->PONE-D-25-62476-->-->Effectiveness of an immersive virtual reality-based therapeutic exercise programme with altered visual feedback in patients with fibromyalgia: A study protocol for a randomised controlled trial-->-->PLOS One Dear Dr. Amer Cuenca, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.-->--> Please submit your revised manuscript by Apr 10 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions --> Comments to the Author 1. Does the manuscript provide a valid rationale for the proposed study, with clearly identified and justified research questions? The research question outlined is expected to address a valid academic problem or topic and contribute to the base of knowledge in the field.--> Reviewer #1: Yes Reviewer #2: Yes ********** -->2. Is the protocol technically sound and planned in a manner that will lead to a meaningful outcome and allow testing the stated hypotheses? The manuscript should describe the methods in sufficient detail to prevent undisclosed flexibility in the experimental procedure or analysis pipeline, including sufficient outcome-neutral conditions (e.g. necessary controls, absence of floor or ceiling effects) to test the proposed hypotheses and a statistical power analysis where applicable. 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(Please upload your review as an attachment if it exceeds 20,000 characters)--> Reviewer #1: Overall Assessment This Study Protocol “Effectiveness of an immersive virtual reality-based therapeutic exercise programme with altered visual feedback in patients with fibromyalgia: A study protocol for a randomised controlled trial” presents a well-designed, two-arm, parallel-group RCT evaluating the effectiveness of an immersive virtual reality (IVR) therapeutic exercise programme incorporating altered visual feedback for individuals with fibromyalgia (FM). From a statistical and methodological perspective, the planned trial is conceptually sound, adheres to SPIRIT 2025 guidance, and incorporates several strengths: prespecified outcomes, a justified sample-size calculation, a detailed randomisation and blinding strategy, and appropriate use of covariate-adjusted models. Nevertheless, several aspects would benefit from clarification or refinement to ensure full transparency, reproducibility, and appropriateness of statistical analyses, especially considering the complexity of the intervention and the breadth of outcomes being collected. My major and minor statistical comments follow. ________________________________________ Major Comments 1. Sample Size Justification: Effect Size Assumptions Need Clarification The sample size uses an assumed SD = 20 and an MCID of ≈14% on the FIQR to detect between-group differences using ANCOVA. However: • The protocol does not state the absolute expected mean FIQR score on which the 14% MCID is computed. • It is unclear whether the authors powered the study for between-group mean difference or group × time interaction. Recommendation: Explicitly report the assumed baseline FIQR mean, the corresponding absolute MCID value, the expected post-intervention difference, and whether the model used in the power calculation aligns with the final analysis approach (ANCOVA vs. repeated-measures ANCOVA). Provide the full numeric parameters used in G*Power for reproducibility. ________________________________________ 2. Choice of Analysis Method: Repeated-Measures ANCOVA May Not Be Appropriate The plan states that two-way repeated-measures ANCOVA will be used, adjusting for covariates (sex, FIQR cluster, baseline scores). Two key concerns: • Repeated-measures ANCOVA is not a standard or well-defined model. • Adjusting for baseline within a repeated-measures framework typically leads to statistical redundancy and may violate model assumptions. Recommendation: Use a linear mixed-effects model (LMM), which is the standard for RCTs with repeated measures, missing data handled under MAR, and covariate adjustment. LMMs also allow random intercepts (and slopes if appropriate), avoid sphericity assumptions, and appropriately model within-subject correlation. ________________________________________ 3. Multiple Secondary Outcomes: No Adjustment Strategy Specified The study includes an exceptionally large set of secondary outcomes (psychological scales, sensory testing, mobility tests, physiological measures, IVR tolerance, motivation, and more). Given the number of endpoints, there is a substantial risk of false positives. Recommendation: Specify a plan for multiplicity control, such as: • Designating key secondary outcomes a priori and controlling FDR across them, or • Applying hierarchical testing, or • Clearly stating that secondary outcomes are exploratory without inferential claim. PLOS ONE’s policies permit exploratory outcomes, but this must be stated unambiguously. ________________________________________ 4. Handling of Missing Data: Multiple Imputation Needs Additional Specification The protocol states that multiple imputations will be used assuming data missing at random (MAR). Issues requiring clarification: • The number of imputations is not specified. • The imputation model variables are not described. • MAR assumption justification is absent. • For longitudinal outcomes, multilevel imputation or model-based approaches are usually preferred. Recommendation: Specify: 1. Number of imputations (≥20 recommended). 