Evidence map›Paper›PMID 38575925›Full record

ArticleBMC public health2024

Characterization of adults concerning the use of a hypothetical mHealth application addressing stress-overeating: an online survey.

Martin Lurz, Kathrin Gemesi, Sophie Laura Holzmann, Birgit Kretzschmar, Monika Wintergerst, Georg Groh, Markus Böhm, Kurt Gedrich, Hans Hauner, Helmut Krcmar and 1 more

Abstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Martin Lurz *Krcmar Lab (I17), Department of Computer Science, Technical University of Munich, Boltzmannstr. 3, 85748, Garching b. München, Germany. martin.lurz@tum.de.
Kathrin Gemesi *Institute for Nutritional Medicine, School of Medicine & Health, Technical University of Munich, Munich, Germany.
Sophie Laura HolzmannInstitute for Nutritional Medicine, School of Medicine & Health, Technical University of Munich, Munich, Germany.
Birgit KretzschmarResearch Group Public Health Nutrition, ZIEL- Institute for Food & Health, Technical University of Munich, Freising, Germany.
Monika WintergerstResearch Group Social Computing, Department of Computer Science, Technical University of Munich, Garching, Germany.
Georg GrohResearch Group Social Computing, Department of Computer Science, Technical University of Munich, Garching, Germany.
Markus BöhmDepartment of Informatics, University of Applied Sciences Landshut, Landshut, Germany.
Kurt GedrichResearch Group Public Health Nutrition, ZIEL- Institute for Food & Health, Technical University of Munich, Freising, Germany.
Hans HaunerInstitute for Nutritional Medicine, School of Medicine & Health, Technical University of Munich, Munich, Germany.
Helmut KrcmarKrcmar Lab (I17), Department of Computer Science, Technical University of Munich, Boltzmannstr. 3, 85748, Garching b. München, Germany.
Christina HolzapfelInstitute for Nutritional Medicine, School of Medicine & Health, Technical University of Munich, Munich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAbout 40% of people respond to stress by consuming more unhealthy foods. This behavior is associated with increased energy intake and the risk of obesity. As mobile health (mHealth) applications (apps) have been shown to be an easy-to-use intervention tool, the characterization of potential app users is necessary to develop target group-specific apps and to increase adherence rates.

methodsThis cross-sectional online survey was conducted in the spring of 2021 in Germany. Sociodemographic data and data on personality (Big Five Inventory, BFI-10), stress-eating (Salzburg Stress Eating Scale, SSES), and technology behavior (Personal Innovativeness in the Domain of Information Technology, PIIT; Technology Acceptance Model 3, TAM 3) were collected.

resultsThe analysis included 1228 participants (80.6% female, mean age: 31.4 ± 12.8 years, mean body mass index (BMI): 23.4 ± 4.3 kg/m

conclusionThis study suggests that individuals who are prone to stress-overeating are highly interested in adopting an mHealth app as support. Participants with a high intention to use an mHealth app seem to have a general affinity towards new technology (PIIT) and appear to be more insecure with conflicting motives regarding their diet.

trial registrationThis survey was registered in the German Clinical Trials Register (Registration number: DRKS00023984).

Indexed as

Mobile ApplicationsTelemedicineAdolescentAdultCross-Sectional StudiesFemaleHumansHyperphagiaMaleObesityYoung AdultBehavioral intentionDigital toolNutritionObesity

Identifiers

PMID38575925
PMCPMC10993539

What OpenQuestion holds

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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.