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1.
Article in English | MEDLINE | ID: mdl-38785210

ABSTRACT

Women age 40-60 are disproportionately affected by health problems that increase their risk for cardiovascular disease (CVD; e.g. hypertension). Social comparisons (i.e. self-evaluations relative to others) are known to influence health in this and other groups, but their nature and consequences in daily life are poorly understood. We conducted an ecological momentary assessment study over 10 days (5x/day) with 75 women ages 40-60 who had ≥1 CVD risk conditions (MAge = 51.6 years, MBMI = 34.0 kg/m2). Using a mix of frequentist and Bayesian analytic approaches, we examined characteristics of women's naturally occurring comparisons and tested predictions from the Identification/Contrast Model within-person (e.g. identifying with an upward target results in positive affect, whereas contrasting results in negative affect). Comparisons occurred at 21% of moments, with considerable within-person variability in response. In line with predictions from the Identification/Contrast Model, women were more likely to experience positive affect after upward identification or downward contrast and more likely to experience negative affect after upward contrast or downward identification, though observed nuances warrant additional consideration. Overall, findings support the Identification/Contrast Model to describe women's comparison experiences as they occur in daily life. Future work should determine pathways between the immediate consequences of comparisons and longer-term health outcomes.

2.
Digit Health ; 9: 20552076231210654, 2023.
Article in English | MEDLINE | ID: mdl-37954685

ABSTRACT

During midlife (ages 40-60), women experience myriad changes that elevate their risk for cardiovascular disease (CVD), including decreased physical activity (PA). Women cite lack of social support for PA and lack of active peers who can serve as role models as key barriers. Digital tools such as web applications can provide exposure to these social inputs; they are also accessible in daily life and require modest time investment. However, as few tools have been designed to meet the unique needs of women in midlife with CVD risk, our research team previously built a web application that is tailored for this population. In the present study, we used a convergent mixed methods design to develop a deep understanding of the feasibility, usability, and acceptability of this web application in a sample of identified end users. Participants (N = 27, MAge = 53 years, MBMI = 32.6 kg/m2) used the web application at the start of each day for 7 days and completed a 1-hour qualitative interview at the end of this test period. Integration of findings from two-level multilevel models (quantitative) and thematic analysis (qualitative) indicated support for the feasibility, usability, and acceptability of the new web application among women in midlife with CVD risk conditions and identified critical opportunities for improving the user experience. Findings also speak to the utility of options for content selection that can meet women's needs in daily life and highlight women's desire for PA resources that prioritize their perspectives.

3.
JMIR Form Res ; 6(7): e36280, 2022 Jul 26.
Article in English | MEDLINE | ID: mdl-35881431

ABSTRACT

BACKGROUND: Women in midlife are vulnerable to developing cardiovascular disease, particularly those who have conditions such as hypertension. Physical activity (PA) can reduce risk, but efforts to promote PA in this population have been only modestly effective. More attention to social influences on PA behavior may be useful, particularly social support and social comparison processes. Activating these processes with digital tools can provide easy access that is flexible to the needs of women in midlife. OBJECTIVE: This paper describes the user-centered design processes of developing and conducting initial evaluation of a personalized and adaptive web application, tailored to the social needs of women in midlife. The goal was to gather feedback from the population of interest, before and during the design process. METHODS: This study was conducted in 4 stages. The first and second authors (DA and AFL) developed technical specifications, informed by their experience with the population of interest. We collected feedback on potential content for the web application with women in midlife using both interviews (5/10, 50%; mean age 47.4, SD 6.66 years; mean BMI 35.3, SD 9.55 kg/m2) and surveys (5/10, 50%; mean age 51, SD 6.60 years; mean BMI 32.7, SD 8.39 kg/m2). We used their feedback to inform support messages and peer profiles (ie, sources of social comparison information). Nine members of the behavioral science team and 3 testers unfamiliar with the web application completed internal testing. We conducted naturalistic functionality testing with a different group of women in midlife (n=5; mean age 50, SD 6.26 years; mean BMI 30.1, SD 5.83 kg/m2), who used the web application as intended for 4 days and provided feedback at the end of this period. RESULTS: Iterative storyboard development resulted in programming specifications for a prototype of the web application. We used content feedback to select and refine the support messages and peer profiles to be added. The following 2 rounds of internal testing identified bugs and other problems regarding the web application's functioning and full data collection procedure. Problems were addressed or logged for future consideration. Naturalistic functionality testing revealed minimal further problems; findings showed preliminary acceptability of the web application and suggested that women may select different social content across days. CONCLUSIONS: A multidisciplinary and user-centered design approach led to a personalized and adaptive web application, tailored to the social needs of women in midlife. Findings from testing with this population demonstrated the feasibility and acceptability of the new application and supported further development toward its use in daily life. We describe several potential uses of the web application and next steps for its development. We also discuss the lessons learned and offer recommendations for future collaborations between behavioral and computer scientists to develop similar tools.

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