2. Variables included in the imputation model (all predictors, outcomes, and auxiliary variables). 3. Whether imputation will be performed separately by randomized group. 4. Whether imputation will incorporate repeated-measures structure (e.g., MICE with time indicators). ________________________________________ 5. Randomisation: Stratification and Block Sizes Are Appropriate but Require Clarification Randomisation uses stratified block permutation across eight strata (sex × FIQR severity clusters) with block sizes 2–4. Concerns: • FIQR cluster derives from Pérez-Aranda et al., but the method for real-time assignment of clusters in the trial is not fully described. • The number of strata (8) for a target N=80 may result in unstable allocation within small strata. Recommendation: Report: • The decision process and data required to assign FIQR cluster at baseline. • Expected sample per stratum and any minimum-size rule. • Whether randomisation performance (e.g., imbalance assessment) will be monitored. ________________________________________ 6. Quantitative Sensory Testing (QST): Statistical Plan Requires Additional Detail The QST section is detailed in measurement procedures, but statistical treatment lacks specification (PPT, TS, CPM outcomes). Required clarifications: • Will PPT, TS, and CPM be analysed as continuous outcomes in ANCOVA/LMM? • Will outliers (common in QST data) be handled through transformation or robust methods? • How will bilateral PPT sites be modelled (averaged, modelled separately, or nested within subject)? ________________________________________ 7. Per-Protocol vs. Intention-to-Treat While ITT analysis is planned, the protocol does not describe conditions for per-protocol analysis or adherence thresholds. Recommendation: Specify whether: • A per-protocol set will also be reported. • Exercise dose or session attendance will be incorporated into sensitivity analyses. ________________________________________ Minor Comments 1. Effect Size Reporting Partial eta-squared is appropriate, but confidence intervals for effect sizes should also be reported when possible. 2. Covariate Inclusion Adjusting only for sex, baseline FIQR, and baseline outcome may be insufficient if strong prognostic variables exist (e.g., medication use, FM symptom severity, kinesiophobia). Consider prespecifying additional adjustment factors or providing a rationale for the limited covariate set. 3. Rescue Medication The plan instructs participants to avoid analgesics 24 hours before assessments. Clarify whether medication use will be recorded and included as a covariate or descriptive variable. 4. Intervention Fidelity Metrics Session-level variables (e.g., RPE, repetitions, pain pre/post) are being collected but are not included in the analysis plan. Clarify whether these will inform adherence, dose-response exploration, or moderation analysis. 5. Adverse Event Statistical Reporting Although AEs will be reported descriptively, specify whether incidence rates between groups will be compared statistically (risk ratios or risk differences). Reviewer #2: Summary The work is a study protocol of a randomized controlled clinical trial research project. The clinical trial is targeted at potentially improving the nonpharmacologic treatment given to fibromyalgia, a very common disease with a substantial impact on global health care and population. In this study, the IVR is designed to manipulate the vision, making the patient feel as though they are experiencing reduced body movement compared to the actual motion. Comments 1. According to your 5th reference (Cabo-Meseguer A, Cerdá-Olmedo G, Trillo-Mata JL. Fibromialgia: prevalencia, perfiles epidemiológicos y costes económicos. Med Clin (Barc). 2017;149:441–448. doi:10.1016/j.medcli.2017.06.008), the annual expenditure of fibromyalgia treatment in Spain is more than 12,993 million euros, but on line 55, you stated 4.2 billion; you need to clarify the discrepancy. Furthermore, your 5th reference is focused on Spain, so if you want to mention figures related to the USA, I recommend you use studies done in the USA. 2. The abbreviations EG and CG are used in lines 122 and 125, but the extended versions are mentioned in176 and 177; correct that. 3. In the “Ethical approval and registration” section, I recommend you include a statement that shows that participants can drop out of the study at any time without any fear of being denied any health service at your hospital or other health care facilities. 