Abstract
Digital health interventions (DHIs), delivered via digital platforms such as internet-based programs, mobile applications or short messages, may improve patient-reported outcomes (PROs), but comparative effectiveness is unclear. We conducted a network meta-analysis of randomized controlled trials in adults undergoing elective surgery under general anesthesia, identified in PubMed, Embase, CENTRAL, and Web of Science to March 1, 2025. Standardized mean differences (SMDs), mean differences (MDs) with minimal important differences (MIDs), and 95% CIs were estimated. Risk of bias was assessed with RoB 2 and certainty of evidence with GRADE. Fifty-six trials (6,154 patients) were included. Extended reality (XR) most effectively reduced perioperative anxiety (SMD 0.60; 95% CI 0.37–0.84; MD 8.05; MID 6.71; moderate-certainty). For postoperative pain, mobile applications (SMD 0.64; 95% CI 0.32–0.95; MD 1.36; MID 1.0; moderate-certainty) and XR (SMD 0.51; 95% CI 0.26–0.76; MD 1.09; MID 1.0; moderate-certainty) were probably effective. For quality of life, 2D video yielded the greatest gain (SMD 0.99; 95% CI 0.11–1.88; MD 0.11; MID 0.05; high-certainty). XR also improved satisfaction (SMD 1.27; 95% CI 0.63–1.91; MD 1.91; MID 0.75; moderate-certainty). These findings suggest that DHIs may improve perioperative PROs.
Data availability
All data generated or analyzed during this study are included in this published article and its supplementary materials. Further datasets used and analyzed during the current study are available from the corresponding author on reasonable request.
References
Weinfurt, K. P. & Reeve, B. B. Patient-reported outcome measures in clinical research. JAMA 328, 472–473 (2022).
Chang, S. S. & Movsas, B. How vital are patient-reported outcomes?J. Natl. Cancer Inst. 114, 347–348 (2022).
Black, N. Patient reported outcome measures could help transform healthcare. BMJ 346, f167–f167 (2013).
Dai, W. et al. Patient-reported outcome-based symptom management versus usual care after lung cancer surgery: a multicenter randomized controlled trial. JCO. 40, 988–996 (2022).
Pearce, F. J. et al. The role of patient-reported outcome measures in trials of artificial intelligence health technologies: a systematic evaluation of ClinicalTrials.gov records (1997–2022). Lancet Digit. Health 5, e160–e167 (2023).
Wang, X. S. & Gottumukkala, V. Patient-reported outcomes: Is this the missing link in patient-centered perioperative care? Best. Pract. Res. Clin. Anaesthesiol. 35, 565–573 (2021).
Tadesse, M. et al. Effect of preoperative anxiety on postoperative pain on patients undergoing elective surgery: prospective cohort study. Ann. Med. Surg. 73, 103190 (2022).
Ni, K., Zhu, J. & Ma, Z. Preoperative anxiety and postoperative adverse events: a narrative overview. Anesthesiol. Perioper. Sci. 1, 23 (2023).
Gu, X., Zhang, Y., Wei, W. & Zhu, J. Effects of preoperative anxiety on postoperative outcomes and sleep quality in patients undergoing laparoscopic gynecological surgery. J. Clin. Med. 12, 1835 (2023).
Holzer, K. J. et al. The impact of compassion-based interventions on perioperative anxiety and depression: a systematic review and meta-analysis. J. Affect. Disord. 365, 476–491 (2024).
Valentijn, P. P. et al. Digital health interventions for musculoskeletal pain conditions: systematic review and meta-analysis of randomized controlled trials. J. Med. Internet Res. 24, e37869 (2022).
McDonald, S., Page, M. J., Beringer, K., Wasiak, J. & Sprowson, A. Preoperative education for hip or knee replacement. Cochrane Database Syst. Rev. 2014, CD003526 (2014).
Guo, P., East, L. & Arthur, A. A preoperative education intervention to reduce anxiety and improve recovery among Chinese cardiac patients: a randomized controlled trial. Int. J. Nurs. Stud. 49, 129–137 (2012).
Amoah, V. M. K. et al. A qualitative assessment of perceived barriers to effective therapeutic communication among nurses and patients. BMC Nurs. 18, 4 (2019).
Lewkowitz, A. K. et al. The effect of digital health interventions on postpartum depression or anxiety: a systematic review and meta-analysis of randomized controlled trials. Am. J. Obstet. Gynecol. 230, 12–43 (2024).
Bodenheimer, T. & Sinsky, C. From triple to quadruple aim: care of the patient requires care of the provider. Ann. Fam. Med. 12, 573–576 (2014).
Shaffer, K. M. et al. Digital health and telehealth in cancer care: a scoping review of reviews. Lancet Digit. Health 5, e316–e327 (2023).
Zakiyah, N. et al. Economic evaluations of digital health interventions for patients with heart failure: systematic review. J. Med. Internet Res. 26, e53500 (2024).
