Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 20 min ago
Peer-reviewedCC BY 4.0PLOS One

Operationalizing independent oversight in large-scale public health cohorts: Monitoring protocol for the NIMHANS–NH–SKAN Stroke project

  • Mutharaju Arelingaiah1
  • Arpitha Arun2
  • Bangalore Sivilan Madhuri3
  • Kibballi Madhukeshwar Akshaya4
  • Huluvadi Shivalingaiah Anwith5
  • Shivappa Usha6
  • Ramesh Holla7
  • Aravind Karinagannanavar8
  • Malatesh Undi9
  • Sharankumar Holyachi10
  • Pradeep S. Banandur11
  • Gautham Melur Sukumar11
8 more authors
  • Banavaram Anniappan Arvind11
  • Betagalli Mallappa Shivaraj12
  • Khandiga Sharma Lakshmi13
  • Thimappa Hegde14
  • Komal Prasad15
  • Lavanya Garady16
  • Ankesh Ashwini17
  • Upashana Medhi18
18 affiliations
  1. 1Department of Psychiatric Social Work, NIMHANS, Bengaluru, Karnataka, India
  2. 2Senior Research Officer, NIMHANS-NH-SKAN Stroke Project, NIMHANS, Bengaluru, Karnataka, India
  3. 3Senior Community Mobiliser, NIMHANS-NH-SKAN Stroke Project, NIMHANS, Bengaluru, Karnataka, India
  4. 4Department of Community Medicine, Yenepoya Medical College, Yenepoya (Deemed to be University), Mangalore, Karnataka, India
  5. 5Department of Community Medicine, Kempegowda Institute of Medical Sciences, Bengaluru, Karnataka, India
  6. 6Department of Community Medicine, Rajarajeswari Medical College and Hospital, Bengaluru, Karnataka, India
  7. 7Department of Community Medicine, Kasturba Medical College Mangalore, Karnataka, Manipal Academic of Higher Education, Manipal, Karnataka, India
  8. 8Department of Community Medicine, K. H. Patil Institute of Medical Sciences, Gadag, Karnataka, India
  9. 9Department of Community Medicine, Karwar Institute of Medical Sciences, Karwar, Karnataka, India
  10. 10Department of Community Medicine, Koppal Institute of Medical Sciences, Koppal, Karnataka, India
  11. 11Department of Epidemiology, NIMHANS, Bengaluru, Karnataka, India
  12. 12Department of Community Medicine, Chikkamagaluru Institute of Medical Sciences, Chikkamagaluru, Karnataka, India
  13. 13Department of Pathology, Sanjay Gandhi Institute of Trauma and Orthopaedics, Bengaluru, Karnataka, India
  14. 14Mazumdar Shaw Medical Centre, Narayana Institute of Neurosciences, Bengaluru, Karnataka, India
  15. 15Neurosurgeon, Narayana Institute of Neurosciences, Bengaluru, Karnataka, India
  16. 16SKAN Research Trust, Bengaluru, Karnataka, India
  17. 17Skan Project, NIMHANS, Bengaluru, Karnataka, India
  18. 18Senior Research Officer, Skan Project, NIMHANS, Bengaluru, Karnataka, India
Read full textDOICite

Abstract

Background: onitoring the implementation of observational studies are rare, unlike experimental studies. This article aims to describe the development and implementation of a monitoring plan and framework for a large-scale cohort and quasi-experimental study: the NIMHANS-NH-SKAN Stroke project. Methods: The monitoring framework was developed through a systematic, stepwise process involving expert consultation workshops conducted by an Independent Monitoring Team in collaboration with the study team. A logical framework approach was used to identify key processes throughout the study implementation. These processes were then disaggregated into critical activities (sub-processes) to generate an initial list of monitoring items. These items were prioritized using the Vital–Essential–Desirable (VED) strategy through consensus building. The shortlisted items were further refined using SMART criteria (Specific, Measurable, Achievable, Relevant, and Time-bound) to finalize checklist components. Key Performance Indicators were subsequently derived from the finalized checklist items. The framework emphasized usability, feasibility, and relevance to implementation, enabling systematic monitoring of critical study processes. Results: A total of 89 activities were identified and consolidated into 10 checklists, from which 16 Key Performance Indicators (KPIs) were derived. The monitoring framework primarily emphasized indicator-based assessment of project outputs and outcomes while ensuring adherence to key processes and quality standards. KPIs were expressed as proportions to facilitate standardized performance assessment across sites and time periods. The checklists comprised a combination of in-person observations and record reviews. Monitoring findings were synthesized into periodic reports based on KPI proportions, enabling objective evaluation of implementation performance and identification of areas requiring corrective action. Conclusions: Timely and systematic monitoring is critical to ensure rigor in large-scale projects, and the approach described in this study provides a replicable framework for the development of robust monitoring systems in similar community-based large-scale observational studies in India and similar countries.