4. The authors need to mention the reason for stratifying the patients by strata, considering sex and FM severity in the manuscript. 5. TENS, AE, and RPE are included only as abbreviations; include the extended versions as well. 6. Your study is going to use fully immersive VR technology, but you described it as immersive VR. According to the classification, immersive can be fully or semi-immersive. Therefore, for better clarity, you should consistently use the term fully immersive VR. Questions 1.Wouldn't using different diagnostic criteria for the diagnosis of fibromyalgia affect your statistical findings? 2.What language are you going to use to assess the FIQR; Central Sensitisation Inventory (CSI); Multidimensional Fatigue Inventory (MFI-20); Pittsburgh Sleep Quality Index (PSQI); EQ-5D-5L? and the other scores? 3. Is the presence of comorbidities like hormonal abnormalities, respiratory illnesses profile of the samples going to be assessed? 4.Did you consider concurrent rheumatological disease, such as rheumatoidarthritis or osteoarthritis, as exclusion criteria? 5. What are the parts of the device to be used in the VR treatment? 6. What types of videos or games are going to be used for VR treatment? 7. What are the operational definitions of the “physiological, psychological, and physical outcomes” of fibromyalgia? 8. Why do you want to include only those patients with a pain intensity score of ≥3? 9. What are the comorbidities and/or symptoms that you will consider as  contraindications for IVR and exercise-based interventions? 10. Why did you select 6 weeks for the duration of therapy? And how did you determine the 2 times per week frequency of sessions? And why did you select 60 minutes for the duration of a session? 11. Why did you choose HTC Vive Pro? 12. Why did you choose 20% arm underestimation during flexion and 10% underestimation during extension specifically? ********** -->7. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .--> Reviewer #1: Yes: Dr Shah-Jalal Sarker Reviewer #2: No ********** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation . NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications. Attachment Submitted filename: Comments.docx pone.0348346.s008.docx (29.6KB, docx) PLoS One. 2026 Jun 4;21(6):e0348346. doi: 10.1371/journal.pone.0348346.r002 Author response to Decision Letter 1 Article notes Copyright and License information Collection date 2026. PMC Copyright notice 25 Mar 2026 RESPONSES TO JOURNAL REQUIREMENTS JOURNAL REQUIREMENTS FIRST REQUIREMENT “Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf” Response We have carefully revised the manuscript to ensure compliance with PLOS ONE’s style requirements, using the journal’s formatting sample files for the main body and for the title, authors, and affiliations page as reference guides. Specifically, we reviewed the manuscript structure, title page, author and affiliation formatting, section layout, and file naming, and we amended these elements where needed so that the revised submission conforms to PLOS ONE formatting requirements. SECOND REQUIREMENT “Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please delete it from any other section.” Response We confirm that the ethics statement is presented in the Methods section of the manuscript, in accordance with PLOS ONE’s requirements. We also ensured that the ethics information is reported consistently across the revised submission materials. THIRD REQUIREMENT “Please provide a complete Data Availability Statement in the submission form, ensuring you include all necessary access information or a reason for why you are unable to make your data freely accessible. If your research concerns only data provided within your submission, please write "All data are in the manuscript and/or supporting information files" as your Data Availability Statement.” Response We have provided a complete Data Availability Statement in the submission form. As this manuscript is a study protocol and does not report study results or generated datasets, the statement clarifies that no datasets were generated or analysed for the current article. It also specifies that, upon trial completion, the de-identified dataset, accompanying data dictionary, and analysis code will be deposited in Zenodo and made publicly available under an open licence, with DOIs to be provided upon release. FOURTH REQUIREMENT “When completing the data availability statement of the submission form, you indicated that you will make your data available on acceptance. We