Evidence map›Paper›PMID 37299733›Full record

ArticleSensors (Basel, Switzerland)2023

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions.

Ahmad Yaser Alhaddad, Hussein Aly, Hoda Gad, Einas Elgassim, Ibrahim Mohammed, Khaled Baagar, Abdulaziz Al-Ali, Kishor Kumar Sadasivuni, John-John Cabibihan, Rayaz A Malik

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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

10 authors.

Ahmad Yaser AlhaddadDepartment of Mechanical and Industrial Engineering, Qatar University, Doha 2713, Qatar.ORCID 0000-0002-6062-5833
Hussein AlyKINDI Center for Computing Research, Qatar University, Doha 2713, Qatar.ORCID 0000-0003-2384-6343
Hoda GadWeill Cornell Medicine-Qatar, Doha 24144, Qatar.
Einas ElgassimWeill Cornell Medicine-Qatar, Doha 24144, Qatar.
Ibrahim MohammedWeill Cornell Medicine-Qatar, Doha 24144, Qatar.
Khaled BaagarHamad Medical Corporation, Doha 3050, Qatar.
Abdulaziz Al-AliKINDI Center for Computing Research, Qatar University, Doha 2713, Qatar.ORCID 0000-0003-0006-2642
Kishor Kumar SadasivuniCenter for Advanced Materials, Qatar University, Doha 2713, Qatar.ORCID 0000-0003-2730-6483
John-John CabibihanDepartment of Mechanical and Industrial Engineering, Qatar University, Doha 2713, Qatar.ORCID 0000-0001-5892-743X
Rayaz A MalikWeill Cornell Medicine-Qatar, Doha 24144, Qatar.ORCID 0000-0002-7188-8903

Funding

Qatar National Research Fund 11S-0110-180247
6 · The paper itself

Abstract

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

Indexed as

Diabetes Mellitus, Type 1HypoglycemiaWearable Electronic DevicesBlood GlucoseBlood Glucose Self-MonitoringHumansLongitudinal StudiesBlood Glucosecontinuous blood glucosedata collectiondiabetes managementlongitudinal monitoringmachine learningwearable devices

Identifiers

PMID37299733
PMCPMC10255223

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.