Evidence map›Paper›PMID 30288504›Full record

ArticleProceedings of the ... International Conference on Embedded Networked Sensor Systems. International Conference on Embedded Networked Sensor Systems2017

mCerebrum: A Mobile Sensing Software Platform for Development and Validation of Digital Biomarkers and Interventions.

Syed Monowar Hossain, Timothy Hnat, Nazir Saleheen, Nusrat Jahan Nasrin, Joseph Noor, Bo-Jhang Ho, Tyson Condie, Mani Srivastava, Santosh Kumar

Abstract read
In one paragraph

Article in Proceedings of the ... International Conference on Embedded Networked Sensor Systems. International Conference on Embedded Networked Sensor Systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

  1. Trial
  2. Article
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  5. Article
  6. mRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2022
    Article
  7. Article
  8. Article
  9. Article
  10. WristPrint: Characterizing User Re-identification Risks from Wrist-worn Accelerometry Data.Conference on Computer and Communications Security : proceedings of the ... conference on computer and communications security. ACM Conference on Computer and Communications Security · 2021
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. SmokingOpp: Detecting the Smoking 'Opportunity' Context Using Mobile Sensors.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2020
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Syed Monowar HossainUniversity of Memphis.
Timothy HnatUniversity of Memphis.
Nazir SaleheenUniversity of Memphis.
Nusrat Jahan NasrinUniversity of Memphis.
Joseph NoorUniversity of California, Los Angeles.
Bo-Jhang HoUniversity of California, Los Angeles.
Tyson CondieUniversity of California, Los Angeles.
Mani SrivastavaUniversity of California, Los Angeles.
Santosh KumarUniversity of Memphis.

Funding

Project-002UG1DA040309 · NIDA · DARTMOUTH COLLEGE · PI Lisa A. Marsch · 2015 to 2026
$37.9M
TRAINING U54EB020404 · NIBIB · UNIVERSITY OF MEMPHIS · PI KUMAR, SANTOSH · 2014 to 2018
$11.3M
Eliminating Tobacco-Related Disparities amount African American SmokersR01MD010362 · NIMHD · UNIVERSITY OF UTAH · PI LAM, CHO YAN · 2016 to 2020
$4.0M
Socioeconomic status, stress, and smoking cessationR01CA190329 · NCI · UNIVERSITY OF UTAH · PI LAM, CHO YAN · 2015 to 2020
$3.4M
ROBAS: A Multimodal Sensor System for Remote Assessment of Oral Health BehaviorsR01DE025244 · NIDCR · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SHETTY, VIVEK · 2015 to 2019
$3.1M
NCI NIH HHS R01 CA190329NIBIB NIH HHS U54 EB020404NIDA NIH HHS UG1 DA040309NIDCR NIH HHS R01 DE025244
6 · The paper itself

Abstract

The development and validation studies of new multisensory biomarkers and sensor-triggered interventions requires collecting raw sensor data with associated labels in the natural field environment. Unlike platforms for traditional mHealth apps, a software platform for such studies needs to not only support high-rate data ingestion, but also share raw high-rate sensor data with researchers, while supporting high-rate sense-analyze-act functionality in real-time. We present CCS CONCEPTS: • ACM REFERENCE FORMAT: Syed Monowar Hossain, Timothy Hnat, Nazir Saleheen, Nusrat Jahan Nasrin, Joseph Noor, Bo-Jhang Ho, Tyson Condie, Mani Srivastava, and Santosh Kumar. 2017. mCerebrum: A Mobile Sensing Software Platform for Development and Validation of Digital Biomarkers and Interventions. In

Indexed as

mHealthmobile sensor big datasoftware architecture

Identifiers

PMID30288504
PMCPMC6168216

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.