strongly recommend all authors decide on a data sharing plan before acceptance, as the process can be lengthy and hold up publication timelines. Please note that, though access restrictions are acceptable now, your entire data will need to be made freely accessible if your manuscript is accepted for publication. This policy applies to all data except where public deposition would breach compliance with the protocol approved by your research ethics board. If you are unable to adhere to our open data policy, please kindly revise your statement to explain your reasoning and we will seek the editor's input on an exemption. Please be assured that, once you have provided your new statement, the assessment of your exemption will not hold up the peer review process.” Response We have revised the Data Availability Statement in the submission form to clarify our data-sharing plan in line with PLOS ONE policy. As this manuscript is a study protocol and does not report study results or generated datasets, the revised statement specifies that no datasets were generated or analysed during the current study. It also states that, upon trial completion, the de-identified dataset, accompanying data dictionary, and analysis code will be deposited in Zenodo and made publicly available under an open licence, with the corresponding DOIs to be provided upon release. FIFTH REQUIREMENT “We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match. When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.” Response Thank you for highlighting this discrepancy. We acknowledge that the grant information reported in the “Funding Information” and “Financial Disclosure” sections was inconsistent. We have reviewed the funding details and will revise the “Funding Information” section at resubmission so that the correct grant number(s) are provided and both sections are fully aligned. SIXTH REQUIREMENT “Thank you for stating the following financial disclosure: This work was supported by grants from the University CEU Cardenal Herrera (GIR25/41). Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." If this statement is not correct you must amend it as needed. Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.” Response Thank you for this clarification. We confirm that the funder’s role was limited to financial support only. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. As requested, we have included the amended Role of Funder statement in the cover letter. SEVENTH AND EIGHTH REQUIREMENT “Please amend your authorship list in your manuscript file to include author Juan Francisco Lisón Párraga” “Please amend the manuscript submission data (via Edit Submission) to include author Juan Francisco Lisón” Response We have reviewed and updated the authorship information across the submission materials to ensure consistency between the manuscript file and the online submission data. In accordance with the author’s preferred publication name, this author is listed as Juan Francisco Lisón in the manuscript, and the submission metadata have been amended accordingly so that the author details are complete, accurate, and fully aligned across the revised submission. NINTH REQUIREMENT “Please amend either the abstract on the online submission form (via Edit Submission) or the abstract in the manuscript so that they are identical.” Response We have reviewed the abstract in both the manuscript and the online submission form and ensured that the two versions are identical across the revised submission. TENTH REQUIREMENT “We note that there is identifying data in the Supporting Information file <S4 Document. Ethics approval (EN).pdf, S5 Document. Ethic approval (ES).pdf>. Due to the inclusion of these potentially identifying data, we have removed this file from your file inventory. Prior to sharing human research participant data, authors should consult with an ethics committee to ensure data are shared in accordance with participant consent and all applicable local laws. Data sharing should never compromise participant privacy. It is therefore not appropriate to publicly share personally identifiable data on human research participants. The following are examples of data that should not be shared: • Name, initials, physical address • Ages more specific than whole numbers • Internet protocol (IP) address • Specific dates (birth dates, death dates, examination dates, etc.) • Contact information such as phone number or email address • Location data • ID numbers that seem specific (long numbers, include initials, titled “Hospital ID”) rather than random (small numbers in numerical order) Data that are not directly identifying may also be inappropriate to share, as in combination they can become identifying. For example, data collected from a small group of participants, vulnerable