Objective
To identify strategies used to recruit and retain underrepresented populations and populations with arthritis or fibromyalgia (FM) into behavioral programs targeting exercise, physical ...activity, or chronic disease self management.
Methods
Five bibliographic databases were searched for articles published between January 2000 and May 2022. The search focused on strategies and best practices for recruiting and retaining underrepresented populations or populations with arthritis or FM into disease self‐management or physical activity/exercise programs. s and full‐text articles were screened for inclusion by 2 independent reviewers, and 2 reviewers extracted data from included articles.
Results
Of the 2,800 articles, a total of 43 publications (31 interventions, 8 reviews, 4 qualitative/descriptive studies) met criteria and were included. The majority of studies focused on physical activity/exercise (n = 36) and targeted African American (n = 17), Hispanic (n = 9), or arthritis populations (n = 7). Recruitment strategies that were frequently used included having race‐ or community‐matched team members, flyers and information sessions in areas frequented by the population, targeted emails/mailings, and word of mouth referrals. Retention strategies used included having race‐ or community‐matched team members, incentives, being flexible, and facilitating attendance. Most studies used multiple recruitment and retention strategies.
Conclusion
This scoping review highlights the importance of a multifaceted recruitment and retention plan for underrepresented populations and populations with arthritis or FM in behavioral intervention programs targeting exercise, physical activity, or chronic disease self management. Additional research is needed to better understand the individual effects of different strategies and the costs associated with the various recruitment/retention methods in underrepresented populations and populations with arthritis.
PURPOSEThe main purpose of this study was to examine the accuracy of wristband activity monitors on measuring step counts at prescribed speeds on a treadmill and under short bouts of common daily ...activities.
METHODSThirty healthy young adults wore three wristband activity monitors on both wrists while walking or jogging on a treadmill at different speeds (54, 80, 107, and 134 m·min) and performing six different common daily activities for 5 min each. The monitors included the Fitbit Flex, the Garmin Vivofit, and the Jawbone UP. The common daily activity conditions included two sitting activities (playing a tablet computer game and folding laundry), two walking activities (pushing a stroller, carrying a bag), and two stair climbing activities (down and up). Absolute percentage error (APE) scores were computed to examine the accuracy between actual observed steps and monitor-detected steps.
RESULTSUnder the treadmill condition, the APE ranged between 1.5% and 9.6%. Accuracy was improved at faster speeds (134 m·min) for all the monitors (APE < 2.5%). In the common daily activity conditions, substantial step counts were registered when folding laundry. All monitors significantly underestimated actual steps (all APE >33%) when pushing a stroller. Higher APE was observed when worn on the dominant wrist under the common daily activity conditions.
CONCLUSIONSThe wristband activity monitors examined were more accurate for measuring step counts between 80 and 134 m·min as compared with a slower speed. Accuracy under each common daily activity condition ranged widely between monitors and activity, with less error when worn on the nondominant wrist. These results will help to inform researchers on the use and accuracy of wristband activity monitors for future studies.
Objective
To determine the effects on weight loss of three abbreviated behavioral weight loss interventions with and without coaching and mobile technology.
Methods
A randomized controlled efficacy ...study of three 6‐month weight loss treatments was conducted in 96 adults with obesity: 1) self‐guided (SELF), 2) standard (STND), or 3) technology‐supported (TECH). STND and TECH received eight in‐person group treatment sessions. SELF and STND used paper diaries to self‐monitor diet, activity, and weight; TECH used a smartphone application with social networking features and wireless accelerometer.
Results
Weight loss was greater for TECH and STND than SELF at 6 months (−5.7 kg 95% confidence interval: −7.2 to −4.1 vs. −2.7 kg 95% confidence interval: −5.1 to −0.3, P < 0.05) but not 12 months. TECH and STND did not differ except that more STND (59%) than TECH (34%) achieved ≥ 5% weight loss at 6 months (P < 0.05). Self‐monitoring adherence was greater in TECH than STND (P < 0.001), greater in both interventions than SELF (P < 0.001), and covaried with weight loss (r(84) = 0.36‐0.51, P < 0.001).