Related

  1. Window leakage in clinical EEG machine learning: matched subject-independent evaluation across ADHD and Parkinson cohorts
    Burak Aggul
    Frontiers in neuroinformaticsPeer-reviewedCC BYFull textNeuroscienceEurope PMC
  2. Comparative characterization of the mucosal eukaryotic virome across independent whole-metagenome shotgun sequencing cohorts
    Hee Sam Na, Jung Hwa Park, Tae Sung Kim +1
    Scientific ReportsPeer-reviewedCC BY 4.0Full textEcologyCrossref
  3. Amino Acid Metabolic Signature Links Pediatric Environmental Enteric Dysfunction to Maternal-Fetal Growth Restriction Across Two Independent Cohorts
    Shahzaib Khan, Nathaniel Ribeiro, Waqasuddin Khan +12
    bioRxivPreprintCC BYFull textSystems & synthetic biologybioRxivPMC
  4. Protocol for monitoring cytoplasmic Ca2+ dynamics using Fura Red-AM on a Zeiss Axio Observer microscope
    Sabrina Beaumier, Marc Bartoli
    STAR ProtocolsPeer-reviewedCC BY-NC-ND 4.0Full textGenetics & genomicsHuman geneticsHAL
  5. DEADMON: A protocol for long-term monitoring of deadwood-associated species
    J. Purhonen, S. Huttunen, A. Kantelinen +2
    bioRxivPreprintCC BY-NC-NDFull textEcologybioRxiv

Cite

BibTeXRIS
BibTeX
@article{arelingaiah2026operationalizing,
  title = {{Operationalizing independent oversight in large-scale public health cohorts: Monitoring protocol for the NIMHANS–NH–SKAN Stroke project}},
  author = {Mutharaju Arelingaiah and Arpitha Arun and Bangalore Sivilan Madhuri and Kibballi Madhukeshwar Akshaya and Huluvadi Shivalingaiah Anwith and Shivappa Usha and Ramesh Holla and Aravind Karinagannanavar and Malatesh Undi and Sharankumar Holyachi and Pradeep S. Banandur and Gautham Melur Sukumar and Banavaram Anniappan Arvind and Betagalli Mallappa Shivaraj and Khandiga Sharma Lakshmi and Thimappa Hegde and Komal Prasad and Lavanya Garady and Ankesh Ashwini and Upashana Medhi},
  journal = {PLOS One},
  year = {2026},
  month = nov,
  doi = {10.1371/journal.pone.0361702},
  url = {https://doi.org/10.1371/journal.pone.0361702},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Operationalizing independent oversight in large-scale public health cohorts: Monitoring protocol for the NIMHANS–NH–SKAN Stroke project
AU  - Mutharaju Arelingaiah
AU  - Arpitha Arun
AU  - Bangalore Sivilan Madhuri
AU  - Kibballi Madhukeshwar Akshaya
AU  - Huluvadi Shivalingaiah Anwith
AU  - Shivappa Usha
AU  - Ramesh Holla
AU  - Aravind Karinagannanavar
AU  - Malatesh Undi
AU  - Sharankumar Holyachi
AU  - Pradeep S. Banandur
AU  - Gautham Melur Sukumar
AU  - Banavaram Anniappan Arvind
AU  - Betagalli Mallappa Shivaraj
AU  - Khandiga Sharma Lakshmi
AU  - Thimappa Hegde
AU  - Komal Prasad
AU  - Lavanya Garady
AU  - Ankesh Ashwini
AU  - Upashana Medhi
PY  - 2026
DA  - 2026/11/04
JO  - PLOS One
DO  - 10.1371/journal.pone.0361702
UR  - https://doi.org/10.1371/journal.pone.0361702
AB  - Background: onitoring the implementation of observational studies are rare, unlike experimental studies. This article aims to describe the development and implementation of a monitoring plan and framework for a large-scale cohort and quasi-experimental study: the NIMHANS-NH-SKAN Stroke project. Methods: The monitoring framework was developed through a systematic, stepwise process involving expert consultation workshops conducted by an Independent Monitoring Team in collaboration with the study team. A logical framework approach was used to identify key processes throughout the study implementation. These processes were then disaggregated into critical activities (sub-processes) to generate an initial list of monitoring items. These items were prioritized using the Vital–Essential–Desirable (VED) strategy through consensus building. The shortlisted items were further refined using SMART criteria (Specific, Measurable, Achievable, Relevant, and Time-bound) to finalize checklist components. Key Performance Indicators were subsequently derived from the finalized checklist items. The framework emphasized usability, feasibility, and relevance to implementation, enabling systematic monitoring of critical study processes. Results: A total of 89 activities were identified and consolidated into 10 checklists, from which 16 Key Performance Indicators (KPIs) were derived. The monitoring framework primarily emphasized indicator-based assessment of project outputs and outcomes while ensuring adherence to key processes and quality standards. KPIs were expressed as proportions to facilitate standardized performance assessment across sites and time periods. The checklists comprised a combination of in-person observations and record reviews. Monitoring findings were synthesized into periodic reports based on KPI proportions, enabling objective evaluation of implementation performance and identification of areas requiring corrective action. Conclusions: Timely and systematic monitoring is critical to ensure rigor in large-scale projects, and the approach described in this study provides a replicable framework for the development of robust monitoring systems in similar community-based large-scale observational studies in India and similar countries.
SN  - 1932-6203
ER  -