populations, or private groups should not be shared if they involve indirect identifiers (such as sex, ethnicity, location, etc.) that may risk the identification of study participants. Additional guidance on preparing raw data for publication can be found in our Data Policy ( https://journals.plos.org/plosone/s/data-availability#loc-human-research-participant data-and-other-sensitive-data) and in the following article: http://www.bmj.com/content/340/bmj.c181.long . Please remove or anonymize all personal information (<specific identifying information in file to be removed>), ensure that the data shared are in accordance with participant consent, and re-upload a fully anonymized data set. Please note that spreadsheet columns with personal information must be removed and not hidden as all hidden columns will appear in the published file.” Response We have reviewed the supporting ethics approval documents and removed non-essential identifying information prior to re-upload. Specifically, personal names and handwritten signatures have been anonymised in the revised files, while retaining the ethics approval information necessary to document institutional approval. The revised supporting files are therefore fully anonymised for public sharing. RESPONSE TO REVIEWERS We would like to express our sincere gratitude to both reviewers for the time, dedication, and thoughtful consideration they have devoted to evaluating our manuscript. We truly appreciate the depth and quality of their comments, which have been highly constructive and insightful. Their observations and recommendations have allowed us to carefully re-examine several aspects of the protocol, clarify key methodological points, improve the precision of our reporting, and strengthen the scientific rationale underlying our decisions. We believe that addressing these comments has significantly enhanced the clarity, transparency, and overall methodological rigor of the manuscript. We are grateful for the opportunity to revise our work in light of these valuable suggestions, which have undoubtedly contributed to improving the quality and robustness of the article. For ease of review, we note that all references cited throughout the document are fully listed in the final bibliography section. REVIEWER 1: MAYOR COMMENTS: FIRST MAYOR COMMENT “Sample Size Justification: Effect Size Assumptions Need Clarification The sample size uses an assumed SD = 20 and an MCID of ≈14% on the FIQR to detect between-group differences using ANCOVA. However: • The protocol does not state the absolute expected mean FIQR score on which the 14% MCID is computed. • It is unclear whether the authors powered the study for between-group mean difference or group × time interaction. Recommendation: Explicitly report the assumed baseline FIQR mean, the corresponding absolute MCID value, the expected post-intervention difference, and whether the model used in the power calculation aligns with the final analysis approach (ANCOVA vs. repeated-measures ANCOVA). Provide the full numeric parameters used in G*Power for reproducibility.” Response We thank the reviewer for this important methodological observation. We have revised the manuscript to clarify the assumptions and ensure full reproducibility of the sample size calculation. Specifically, we now report the assumed baseline FIQR mean used to operationalise the ≈14% MCID, and we translate this to an absolute between-group difference. Assuming a baseline mean FIQR score of 65 points, a 14% difference corresponds to 9.1 FIQR points. With an assumed SD of 20, this corresponds to an effect size of d ≈ 0.46 and Cohen’s f = 0.23. We also clarify that the trial was powered to detect the baseline-adjusted between-group difference at the post-intervention assessment (i.e., not a group × time interaction). Although the a priori calculation was performed using an ANCOVA framework, the primary analysis will use a prespecified two-timepoint linear mixed-effects model; the post-intervention between-group contrast from this model targets the same baseline-adjusted post-intervention comparison as the ANCOVA used for the sample size calculation. For reproducibility, the G*Power inputs are now explicitly provided: G*Power 3.1.9.2; test family: F tests; statistical test: ANCOVA: fixed effects, main effects and interactions; number of groups: 2; number of covariates: 1 (baseline FIQR); effect size f = 0.23; α = 0.05 (two-sided); power (1–β) = 0.80; allocation ratio 1:1; total N = 64 (32 per group), inflated to N = 80 to allow for 20% dropout. Accordingly, the revised manuscript text now reads as follows: “Sample size was calculated using G*Power 3.1.9.2 based on standard methodological guidelines. The primary outcome is the impact of FM on patients' lives, assessed with FIQR. The minimum clinically