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Photobiomodulation efficacy in age-related macular degeneration: a systematic review and meta-analysis of randomized clinical trials

• Tiago N. O. Rassi 1 , 4   na1 ,
• Lucas M. Barbosa   ORCID: orcid.org/0000-0003-3408-2115 2   na1 ,
• Sacha Pereira 3 ,
• Eduardo A. Novais 4 ,
• Fernando Penha 4 , 5 ,
• Luiz Roisman 4 &
• Mauricio Maia 4  

International Journal of Retina and Vitreous volume  10 , Article number:  54 ( 2024 ) Cite this article

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Age-related macular degeneration (AMD) is a leading cause of vision loss. Photobiomodulation (PBM) offers a controversial approach for managing dry AMD, aiming to halt or reverse progression through mitochondrial activity modulation. However, the efficacy and clinical relevance of PBM as a potential approach for managing dry AMD remain debated.

We systematically searched PubMed, Embase, and Cochrane databases for randomized controlled trials (RCTs) comparing PBM versus a sham in patients with dry AMD. We performed trial sequential analysis (TSA) and minimal clinically important difference (MCID) calculations to assess statistical and clinical significance applying a random-effects model with 95% confidence intervals (CI).

We included three RCTs comprising 247 eyes. The pooled analysis showed that PBM significant improved BCVA (MD 1.76 letters; 95% CI: 0.04 to 3.48) and drusen volume (MD -0.12 mm³; 95% CI: -0.22 to -0.02) as compared with a sham control. However, the TSA indicated that the current sample sizes were insufficient for reliable conclusions. No significant differences were observed in GA area. The MCID analysis suggested that the statistically significant results did not translate into clinically significant benefits. In the quality assessment, all studies were deemed to have a high risk of bias.

This meta-analysis points limitations in the current evidence base for PBM in dry AMD treatment, with issues around small sample sizes. Statistically significant improvements do not translate into clinical benefits. The research underscores need for larger RCTs to validate PBM’s therapeutic potential for dry AMD.

Age-related macular degeneration (AMD) significantly impacts global visual health, particularly its advanced forms, such as geographic atrophy (GA), which leads to severe visual impairment and blindness. With population aging, the prevalence of AMD is expected to increase, highlighting the urgency for effective treatments and management strategies to mitigate its impact on quality of life and burden on healthcare systems [ 1 ].

Current therapeutic options for dry AMD are scarce and focus on lowering the progression to advanced stages such as GA, although their efficacy is often questionable [ 2 ]. Until recently, there were no treatments available specifically for GA. In 2023, the FDA approved two complement inhibitors for slowing the progression rate of GA areas [ 3 , 4 ]. However, accessibility remains a major challenge. This underscores the critical need for novel therapies that can halt or ideally reverse the progression of dry AMD and GA, thereby preserving visual function.

Photobiomodulation (PBM) is a therapeutic option for dry AMD, focusing on slowing disease progression by influencing mitochondrial activity, reducing oxidative stress, and modulating inflammation through LEDs at specific wavelengths (590, 660, 850 nm) [ 5 , 6 ]. Despite anecdotal reports and early studies indicating potential benefits, such as improved microperimetry outcomes for some patients, [ 7 ] its efficacy and scientific validity in preventing the progression from dry AMD to GA are subject of substantial controversy [ 8 , 9 ].

Herein, we perform an updated meta-analysis of randomized controlled trials (RCTs) to evaluate the efficacy of PBM versus a sham procedure in patients with dry AMD. We performed a trial sequential analysis (TSA) to evaluate if the sample was sufficient for making statistical inference [ 10 , 11 , 12 ] and assessed the minimum clinically important differences (MCID) calculated by pooled standard deviation (SD) to check if any statistical differences would translate to clinical significance [ 13 , 14 ].

Our study was performed and reported following the Cochrane Collaboration Handbook for Systematic Reviews of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Statement guidelines [ 15 , 16 ]. The protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO) under protocol number CRD42024521983.

Data source and search strategy

We systematically searched PubMed, Embase, and Cochrane databases. Our search was last updated in February 2024. The search terms included “photobiomodulation” and “age-related macular degeneration”. The complete search strategy is provided in Supplemental Methods 3. All records retrieved were independently assessed by two authors, L.M.B. and T.N.O.R., and a decision regarding full-text retrieval was arbitrated by consensus between them. Full texts were reviewed by L.M.B. and T.N.O.R. and discussed regarding inclusion and exclusion criteria. References of eligible papers and systematic reviews were also searched for additional studies of interest. Conference abstracts and prospective trials were also searched.

Eligibility criteria

There was no restriction regarding publication date, status, or language. We considered studies eligible for inclusion if they [ 1 ] were RCTs; [ 2 ] directly compared PBM with sham; [ 3 ] included patients with diagnosed non-exudative AMD.

Our clinical outcomes of interest were: [ 1 ] last visit best corrected visual acuity (BCVA); last visit drusen volume in mm 3 ; last visit GA area in mm 2 . Our pooled analyses last visit included a follow-up of at least 9 months.

Risk of bias assessment

Two independent authors (TR. and S.F.P.) assessed the risk of bias in the included RCTs using the Cochrane tool for assessing the risk of bias in randomized controlled trials (RoB-2) [ 17 ]. Disagreements were resolved through consensus.

Statistical analysis

We applied the Mantel-Haenszel random-effects model with a restricted maximum likelihood variance estimator for all outcomes. We pooled risk ratios (RR) with 95% confidence intervals (CI) for binary endpoints and mean differences (MD) with 95% CI for continuous endpoints. When needed, we extracted data using the WebPlotDigitizer tool.

We assessed heterogeneity with Cochran’s Q and I 2 statistics, with p  ≤ 0.10 indicating statistical significance for heterogeneity. We determined the between-study heterogeneity based on I 2 values of 0%, ≤ 25%, ≤ 50%, and > 50%, indicating no observed, low, moderate, and substantial heterogeneity, respectively. All statistical analyses were performed using R version 4.3.2.

Trial Sequential Analysis

We performed TSA using the TSA software (Copenhagen Trial Unit, Centre for Clinical Intervention Research, Copenhagen, Denmark) on the outcomes of BCVA, drusen volume, and GA area. We utilized a MD measure of effect and a random-effects model, setting a conventional 95% CI. The analysis incorporated a two-sided conventional boundary with 5% types I error rate. Alpha-spending boundaries were established using a two-sided boundary type, maintaining a 5% types I error rate and an 80% statistical power. The alpha and beta spending function adopted was the O’Brien-Fleming approach. In determining the required information size (RIS), we opted for an empirical method with heterogeneity correction, applying the model variance to accommodate study variability.

Minimal clinically important difference

We established the MCID for each outcome exhibiting statistical differences by calculating the pooled standard deviation (SD) and then multiplying this pooled SD by 0.5 [ 13 , 14 , 18 ].

Study selection and characteristics

Our systematic review initially yielded 150 results. After removal of duplicates and screening based on title and abstract, 10 full-text articles were reviewed for possible inclusion. Finally, three RCTs fulfilled our inclusion criteria and were included in the analysis, [ 7 , 19 , 20 ] comprising a pooled population of 247 eyes, of whom 151 (61%) were randomized to the PBM group. Comprehensive details of the study selection are detailed in Fig.  1 .

PRISMA flow diagram of study screening and selection. Abbreviations PBM, photobiomodulation; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analysis

The mean age was 75.1 years. Total follow-up ranged from 9 to 13 months. All included studies were sham-controlled. Individual study characteristics are detailed in Table  1 [ 7 , 19 , 20 ].

Clinical endpoints

PBM showed a statistically significant improvement in BCVA over sham treatment, with a MD of 1.76 ETDRS letters among 241 eyes (95% CI [0.04; 3.48], p  = 0.04) despite a high heterogeneity (I²=77%), as shown in Fig.  2 . However, while statistically significant, the observed improvement did not meet the threshold for clinical relevance as defined by the MCID of 6.8 ETDRS letters. The TSA also indicated a RIS of 555 eyes for statistical inference, as the z-curve did not meet the monitoring boundary, suggesting the current sample size is insufficient, as shown in Fig.  3 .

Forest plot for best corrected visual acuity (BCVA). There was a slight overall improvement favoring PBM versus sham with a mean difference of 1.76 ( P  = 0.04). Abbreviations CI, confidence intervals; MD, mean difference; PBM, photobiomodulation; SD, standard deviation; IV, inverse variance

Figure  3 shows a TSA for evaluating treatment efficacy in a cumulative meta-analysis. On the x-axis, the number of eyes reaches 241 across 3 studies, as shown by the blue curve. The y-axis measures the Z-score, assessing statistical deviation from the null hypothesis. The curve falls short of the required information size (555 eyes), indicated by the perpendicular line, suggesting more data is needed for a robust conclusion. The curve does not cross the monitoring boundaries, which, along with the conventional ± 1.96 Z-score boundaries, assess significance; therefore, the analysis does not conclusively favor either treatment group over the other

Anatomical endpoints

There was no significant difference between groups in GA area (73 eyes; MD -0.53 mm 2 ; 95% CI [-1.44; 0.37]; p  = 0.25; I 2  = 0%), as shown in Fig.  4 . However, TSA indicated that a RIS of 436 eyes would be necessary for a statistical inference, as shown in Fig.  5 . Moreover, the z-curve did not reach the monitoring boundary.

Forest plot for of geographic atrophy (GA) area between Photobiomodulation (PBM) and sham treatment. The combined results yield a mean difference of -0.53, indicating no significant difference between PBM and SHAM treatments in reducing GA area ( P  = 0.25). Abbreviations CI, confidence intervals; MD, mean difference; PBM, photobiomodulation; SD, standard deviation; IV, inverse variance

TSA for GA area. On the x-axis, the number of eyes reaches 73 across 3 studies, as shown by the blue curve. The y-axis measures the Z-score, assessing statistical deviation from the null hypothesis. The curve falls short of the required information size (436 eyes), suggesting that more data are needed for a robust conclusion. The curve does not cross the monitoring boundaries, which, along with the conventional ± 1.96 Z-score boundaries, assess significance; therefore, the analysis does not conclusively favor either treatment group over the other. Abbreviations PBM, photobiomodulation

As compared with a sham procedure, PBM significantly reduced drusen volume (242 eyes; MD -0.12mm 3 ; 95% CI [-0.22; -0.02]; p  = 0.02; I 2  = 74%), as shown in Fig.  6 . However, the observed improvement did not meet the threshold for clinical relevance as defined by the MCID of 0.39 mm 3 . In addition, TSA indicated that a RIS of 444 eyes would be necessary for statistical inference, indicating insufficient sample size, as shown in Fig.  7 . Moreover, the z-curve did not reach the monitoring boundary.

