Linda Hartley-Clark

Preventing excessive gestational weight gain: a systematic review of interventions

Women who gain excessive weight during pregnancy have an increased risk of post‐partum obesity, and retention of gestational weight gain (GWG) post birth is a strong predictor of maternal overweight/obesity a decade or more after the birth. The aim of the current review was to identify, and evaluate the effect of key variables designed to modify risk factors for excessive weight gain in pregnant women that have been targeted in interventions over the last decade. The 10 interventions focused primarily on behavioural changes in relation to physical activity and/or to eating. While six studies reported significantly less weight gain in the intervention women, only three showed that women in the intervention were significantly more likely to gain within recommended guidelines. GWG was reduced in only normal‐weight, low‐income, obese, or overweight women, or not at all. Only one study reported a reduction in GWG in women with body mass indexes spanning the normal, overweight and obese categories. The findings were inconsistent in relation to what factors need to be targeted in intervention programmes to reduce GWG. Consideration of psychological factors relevant to pregnancy, in addition to behavioural changes in relation to eating and physical activity, is suggested for future intervention studies.

A conceptual model of psychosocial risk and protective factors for excessive gestational weight gain

OBJECTIVE nearly half of all women exceed the guideline recommended pregnancy weight gain for their Body Mass Index (BMI) category. Excessive gestational weight gain (GWG) is correlated positively with postpartum weight retention and is a predictor of long-term, higher BMI in mothers and their children. Psychosocial factors are generally not targeted in GWG behaviour change interventions, however, multifactorial, conceptual models that include these factors, may be useful in determining the pathways that contribute to excessive GWG. We propose a conceptual model, underpinned by health behaviour change theory, which outlines the psychosocial determinants of GWG, including the role of motivation and self-efficacy towards healthy behaviours. This model is based on a review of the existing literature in this area. ASSESSMENT AND CONCLUSION there is increasing evidence to show that psychosocial factors, such as increased depressive symptoms, anxiety, lower self-esteem and body image dissatisfaction, are associated with excessive GWG. What is less known is how these factors might lead to excessive GWG. Our conceptual model proposes a pathway of factors that affect GWG, and may be useful for understanding the mechanisms by which interventions impact on weight management during pregnancy. This involves tracking the relationships among maternal psychosocial factors, including body image concerns, motivation to adopt healthy lifestyle behaviours, confidence in adopting healthy lifestyle behaviours for the purposes of weight management, and actual behaviour changes. IMPLICATIONS FOR PRACTICE health-care providers may improve weight gain outcomes in pregnancy if they assess and address psychosocial factors in pregnancy.

Using dynamic factor analysis to provide insights into data reliability in experience sampling studies.

The past 2 decades have seen increasing use of experience sampling methods (ESMs) to gain insights into the daily experience of affective states (e.g., its variability, as well as antecedents and consequences of temporary shifts in affect). Much less attention has been given to methodological challenges, such as how to ensure reliability of test scores obtained using ESM. The present study demonstrates the use of dynamic factor analysis (DFA) to quantify reliability of test scores in ESM contexts, evaluates the potential impact of unreliable test scores, and seeks to identify characteristics of individuals that may account for their unreliable test scores. One hundred twenty-seven participants completed baseline measures (demographics and personality traits), followed by a 7-day ESM phase in which positive and negative state affect were measured up to 6 times per day. Analyses showed that although at the sample level, scores on these affect measures exhibited adequate levels of reliability, up to one third of participants failed to meet conventional standards of reliability. Where these low reliability estimates were not significantly associated with personality factors, they could—in some cases—be explained by model misspecification where a meaningful alternative structure was available. Despite these potential differences in factor structure across participants, subsequent modeling with and without these “unreliable” cases showed similar substantive results. Hence, the present findings suggest typical analyses based on ESM data may be robust to individual differences in data structure and/or quality. Ways to augment the DFA approach to better understand unreliable cases are discussed.