Evidence mapPaperPMID 38596697Full record

ReviewJAMIA open2024

The Bitemporal Lens Model-toward a holistic approach to chronic disease prevention with digital biomarkers.

Filipe Barata, Jinjoo Shim, Fan Wu, Patrick Langer, Elgar Fleisch

Abstract readReview
In one paragraph

Review in JAMIA open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. 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

5 authors.

Filipe BarataCentre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland.ORCID https://orcid.org/0000-0002-3905-2380
Jinjoo ShimCentre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland.ORCID https://orcid.org/0000-0003-0226-7369
Fan WuCentre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland.
Patrick LangerCentre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland.
Elgar FleischCentre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers. Materials and Methods: The Bitemporal Lens Model integrates the change-point model, focusing on critical disease-specific parameters, and the recurrent-pattern model, emphasizing lifestyle and behavioral patterns, for early risk identification. Results: By incorporating both the change-point and recurrent-pattern models, the Bitemporal Lens Model offers a comprehensive approach to preventive healthcare, enabling a more nuanced understanding of individual health trajectories, demonstrated through its application in cardiovascular disease prevention. Discussion: We explore the benefits of the Bitemporal Lens Model, highlighting its capacity for personalized risk assessment through the integration of two distinct lenses. We also acknowledge challenges associated with handling intricate data across dual temporal dimensions, maintaining data integrity, and addressing ethical concerns pertaining to privacy and data protection. Conclusion: The Bitemporal Lens Model presents a novel approach to enhancing preventive healthcare effectiveness.

Indexed as

biomedical sensorscardiovascular diseasedigital biomarkersdigital healthdigital health interventionshealth monitoringhealth promotionmobile healthpersonalized preventionprecision healthpredictive analyticspreventive medicinetelehealthwearable technology

Identifiers

PMID38596697
PMCPMC11000821

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.