Forest plot for drusen volume. Results show a small mean difference of -0.12 mm³, with overall findings favoring PBM ( P  = 0.02). Abbreviations CI, confidence intervals; MD, mean difference; PBM, photobiomodulation; SD, standard deviation; IV, inverse variance

TSA for drusen volume. On the x-axis, the number of eyes reaches 242 across 3 studies, as shown by the blue curve’s progression. The y-axis measures the Z-score, assessing statistical deviation from the null hypothesis. The curve falls short of the required information size (444 eyes), indicated by the perpendicular line, suggesting more data are needed for statistical inference. The curve does not cross the monitoring boundaries, which, along with the conventional ± 1.96 Z-score boundaries, assess significance; therefore, the analysis does

Risk of Bias Assessment

Using the Cochrane Collaboration’s RoB-2 tool, our quality assessment suggests that all three RCTs are at a high risk for bias. The primary concern was related to bias in measuring outcomes. Additionally, one of the studies experienced issues with bias due to missing outcome data attributed to disruptions caused by COVID-19 [ 20 ]. Individual RCT appraisal is detailed in Fig.  8 .

This meta-analysis included three RCTs with 247 eyes to assess the efficacy of PBM in patients with dry AMD. Our pooled data showed an improvement in BCVA and drusen volume in patients treated with PBM as compared with a sham with no significant difference in terms of progression of GA area. However, these statistical inferences could not be confirmed due to insufficient sample size, as indicated by the TSA. Even if the TSA was favorable, BCVA and drusen volume were not clinically significant, as they did not meet the MCID.

One may argue that the significant findings of RCTs of PBM therapy for dry AMD may not translate into clinical benefits. The largest RCT on the subject found a MD of 2.4 ETDRS letters compared with sham [ 19 ]. Nonetheless, visual acuity measurements in intermediate AMD may vary by an average of 9 ETDRS letters in patients who do not receive any treatment, much higher than the above cited MD [ 21 ]. For instance, the established MCID for photodynamic therapy in patients with neovascular membranes is 7.5 letters [ 22 ]. Of note, the FDA requires a minimum improvement of at least 15 letters for approving a pharmacological intervention in this setting [ 23 ]. Therefore, it could be contended that the benefits of PBM therapy do not meet clinical significance, which indeed was corroborated by our findings through the MCIDs results.

In addition, inadequate sample sizes limit the primary studies from definitively assessing the efficacy of PBM for dry AMD, as highlighted by previous meta-analyses that were only able to collect data from 2 studies and 96 eyes [ 8 ]. The individual trials, LIGHTSITE I and II, [ 7 , 20 ] also recognized the constraints of their small cohorts. On top of the limited sample size, there are only three RCTs evaluating PBM for dry AMD, highlighting the need for additional and larger RCTs. Additionally, some might argue that the pooled sample size lacked statistical power for measuring outcomes such as the drusen volume. This issue arises because drusen size may vary, and drusen regression is a well-described phenomenon in the natural course of the disease, [ 24 , 25 ] underscoring the need for larger sample sizes to draw more robust conclusions [ 26 ]. All these data and insights were corroborated by our TSA, which revealed that the existing pooled sample did not meet the required information size to make statistical inferences.

A significant challenge in evaluating treatments for dry AMD is selecting appropriate clinical endpoints. The FDA only recognizes GA volume as a valid outcome for dry AMD, whereas visual acuity and changes in drusen volume are not accepted by the regulatory agency [ 3 ]. This obstacle in finding appropriate measuring outcomes may explain the barriers that current studies on PBM face when trying to assess treatment efficacy in dry AMD. This is reflected heavily in the quality assessment, where all the studies were deemed to be at high risk of bias, consistent with evaluation of a previous meta-analysis [ 8 ]. One of the reasons for this high risk of bias was the reliance on BCVA as a measure of efficacy.

It is highly questionable whether BCVA stands as an optimal measure for treatment efficacy for drusen, since visual acuity may not be sensitive enough to detect changes in visual function in patients with intermediate AMD [ 27 ]. Another study showed lack of correlation between large drusen and BCVA [ 28 ]. Visual acuity has also shown major variations in intermediate AMD, which could potentially interfere with results. [ 21 ].

Additionally, the application of short-term drusen volume tracking as an effective endpoint for assessing efficacy in AMD has its restrictions. Studies with extended durations have demonstrated that a reduction in drusen can actually be indicative of a risk for progressing to advanced stages of AMD [ 3 , 24 , 25 ].

Our study has limitations. First, the small size of our pooled population may have hindered our statistical power, despite the inclusion of all studies that met eligibility criteria. Second, the absence of patient-level data precluded assessment of subgroup analyses and whether individual factors may interfere in the relative efficacy of PBM in this patient population. Finally, we could not assess the incidence of new-onset GA owing to the incomplete reporting in some of the individual studies.

In this meta-analysis evaluating PBM therapy for patients with dry AMD, there was a statistically significant improvement in visual acuity and drusen volumes, but not in incidence of GA. However, definitive statistical inferences are limited by an insufficient sample size, as indicated by the TSA. In addition, the significant results in terms of visual acuity and drusen volumes did not translate into clinically important benefits, as they did not meet the MCID casting doubt on PBM’s real-world efficacy. Larger RCTs with longer follow ups and more appropriate outcome measures are warranted to conclusively evaluate the role of PBM in patients with dry AMD.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Related macular degeneration

Related eye disease studies

Corrected visual acuity

Confidence intervals

Early treatment of diabetic retinopathy study

Food and Drug Administration

• Geographic atrophy

Minimum clinically important differences

Mean difference

• Photobiomodulation

Preferred Reporting Items for Systematic Reviews and Meta-Analysis

International Prospective Register of Systematic Reviews

Randomized controlled trial

Required information size

Risk of Bias Assessment Tool 2

Standard deviation

Trial sequential analysis

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Acknowledgements

We thank Rhanderson Cardoso, MD, FACC for his review of the manuscript.

Author information

Tiago N. O. Rassi and Lucas M. Barbosa contributed equally to this work.

Authors and Affiliations

Department of Ophthalmology, Banco de Olhos Foundation of Goiás, Goiânia, Brazil

Tiago N. O. Rassi

Department of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil

Lucas M. Barbosa

Department of Medicine, Faculty of Medical Science of Paraíba, João Pessoa, Brazil

Sacha Pereira

Department of Ophthalmology, Federal University of São Paulo, São Paulo, Brazil

Tiago N. O. Rassi, Eduardo A. Novais, Fernando Penha, Luiz Roisman & Mauricio Maia

Department of Ophthalmology, Regional University of Blumenau, Blumenau, Brazil

Fernando Penha

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Rassi, T.N., Barbosa, L.M., Pereira, S. et al. Photobiomodulation efficacy in age-related macular degeneration: a systematic review and meta-analysis of randomized clinical trials. Int J Retin Vitr 10 , 54 (2024). https://doi.org/10.1186/s40942-024-00569-x

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Sentiment-aware drug recommendations with a focus on symptom-condition mapping

• Original Research
• Published: 19 August 2024

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• E. Anbazhagan   ORCID: orcid.org/0009-0005-8149-5600 1 ,
• E. Sophiya 2 &
• R. Prasanna Kumar 1  

The adoption of digital health records and the rise of online medical forums resulted in massive volumes of unstructured healthcare data. Most of the data used by traditional drug recommendation systems is obtained from patient Electronic Health Records (EHR) and subjective feedback and experiences included in patient evaluations. Nevertheless, the current systems based on sentiment analysis fail consider Symptom based diagnosis whereas researches that proposes Graph models doesn’t not include patient satisfaction and Health History as some has specific needs. To address the draw backs of existing drug recommendation systems, this study suggests a novel approach that combines symptom-disease mapping with sentiment analysis of patient reviews. The primary objective of the research is to utilize machine learning classifiers to make symptom-based predictions about probable medical conditions as Phase I. Then, before being fed into sequence network and machine learning models, patient reviews that are relevant to the predicted condition are filtered as Phase II. This method generates probabilities for suggesting certain drugs by evaluating sentiments and incorporating review ratings. With a Performance score of Ensemble Model up to 99.25% in Phase I and accuracy of 99.45% for sentiment analyser in Phase II. The performance of the model was evaluated based on accuracy, Receiver Operating Characteristic Curve (ROC)-Area Under Curve (AUC) score, sensitivity, selectivity. The proposed system helps in recommending the optimal drug for any type of symptom samples which is available in database.

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E. Anbazhagan: Manuscript draft preparation, Methodology, Experimentation, Algorithm design. E. Sophiya: Algorithm design, Conceptualization, Supervision, Editing. R. Prasanna Kumar: Algorithm design, Experimentation, Supervision, Editing.

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Anbazhagan, E., Sophiya, E. & Prasanna Kumar, R. Sentiment-aware drug recommendations with a focus on symptom-condition mapping. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-02091-7

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Hyaluronic acid in bone regeneration: systematic review and meta-analysis.

