Experiences of African American Breast Cancer Survivors Using Digital Scales and Activity Trackers in a Weight Gain Prevention Intervention: Qualitative Study

A Cross-Sectional Study of Grocery Shopping Factors of Importance among Food-Insecure African Americans

The objective of this study was to (1) assess via cross-sectional survey the prevalence of food insecurity among African Americans [AAs] after their most recent grocery shopping trip, and (2) examine the grocery shopping factors of importance and characteristics of food-insecure AA grocery shoppers. Most (70.4%) were food-insecure. Food-insecure grocery shoppers were significantly more likely to be younger, less educated, who often skipped meals and/or practiced fasting, accessed a food pantry, were SNAP recipients, were considered to not be in "good" health, and who had higher BMI compared to food-secure shoppers (p ≤ 0.03 * for all). Our data showed that AAs shopped for groceries a mean 2.20 ± 1.29 times per week, for low prices (72.1%), without a weekly budget (58.9%), with a grocery list (44.6%) or using an app (27.6%), for high-quality vegetables (27.5%), for good customer service (22.9%), for store brands (20.8%) and name brands (17.9%).Food-insecure shoppers were significantly more likely to grocery shop more times per week, have a weekly budget, and use an app, but were significantly less likely to report store brands, name brands, good customer service, and high-quality vegetables as grocery factors of importance (p ≤ 0.03 * for all). Grocery strategies such as shopping with a grocery app and/or grocery list could help food-insecure AAs reduce grocery trips, promote meal planning to save money, and avoid skipping meals/fasting, while eating healthier.

Barriers and facilitators for weight management interventions in breast cancer patients: a systematic review of qualitative studies

INTRODUCTION

This systematic review and meta-synthesis of qualitative studies provides an overview of barriers and facilitators that breast cancer patients experience in weight management interventions.

METHODS

We included qualitative studies describing barriers and facilitators for weight management interventions as experienced by adult breast cancer patients after the completion of initial treatment . The data was extracted and using thematic analysis.

RESULTS

After analysis, eleven themes were determined. Six of those themes could be linked to the Attitude, Social Influence and self Efficacy (ASE)-model. Physical and mental benefits, anticipated regret and a lack of motivation were linked to attitude. Integrating a weight management programme in daily life, stigma and fears were linked to self-efficacy. With regard to the social influence determinant, encouragement and discouragement by family members were developed as a theme. Four additional themes were conducted related to weight management behaviour; external barriers, economic barriers, cultural barriers and physical barriers. In addition, integrating weight management in cancer care was described as a separate theme.

CONCLUSIONS

Several disease specific issues, including feeling stigmatized after cancer treatment and treatment-related side effects and peer-support should be given specific attention to maximize adherence of weight management programmes.

Collecting health-related research data using consumer-based wireless smart scales

BACKGROUND

Serious public-health concerns such as overweight and obesity are in many cases caused by excess intake of food combined with decreases in physical activity. Smart scales with wireless data transfer can, together with smart watches and trackers, observe changes in the population's health. They can present us with a picture of our metabolism, body health, and disease risks. Combining body composition data with physical activity measurements from devices such as smart watches could contribute to building a human digital twin.

OBJECTIVE

The objectives of this study were to (1) investigate the evolution of smart scales in the last decade, (2) map status and supported sensors of smart scales, (3) get an overview of how smart scales have been used in research, and (4) identify smart scales for current and future research.

METHOD

We searched for devices through web shops and smart scale tests/reviews, extracting data from the manufacturer's official website, user manuals when available, and data from web shops. We also searched scientific literature databases for smart scale usage in scientific papers.

RESULT

We identified 165 smart scales with a wireless connection from 72 different manufacturers, released between 2009 and end of 2021. Of these devices, 49 (28%) had been discontinued by end of 2021. We found that the use of major variables such as fat and muscle mass have been as good as constant over the years, and that minor variables such as visceral fat and protein mass have increased since 2015. The main contribution is a representative overview of consumer grade smart scales between 2009 and 2021.

CONCLUSION

The last six years have seen a distinct increase of these devices in the marketplace, measuring body composition with bone mass, muscle mass, fat mass, and water mass, in addition to weight. Still, the number of research projects featuring connected smart scales are few. One reason could be the lack of professionally accurate measurements, though trend analysis might be a more feasible usage scenario.

Patterns of Fitbit Use and Activity Levels Among African American Breast Cancer Survivors During an eHealth Weight Loss Randomized Controlled Trial

PURPOSE

This study examined adherence with a physical activity tracker and patterns of activity among different subgroups of African American/Black breast cancer survivors (AABCS).

DESIGN

Secondary analysis of weight loss trial that used an activity tracker (FitBit) with or without a commercial eHealth program (SparkPeople) over 12 months.

SETTING AND SUBJECTS

AABCS (N = 44) in New Jersey.

MEASURES AND ANALYSIS

Adherence with tracker use, steps per day, and active minutes per week were compared by demographic and clinical characteristics using nonparametric statistics.

RESULTS

Median adherence was over 6 days per week throughout the 12-months. Adherence was significantly correlated with steps and active minutes (p < 0.015). Groups with lower adherence included: those with 5 or more conditions (p = 0.039), had higher number of household members (p = 0.008), and younger than 60 years (p = 0.044). Median number of steps per day remained consistently around 7000 throughout 12 months. Factors associated with lower activity included: age > 60; retirement; higher number of household members, comorbidity, or baseline BMI; and those in the SparkPeople + Fitbit group. Self-monitoring, goal setting, and self-efficacy were significantly correlated with activity levels (p < 0.05).

CONCLUSION

Use of a physical activity tracker may help increase activity levels in AABCS. Certain subgroups, e.g. those older than age 60 years, retired, with BMI over 40, higher number of comorbidities or more household members, may require additional interventions.