Maintenance of weight loss and behaviour. dietary intervention: 1 year follow up

Transcription

1 Institute of Psychological Sciences FACULTY OF MEDICINE AND HEALTH Maintenance of weight loss and behaviour change Dropouts following and a 12 Missing week healthy Data eating dietary intervention: 1 year follow up Kyriaki Iria Myrissa Human Appetite Research Unit Supervisors: Professor Louise Dye, Dr Clare Lawton, Dr Arief Gusnanto

2 Overview of Year 1 study 12 week dietary intervention Diet A (Healthy eating) or Diet B (High Fibre and Healthy Eating) Design: A single blind randomised controlled trial Primary outcome: Changes in body weight Secondary outcomes: Body composition, appetite control, Eating behaviour, Dietary intake Follow up (+12 months) study

3 Issues related to the study Dropouts (attrition, lost to follow up) Attrition rates vary from 10% to 80% depending on the type and setting of the intervention (eg 20 out of 40 women did not attend follow up as of April 2012) Missing data (eg incomplete or missing diaries, dropouts)

4 Dropout

5 When people drop out? Before starting the intervention During the intervention Lost to follow up (fail to return for follow up visits, withdrawal)

6 Why people dropout? Evidence is mixed; a range of potential factors related to attrition/dropouts Demographic: age, education Weight/shape factors: body satisfaction, body image Dieting/eating behaviour factors: past dieting attempts, initial weight loss Personality factors: self efficacy Psychological health: mental health Other: travel distance to the clinic, financial difficulties, personal problems Moroshko, I., Brennan, L. & O'Brien, P. (2011). Predictors of dropout in weight loss interventions: a systematic review of the literature, Obesity Review, 12(11),

7 How can we reduce dropouts? Shorter, simpler study protocols that minimize participant burden tend to reduce dropout Identifying factors contributing to attrition might help to identify people at higher risk and/or target interventions Facilitate special assistance and support Positive relationship between research staff and participants BUT Some dropouts are inevitable with clinical trials..

8 Missing Data

9 Types of missing data Missing Completely at Random (MCAR) a missing subject or missing data point, occurs for completely random reasons, at any time point Missing at Random (MAR) the alternative of MCAR, suggesting that what caused the data to be missing does not depend upon the missing data itself Missing not at random (MNAR) when the probability of dropout depends on the unobserved outcomes, as well as on the observed data

10 How to handle missing data? o Last observation carried forward (LOCF): makes use of baseline measurements and any observed intermediate measurements by carrying forward the last observation to the final time point for participants who dropout

11 Dealing with missing data o Multiple imputations: creates a set of complete data by imputing each missing value using existing values from the data o Maximum likelihood estimation: models all the data that were obtained, adds some error variance and estimates the parameter values that make the observed data maximally likely

12 Conclusion Range of statistical methods available; all have underlying assumptions which vary widely in plausibility Ad hoc imputation methods should be avoided Multiple imputation, longitudinal modelling Choice of method depends on context and presumed reasons for dropouts

13 Summary Effectiveness of weight loss interventions can be improved by reducing attrition Important to try to obtain follow up data even from participants who dropout- to understand reasons for drop out and relevance of study MAR, MNAR Ongoing monitoring of missing data helps investigators understand and address reasons for attrition Careful decisions about how to deal with missing data are important

14 Thank you!