Asking questions changes health-related behavior: an updated systematic review and meta-analysis

Miles, Lisa; Rodrigues, Angela; Sniehotta, Falko F; French, David P.

doi:10.1016/j.jclinepi.2020.03.014

Cited by 22 publications

(30 citation statements)

References 22 publications

(67 reference statements)

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“…This resonates with prior research demonstrating that monitoring via strategies such as audit and feedback can yield small but positive improvements in practice [ 40 , 41 ]. Research on measurement reactivity also indicates that prompting research participants to reflect on a behavior can result in behavior changes and introduce bias to clinical trial results [ 13 , 42 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

Unintended consequences: a qualitative study exploring the impact of collecting implementation process data with phone interviews on implementation activities

Gruß

Bunce

Davis

et al. 2020

Implement Sci Commun

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Background Qualitative data are crucial for capturing implementation processes, and thus necessary for understanding implementation trial outcomes. Typical methods for capturing such data include observations, focus groups, and interviews. Yet little consideration has been given to how such methods create interactions between researchers and study participants, which may affect participants’ engagement, and thus implementation activities and study outcomes. In the context of a clinical trial, we assessed whether and how ongoing telephone check-ins to collect data about implementation activities impacted the quality of collected data, and participants’ engagement in study activities. Methods Researchers conducted regular phone check-ins with clinic staff serving as implementers in an implementation study. Approximately 1 year into this trial, 19 of these study implementers were queried about the impact of these calls on study engagement and implementation activities. The two researchers who collected implementation process data through phone check-ins with the study implementers were also interviewed about their perceptions of the impact of the check-ins. Results Study implementers’ assessment of the check-ins’ impact fell into three categories: (1) the check-ins had no effect on implementation activities, (2) the check-ins served as a reminder about study participation (without relating a clear impact on implementation activities), and (3) the check-ins caused changes in implementation activities. The researchers similarly perceived that the phone check-ins served as reminders and encouraged some implementers’ engagement in implementation activities; their ongoing nature also created personal connections with study implementers that may have impacted implementation activities. Among some study implementers, anticipation of the check-in calls also improved their ability to recount implementation activities and positively affected quality of the data collected. Conclusion These results illustrate the potential impact of qualitative data collection on implementation activities during implementation science trials. Mitigating such effects may prove challenging, but acknowledging these consequences—or even embracing them, perhaps by designing data collection methods as implementation strategies—could enhance scientific rigor. This work is presented to stimulate debate about the complexities involved in capturing data on implementation processes using common qualitative data collection methods. Trial registration ClinicalTrials.gov, NCT02325531. Registered 15 December 2014.

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Section: Discussionmentioning

confidence: 99%

Unintended consequences: a qualitative study exploring the impact of collecting implementation process data with phone interviews on implementation activities

Gruß

Bunce

Davis

et al. 2020

Implement Sci Commun

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“…Other experiments of RPEs include those estimating the effects of social desirability considerations in reporting on one's own behaviour [20], and other studies of the effects of obtaining informed consent [21]. Most studies, however, concern the effects of assessment or measurement, for which systematic review evidence indicates there are small effects across multiple topics [10].…”

Section: Studies Of Research Participation Effectsmentioning

confidence: 99%

“…The CONSORT Statement [7] recommends that sources of potential bias be discussed as study limitations in trials, and while such discussion is important to aid the interpretation of findings, it is rare that analyses and effect estimates account for them. This may be for several reasons, including a belief that any such biases are small enough to be ignored, and this may often be the case [8,9], or that apart from measurement reactivity [10], it is simply hard to make any informed adjustment for them. The reasoning goes that all participants are involved in such activities in RCTs, and that differences among participants will be equally divided between treatment groups due to randomisation.…”

Section: Introductionmentioning

confidence: 99%

Causal models accounted for research participation effects when estimating effects in a behavioral intervention trial

Bendtsen

McCambridge

2021

Journal of Clinical Epidemiology

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…In many countries, the prevalence of obesity and related complications such as type 2 diabetes continue to increase 1,2 . Interventions aimed solely at improving diet and physical activity ignore other potentially modifiable and important factors.…”

Section: Introductionmentioning

confidence: 99%

“…Monitoring of intake with or without feedback is a common behaviour change technique and the Mandometer has the potential of changing the behaviour it measures. This deserves a mention, here is the latest relevant systematic review https://www.sciencedirect.com/science/article/pii/S0895435619311461 1 . There is a confusion about the nature of the study.…”

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confidence: 99%

Piloting the objective measurement of eating behaviour at a population scale: a nested study within the Avon Longitudinal Study of Parents and Children

Wade¹,

Clifford²,

Simpkin³

et al. 2020

Wellcome Open Res

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Background: Effective measurement and adaption of eating behaviours, such as eating speed, may improve weight loss and weight over time. We assessed whether the Mandometer, a portable weighing scale connected to a computer that generates a graph of food removal rate from the plate to which it is connected, together with photo-imaging of food, might prove an effective approach to measuring eating behaviours at large scale. Methods: We deployed the Mandometer in the home environment to measure main meals over three days of 95 21-year-old participants of the Avon Longitudinal Study of Parents and Children. We used multi-level models to describe food weight and eating speed and, as exemplar analyses, examined the relationship of eating behaviours with body mass index (BMI), dietary composition (fat content) and genotypic variation (the FTO rs9939609 variant). Using this pilot data, we calculated the sample size required to detect differences in food weight and eating speed between groups of an exposure variable. Results: All participants were able to use the Mandometer effectively after brief training. In exemplar analyses, evidence suggested that obese participants consumed more food than those of "normal" weight (i.e., BMI 19 to <25 kg/m2) and that A/A FTO homozygotes (an indicator of higher weight) ate at a faster rate compared to T/T homozygotes. There was also some evidence that those with a high-fat diet consumed less food than those with a low-fat diet, but no strong evidence that individuals with medium- or high-fat diets ate at a faster rate. Conclusions: We demonstrated the potential for assessing eating behaviour in a short-term home setting and combining this with information in a research setting. This study may offer the opportunity to design interventions tailored for at-risk eating behaviours, offering advantages over the “one size fits all” approach of current failing obesity interventions.

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Asking questions changes health-related behavior: an updated systematic review and meta-analysis

Cited by 22 publications

References 22 publications

Unintended consequences: a qualitative study exploring the impact of collecting implementation process data with phone interviews on implementation activities

Unintended consequences: a qualitative study exploring the impact of collecting implementation process data with phone interviews on implementation activities

Causal models accounted for research participation effects when estimating effects in a behavioral intervention trial

Piloting the objective measurement of eating behaviour at a population scale: a nested study within the Avon Longitudinal Study of Parents and Children

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