Tailored notification encouraging examinees with abnormal glucose levels in health checkups to seek medical care.

Miki Nagafuchi, Hirokazu Takahashi, Keizo Anzai, Miki Hidaka, Tsuyoshi Matsushita, Kimie Fujita

Abstract read

In one paragraph

Article in Diabetology international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Miki NagafuchiDepartment of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka 812-8582 Japan.

Hirokazu TakahashiDivision of Metabolism and Endocrinology, Faculty of Medicine, Saga University, 5‑1‑1 Nabeshima, Saga City, Saga 849-8501 Japan.

Keizo AnzaiDivision of Metabolism and Endocrinology, Faculty of Medicine, Saga University, 5‑1‑1 Nabeshima, Saga City, Saga 849-8501 Japan.

Miki HidakaSaga branch of the Japanese Health Insurance Association, 7-28 Gofukumachi, Saga City, Saga 840-0824 Japan.

Tsuyoshi MatsushitaSaga branch of the Japanese Health Insurance Association, 7-28 Gofukumachi, Saga City, Saga 840-0824 Japan.

Kimie FujitaDepartment of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka 812-8582 Japan.ORCID 0000-0002-3629-320X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aims of this study were to generate a written notification message using social marketing methods to encourage examinees with abnormal glucose levels to seek medical care and to evaluate the optimized message. A semi-structured interview was conducted among examinees with abnormal glucose levels who had never visited a medical clinic for diagnosis of hyperglycemia. Patients were divided into two segments. A tailored notification letter was developed according to these segments using a social marketing approach. Other examinees were prospectively enrolled as the control group. The tailored notification for Segment 1 included a web code to access a health care provider. Future loss of time and money was emphasized in the message for Segment 2. According to responses in a telephone interview, 654 examinees with abnormal fasting glucose levels in a health checkup were divided into Group 1 ( SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13340-021-00534-1.

Indexed as

DiabetesHealth checkupNudging interventionSocial marketingTailored message

Identifiers

PMID35059262

DOI10.1007/s13340-021-00534-1

PMCPMC8733040

What OpenQuestion holds

Textmetadata

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Registered trials

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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.