Evidence map›Paper›PMID 41299799›Full record

ArticleBMC psychology2025

Freiburg mindfulness inventory (FMI) short form and revised form (FMI-13R) - norm scores and psychometrics in a representative German sample.

Harald Walach, Sebastian Sauer, Niko Kohls, Nina Rose, Stefan Schmidt

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Article in BMC psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Harald WalachNext Society Institute, Kazimieras Simonavicius University, Vilnius, Lithuania.ORCID http://orcid.org/0000-0003-4603-7717
Sebastian SauerDept. Psychology, University of Applied Sciences, Ansbach, Germany.ORCID http://orcid.org/0000-0003-1515-8348
Niko KohlsFaculty of Applied Natural Sciences and Health, Coburg University of Applied Sciences, Coburg, Germany.ORCID http://orcid.org/0000-0002-9761-4839
Nina RosePrivate practice, Oldenburg, Germany.
Stefan SchmidtDept. of Psychosomatic Medicine and Psychotherapy, Medical Faculty, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany. stefan.schmidt@uniklinik-freiburg.de.ORCID http://orcid.org/0000-0003-4858-4220

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Freiburg Mindfulness Inventory (FMI) in its short form is one of the most frequently used instruments in research to measure self-attributed mindfulness levels. Despite the widespread use, there is no calibration sample available until now. Thus, we decided to calibrate it in a representative German sample as norm values are of relevance for clinicians and researchers alike. In addition, we tried to replicate the instruments’ psychometric properties.

methodsA sample of 1,012 respondents was recruited from a commercial online panel, approximating a representative sample of the German population. Participants filled in the FMI-14 (short from). We performed psychometric analyses and calculated a Confirmatory Factor Analysis (CFA). We also computed a measurement invariance analysis and a LASSO regression to identify population variables that predict FMI scores. To gauge external validity of the instrument, we also presented the FMI together with four items of the Patient Health Questionnaire (PHQ-4).

resultsWe obtained the best psychometric properties with a revised version of the FMI-13R, where the only negatively coded item was removed due to lack of fit. McDonald’s Omega was found to be 0.88 and the mean item-scale intercorrelation was 0.36. The construct is unidimensional, with two highly correlated subconstructs, Presence and Acceptance (r = 0.93), which can be separated for conceptual reasons if desired. The CFA supported these models. Age and gender stratified norm scores were calculated and reported. Regression analyses and partial correlation analyses showed negative correlation of FMI-13R scores with measures of anxiety and depression, as expected, and thereby support our previous findings that the instrument is valid and that the Acceptance component of mindfulness is the one that is associated with positive effects on health-related parameters.

conclusionThe FMI-13R is a psychometrically sound and valid instrument for the assessment of mindfulness. Norm scores can now be used by clinicians and researchers to classify individuals or samples into a range of values compared to the German population.

Indexed as

MindfulnessPsychometricsAdolescentAdultAgedFactor Analysis, StatisticalFemaleGermanyHumansMaleMiddle AgedReproducibility of ResultsSurveys and QuestionnairesYoung AdultAcceptanceAssessment of mindfulnessFMINorm dataPresence

Identifiers

PMID41299799
PMCPMC12670850

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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.