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.

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Background Caregivers play a pivotal role in maintaining an economically viable health care system, yet they are characterized by low levels of psychological well-being and consistently report unmet needs for psychological support. Mobile app–based (mobile health [mHealth]) interventions present a novel approach to both reducing stress and improving well-being. Objective This study aims to evaluate the effectiveness of a self-guided mobile app–based psychological intervention for people providing care to family or friends with a physical or mental disability. Methods In a randomized, single-blind, controlled trial, 183 caregivers recruited through the web were randomly allocated to either an intervention (n=73) or active control (n=110) condition. The intervention app contained treatment modules combining daily self-monitoring with third-wave (mindfulness-based) cognitive-behavioral therapies, whereas the active control app contained only self-monitoring features. Both programs were completed over a 5-week period. It was hypothesized that intervention app exposure would be associated with decreases in depression, anxiety, and stress, and increases in well-being, self-esteem, optimism, primary and secondary control, and social support. Outcomes were assessed at baseline, postintervention, and 3-4 months postintervention. App quality was also assessed. Results In total, 25% (18/73) of the intervention participants were lost to follow-up at 3 months, and 30.9% (34/110) of the participants from the wait-list control group dropped out before the postintervention survey. The intervention group experienced reductions in stress (b=−2.07; P=.04) and depressive symptoms (b=−1.36; P=.05) from baseline to postintervention. These changes were further enhanced from postintervention to follow-up, with the intervention group continuing to report lower levels of depression (b=−1.82; P=.03) and higher levels of emotional well-being (b=6.13; P<.001), optimism (b=0.78; P=.007), self-esteem (b=−0.84; P=.005), support from family (b=2.15; P=.001), support from significant others (b=2.66; P<.001), and subjective well-being (b=4.82; P<.001). On average, participants completed 2.5 (SD 1.05) out of 5 treatment modules. The overall quality of the app was also rated highly, with a mean score of 3.94 out of a maximum score of 5 (SD 0.58). Conclusions This study demonstrates that mHealth psychological interventions are an effective treatment option for caregivers experiencing high levels of stress. Recommendations for improving mHealth interventions for caregivers include offering flexibility and customization in the treatment design. Trial Registration Australian New Zealand Clinical Trial Registry ACTRN12616000996460; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=371170

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. (PsycINFO Database Record

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