Wu, K. A. et al. Digital health for patients undergoing cardiac surgery: a systematic review. Healthcare 11, 2411 (2023).
van der Meij, E. et al. Personalised perioperative care by e-health after intermediate-grade abdominal surgery: a multicentre, single-blind, randomised, placebo-controlled trial. Lancet 392, 51–59 (2018).
Hao, X. et al. Control Tower in the hospital: a structure–process–outcome systemic review of telemedicine systems for interprofessional collaboration. npj Digit. Med. 8, 669 (2025).
Kondylakis, H. et al. A digital health intervention for stress and anxiety relief in perioperative care: protocol for a feasibility randomized controlled trial. JMIR Res. Protoc. 11, e38536 (2022).
Walker, E. M. K., Bell, M., Cook, T. M., Grocott, M. P. W. & Moonesinghe, S. R. Patient reported outcome of adult perioperative anaesthesia in the United Kingdom: a cross-sectional observational study. Br. J. Anaesth. 117, 758–766 (2016).
Gobbo, M. et al. Experience and needs during perioperative care: a focus group study. Patient Prefer. Adherence 14, 891–902 (2020).
Lin, S.-J. et al. Perioperative application of chatbots: a systematic review and meta-analysis. BMJ Health Care Inf. 31, e100985 (2024).
Luo, Z. et al. Digital health interventions in pediatric perioperative care: a network meta-analysis. JAMA Pediatr. 179, 1153 (2025).
Colloca, L. et al. Telehealth virtual reality intervention reduces chronic pain in a randomized crossover study. npj Digit. Med. 8, 192 (2025).
Grygorian, A., Montano, D., Shojaa, M., Ferencak, M. & Schmitz, N. Digital health interventions and patient safety in abdominal surgery. JAMA Netw. Open 7, e248555 (2024).
Javadi, N., Gwilt, I. & Davis, A. Potential benefits of designing immersive technologies to reduce anxiety in the perioperative patient journey. Proc. DRS https://doi.org/10.21606/drs.2022.491 (2022).
Gentili, A. et al. The cost-effectiveness of digital health interventions: a systematic review of the literature. Front. Public Health 10, 787135 (2022).
Khalili-Mahani, N. & Tran, S. The bigger picture of digital interventions for pain, anxiety and stress: a systematic review of 1200+ controlled trials. in Lecture Notes in Computer Science (ed. Duffy, V. G.) 67–78, https://doi.org/10.1007/978-3-031-06018-2_5 (Springer International Publishing, 2022).
Chang, H., Zhou, J., Chen, Y., Wang, X. & Wang, Z. Comparative effectiveness of eHealth interventions on the exercise endurance and quality of life of patients with COPD: a systematic review and network meta-analysis. J. Clin. Nurs. 33, 3711–3720 (2024).
Slattery, B. W. et al. An evaluation of the effectiveness of the modalities used to deliver electronic health interventions for chronic pain: systematic review with network meta-analysis. J. Med. Internet Res. 21, e11086 (2019).
Zangger, G. et al. Benefits and harms of digital health interventions promoting physical activity in people with chronic conditions: systematic review and meta-analysis. J. Med. Internet Res. 25, e46439 (2023).
Knight, S. R. et al. Mobile devices and wearable technology for measuring patient outcomes after surgery: a systematic review. npj Digit. Med. 4, 157 (2021).
Jang, S. et al. A systematic review and meta-analysis of the effects of rehabilitation using digital healthcare on musculoskeletal pain and quality of life. J. Pain Res. ume 16, 1877–1894 (2023).
Hewitt, S., Sephton, R. & Yeowell, G. The effectiveness of digital health interventions in the management of musculoskeletal conditions: systematic literature review. J. Med. Internet Res. 22, e15617 (2020).
Kaihara, T. et al. Efficacy of digital health interventions on depression and anxiety in patients with cardiac disease: a systematic review and meta-analysis. Eur. Heart J. Digit. Health 3, 445–454 (2022).
Zhang, Y. et al. Digital health psychosocial intervention in adult patients with cancer and their families: systematic review and meta-analysis. JMIR Cancer 10, e46116 (2024).
Moghimian, M. et al. The effect of digital health interventions on postpartum depression or anxiety: a systematic review and meta-analysis of randomized controlled trials. Am. J. Obstet. Gynecol. 230, 12–43 (2024).
Katz, M. E. et al. Digital health interventions for hypertension management in US populations experiencing health disparities. JAMA Netw. Open 7, e2356070 (2024).
Shimada, K. et al. Artificial intelligence-assisted interventions for perioperative anesthetic management: a systematic review and meta-analysis. BMC Anesthesiol. 24, 306 (2024).
Wang, B. et al. A systematic literature review on integrating AI-powered smart glasses into digital health management for proactive healthcare solutions. npj Digit. Med. 8, 410 (2025).