important difference between groups was set at ≈14%, based on previous research [37]. Assuming a baseline mean FIQR score of 65 points, this corresponds to an absolute between-group difference of 9.1 FIQR points. With α = 0.05 (two-sided), 80% power, and an assumed SD of 20, the trial was powered to detect this baseline-adjusted post-intervention between-group difference using an ANCOVA framework with baseline FIQR as a covariate. This corresponds to d ≈ 0.46 (9.1/20), equivalent to Cohen’s f = 0.23. G*Power inputs were: test family = F tests; statistical test = ANCOVA: fixed effects, main effects and interactions; number of groups = 2; number of covariates = 1 (baseline FIQR); effect size f = 0.23; α = 0.05; power (1−β) = 0.80. This yielded 32 participants per group (64 total). Allowing for 20% dropout, the target sample was increased to 80 participants. This calculation was designed to detect the baseline-adjusted between-group difference in FIQR at the post-intervention assessment and corresponds to the post-intervention between-group contrast estimated from the prespecified two-time point LMM.” SECOND MAYOR COMMENT “Choice of Analysis Method: Repeated-Measures ANCOVA May Not Be Appropriate The plan states that two-way repeated-measures ANCOVA will be used, adjusting for covariates (sex, FIQR level, baseline scores). Two key concerns: • Repeated-measures ANCOVA is not a standard or well-defined model. • Adjusting for baseline within a repeated-measures framework typically leads to statistical redundancy and may violate model assumptions.” Recommendation: Use a linear mixed-effects model (LMM), which is the standard for RCTs with repeated measures, missing data handled under MAR, and covariate adjustment. LMMs also allow random intercepts (and slopes if appropriate), avoid sphericity assumptions, and appropriately model within-subject correlation. Response We thank the reviewer for this valuable statistical recommendation. We agree that the term “two-way repeated-measures ANCOVA” is not a standard, well-defined framework for longitudinal RCT data and that adjusting for baseline within a repeated-measures ANCOVA structure may be statistically redundant and conceptually unclear. In response, we have revised the analysis plan to use linear mixed-effects models (LMMs), which are the standard approach for repeated measures in RCTs. The LMM will include fixed effects for group, time, and the group × time interaction, with a participant-specific random intercept to account for within-subject correlation, and adjustment for prespecified covariates (sex and baseline FM severity level, where applicable). Given that outcomes are assessed at baseline and post-intervention only, the primary treatment effect is the between-group difference in change from baseline to post-intervention (i.e., the group × time interaction). For this two-timepoint setting, we specify a constrained longitudinal data analysis (cLDA) parameterisation (baseline means constrained equal), in which the treatment effect can be expressed as the post-intervention between-group contrast and aligns with the baseline-adjusted ANCOVA estimand used for the sample size calculation. Accordingly, we have revised the “Statistical methods” section, and the manuscript now reads as follows: “Primary and secondary outcomes will be analysed using linear mixed-effects models (LMMs), including fixed effects for group, time, and the group × time interaction, with a random intercept for participants to account for within-subject correlation. Outcomes are assessed at baseline and post-intervention only. Models will be adjusted for prespecified covariates (sex an Attachment Submitted filename: RESPONSE TO REVIEWERS.docx pone.0348346.s010.docx (165.8KB, docx) PLoS One. doi: 10.1371/journal.pone.0348346.r003 Decision Letter 1 Hui-Juan Cao Author information Copyright and License information Roles Hui-Juan Cao : Academic Editor © 2026 Hui-Juan Cao This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 15 Apr 2026 Effectiveness of a fully immersive virtual reality-based therapeutic exercise programme with altered visual feedback in patients with fibromyalgia: A study protocol for a randomised controlled trial PONE-D-25-62476R1 Dear Dr. Cuenca, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. 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Does the manuscript provide a valid rationale for the proposed study, with clearly identified and justified research questions? The research question outlined is expected to address a valid academic problem or topic and contribute to the base of knowledge in the field.