Evidence map›Paper›PMID 30382448›Full record

ArticleHealth care management science2020

The value of mHealth for managing chronic conditions.

Saligrama Agnihothri, Leon Cui, Mohammad Delasay, Balaraman Rajan

Abstract read
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In one paragraph

Article in Health care management science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 6 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

42 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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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

4 authors.

Saligrama AgnihothriSchool of Management, Binghamton University, Binghamton, NY, 13902-6000, USA. agni@binghamton.edu.
Leon CuiSchool of Management, Binghamton University, Binghamton, NY, 13902-6000, USA.
Mohammad DelasayCollege of Business, Stony Brook University, Stony brook, NY, 11794, USA.
Balaraman RajanCollege of Business & Economics, California State University East Bay, Hayward, CA, 94542, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic conditions place a high cost burden on the healthcare system and deplete the quality of life for millions of Americans. Digital innovations such as mobile health (mHealth) technology can be used to provide efficient and effective healthcare. In this research we explore the use of mobile technology to manage chronic conditions such as diabetes and hypertension. There is ample empirical evidence in the healthcare literature showing that patients who use mHealth observe improvement in their health. However, an analytical study that quantifies the benefit of using mHealth is lacking. The benefit of using mHealth depends on many factors such as the current health condition of the patient, pattern of disease progression, frequency of measurement and intervention, the effectiveness of intervention, and the cost of measuring. Stochastic modeling is a suitable approach to take these factors into consideration to evaluate the benefit of mHealth. In this paper, we model the disease progression with the help of a Markov chain and quantify the benefits of measuring and intervention taking into consideration the above-mentioned factors. We compare two different modes for measuring and intervention, mHealth mode and conventional office visit mode, and evaluate the impact of these factors on health outcome.

Indexed as

Chronic DiseaseDisease ManagementTelemedicineDisease ProgressionHumansMarkov ChainsOffice VisitsQuality-Adjusted Life YearsChronic conditionsMarkov modelsmHealth

Identifiers

PMID30382448

What OpenQuestion holds

Textmetadata
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.