Lee, H., Chen, Q. & Ma, D. AI aiding perioperative anaesthetic management: on the way but not ready yet. Anesthesiol. Perioper. Sci. 2, 43 (2024).
Hutton, B. et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann. Intern. Med. 162, 777–784 (2015).
Sterne, J. A. C. et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ 366, l4898 (2019).
Andrade, C. Mean difference, standardized mean difference (SMD), and their use in meta-analysis: as simple as it gets. J. Clin. Psychiatry 81, 11349 (2020).
Norman, G. R., Sloan, J. A. & Wyrwich, K. W. Interpretation of changes in health-related quality of life: the remarkable universality of half a standard deviation. Med. Care 41, 582–592 (2003).
Myles, P. S. et al. Measuring acute postoperative pain using the visual analog scale: the minimal clinically important difference and patient acceptable symptom state. Br. J. Anaesth. 118, 424–429 (2017).
Neupane, B., Richer, D., Bonner, A. J., Kibret, T. & Beyene, J. Network meta-analysis using R: a review of currently available automated packages. PLoS ONE 9, e115065 (2014).
Higgins, J. P. T. & Thompson, S. G. Quantifying heterogeneity in a meta-analysis. Stat. Med. 21, 1539–1558 (2002).
Dias, S., Welton, N. J., Caldwell, D. M. & Ades, A. E. Checking consistency in mixed treatment comparison meta-analysis. Stat. Med. 29, 932–944 (2010).
Egger, M., Davey Smith, G., Schneider, M. & Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 315, 629–634 (1997).
Izcovich, A., Chu, D. K., Mustafa, R. A., Guyatt, G. & Brignardello-Petersen, R. A guide and pragmatic considerations for applying GRADE to network meta-analysis. BMJ. 381, e074495 (2023).
Schünemann, H. J. et al. GRADE guidelines: 21 part 1. Study design, risk of bias, and indirectness in rating the certainty across a body of evidence for test accuracy. J. Clin. Epidemiol. 122, 129–141 (2020).
Schünemann, H. J. et al. GRADE guidelines: 21 part 2. Test accuracy: inconsistency, imprecision, publication bias, and other domains for rating the certainty of evidence and presenting it in evidence profiles and summary of findings tables. J. Clin. Epidemiol. 122, 142–152 (2020).
Brignardello-Petersen, R. et al. GRADE approach to drawing conclusions from a network meta-analysis using a minimally contextualised framework. BMJ m3900. https://doi.org/10.1136/bmj.m3900 (2020).
Acknowledgements
The Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant No. 2023ZD0501801 to R.Z. and G.C., 2025ZD0550604 to S.L.), the Sichuan Province Health Research Project (Grant No. ZH2025-103 to T.Z.), the National Natural Science Foundation of China (Grant No. 82371280 to M.O., 72342014 to S.L.), the Science and Technology Department of Sichuan Province (Grant No. 2024ZDZX0017 to X.P., 2023NSFSC1565 to M.O., 2024YFFK0100 to Y.W.), 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (Grant No. ZYYC24001 to S.L.), and the Science and Technology Department of Sichuan Province (Grant No. 2024NSFSC0624 to L.Y.).
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Z.L., R.Z., J.W., and K.N. had full access to all study data and take responsibility for the integrity and accuracy of the analysis. Z.L., R.Z., J.W., and K.N. contributed equally as co-first authors. Z.L., R.Z., J.W., K.N., X.P., L.C., P.L., S.D., M.O., X.H., L.Y., Y.W., G.C., S.L., and T.Z. conceived and designed the study. Z.L., R.Z., J.W., K.N., X.P., L.C., P.L., S.D., M.O., X.H., L.Y., Y.W., G.C., S.L., and T.Z. contributed to data acquisition, analysis, and interpretation. Z.L., R.Z., J.W., K.N., X.P., L.C., P.L., S.D., X.H., and T.Z. drafted the manuscript. Z.L., R.Z., J.W., K.N., X.P., L.C., P.L., S.D., M.O., X.H., L.Y., Y.W., G.C., S.L., and T.Z. critically revised the manuscript for important intellectual content. Z.L., R.Z., J.W., K.N., X.H., and L.C. performed the statistical analysis. R.Z., G.C., S.L., T.Z., M.O., X.P., Y.W., and L.Y. obtained funding. Z.L., R.Z., J.W., K.N., X.P., L.C., P.L., S.D., X.H., L.Y., Y.W., G.C., and T.Z. provided administrative, technical, or material support. X.H., S.L., and T.Z. supervised the study. Z.L., R.Z., J.W., and K.N. conducted article screening, data extraction, and risk of bias assessment. X.H., S.L., and T.Z. guided the analysis plan and background research. All authors reviewed and approved the final manuscript.
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Luo, Z., Zhou, R., Wei, J. et al. Digital health interventions for perioperative patient-reported outcomes: a network meta-analysis. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02398-8
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DOI: https://doi.org/10.1038/s41746-026-02398-8
