Hierarchies of evidence applied to lifestyle Medicine (HEALM): introduction of a strength-of-evidence approach based on a methodological systematic review

Katz, David L.; Karlsen, Micaela; Chung, Mei; Shams-White, Marissa; Green, L. W.; Fielding, Jonathan E.; Saito, Amornrat; Willett, Walter C.

doi:10.1186/s12874-019-0811-z

Cited by 64 publications

(67 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A limitation of our work is the subjective nature of our conclusion classification system, although this was done with high inter-rater agreements, which was confirmed outside of the review team. Secondly, the GRADE approach may be less applicable for lifestyle interventions as the evidence from large and well-conducted RCTs is often absent; more lenient rules for appraising the evidence have been suggested (e.g., HEALM [ 63 ]). Thirdly, the present research is limited to the field of diet and depression but similar inferences may apply to different exposures, like nutraceuticals, and different outcomes, like cardiovascular health (our GRADE evaluation of other patient-relevant health outcomes already indicated low to very low certainty-evidence also in these fields of study; see S1 Appendix ).…”

Section: Discussionmentioning

confidence: 99%

Impact of review method on the conclusions of clinical reviews: A systematic review on dietary interventions in depression as a case in point

et al. 2020

View full text Add to dashboard Cite

Background The recommendations of experts who write review articles are a critical determinant of the adaptation of new treatments by clinicians. Several types of reviews exist (narrative, systematic, meta-analytic), and some of these are more vulnerable to researcher bias than others. Recently, the interest in nutritional interventions in psychiatry has increased and many experts, who are often active researchers on this topic, have come to strong conclusions about the benefits of a healthy diet on depression. In a young and active field of study, we aimed to investigate whether the strength of an author’s conclusion is associated with the type of review article they wrote. Methods Systematic searches were performed in PubMed, Web of Science, Cochrane Database of Systematic Reviews, and Google Scholar for narrative reviews and systematic reviews with and without meta-analyses on the effects of diet on depression (final search date: May 30 th , 2020). Conclusions were extracted from the abstract and discussion section and rated as strong, moderate, or weak by independent raters who were blind to study type. A benchmark on legitimate conclusion strength was based on a GRADE assessment of the highest level of evidence. This systematic review was registered with PROSPERO, number CRD42020141372. Findings 24 narrative reviews, 12 systematic reviews, and 14 meta-analyses were included. In the abstract, 33% of narrative reviews and 8% of systematic reviews came to strong conclusions, whereas no meta-analysis did. Narrative reviews were 8.94 (95% CI: 2.17, 36.84) times more likely to report stronger conclusions in the abstract than systematic reviews with and without meta-analyses. These findings were similar for conclusions in the discussion section. Narrative reviews used 45.6% fewer input studies and were more likely to be written by authors with potential conflicts of interest. A study limitation is the subjective nature of the conclusion classification system despite high inter-rater agreements and its confirmation outside of the review team. Conclusions We have shown that narrative reviews come to stronger conclusions about the benefits of a healthy diet on depression despite inconclusive evidence. This finding empirically underscores the importance of a systematic method for summarizing the evidence of a field of study. Journal editors may want to reconsider publishing narrative reviews before meta-analytic reviews are available.

show abstract

Section: Discussionmentioning

confidence: 99%

Impact of review method on the conclusions of clinical reviews: A systematic review on dietary interventions in depression as a case in point

et al. 2020

View full text Add to dashboard Cite

show abstract

“…36 Another example is the Hierarchies of Evidence Applied to Lifestyle Medicine (HEALM) tool from the American College of Lifestyle Medicine that illustrates how to evaluate the specific contributions of diverse research methods to understanding lifetime effects of health behaviors. 37 Recommendation #4: Use methods that better target learners…”

Section: Recommendation #2: Conduct and Document Needs Assessmentsmentioning

confidence: 99%

Keeping Integrative Medicine Continuing Medical Education on the Cutting Edge—and Compliant

Ring

Majd

Mehta

2020

The Journal of Alternative and Complementary Medicine

View full text Add to dashboard Cite

“…While it is not part of the taxonomic method, taxonomic meta-analysis is compatible with the use of strength of evidence assessments for systematic reviews using diverse sets of evidence (not just randomized controlled trials [RCTs]) such as HEALM. 18 For the COEB project, the need for inclusion of diverse research designs and data inclusion was supported by input by the project External Expert Panel. With that support, however, standardized inclusion criteria were developed as discussed in the accompanying Methods article.…”

Section: How Taxonomic Meta-analysis Differs From Conventional Meta-amentioning

confidence: 99%

Childhood Obesity Evidence Base Project: A Rationale for Taxonomic versus Conventional Meta-Analysis

et al. 2020

View full text Add to dashboard Cite

Introduction: There is a great need for analytic techniques that allow for the synthesis of learning across seemingly idiosyncratic interventions. Objectives: The primary objective of this paper is to introduce taxonomic meta-analysis and explain how it is different from conventional meta-analysis. Results: Conventional meta-analysis has previously been used to examine the effectiveness of childhood obesity prevention interventions. However, these tend to examine narrowly defined sections of obesity prevention initiatives, and as such, do not allow the field to draw conclusions across settings, participants, or subjects. Compared with conventional meta-analysis, taxonomic metaanalysis widens the aperture of what can be examined to synthesize evidence across interventions with diverse topics, goals, research designs, and settings. A component approach is employed to examine interventions at the level of their essential features or activities to identify the concrete aspects of interventions that are used (intervention components), characteristics of the intended populations (target population or intended recipient characteristics), and facets of the environments in which they operate (contextual elements), and the relationship of these components to effect size. In addition, compared with conventional meta-analysis methods, taxonomic meta-analyses can include the results of natural experiments, policy initiatives, program implementation efforts and highly controlled experiments (as examples) regardless of the design of the report being analyzed as long as the intended outcome is the same. It also characterizes the domain of interventions that have been studied. Conclusion: Taxonomic meta-analysis can be a powerful tool for summarizing the evidence that exists and for generating hypotheses that are worthy of more rigorous testing.

show abstract

Hierarchies of evidence applied to lifestyle Medicine (HEALM): introduction of a strength-of-evidence approach based on a methodological systematic review

Cited by 64 publications

References 59 publications

Impact of review method on the conclusions of clinical reviews: A systematic review on dietary interventions in depression as a case in point

Impact of review method on the conclusions of clinical reviews: A systematic review on dietary interventions in depression as a case in point

Keeping Integrative Medicine Continuing Medical Education on the Cutting Edge—and Compliant

Childhood Obesity Evidence Base Project: A Rationale for Taxonomic versus Conventional Meta-Analysis

Contact Info

Product

Resources

About