Efficacy of a Smartphone App Intervention for Reducing Caregiver Stress: Randomized Controlled Trial.

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.

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. (PsycINFO Database Record

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.