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Lorenzi, C.; Leggeri, A.; Cammarota, I.; Carosi, P.; Mazzetti, V.; Arcuri, C. Hyaluronic Acid in Bone Regeneration: Systematic Review and Meta-Analysis. Dent. J. 2024 , 12 , 263. https://doi.org/10.3390/dj12080263

Lorenzi C, Leggeri A, Cammarota I, Carosi P, Mazzetti V, Arcuri C. Hyaluronic Acid in Bone Regeneration: Systematic Review and Meta-Analysis. Dentistry Journal . 2024; 12(8):263. https://doi.org/10.3390/dj12080263

Lorenzi, Claudia, Andrea Leggeri, Ilaria Cammarota, Paolo Carosi, Vincenzo Mazzetti, and Claudio Arcuri. 2024. "Hyaluronic Acid in Bone Regeneration: Systematic Review and Meta-Analysis" Dentistry Journal 12, no. 8: 263. https://doi.org/10.3390/dj12080263

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Published on 19.8.2024 in Vol 26 (2024)

Immersive and Nonimmersive Virtual Reality–Assisted Active Training in Chronic Musculoskeletal Pain: Systematic Review and Meta-Analysis

Authors of this article:

• Hermione Hin Man Lo 1, 2 , BN, MScN, MPH   ; 
• Mengting Zhu 2 , MSc   ; 
• Zihui Zou 2 , MPH   ; 
• Cho Lee Wong 1 , BN, MSc, PhD   ; 
• Suzanne Hoi Shan Lo 1 , BN, MSc, PhD   ; 
• Vincent Chi-Ho Chung 2 , B Chin Med, BSs Biomed Sci, MSc, PhD   ; 
• Samuel Yeung-Shan Wong 2 , MD, MPH, CCFP   ; 
• Regina Wing Shan Sit 2 , MD, MBBS, DCH, DPD, PDip Community Geriatrics, DipMED  

1 Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)

2 Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)

Corresponding Author:

Regina Wing Shan Sit, MD, MBBS, DCH, DPD, PDip Community Geriatrics, DipMED

Jockey Club School of Public Health and Primary Care

Faculty of Medicine

The Chinese University of Hong Kong

Room 209, Public Health Building, Prince of Wales Hospital

30-32 Ngan Shing Street, Shatin, New Territories

China (Hong Kong)

Phone: 852 2252 8452

Email: [email protected]

Background: Virtual reality (VR) in different immersive conditions has been increasingly used as a nonpharmacological method for managing chronic musculoskeletal pain.

Objective: We aimed to assess the effectiveness of VR-assisted active training versus conventional exercise or physiotherapy in chronic musculoskeletal pain and to analyze the effects of immersive versus nonimmersive VR on pain outcomes.

Methods: This systematic review of randomized control trials (RCTs) searched PubMed, Scopus, and Web of Science databases from inception to June 9, 2024. RCTs comparing adults with chronic musculoskeletal pain receiving VR-assisted training were included. The primary outcome was pain intensity; secondary outcomes included functional disability and kinesiophobia. Available data were pooled in a meta-analysis. Studies were graded using the Cochrane Risk-of-Bias Tool version 2.

Results: In total, 28 RCTs including 1114 participants with some concerns for a high risk of bias were identified, and 25 RCTs were included in the meta-analysis. In low back pain, short-term outcomes measured post intervention showed that nonimmersive VR is effective in reducing pain (standardized mean difference [SMD] –1.79, 95% CI –2.72 to –0.87; P <.001), improving disability (SMD –0.44, 95% CI –0.72 to –0.16; P =.002), and kinesiophobia (SMD –2.94, 95% CI –5.20 to –0.68; P =.01). Intermediate-term outcomes measured at 6 months also showed that nonimmersive VR is effective in reducing pain (SMD –8.15, 95% CI –15.29 to –1.01; P =.03), and kinesiophobia (SMD –4.28, 95% CI –8.12 to –0.44; P =.03) compared to conventional active training. For neck pain, immersive VR reduced pain intensity (SMD –0.55, 95% CI –1.02 to –0.08; P =.02) but not disability and kinesiophobia in the short term. No statistical significances were detected for knee pain or other pain regions at all time points. In addition, 2 (8%) studies had a high risk of bias.

Conclusions: Both nonimmersive and immersive VR–assisted active training is effective in reducing back and neck pain symptoms. Our study findings suggest that VR is effective in alleviating chronic musculoskeletal pain.

Trial Registration: PROSPERO CRD42022302912; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=302912

Introduction

Chronic musculoskeletal pain is a worldwide health problem with varying effects on physical, psychological, and social functioning [ 1 , 2 ]. According to the Global Burden of Disease Study of 2019, chronic musculoskeletal pain, especially low back pain, is the leading cause of disability worldwide, resulted in 149 million years of life lost [ 3 ]. The burden is expected to increase with an aging population and longevity [ 4 ].

Multimodal care is often needed in the management of chronic pain, with the aim of maintaining physical functioning and psychosocial well-being [ 5 ]. Exercise therapy is a well-known nonpharmacological modality in chronic pain management, with positive effects on pain intensity, physical function, sleep, and the quality of life [ 6 ]. Its additional benefits on happiness through the release of endorphins, serotonins, dopamine, and other “reward” chemicals have been demonstrated among individuals with chronic pain and depression [ 7 , 8 ]. Recent studies have suggested that technological advancements may increase the attractiveness of these active physical training programs, thus further improving compliance, adherence, and clinical outcomes [ 9 , 10 ].

Virtual reality (VR) is a digital technology that creates “a sense of presence in an computer-generated, three-dimensional, interactive environment with different immersive conditions” through head-mounted devices (HMDs), body-tracking sensors, and direct user input devices [ 11 ]. The use of VR promotes physical activity by increasing energy expenditure for fitness [ 12 ]. The capability of VR to reduce pain has mostly been attributed to active distraction (visual, auditory, and tactile input through interaction with a VR environment), which is understood to reduce resources available for the perception and elaboration of pain, thus diminishing subjective pain experience [ 13 ]. Gaming technology with motivational and affectively rewarding elements, as well as the goal-oriented interaction with a virtual environment, is suggested to have greater pain reduction [ 14 ].

An increasing number of trials have evaluated the role of VR-assisted active training in chronic musculoskeletal pain. Yet, within VR applications, an important distinction can be made between immersive and nonimmersive media, which differ in spatial presence [ 15 ]. With immersive technology, participants view the full panorama and are essentially inside the created environment. In a nonimmersive environment, virtual content is based on how the device (personal computer, smartphone, or tablet) is moved or rotated, and participants are only external observers. Whether immersive or nonimmersive VR is better for pain management remains unclear.

The aim of this systematic review was to assess the effectiveness of VR-assisted active training versus conventional active controls for musculoskeletal pain in different regions and to analyze the effects of immersive versus nonimmersive VR on validated pain outcomes.

Study Design

This systematic review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines [ 16 ]. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) registry (ID CRD42022302912).

Search Strategy

Two independent reviewers (authors HHML and ZZ) independently screened papers from PubMed, Scopus, and the Web of Science. A systematic search was conducted from inception to June 9, 2024. A backward reference search was used to increase the yield of eligible studies. Only English papers with full text available were included.

Our search strategy had 2 components for “chronic primary musculoskeletal pain” and “virtual reality.” Keywords for the population were “cervical pain” OR “neck pain” OR “shoulder pain” OR “thoracic pain” OR “back pain” OR “low back pain” OR “joint pain” OR “arthralgia” OR “knee pain” OR “ankle pain” OR “limb pain” OR “osteoarthritis NOT structural” or “degenerative joint.” Keywords for the intervention were “virtual reality” OR “augmented reality” OR “mixed reality.” Please refer to Multimedia Appendix 1 for detailed search strategies.

Eligibility Criteria

All parallel or cross-over randomized controlled trials (RCTs) that evaluated the effectiveness of VR-assisted active training in chronic musculoskeletal pain were included. Our review included both 2-arm and multiarm trials [ 17 ]. Chronic musculoskeletal pain was defined as pain that lasts for more than 3 months persistently or intermittently, including regional pain (joints, limbs, back, or neck), a degenerative joint condition (eg, osteoarthritis), and musculoskeletal complaints that fall under the “chronic primary pain” classification of the International Classification of Disease, 11th Revision [ 18 ]. We included all VR interventions that create synchronized motion-based interactions with computer-generated objects and provide a sense of “presence” for users. Presence is defined as VR users’ feeling of being immersed in a computer-generated environment via HMDs (immersive) or screens (nonimmersive), user input devices, body motion sensors, or commercial video game consoles [ 19 , 20 ]. To compare the effects of VR-assisted active training, we included studies with control groups using conventional exercise therapy or physiotherapy. Detailed inclusion and exclusion criteria are shown in Textbox 1 .

Inclusion criteria

• Randomized controlled trials (RCTs)
• Aged ≥18 years, with chronic musculoskeletal pain
• Virtual reality (VR)–assisted exercise therapy or physiotherapy
• Active training in comparison groups, including exercise therapy or conventional active physiotherapy

Exclusion criteria

• Cancer-related pain or autoimmune arthritis
• Psychotherapies
• Waitlist controls/daily life routine
• Passive physiotherapy in controls
• VR-assisted controls

Outcome Measures

The primary outcome was pain intensity. To be eligible, studies had to measure pain intensity using the Visual Analog Scale, the Numerical Rating Scale, the McGill Pain Questionnaire, the Chronic Pain Grade Scale, or other validated questionnaires [ 21 , 22 ]. Secondary outcomes were functional disability measured using disease-specific scales, such as the Roland-Morris Disability Questionnaire (RMDQ) [ 23 ] or the Oswestry Disability Index (ODI) [ 24 ] for chronic low back pain; the Neck Disability Index or the Neck Pain Disability Scale for chronic neck pain [ 25 - 27 ]; the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) for knee pain [ 28 ]; the Disabilities of the Arm, Shoulder, and Hand Questionnaire for shoulder pain [ 29 ]; and other validated questionnaires. Psychological status was measured through kinesiophobia, an emotional and cognitive factor that leads to maladaptive behaviors [ 30 , 31 ]; studies have shown that kinesiophobia is associated with more pain and disability and a lower quality of life [ 30 ]. Kinesiophobia was assessed using the Fear-Avoidance Beliefs Questionnaire [ 32 ] and the TAMPA Scale of Kinesiophobia [ 33 , 34 ].

Study Selection and Data Extraction

Studies retrieved from the databases were uploaded to Covidence online systematic review software (Veritas Health Innovation). Two independent reviewers (HHML and ZZ) performed title and abstract screening of the retrieved literature for potentially eligible trials. The full text of selected papers was then retrieved and screened against the inclusion and exclusion criteria. Both reviewers (HHML and ZZ) independently judged the eligibility of the full text retrieved, and disagreements between the 2 reviewers were resolved by a third reviewer (author RWSS).

