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Peer-reviewedCC BY 4.0Research in Nursing & Health

The Technology Dependence Index (TDI): Development and Validation in Neonatal and Pediatric Populations

  • Ashlee J. Vance1,2,3
  • Matthew Peterson4
  • Hayley H. Estrem4
  • Christine A. Fortney5,6
  • Sharron L. Docherty7,8
  • Debra H. Brandon7,8
8 affiliations
  1. 1Center for Healthcare Improvement Henry Ford Health Detroit Michigan USA
  2. 2College of Nursing Michigan State University East Lansing Michigan USA
  3. 3Department of Pediatrics, College of Human Medicine Michigan State University East Lansing Michigan USA
  4. 4School of Nursing The University of North Carolina at Wilmington Wilmington North Carolina USA
  5. 5College of Nursing The Ohio State University Columbus Ohio USA
  6. 6Center for Biobehavioral Health Nationwide Children's Hospital Research Institute Columbus Ohio USA
  7. 7Division of Nursing Science and Family Centered Health, School of Nursing Duke University Durham North Carolina USA
  8. 8Department of Pediatrics, School of Medicine Duke University Durham North Carolina USA
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Abstract

The survival of infants and children with complex medical conditions continues to increase, underscoring the need to quantify technology dependence more effectively. Existing measures emphasize mortality risk and fail to capture the burden of ongoing technological support. To address this gap, the Technology Dependence Index (TDI) was developed and initially validated as a standardized measure capturing the technology‐dependence component of neonatal and pediatric caregiving complexity. Technology dependence is defined as the complexity and intensity of the skilled, health‐related caregiving an infant or child needs to sustain physiological functioning, beyond the presence of any single device. Pooled data from six studies yielded 818 TDI observations across 265 infants. Inter‐rater reliability was moderate (κ = 0.61), with intraclass correlations from 0.90 to 0.99 and at least 90% agreement for 10 of 12 domains, noting the medication domain had the lowest inter‐rater reliability (45%–48%). Principal components analysis supported a multidimensional internal structure, with the 12 domains loading onto three retained factors. Researchers and clinicians can use the TDI retrospectively through chart review, prospectively in real time, or via parent report, making it adaptable to both research and clinical settings.

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Cite

BibTeXRIS
BibTeX
@article{vance2026technology,
  title = {{The Technology Dependence Index (TDI): Development and Validation in Neonatal and Pediatric Populations}},
  author = {Ashlee J. Vance and Matthew Peterson and Hayley H. Estrem and Christine A. Fortney and Sharron L. Docherty and Debra H. Brandon},
  journal = {Research in Nursing \& Health},
  year = {2026},
  month = aug,
  doi = {10.1002/nur.70121},
  url = {https://doi.org/10.1002/nur.70121},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - The Technology Dependence Index (TDI): Development and Validation in Neonatal and Pediatric Populations
AU  - Ashlee J. Vance
AU  - Matthew Peterson
AU  - Hayley H. Estrem
AU  - Christine A. Fortney
AU  - Sharron L. Docherty
AU  - Debra H. Brandon
PY  - 2026
DA  - 2026/08/15
JO  - Research in Nursing & Health
DO  - 10.1002/nur.70121
UR  - https://doi.org/10.1002/nur.70121
AB  - The survival of infants and children with complex medical conditions continues to increase, underscoring the need to quantify technology dependence more effectively. Existing measures emphasize mortality risk and fail to capture the burden of ongoing technological support. To address this gap, the Technology Dependence Index (TDI) was developed and initially validated as a standardized measure capturing the technology‐dependence component of neonatal and pediatric caregiving complexity. Technology dependence is defined as the complexity and intensity of the skilled, health‐related caregiving an infant or child needs to sustain physiological functioning, beyond the presence of any single device. Pooled data from six studies yielded 818 TDI observations across 265 infants. Inter‐rater reliability was moderate (κ = 0.61), with intraclass correlations from 0.90 to 0.99 and at least 90% agreement for 10 of 12 domains, noting the medication domain had the lowest inter‐rater reliability (45%–48%). Principal components analysis supported a multidimensional internal structure, with the 12 domains loading onto three retained factors. Researchers and clinicians can use the TDI retrospectively through chart review, prospectively in real time, or via parent report, making it adaptable to both research and clinical settings.
SN  - 0160-6891
SN  - 1098-240X
ER  -