--> Reviewer #1: Yes ********** -->2. Is the protocol technically sound and planned in a manner that will lead to a meaningful outcome and allow testing the stated hypotheses? The manuscript should describe the methods in sufficient detail to prevent undisclosed flexibility in the experimental procedure or analysis pipeline, including sufficient outcome-neutral conditions (e.g. necessary controls, absence of floor or ceiling effects) to test the proposed hypotheses and a statistical power analysis where applicable. As there may be aspects of the methodology and analysis which can only be refined once the work is undertaken, authors should outline potential assumptions and explicitly describe what aspects of the proposed analyses, if any, are exploratory.--> Reviewer #1: Yes ********** -->3. Is the methodology feasible and described in sufficient detail to allow the work to be replicable? Descriptions of methods and materials in the protocol should be reported in sufficient detail for another researcher to reproduce all experiments and analyses. The protocol should describe the appropriate controls, sample size calculations, and replication needed to ensure that the data are robust and reproducible.--> Reviewer #1: Yes ********** -->4. Have the authors described where all data underlying the findings will be made available when the study is complete? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception, at the time of publication. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: Yes ********** -->5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.--> Reviewer #1: Yes ********** -->6. Review Comments to the Author Please use the space provided to explain your answers to the questions above and, if applicable, provide comments about issues authors must address before this protocol can be accepted for publication. You may also include additional comments for the author, including concerns about research or publication ethics. You may also provide optional suggestions and comments to authors that they might find helpful in planning their study. (Please upload your review as an attachment if it exceeds 20,000 characters)--> Reviewer #1: The authors have made substantial improvements to the manuscript. The protocol is now technically sound and suitable for publication. However, the following minor points should be considered to improve clarity and interpretability: Clarity of Statistical Framework The description of the LMM and cLDA approach is technically correct but may be difficult for non-statistical readers. A brief plain-language explanation of the primary treatment effect would improve accessibility. Missing Data Assumption (MAR) While the analytical approach is appropriate, the manuscript would benefit from a short statement discussing the plausibility and limitations of the missing-at-random assumption in this clinical context. Secondary Outcomes Although appropriately designated as exploratory, the large number of secondary outcomes remains extensive. Highlighting a small subset of key secondary outcomes may aid interpretability. Readability Some sections—particularly the statistical methods—could be streamlined to improve readability without reducing methodological rigor. ********** -->7. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .--> Reviewer #1: Yes: Dr Shah-Jalal Sarker ********** PLoS One. doi: 10.1371/journal.pone.0348346.r004 Acceptance letter Hui-Juan Cao Author information Copyright and License information Roles Hui-Juan Cao : Academic Editor © 2026 Hui-Juan Cao This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice PONE-D-25-62476R1 PLOS One Dear Dr. Amer-Cuenca, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing . If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Hui-Juan Cao Academic Editor PLOS One Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials S1 Checklist. SPIRIT 2025 checklist. (DOCX) pone.0348346.s001.docx (35KB, docx) S1 Protocol. Spanish protocol. (DOCX) pone.0348346.s002.docx (5MB, docx) S2 Protocol. English protocol. (DOCX) pone.0348346.s003.docx (4.9MB, docx) S3 Appendix. Intervention. (DOCX) pone.0348346.s004.docx (7.7MB, docx) S4 Document. Ethics approval (EN). (PDF) pone.0348346.s005.pdf (62.5KB, pdf) S5 Document. Ethics approval (ES). (PDF) pone.0348346.s006.pdf (71.8KB, pdf) S6 Checklist. Human participants research checklist. (DOCX) pone.0348346.s007.docx (57.4KB, docx) Attachment Submitted filename: Comments.docx pone.0348346.s008.docx (29.6KB, docx) Attachment Submitted filename: RESPONSE TO REVIEWERS.docx pone.0348346.s010.docx (165.8KB, docx) Data Availability Statement No datasets were generated or analysed during the current study, as this manuscript reports a study protocol only. Upon trial completion, the de-identified dataset, accompanying data dictionary, and analysis code will be deposited in Zenodo and made publicly available under an open licence; DOIs will be provided upon release. 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