Data were then extracted from the selected papers by the first reviewer (HHML) and cross-checked by the second reviewer (ZZ). The extraction table headings included the first author, the year of publication, chronic pain subtypes, the sample size analyzed, the intervention group, the control group, the dosage of interventions, the mean age, outcomes (in terms of the mean difference [MD]), and assessment time points in weeks. Two independent reviewers (HHML and ZZ) then extracted MDs and SDs for the following domains: (1) pain intensity, (2) disability, and (3) kinesiophobia.

Risk-of-Bias Assessment

Selected studies were assessed according to the intention to treat using the Cochrane Risk-of-Bias Tool version 2 (RoB 2). RoB 2 covers all types of biases that affect the results of RCTs: (1) bias due to randomization, (2) bias caused by deviations from intended interventions, (3) missing outcome data that results in bias, (4) bias in outcome measurement, and (5) the selection bias of reported results. Two reviewers (HHML and MZ) assessed the risk of bias independently for the selected papers, while a third reviewer (RWSS) acted as the arbitrator. A conclusion for risk-of-bias judgment was made by consensus.

Publication bias was assessed for the meta-analysis with 10 or more RCTs and was determined by constructing a funnel plot with the standard error against the effect size [ 35 ].

Quality of Evidence

The Grading of Recommendation Assessment, Development, and Evaluation (GRADE) approach was used to assess the quality of evidence with GRADEpro software [ 36 ]. The quality of evidence for pain intensity, disability, and kinesiophobia at different time points was assessed separately. Evidence was downgraded if (1) the risk of bias was apparent (1 study showed a high risk, and 24 studies showed some concerns); (2) inconsistency was demonstrated, with I 2 >50%; (3) there was indirectness in participants or comparators (ie, whether participants or comparators aligned and compared with our research question, eg, participants had chronic musculoskeletal pain and comparators used conventional active training/physical therapy); (4) evidence of imprecision (when the effect size was large, ie standardized mean difference [SMD]>0.8 or MD>minimally clinically important difference [MCID; 95% CI], but the total sample size was small) [ 37 ]; or (5) there was publication bias (funnel plots were visually inspected when at least 10 trials were included in the meta-analysis). An overall GRADE rating was agreed upon (HHML and MZ) for each included study at 4 levels: very low, low, moderate, and high.

Statistical Analysis

All meta-analyses were conducted using Review Manager (RevMan version 5.4) software (Cochrane). Pairwise meta-analysis was performed using a random effects model according to nonimmersive or immersive VR interventions [ 38 ]. Pain intensity, disability, and kinesiophobia were analyzed according to pain regions. Regarding the assessment time points, analysis was conducted in the immediate postintervention period (short term) and at 6 months (intermediate term). In our study, we defined short-term pain outcomes at 12 weeks and intermediate-term pain outcomes at 6 months. These time frames were established based on our clinical experience and the expected response timeline for the pain interventions used. We chose a 12-week period for assessing short-term pain outcomes as it aligns with the typical clinical trajectory observed in patients receiving pain interventions. During this period, patients are likely to experience the initial benefits of the treatment, and early therapeutic effects are most evident. The 24-week mark was selected to represent intermediate-term pain outcomes based on the continued progression of therapeutic effects and sustained pain relief observed in clinical practice. SMDs were used to measure continuous outcomes with more than 1 measuring scale. The SMD was clinically interpreted as Cohen d (SMD 0.2 was considered a small effect, 0.5 was considered a moderate effect and clinically important, and 0.8 was considered a large effect) [ 39 , 40 ]. Weighted mean differences (MDs) with 95% CIs were used to measure continuous outcomes within a domain on similar scales, and relevant minimal clinical important differences were used to assess clinical significance. Heterogeneity between studies was further explored by referring to I 2 statistics for all outcomes: I 2 <25%, low heterogeneity; I 2 =25%-50%, moderate heterogeneity; and I 2 >50%, high heterogeneity [ 41 ].

Eligible Studies

A total of 2753 papers were screened after removing duplicates. After title and abstract screening, 30 (1.1%) papers were selected for full-text retrieval and screening. The remaining reports and 6 papers from the backward reference search were screened by 2 independent reviewers (HHML and MZ), and 8 (22.2%) papers were excluded for the following reasons: not RCTs (n=3, 37.5%), passive physiotherapy in controls (n=1, 12.5%), VR-assisted controls (n=1, 12.5%), waitlist controls (n=1, 12.5%), and duplicate studies (n=2, 25%). A total of 28 (93.3%) of 30 studies were included in the final qualitative synthesis and 25 (83.3%) of the 30 studies were included in the meta-analysis synthesis ( Figure 1 ) [ 42 - 65 ]. Furthermore, 3 (10.7%) studies could not be pooled: 1 (33.3%) single-out nonimmersive VR study on neck pain [ 66 ], 1 (33.3%) RCT on different joint pains [ 60 ], and 1 (33.3%) study on shoulder pain [ 59 ].

Characteristics of Included Trials

Table S1 in Multimedia Appendix 2 summarizes all the 28 RCTs [ 42 - 69 ] selected. The mean age of the study sample was 42.50 (SD 3.49) years in the VR intervention group and 42.93 (SD 3.73) years in the control group. The sample sizes ranged from 19 (1.7%) to 84 (7.3%), with a total sample size of 1144. The average proportion of female participants was 49.3% (n=564) in all trials, excluding 3 (10.7%) trials [ 42 , 58 , 67 ] that did not provide participants’ sex ratio. In addition, 18 (64.3%) studies [ 42 - 47 , 54 - 63 , 66 - 68 ] used nonimmersive VR, while 8 studies [ 48 - 53 , 64 , 65 , 69 ] used immersive VR. Each VR session lasted from 10 to 45 minutes (mean 26.54, SD 9.05), except 4 (28.6%) studies [ 43 , 48 , 49 , 65 ] that did not report the VR intervention time. The mean total number of sessions was 15.46 (SD 7.18), and the frequency ranged from a single session to 5 sessions/week. Control groups included conventional physical therapy, such as balance and core stabilization training, walking, and audio-guided, isokinetic dynamometer–assisted, proprioceptive, kinematic, and sensorimotor exercises. Furthermore, 4 (14.3%) studies [ 53 , 60 , 64 , 69 ] reported no adverse events during the trials. Minor simulator sickness symptoms (eg, dizziness, nausea, headache) were reported in 2 (7.1%) studies [ 50 , 51 ].

All included studies were assessed with RoB 2 ( Figure 2 ). Overall, 26 (92.9%) studies [ 42 - 44 , 46 - 63 , 66 - 70 ] were rated as having “some concerns” of bias, and 2 (7.1%) studies [ 45 , 65 ] were rated as having a “high risk” of bias. In the domain of “bias arising from the randomization process,” 15 (53.6%) studies [ 42 - 44 , 46 - 49 , 51 , 57 , 61 , 63 , 65 , 67 - 69 ] had low bias and 13 (46.4%) studies [ 45 , 50 , 52 - 56 , 58 - 60 , 62 , 64 , 66 ] had some bias. In the domain of “deviations from the intended interventions,” all 28 (100%) studies had some bias due to failure to blind participants or delivery of interventions. In the domain of “missing outcome data,” 24 (85.7%) studies [ 42 - 44 , 46 - 51 , 53 - 55 , 57 - 61 , 63 - 66 , 68 , 69 ] had low risk of bias, 3 (10.7%) studies [ 52 , 56 , 62 ] had some concerns regarding bias, and 1 (3.6%) study [ 45 ] had high risk of bias. In the domain of “bias arising from measurement of the outcome,” 12 (42.9%) studies [ 44 , 46 - 51 , 53 - 55 , 57 - 61 , 63 , 64 , 66 , 67 ] had low bias and 16 (57.1%) studies [ 42 , 43 , 45 , 52 , 53 , 55 , 56 , 62 , 65 , 68 , 69 ] had some bias. The absence of blinding outcome assessors or data analysts was observed in studies with some bias. In the domain of “selection of reported results,” 7 (25%) studies [ 45 - 47 , 51 , 52 , 60 , 64 , 66 ] had low risk of bias, 20 (71.4%) studies [ 42 - 44 , 47 , 49 , 50 , 53 - 59 , 61 - 63 , 67 - 69 ] had some concerns regarding bias, and 1 (3.6%) study [ 65 ] had high risk of bias. This domain was mainly affected by the absence of prespecified analysis plans in preliminary and pilot studies.

Pain Region: Back

Pain intensity.

In the short term, 15 (53.6%; n=563, 49.2%, participants) of the 28 RCTs [ 43 - 48 , 54 - 58 , 62 - 64 , 67 ] were eligible for pooling. Results from 13 (46.4%) RCTs favored the use of nonimmersive VR in reducing pain intensity (SMD –1.79, 95% CI –2.72 to –0.87; P <.001), with high heterogeneity ( I 2 = 94%) compared to active training. Results of immersive VR from 2 (7.1%) RCTs were statistically insignificant (SMD 0.04, 95% CI –1.10 to 1.19; P =.94), as shown in Figure 3 a. Visual inspection of funnel plots ( Figure 3 b) indicated publication bias in our meta-analysis.

In the intermediate term, 4 (14.3%; n=135, 11.8%, participants) RCTs [ 44 - 47 ] were eligible for pooling; again, results favored nonimmersive VR in reducing pain intensity (SMD –8.15, 95% CI –15.29 to –1.01; P =.03), with high heterogeneity ( I 2 = 98%), as shown in Figure 3 c.

Functional Disability

Of the 28 RCTs, 6 (21.4%; n=229, 20%, participants) [ 45 , 48 , 54 , 55 , 58 , 67 ] were eligible for pooling. The ODI was extracted over the RMDQ in the pooling because of its higher reliability and relatively lower measurement error [ 71 ]. Results favored the use of nonimmersive VR over conventional active training in improving back disability in the short term (SMD –0.44, 95% CI –0.72 to –0.16; P =.002) and of low heterogeneity ( I 2 =6%), as shown in Figure 4 .

Kinesiophobia

In the short term, pooled results of 5 (17.9%; n=135, 11.8%, participants) of the 28 RCTs [ 45 - 47 , 55 ] favored nonimmersive VR over conventional active training (SMD –2.94, 95% CI –5.20 to –0.68; P =.01), with high heterogeneity ( I 2 =95%). Pooled results of immersive VR from 2 (7.1%; n=66, 5.8%, participants) RCTs were statistically insignificant (SMD –1.17, 95% CI –2.59 to 0.26; P =.11), with high heterogeneity ( I 2 =85%), as shown in Figure 5 a.

