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<p>Smartphones have become essential tools in healthcare, particularly in assessing human posture. This systematic review evaluated mobile applications that utilize photogrammetry to assess body alignment in both the sagittal and coronal planes. The review adhered to PRISMA 2020 guidelines and was registered in PROSPERO (CRD42024573433). We conducted a search across multiple databases, including PubMed, Scopus, Web of Science, ACM Digital Library, Embase, and Google Scholar. Studies were included if they reported the development, testing, or validation of smartphone apps for posture analysis. Excluded were studies that used wearables, radiographic or sensor-based methods, or those without full-text availability. Two authors independently screened the studies and extracted relevant data. The COSMIN checklist was employed to assess the quality of the studies. A meta-analysis using MedCalc calculated the intraclass correlation coefficients (ICCs) and Pearson’s r for assessing reliability and validity. A total of 29 studies involving 1,910 participants were included in the review. The pooled ICCs demonstrated excellent test-retest reliability (ICC = 0.904) and inter-rater reliability (ICC = 0.889) for the craniovertebral angle, while measurements for hip tilt, head tilt, and acromion alignment showed moderate to excellent reliability. Limitations of this review include the restriction to English-language studies, heterogeneity in methods, small sample sizes, and inconsistent reporting (e.g., SEM, MAD). These findings support the clinical utility of smartphone applications for posture assessment.</p>

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Photogrammetry-based smartphone applications for spinal posture assessment: a systematic review and meta-analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月15日

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  • Mina Karbalaeimahdi,
  • Hooman Minoonejad,
  • Seyed Hamed Mousavi,
  • Reza Rajabi

摘要

Smartphones have become essential tools in healthcare, particularly in assessing human posture. This systematic review evaluated mobile applications that utilize photogrammetry to assess body alignment in both the sagittal and coronal planes. The review adhered to PRISMA 2020 guidelines and was registered in PROSPERO (CRD42024573433). We conducted a search across multiple databases, including PubMed, Scopus, Web of Science, ACM Digital Library, Embase, and Google Scholar. Studies were included if they reported the development, testing, or validation of smartphone apps for posture analysis. Excluded were studies that used wearables, radiographic or sensor-based methods, or those without full-text availability. Two authors independently screened the studies and extracted relevant data. The COSMIN checklist was employed to assess the quality of the studies. A meta-analysis using MedCalc calculated the intraclass correlation coefficients (ICCs) and Pearson’s r for assessing reliability and validity. A total of 29 studies involving 1,910 participants were included in the review. The pooled ICCs demonstrated excellent test-retest reliability (ICC = 0.904) and inter-rater reliability (ICC = 0.889) for the craniovertebral angle, while measurements for hip tilt, head tilt, and acromion alignment showed moderate to excellent reliability. Limitations of this review include the restriction to English-language studies, heterogeneity in methods, small sample sizes, and inconsistent reporting (e.g., SEM, MAD). These findings support the clinical utility of smartphone applications for posture assessment.