Evidence map›Paper›PMID 31443058›Full record

ArticleIEEE journal of biomedical and health informatics2020

Context-Aware Delivery of Ecological Momentary Assessment.

Samaneh Aminikhanghahi, Maureen Schmitter-Edgecombe, Diane J Cook

Abstract read
In one paragraph

Article in IEEE journal of biomedical and health informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Ask Less, Learn More: Adapting Ecological Momentary Assessment Survey Length by Modeling Question-Answer Information Gain.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024
    Article
  8. Article
  9. Article
  10. Application of Digital Medicine in Addiction.Journal of Shanghai Jiaotong University (science) · 2022
    Article
  11. Observational
  12. Real-Time Monitoring of Suicide Risk among Adolescents: Potential Barriers, Possible Solutions, and Future Directions.Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53
    Article
  13. 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

3 authors.

Samaneh Aminikhanghahi
Maureen Schmitter-Edgecombe
Diane J Cook

Funding

Multidisciplinary undergraduate training program in Health-assistive Smart EnvironmentsR25AG046114 · NIA · WASHINGTON STATE UNIVERSITY · PI COOK, DIANE JOYCE, MINOR, BRYAN · 2014 to 2025
$3.4M
Multi-modal functional health assessment and intervention for individuals experiencing cognitive declineR01AG065218 · NIA · WASHINGTON STATE UNIVERSITY · PI COOK, DIANE JOYCE, SCHMITTER-EDGECOMBE, MAUREEN · 2020 to 2024
$3.3M
Smart Technologies for Health Assessment and AssistanceR01EB009675 · NIBIB · WASHINGTON STATE UNIVERSITY · PI COOK, DIANE JOYCE, SCHMITTER-EDGECOMBE, MAUREEN · 2010 to 2017
$2.6M
Smart environment technology for longitudinal behavior analysis and interventionR01EB015853 · NIBIB · WASHINGTON STATE UNIVERSITY · PI COOK, DIANE JOYCE, SCHMITTER-EDGECOMBE, MAUREEN · 2012 to 2015
$1.5M
Development of an Online Course Suite in Tools for Analysis of Sensor-Based Behavioral Health Data (AHA!)R25EB024327 · NIBIB · WASHINGTON STATE UNIVERSITY · PI COOK, DIANE JOYCE · 2017 to 2019
$506k
Automated Health Assessment through Mobile Sensing and Machine Learning of Daily ActivitiesR41EB029774 · NIBIB · ADAPTELLIGENCE, LLC · PI COOK, DIANE JOYCE, HOLDER, LAWRENCE · 2019 to 2019
$348k
NIA NIH HHS R01 AG065218NIA NIH HHS R25 AG046114NIBIB NIH HHS R01 EB009675NIBIB NIH HHS R01 EB015853NIBIB NIH HHS R25 EB024327NIBIB NIH HHS R41 EB029774
6 · The paper itself

Abstract

Ecological Momentary Assessment (EMA) is an in-the-moment data collection method which avoids retrospective biases and maximizes ecological validity. A challenge in designing EMA systems is finding a time to ask EMA questions that increases participant engagement and improves the quality of data collection. In this work, we introduce SEP-EMA, a machine learning-based method for providing transition-based context-aware EMA prompt timings. We compare our proposed technique with traditional time-based prompting for 19 individuals living in smart homes. Results reveal that SEP-EMA increased participant response rate by 7.19% compared to time-based prompting. Our findings suggest that prompting during activity transitions makes the EMA process more usable and effective by increasing EMA response rates and mitigating loss of data due to low response rates.

Indexed as

AlgorithmsEcological Momentary AssessmentAgedFemaleHuman ActivitiesHumansMaleMiddle AgedPattern Recognition, AutomatedRetrospective StudiesTime Factors

Identifiers

PMID31443058
PMCPMC8059357

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.