In the intermediate term, 3 (10.7%; n=105, 9.2%, participants) RCTs [ 45 - 47 ] were pooled. Pooled results favored nonimmersive VR in kinesiophobia (SMD –4.28, 95% CI –8.12 to –0.44; P =.03), with high heterogeneity ( I 2 =96%), as shown in Figure 5 b.

Pain Region: Neck

Of the 28 RCTs, 7 (25%; n=316, 6.9%, participants) [ 49 - 53 , 65 , 69 ] were eligible for pooling for immersive VR. Pooled results favored immersive VR over conventional active training in reducing pain intensity in the short term (SMD –0.55, 95% CI –1.02 to –0.08; P =.02), with high heterogeneity ( I 2 =75%), as shown in Figure 6 .

Of the 28 RCTs, 6 (21.4%; n=282, 24.7%, participants) [ 49 - 52 , 65 , 69 ] were eligible for pooling for immersive VR. Pooled results favored immersive VR over conventional active training in reducing neck disability in the short term (MD –2.59, 95% CI –3.51 to –1.67; P <.001) and no heterogeneity ( I 2 =0%), as shown in Figure 7 .

Of the 28 RCTs, 3 (10.7%; n=153, 13.4%, participants) [ 49 - 51 ] were eligible for short-term pooling. Pooled results showed no significant effect of immersive VR no kinesiophobia (SMD –0.09, 95% CI –0.40 to 0.23; P =.59) and no heterogeneity ( I 2 =0%), as shown in Figure 8 .

Pain Region: Knee

Of the 28 RCTs, 3 (10.7%; n=160, 14%, participants) [ 42 , 61 , 68 ] on nonimmersive VR were pooled in the short term (SMD –0.74, 95% CI –1.86 to 0.37; P =.19), with high heterogeneity ( I 2 =90%), as shown in Figure 9 .

Of the 28 RCTs, 3 (10.7%; n=160, 14%, participants) [ 42 , 61 , 68 ] were pooled in the short term. Pooled results (MD –11.36, 95% CI –33.95 to 11.23; P =.32) suggested no statistical and clinical significance for nonimmersive VR versus controls, with high heterogeneity ( I 2 =98%), as shown in Figure 10 .

However, due to the small number of studies, pooling was not possible for chronic shoulder, hip, and other joint pain subgroups.

Quality of Evidence With the GRADE Approach

The overall quality of evidence ranged from very low to moderate in both nonimmersive VR ( Table 1 ) and immersive VR ( Table 2 ). In low back pain, the assessment showed very low certainty for both nonimmersive and immersive VR–assisted physical therapy in reducing pain intensity and kinesiophobia in the short and the intermediate term and low certainty for nonimmersive VR in reducing functional disability. For neck pain, we found low-to-moderate certainty for immersive VR–assisted physical therapy in reducing pain, functional disability, and kinesiophobia. For knee pain, very low certainty for nonimmersive VR–assisted physical therapy had no statistical significance for improving pain and functional disability in the short term.

a VR: virtual reality.

b Time points: “short term” defined as postintervention; “intermediate term” defined as 6 months.

c SMD: standardized mean difference.

d MD: mean difference.

e Certainty of evidence: high, further research is very unlikely to change our confidence in the estimate of effects; moderate, further research is likely to have an important impact on our confidence in the estimate of effects and may change the estimate; low, further research is very likely to have an important impact on our confidence in the estimate of effects and is likely to change the estimate; very low, any estimate of effect is very uncertain.

f GRADE: Grading of Recommendations, Assessment, Development, and Evaluations.

g Downgraded by 3 levels as the risk of bias was unclear or high in most included studies (–1), there was inconsistency in results (I 2 >50%; –1), there was imprecision due to a large effect size (SMD>0.8 or MD>MCID, 95% CI), but the total sample size was small (–1).

h Downgraded by 2 levels as the risk of bias was unclear or high in most included studies (–1), and there was indirectness in comparators (–1).

b “Short term” was defined as postintervention.

g Downgraded by 3 levels as the risk of bias was unclear or high in most included studies (–1), there was inconsistency in results (I 2 >50%; –1), and there was indirectness in comparators (–1).

h Downgraded by 2 levels as the risk of bias was unclear in most included studies (–1) and there was indirectness in comparators (–1).

i Downgraded by 2 levels as the risk of bias was unclear in most included studies (–1), there was imprecision due to a large effect size (SMD>0.8 or MD>MCID, 95% CI), but the total sample size was small (–1).

j Downgraded by 1 level as the risk of bias was unclear or high in most included studies (–1).

Principal Findings

For back pain, very-low-to-low-certainty evidence suggests that nonimmersive VR–assisted training is superior to conventional training in reducing pain, improving disability, and improving kinesiophobia in the short term, and the superior effects on pain and kinesiophobia are sustained in the intermediate term. The effect sizes detected in this study were large for pain intensity in the short and the intermediate term (ie, –1.50 and –8.15, respectively). The effect size for disability of –0.44 was moderate. The effect sizes were also large for kinesiophobia, at –2.29 in the short term and –4.28 in the intermediate term. For neck pain, low-to-moderate-certainty evidence suggests that immersive VR is effective in reducing pain and disability in the short term; the effect size of –0.55 was moderate. The mean difference in disability was –2.59, which was lower than the MCID for a neck disability change of –7.5 [ 72 ]. However, no statistically significant effects were detected on kinesiophobia in both short and intermediate terms. For knee pain, only nonimmersive VR was available, and we did not detect any statistically significant difference between nonimmersive VR and control groups in knee pain and function. There are only a few studies on other pain regions, such as the shoulder, hip, and mixed musculoskeletal regions, so pooling was not possible, and the evidence was inconclusive. Our findings suggest that VR-assisted training is superior to conventional active training in managing chronic musculoskeletal pain.

When investigating the effect size of the included studies, especially in nonimmersive VR pooling for chronic back pain, we found that 5 main studies [ 44 , 46 , 47 , 63 , 67 ], which were supervised by physiotherapists in both intervention and control arms, showed large short- and intermediate-term pain reduction effects. Supervision in exercise therapy enhances treatment adherence, achieving the high dosage necessary to demonstrate the VR-assisted effect [ 73 ]. Conversely, 5 studies [ 50 , 52 , 53 , 65 , 69 ] on immersive VR–assisted training for neck pain incorporated conservative treatments, such as strengthening and kinematic exercises, to augment their therapeutic effects. These differences in intervention and control designs may have introduced heterogeneity across trials, alongside variations in the participants’ mean age, the athletes’ training background, the dosage of interventions, and the diversity of different VR hardware and software. For example, the hardware included horse simulators, the ProKin system, the Nintendo Wii system, Kinect Xbox 360, the Biodex Balance system, and high-definition television equipped with motion sensors. Although heterogeneity was high, it was inevitable in VR trials due to the unique features of innovations in digital technology. Therefore, we suggested that the high heterogeneity might affect the generalizability of results but should not demerit the clinical effects of VR in reducing back and neck pain.

Comparing the clinical effectiveness of immersive and nonimmersive VR is challenging because most studies on chronic low back pain have used nonimmersive VR, while those on chronic neck pain have used immersive VR. This discrepancy may be due to the use of HMDs in detecting cervical kinematics and range of motion during active training for neck pain, which is not required in studies on back pain. Our systematic review revealed that most studies on back pain have used software comprising ready-made recreational VR games or virtual simulated environments. Meanwhile, immersive therapeutic software aimed at creating presence, learning, and habit building has recently emerged for treating chronic low back pain [ 74 ]. This development extends the usefulness of immersive VR–assisted interventions by improving pain interference with activity, mood, and stress, which are commonly found in patients with chronic musculoskeletal pain.

Comparison With Other Reviews

Ahern et al [ 19 ] conducted a review of VR in patients with neck and back pain, with only 2 trials eligible for quantitative synthesis; they nevertheless reached the same conclusion that VR is effective in reducing back and neck pain intensity. Brea-Gómez et al [ 75 ] conducted a comprehensive review of studies on chronic back pain; 14 studies were included in the systematic review and 11 in the meta-analysis. Similar to our results, significant differences were found in favor of VR compared to control interventions in pain intensity and kinesiophobia in the short term, with effect sizes of –1.92 and –8.96, respectively; although they showed a trend favoring VR in reducing disability, only 2 trials were included in pooling, and the results were not statistically significant [ 75 ]. We included 6 trials in our pooling, allowing us to detect a more accurate effect size, with smaller CIs and statistical significance. Bordeleau et al [ 76 ] also reviewed the use of VR in back pain; 16 trials were included in the meta-analysis, and similar results were found, with VR statistically significantly improving back pain intensity over control interventions. Yet, the authors did not evaluate the role of VR in back pain disability and did not analyze the effects based on the level of immersiveness [ 76 ]. Li et al [ 77 ] found similar immediate VR effects on back pain but not at 3-6 months, possibly due to high heterogeneity and inconsistency from pooling waitlist controls, violating the assumption of a common effect size [ 78 ]. Our findings on reducing pain intensity and disability were also similar to those of the systematic reviews conducted by Guo et al [ 79 ] and Brea-Gomez et al [ 80 ]. Byra and Czernicki [ 81 ] reviewed the effectiveness of VR rehabilitation in knee and hip osteoarthritis with or without arthroplasty; meta-analysis was not performed due to heterogeneous study populations and outcome measurements [ 81 ]. Although we found a trend favoring the use of VR in knee pain, the small number of studies limited its power to detect statistical significance [ 81 ]. Kantha et al [ 82 ] supported the use of VR over conventional physical therapy in reducing pain but not in improving disability; their meta-analysis of 5 included studies also favored the use of nonimmersive VR, which was similar to our results [ 82 ]. Yet, their results were drawn from pooling of mixed pain regions.

Strengthens and Limitations

The strengths of this study include a comprehensive review of VR in different musculoskeletal pain regions, not only in the short term but also in the intermediate term. This is the first study to evaluate the degree of immersiveness in VR-assisted active training on validated pain outcomes. We used a rigorous methodology that conformed to best-practice guidelines [ 16 ].

There were several limitations. Although we increased the number of VR studies, the total participant sample size was still small, and quantitative syntheses included a small number of studies in most comparisons. For the same reason, we were unable to generate funnel plots to assess publication bias for most outcomes [ 35 ]. Finally, since chronic pain is a biopsychosocial condition, the inclusion of only VR-assisted active training and the exclusion of VR-assisted psychotherapy might potentially underestimate the true effect of VR on chronic pain [ 83 ].

Future Research and Clinical Implications

Future research needs to focus on the long-term effects of VR-assisted active training on chronic pain management. The joy and pleasure associated with VR interactions are attractive, but the excitement will fade with time. Therefore, it is essential to evaluate participants’ adherence to VR interventions [ 9 , 84 ]. Furthermore, trials should be conducted to evaluate other pain, especially knee pain, given that it is the most prevalent condition in the aging population [ 85 ]. The mechanism of action of VR in pain regulation will need to be elucidated for the best design of VR apps. Finally, cost-effectiveness should be evaluated to inform resource allocation of VR in clinical practice.

In summary, our study found that nonimmersive VR–assisted active training is superior to conventional active training in reducing pain intensity, functional disability, and kinesiophobia in low back pain in the short and the intermediate term. Immersive VR–assisted active training is effective in reducing the intensity of neck pain. Evidence on knee pain, shoulder pain, and hip pain remains inconclusive due to the small number of studies. Further high-quality VR trials with longer-term follow-up, adequate sample sizes, and cost-effectiveness analysis will inform the role of VR with different immersive levels in chronic musculoskeletal pain management.

Acknowledgments

We thank Dr Alexander Stamenkovic (PhD, Virginia Commonwealth University) and his research team for sharing their findings that supported our manuscript development.

Conflicts of Interest

None declared.

Search strategies.

Summary of included studies.

PRISMA checklist.

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• Rezaei I, Razeghi M, Ebrahimi S, Kayedi S, Rezaeian Zadeh A. A novel virtual reality technique (Cervigame©) compared to conventional proprioceptive training to treat neck pain: a randomized controlled trial. J Biomed Phys Eng. Nov 18, 2018;9(3):355-366. [ CrossRef ]
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• Guo Q, Zhang L, Han LL, Gui C, Chen G, Ling C, et al. Effects of virtual reality therapy combined with conventional rehabilitation on pain, kinematic function, and disability in patients with chronic neck pain: randomized controlled trial. JMIR Serious Games. Apr 24, 2024;12:e42829. [ FREE Full text ] [ CrossRef ] [ Medline ]
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• Chiarotto A, Maxwell L, Terwee C, Wells G, Tugwell P, Ostelo RW. Roland-Morris Disability Questionnaire and Oswestry Disability Index: which has better measurement properties for measuring physical functioning in nonspecific low back pain? Systematic review and meta-analysis. Phys Ther. Oct 2016;96(10):1620-1637. [ CrossRef ] [ Medline ]
• Carreon LY, Glassman SD, Campbell MJ, Anderson PA. Neck Disability Index, Short Form-36 Physical Component Summary, and pain scales for neck and arm pain: the minimum clinically important difference and substantial clinical benefit after cervical spine fusion. Spine J. Jun 2010;10(6):469-474. [ CrossRef ] [ Medline ]
• Hayden JA, van Tulder MW, Tomlinson G. Systematic review: strategies for using exercise therapy to improve outcomes in chronic low back pain. Ann Intern Med. May 03, 2005;142(9):776-785. [ CrossRef ] [ Medline ]
• Maddox T, Sparks CY, Oldstone L, Chibbaro M, Sackman J, Judge E, et al. Perspective: the promise of virtual reality as an immersive therapeutic. J Med Ext Real. Jan 01, 2024;1(1):13-20. [ CrossRef ]
• Brea-Gómez B, Torres-Sánchez I, Ortiz-Rubio A, Calvache-Mateo A, Cabrera-Martos I, López-López L, et al. Virtual reality in the treatment of adults with chronic low back pain: a systematic review and meta-analysis of randomized clinical trials. Int J Environ Res Public Health. Nov 11, 2021;18(22):11806. [ FREE Full text ] [ CrossRef ] [ Medline ]
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• Li R, Li Y, Kong Y, Li H, Hu D, Fu C, et al. Virtual reality–based training in chronic low back pain: systematic review and meta-analysis of randomized controlled trials. J Med Internet Res. Feb 26, 2024;26:e45406. [ FREE Full text ] [ CrossRef ] [ Medline ]
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Abbreviations

Edited by T de Azevedo Cardoso; submitted 07.05.23; peer-reviewed by M Gasmi , J Aulenkamp, PG Vajargah; comments to author 10.05.24; revised version received 20.06.24; accepted 04.07.24; published 19.08.24.

©Hermione Hin Man Lo, Mengting Zhu, Zihui Zou, Cho Lee Wong, Suzanne Hoi Shan Lo, Vincent Chi-Ho Chung, Samuel Yeung-Shan Wong, Regina Wing Shan Sit. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

• Introduction
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• Article Information

Contributing studies for clinically elevated depression symptoms are presented in order of largest to smallest prevalence rate. Square data markers represent prevalence rates, with lines around the marker indicating 95% CIs. The diamond data marker represents the overall effect size based on included studies.

Contributing studies for clinically elevated anxiety symptoms are presented in order of largest to smallest prevalence rate. Square data markers represent prevalence rates, with lines around the marker indicating 95% CIs. The diamond data marker represents the overall effect size based on included studies.

eTable 1. Example Search Strategy from Medline

eTable 2. Study Quality Evaluation Criteria

eTable 3. Quality Assessment of Studies Included

eTable 4. Sensitivity analysis excluding low quality studies (score=2) for moderators of the prevalence of clinically elevated depressive symptoms in children and adolescence during COVID-19

eTable 5. Sensitivity analysis excluding low quality studies (score=2) for moderators of the prevalence of clinically elevated anxiety symptoms in children and adolescence during COVID-19

eFigure 1. PRISMA diagram of review search strategy

eFigure 2. Funnel plot for studies included in the clinically elevated depressive symptoms

eFigure 3. Funnel plot for studies included in the clinically elevated anxiety symptoms

• Pediatric Depression and Anxiety Doubled During the Pandemic JAMA News From the JAMA Network October 5, 2021 Anita Slomski
• Guidelines Synopsis: Screening for Anxiety in Adolescent and Adult Women JAMA JAMA Clinical Guidelines Synopsis March 8, 2022 This JAMA Clinical Guidelines Synopsis summarizes the 2020 Women’s Preventive Services Initiative recommendation on screening for anxiety in adolescent and adult women. Tiffany I. Leung, MD, MPH; Adam S. Cifu, MD; Wei Wei Lee, MD, MPH
• Addressing the Global Crisis of Child and Adolescent Mental Health JAMA Pediatrics Editorial November 1, 2021 Tami D. Benton, MD; Rhonda C. Boyd, PhD; Wanjikũ F.M. Njoroge, MD
• Effect of the COVID-19 pandemic on Adolescents With Eating Disorders JAMA Pediatrics Comment & Response February 1, 2022 Thonmoy Dey, BSc; Zachariah John Mansell, BSc; Jasmin Ranu, BSc

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Racine N , McArthur BA , Cooke JE , Eirich R , Zhu J , Madigan S. Global Prevalence of Depressive and Anxiety Symptoms in Children and Adolescents During COVID-19 : A Meta-analysis . JAMA Pediatr. 2021;175(11):1142–1150. doi:10.1001/jamapediatrics.2021.2482

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Global Prevalence of Depressive and Anxiety Symptoms in Children and Adolescents During COVID-19 : A Meta-analysis

• 1 Department of Psychology, University of Calgary, Calgary, Alberta, Canada
• 2 Alberta Children’s Hospital Research Institute, Calgary, Alberta, Canada
• Editorial Addressing the Global Crisis of Child and Adolescent Mental Health Tami D. Benton, MD; Rhonda C. Boyd, PhD; Wanjikũ F.M. Njoroge, MD JAMA Pediatrics
• News From the JAMA Network Pediatric Depression and Anxiety Doubled During the Pandemic Anita Slomski JAMA
• JAMA Clinical Guidelines Synopsis Guidelines Synopsis: Screening for Anxiety in Adolescent and Adult Women Tiffany I. Leung, MD, MPH; Adam S. Cifu, MD; Wei Wei Lee, MD, MPH JAMA
• Comment & Response Effect of the COVID-19 pandemic on Adolescents With Eating Disorders Thonmoy Dey, BSc; Zachariah John Mansell, BSc; Jasmin Ranu, BSc JAMA Pediatrics

Question   What is the global prevalence of clinically elevated child and adolescent anxiety and depression symptoms during COVID-19?

Findings   In this meta-analysis of 29 studies including 80 879 youth globally, the pooled prevalence estimates of clinically elevated child and adolescent depression and anxiety were 25.2% and 20.5%, respectively. The prevalence of depression and anxiety symptoms during COVID-19 have doubled, compared with prepandemic estimates, and moderator analyses revealed that prevalence rates were higher when collected later in the pandemic, in older adolescents, and in girls.

Meaning   The global estimates of child and adolescent mental illness observed in the first year of the COVID-19 pandemic in this study indicate that the prevalence has significantly increased, remains high, and therefore warrants attention for mental health recovery planning.

Importance   Emerging research suggests that the global prevalence of child and adolescent mental illness has increased considerably during COVID-19. However, substantial variability in prevalence rates have been reported across the literature.

Objective   To ascertain more precise estimates of the global prevalence of child and adolescent clinically elevated depression and anxiety symptoms during COVID-19; to compare these rates with prepandemic estimates; and to examine whether demographic (eg, age, sex), geographical (ie, global region), or methodological (eg, pandemic data collection time point, informant of mental illness, study quality) factors explained variation in prevalence rates across studies.

Data Sources   Four databases were searched (PsycInfo, Embase, MEDLINE, and Cochrane Central Register of Controlled Trials) from January 1, 2020, to February 16, 2021, and unpublished studies were searched in PsycArXiv on March 8, 2021, for studies reporting on child/adolescent depression and anxiety symptoms. The search strategy combined search terms from 3 themes: (1) mental illness (including depression and anxiety), (2) COVID-19, and (3) children and adolescents (age ≤18 years). For PsycArXiv , the key terms COVID-19 , mental health , and child/adolescent were used.

Study Selection   Studies were included if they were published in English, had quantitative data, and reported prevalence of clinically elevated depression or anxiety in youth (age ≤18 years).

Data Extraction and Synthesis   A total of 3094 nonduplicate titles/abstracts were retrieved, and 136 full-text articles were reviewed. Data were analyzed from March 8 to 22, 2021.

Main Outcomes and Measures   Prevalence rates of clinically elevated depression and anxiety symptoms in youth.

Results   Random-effect meta-analyses were conducted. Twenty-nine studies including 80 879 participants met full inclusion criteria. Pooled prevalence estimates of clinically elevated depression and anxiety symptoms were 25.2% (95% CI, 21.2%-29.7%) and 20.5% (95% CI, 17.2%-24.4%), respectively. Moderator analyses revealed that the prevalence of clinically elevated depression and anxiety symptoms were higher in studies collected later in the pandemic and in girls. Depression symptoms were higher in older children.

Conclusions and Relevance   Pooled estimates obtained in the first year of the COVID-19 pandemic suggest that 1 in 4 youth globally are experiencing clinically elevated depression symptoms, while 1 in 5 youth are experiencing clinically elevated anxiety symptoms. These pooled estimates, which increased over time, are double of prepandemic estimates. An influx of mental health care utilization is expected, and allocation of resources to address child and adolescent mental health concerns are essential.

Prior to the COVID-19 pandemic, rates of clinically significant generalized anxiety and depressive symptoms in large youth cohorts were approximately 11.6% 1 and 12.9%, 2 respectively. Since COVID-19 was declared an international public health emergency, youth around the world have experienced dramatic disruptions to their everyday lives. 3 Youth are enduring pervasive social isolation and missed milestones, along with school closures, quarantine orders, increased family stress, and decreased peer interactions, all potential precipitants of psychological distress and mental health difficulties in youth. 4 - 7 Indeed, in both cross-sectional 8 , 9 and longitudinal studies 10 , 11 amassed to date, the prevalence of youth mental illness appears to have increased during the COVID-19 pandemic. 3 However, data collected vary considerably. Specifically, ranges from 2.2% 12 to 63.8% 13 and 1.8% 12 to 49.5% 13 for clinically elevated depression and anxiety symptoms, respectively. As governments and policy makers deploy and implement recovery plans, ascertaining precise estimates of the burden of mental illness for youth are urgently needed to inform service deployment and resource allocation.

Depression and generalized anxiety are 2 of the most common mental health concerns in youth. 14 Depressive symptoms, which include feelings of sadness, loss of interest and pleasure in activities, as well as disruption to regulatory functions such as sleep and appetite, 15 could be elevated during the pandemic as a result of social isolation due to school closures and physical distancing requirements. 6 Generalized anxiety symptoms in youth manifest as uncontrollable worry, fear, and hyperarousal. 15 Uncertainty, disruptions in daily routines, and concerns for the health and well-being of family and loved ones during the COVID-19 pandemic are likely associated with increases in generalized anxiety in youth. 16

When heterogeneity is observed across studies, as is the case with youth mental illness during COVID-19, it often points to the need to examine demographic, geographical, and methodological moderators. Moderator analyses can determine for whom and under what circumstances prevalence is higher vs lower. With regard to demographic factors, prevalence rates of mental illness both prior to and during the COVID-19 pandemic are differentially reported across child age and sex, with girls 17 , 18 and older children 17 , 19 being at greater risk for internalizing disorders. Studies have also shown that youth living in regions that experienced greater disease burden 2 and urban areas 20 had greater mental illness severity. Methodological characteristics of studies also have the potential to influence the estimated prevalence rates. For example, studies of poorer methodological quality may be more likely to overestimate prevalence rates. 21 The symptom reporter (ie, child vs parent) may also contribute to variability in the prevalence of mental illness across studies. Indeed, previous research prior to the pandemic has demonstrated that child and parent reports of internalizing symptoms vary, 22 with children/adolescents reporting more internalizing symptoms than parents. 23 Lastly, it is important to consider the role of data collection timing on potential prevalence rates. While feelings of stress and overwhelm may have been greater in the early months of the pandemic compared with later, 24 extended social isolation and school closures may have exerted mental health concerns.

Although a narrative systematic review of 6 studies early in the pandemic was conducted, 8 to our knowledge, no meta-analysis of prevalence rates of child and adolescent mental illness during the pandemic has been undertaken. In the current study, we conducted a meta-analysis of the global prevalence of clinically elevated symptoms of depression and anxiety (ie, exceeding a clinical cutoff score on a validated measure or falling in the moderate to severe symptom range of anxiety and depression) in youth during the first year of the COVID-19 pandemic. While research has documented a worsening of symptoms for children and youth with a wide range of anxiety disorders, 25 including social anxiety, 26 clinically elevated symptoms of generalized anxiety are the focus of the current meta-analysis. In addition to deriving pooled prevalence estimates, we examined demographic, geographical, and methodological factors that may explain between-study differences. Given that there have been several precipitants of psychological distress for youth during COVID-19, we hypothesized that pooled prevalence rates would be higher compared with prepandemic estimates. We also hypothesized that child mental illness would be higher among studies with older children, a higher percentage of female individuals, studies conducted later in the pandemic, and that higher-quality studies would have lower prevalence rates.

This systematic review was registered as a protocol with PROSPERO (CRD42020184903) and the Preferred Reporting Items for Systematic Reviews and Meta-analyses ( PRISMA ) reporting guideline was followed. 27 Ethics review was not required for the study. Electronic searches were conducted in collaboration with a health sciences librarian in PsycInfo, Cochrane Central Register of Controlled Trials (CENTRAL), Embase, and MEDLINE from inception to February 16, 2021. The search strategy (eTable 1 in the Supplement ) combined search terms from 3 themes: (1) mental illness (including depression and anxiety), (2) COVID-19, and (3) children and adolescents (age ≤18 years). Both database and subject headings were used to search keywords. As a result of the rapidly evolving nature of research during the COVID-19 pandemic, we also searched a repository of unpublished preprints, PsycArXiv . The key terms COVID-19 , mental health , and child/adolescent were used on March 8, 2021, and yielded 38 studies of which 1 met inclusion criteria.

The following inclusion criteria were applied: (1) sample was drawn from a general population; (2) proportion of individuals meeting clinical cutoff scores or falling in the moderate to severe symptom range of anxiety or depression as predetermined by validated self-report measures were provided; (3) data were collected during COVID-19; (4) participants were 18 years or younger; (5) study was empirical; and (6) studies were written in English. Samples of participants who may be affected differently from a mental health perspective during COVID-19 were excluded (eg, children with preexisting psychiatric diagnoses, children with chronic illnesses, children diagnosed or suspected of having COVID-19). We also excluded case studies and qualitative analyses.

Five (N.R., B.A.M., J.E.C., R.E. and J.Z.) authors used Covidence software (Covidence Inc) to review all abstracts and to determine if the study met criteria for inclusion. Twenty percent of abstracts reviewed for inclusion were double-coded, and the mean random agreement probability was 0.89; disagreements were resolved via consensus with the first author (N.R.). Two authors (N.R. and B.A.M.) reviewed full-text articles to determine if they met all inclusion criteria and the percent agreement was 0.80; discrepancies were resolved via consensus.

When studies met inclusion criteria, prevalence rates for anxiety and depression were extracted, as well as potential moderators. When more than 1 wave of data was provided, the wave with the largest sample size was selected. For 1 study in which both parent and youth reports were provided, 26 the youth report was selected, given research that they are the reliable informants of their own behavior. 28 The following moderators were extracted: (1) study quality (see the next subsection); (2) participant age (continuously as a mean); (3) sex (% female in a sample); (4) geographical region (eg, East Asia, Europe, North America), (5) informant (child, parent), (6) month in 2020 when data were collected (range, 1-12). Data from all studies were extracted by 1 coder and the first author (N.R.). Discrepancies were resolved via consensus.

Adapted from the National Institute of Health Quality Assessment Tool for Observation Cohort and Cross-Sectional Studies, a short 5-item questionnaire was used (eTable 2 in the Supplement ). 29 Studies were given a score of 0 (no) or 1 (yes) for each of the 5 criteria (validated measure; peer-reviewed, response rate ≥50%, objective assessment, sufficient exposure time) and summed to give a total score of 5. When information was unclear or not provided by the study authors, it was marked as 0 (no).

All included studies are from independent samples. Comprehensive Meta-Analysis version 3.0 (Biostat) software was used for data analysis. Pooled prevalence estimates with associated 95% confidence intervals around the estimate were computed. We weighted pooled prevalence estimates by the weight of the inverse of their variance, which gives greater weight to large sample sizes.

We used random-effects models to reflect the variations observed across studies and assessed between-study heterogeneity using the Q and I 2 statistics. Pooled prevalence is reported as an event rate (ie, 0.30) but interpreted as prevalence (ie, 30.0%). Significant Q statistics and I 2 values more than 75% suggest moderator analyses should be explored. 30 As recommended by Bornstein et al, 30 we examined categorical moderators when k of 10 or higher and a minimum cell size of k more than 3 were available. A P value of .05 was considered statistically significant. For continuous moderators, random-effect meta-regression analyses were conducted. Publication bias was examined using the Egger test 31 and by inspecting funnel plots for symmetry.

Our electronic search yielded 3094 nonduplicate records (eFigure 1 in the Supplement ). Based on the abstract review, a total of 136 full-text articles were retrieved to examine against inclusion criteria, and 29 nonoverlapping studies 10 , 12 , 13 , 17 , 19 , 20 , 26 , 32 - 53 met full inclusion criteria.

A total of 29 studies were included in the meta-analyses, of which 26 had youth symptom reports and 3 studies 39 , 42 , 48 had parent reports of child symptoms. As outlined in Table 1 , across all 29 studies, 80 879 participants were included, of which the mean (SD) perecentage of female individuals was 52.7% (12.3%), and the mean age was 13.0 years (range, 4.1-17.6 years). All studies provided binary reports of sex or gender. Sixteen studies (55.2%) were from East Asia, 4 were from Europe (13.8%), 6 were from North America (20.7%), 2 were from Central America and South America (6.9%), and 1 study was from the Middle East (3.4%). Eight studies (27.6%) reported having racial or ethnic minority participants with the mean across studies being 36.9%. Examining study quality, the mean score was 3.10 (range, 2-4; eTable 3 in the Supplement ).

The pooled prevalence from a random-effects meta-analysis of 26 studies revealed a pooled prevalence rate of 0.25 (95% CI, 0.21-0.30; Figure 1 ) or 25.2%. The funnel plot was symmetrical (eFigure 2 in the Supplement ); however, the Egger test was statistically significant (intercept, −9.5; 95% CI, −18.4 to −0.48; P  = .02). The between-study heterogeneity statistic was significant ( Q  = 4675.91; P  < .001; I 2  = 99.47). Significant moderators are reported below, and all moderator analyses are presented in Table 2 .

As the number of months in the year increased, so too did the prevalence of depressive symptoms ( b  = 0.26; 95% CI, 0.06-0.46). Prevalence rates were higher as child age increased ( b  = 0.08; 95% CI, 0.01-0.15), and as the percentage of female individuals ( b  = 0.03; 95% CI, 0.01-0.05) in samples increased. Sensitivity analyses removing low-quality studies were conducted (ie, scores of 2) 32 , 43 (eTable 4 in the Supplement ). Moderators remained significant, except for age, which became nonsignificant ( b  = 0.06; 95% CI, −0.02 to 0.13; P  = .14).

The overall pooled prevalence rate across 25 studies for elevated anxiety was 0.21 (95% CI, 0.17-0.24; Figure 2 ) or 20.5%. The funnel plot was symmetrical (eFigure 3 in the Supplement ) and the Egger test was nonsignificant (intercept, −6.24; 95% CI, −14.10 to 1.62; P  = .06). The heterogeneity statistic was significant ( Q  = 3300.17; P  < .001; I 2  = 99.27). Significant moderators are reported below, and all moderator analyses are presented in Table 3 .

As the number of months in the year increased, so too did the prevalence of anxiety symptoms ( b  = 0.27; 95% CI, 0.10-0.44). Prevalence rates of clinically elevated anxiety was higher as the percentage of female individuals in the sample increased ( b  = 0.04; 95% CI, 0.01-0.07) and also higher in European countries ( k  = 4; rate = 0.34; 95% CI, 0.23-0.46; P  = .01) compared with East Asian countries ( k  = 14; rate = 0.17; 95% CI, 0.13-0.21; P  < .001). Lastly, the prevalence of clinically elevated anxiety was higher in studies deemed to have poorer quality ( k  = 21; rate = 0.22; 95% CI, 0.18-0.27; P  < .001) compared with studies with better study quality scores ( k  = 4; rate = 0.12; 95% CI, 0.07-0.20; P  < .001). Sensitivity analyses removing low quality studies (ie, scores of 2) 32 , 43 yielded the same pattern of results (eTable 5 in the Supplement ).

The current meta-analysis provides a timely estimate of clinically elevated depression and generalized anxiety symptoms globally among youth during the COVID-19 pandemic. Across 29 samples and 80 879 youth, the pooled prevalence of clinically elevated depression and anxiety symptoms was 25.2% and 20.5%, respectively. Thus, 1 in 4 youth globally are experiencing clinically elevated depression symptoms, while 1 in 5 youth are experiencing clinically elevated anxiety symptoms. A comparison of these findings to prepandemic estimates (12.9% for depression 2 and 11.6% for anxiety 1 ) suggests that youth mental health difficulties during the COVID-19 pandemic has likely doubled.

The COVID-19 pandemic, and its associated restrictions and consequences, appear to have taken a considerable toll on youth and their psychological well-being. Loss of peer interactions, social isolation, and reduced contact with buffering supports (eg, teachers, coaches) may have precipitated these increases. 3 In addition, schools are often a primary location for receiving psychological services, with 80% of children relying on school-based services to address their mental health needs. 54 For many children, these services were rendered unavailable owing to school closures.

As the month of data collection increased, rates of depression and anxiety increased correspondingly. One possibility is that ongoing social isolation, 6 family financial difficulties, 55 missed milestones, and school disruptions 3 are compounding over time for youth and having a cumulative association. However, longitudinal research supporting this possibility is currently scarce and urgently needed. A second possibility is that studies conducted in the earlier months of the pandemic (February to March 2020) 12 , 51 were more likely to be conducted in East Asia where self-reported prevalence of mental health symptoms tends to be lower. 56 Longitudinal trajectory research on youth well-being as the pandemic progresses and in pandemic recovery phases will be needed to confirm the long-term mental health implications of the COVID-19 pandemic on youth mental illness.

Prevalence rates for anxiety varied according to study quality, with lower-quality studies yielding higher prevalence rates. It is important to note that in sensitivity analyses removing lower-quality studies, other significant moderators (ie, child sex and data collection time point) remained significant. There has been a rapid proliferation of youth mental health research during the COVID-19 pandemic; however, the rapid execution of these studies has been criticized owing to the potential for some studies to sacrifice methodological quality for methodological rigor. 21 , 57 Additionally, several studies estimating prevalence rates of mental illness during the pandemic have used nonprobability or convenience samples, which increases the likelihood of bias in reporting. 21 Studies with representative samples and/or longitudinal follow-up studies that have the potential to demonstrate changes in mental health symptoms from before to after the pandemic should be prioritized in future research.

In line with previous research on mental illness in childhood and adolescence, 58 female sex was associated with both increased depressive and anxiety symptoms. Biological susceptibility, lower baseline self-esteem, a higher likelihood of having experienced interpersonal violence, and exposure to stress associated with gender inequity may all be contributing factors. 59 Higher rates of depression in older children were observed and may be due to puberty and hormonal changes 60 in addition to the added effects of social isolation and physical distancing on older children who particularly rely on socialization with peers. 6 , 61 However, age was not a significant moderator for prevalence rates of anxiety. Although older children may be more acutely aware of the stress of their parents and the implications of the current global pandemic, younger children may be able to recognize changes to their routine, both of which may contribute to similar rates of anxiety with different underlying mechanisms.

In terms of practice implications, a routine touch point for many youth is the family physician or pediatrician’s office. Within this context, it is critical to inquire about or screen for youth mental health difficulties. Emerging research 42 suggests that in families using more routines during COVID-19, lower child depression and conduct problems are observed. Thus, a tangible solution to help mitigate the adverse effects of COVID-19 on youth is working with children and families to implement consistent and predictable routines around schoolwork, sleep, screen use, and physical activity. Additional resources should be made available, and clinical referrals should be placed when children experience clinically elevated mental distress. At a policy level, research suggests that social isolation may contribute to and confer risk for mental health concerns. 4 , 5 As such, the closure of schools and recreational activities should be considered a last resort. 62 In addition, methods of delivering mental health resources widely to youth, such as group and individual telemental health services, need to be adapted to increase scalability, while also prioritizing equitable access across diverse populations. 63

There are some limitations to the current study. First, although the current meta-analysis includes global estimates of child and adolescent mental illness, it will be important to reexamine cross-regional differences once additional data from underrepresented countries are available. Second, most study designs were cross-sectional in nature, which precluded an examination of the long-term association of COVID-19 with child mental health over time. To determine whether clinically elevated symptoms are sustained, exacerbated, or mitigated, longitudinal studies with baseline estimates of anxiety and depression are needed. Third, few studies included racial or ethnic minority participants (27.6%), and no studies included gender-minority youth. Given that racial and ethnic minority 64 and gender-diverse youth 65 , 66 may be at increased risk for mental health difficulties during the pandemic, future work should include and focus on these groups. Finally, all studies used self- or parent-reported questionnaires to examine the prevalence of clinically elevated (ie, moderate to high) symptoms. Thus, studies using criterion standard assessments of child depression and anxiety disorders via diagnostic interviews or multimethod approaches may supplement current findings and provide further details on changes beyond generalized anxiety symptoms, such symptoms of social anxiety, separation anxiety, and panic.

Overall, this meta-analysis shows increased rates of clinically elevated anxiety and depression symptoms for youth during the COVID-19 pandemic. While this meta-analysis supports an urgent need for intervention and recovery efforts aimed at improving child and adolescent well-being, it also highlights that individual differences need to be considered when determining targets for intervention (eg, age, sex, exposure to COVID-19 stressors). Research on the long-term effect of the COVID-19 pandemic on mental health, including studies with pre– to post–COVID-19 measurement, is needed to augment understanding of the implications of this crisis on the mental health trajectories of today’s children and youth.

Corresponding Author: Sheri Madigan, PhD, RPsych, Department of Psychology University of Calgary, Calgary, AB T2N 1N4, Canada ( [email protected] ).

Accepted for Publication: May 19, 2021.

Published Online: August 9, 2021. doi:10.1001/jamapediatrics.2021.2482

Author Contributions: Drs Racine and Madigan had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Racine, Madigan.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Racine, McArthur, Eirich, Zhu, Madigan.

Critical revision of the manuscript for important intellectual content: Racine, Cooke, Eirich, Madigan.

Statistical analysis: Racine, McArthur.

Administrative, technical, or material support: Madigan.

Supervision: Racine, Madigan.

Conflict of Interest Disclosures: Dr Racine reported fellowship support from Alberta Innovates. Dr McArthur reported a postdoctoral fellowship award from the Alberta Children’s Hospital Research Institute. Ms Cooke reported graduate scholarship support from Vanier Canada and Alberta Innovates Health Solutions outside the submitted work. Ms Eirich reported graduate scholarship support from the Social Science and Humanities Research Council. No other disclosures were reported.

Additional Contributions: We acknowledge Nicole Dunnewold, MLIS (Research and Learning Librarian, Health Sciences Library, University of Calgary), for her assistance with the search strategy, for which they were not compensated outside of their salary. We also acknowledge the contribution of members of the Determinants of Child Development Laboratory at the University of Calgary, in particular, Julianna Watt, BA, and Katarina Padilla, BSc, for their contribution to data extraction, for which they were paid as research assistants.

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