Conclusions
Abbreviated behavioral counseling can produce clinically meaningful weight loss regardless of whether self‐monitoring is performed on paper or smartphone, but long‐term superiority over standard of care self‐guided treatment is challenging to maintain.
Qualitative descriptive study using semi-structured interviews OBJECTIVE: The purpose of this study was to examine the barriers and facilitators of weight management in individuals with spinal cord ...injury (SCI) from the perspective of SCI health care providers.
Veterans Health Administration and Midwest Regional SCI Care Systems.
Health care providers (n = 25) who care for individuals with SCI completed semi-structured interviews. Thematic analysis methodology was used to identify emergent themes around barriers and facilitators to weight management in SCI.
Sixteen subthemes emerged for barriers and seven subthemes emerged for facilitators for weight management in SCI. Barriers included individual-level factors (e.g., physical ability/mobility limitations, lack of interest, psychological obstacles, lack of knowledge, poor dietary strategies), socio-environmental factors (e.g., challenges with family support, lack of access to weight management resources, dependency on others, difficulties obtaining weight measurement), and organizational factors (e.g., lack of integration/inconsistent weight management support from healthcare systems, pushing calorie intake early post-injury). Facilitators included individual-level factors (e.g., motivation, education/knowledge, participation in exercise and physical activity) and socio-environmental factors (e.g., positive support network, access to/use of healthy dietary strategies, access to exercise facilities/adaptive equipment, participating in weight management with others).
Healthcare providers identified individual-level, socio-environmental, and organizational barriers and facilitators that influence weight management efforts in individuals with SCI. Future weight management resources and programs should consider addressing common barriers identified by healthcare providers, individuals with SCI, and their caregivers, and develop strategies to promote facilitators to enhance weight management in this population.
ABSTRACT
Introduction
Obesity is a significant health concern for veterans and individuals with spinal cord injury, yet screening for overweight/obesity can be challenging. This study examines how ...healthcare providers screen for overweight/obesity and the challenges encountered in identifying overweight/obesity in veterans and persons with spinal cord injury.
Materials and Methods
Healthcare providers who provide care for persons with spinal cord injury completed a semi-structured interview. The interview explored their perspectives on measuring overweight/obesity in persons with spinal cord injury and the challenges they faced. Thematic analysis was used to identify themes that emerged from the interviews.
Results
Twenty-five providers (88% female with an average experience of 9.6 ± 7.3 years in providing care for spinal cord injury patients) participated in the interviews. The themes described the health indicators and equipment used to assess overweight/obesity, provider concerns regarding measurement, and criteria for classifying overweight/obesity. Body weight and body mass index were the most commonly used indicators. However, concerns were raised regarding accuracy of these measures for spinal cord injury patients, as well as issues related to the accessibility, calibration, and usability of the equipment. Many providers reported using standard body mass index ranges and categories instead of those specific to spinal cord injury.
Conclusion
This study identified the most commonly used indicators of weight or body composition in veterans and persons with spinal cord injury and highlighted providers’ concerns with these measures. Future research is needed to identify the most feasible, accurate, and appropriate health indicators that could be used in a clinical setting to identify overweight and obesity in this population.
Standard behavioral weight loss interventions often set uniform physical activity (PA) goals and promote PA self-monitoring; however, adherence remains a challenge, and recommendations may not ...accommodate all individuals. Identifying patterns of PA goal attainment and self-monitoring behavior will offer a deeper understanding of how individuals adhere to different types of commonly prescribed PA recommendations (ie, minutes of moderate-to-vigorous physical activity MVPA and daily steps) and guide future recommendations for improved intervention effectiveness.
This study examined weekly patterns of adherence to step-based and minute-based PA goals and self-monitoring behavior during a 6-month online behavioral weight loss intervention.
Participants were prescribed weekly PA goals for steps (7000-10,000 steps/day) and minutes of MVPA (50-200 minutes/week) as part of a lifestyle program. Goals gradually increased during the initial 2 months, followed by 4 months of fixed goals. PA was self-reported daily on the study website. For each week, participants were categorized as adherent if they self-monitored their PA and met the program PA goal, suboptimally adherent if they self-monitored but did not meet the program goal, or nonadherent if they did not self-monitor. The probability of transitioning into a less adherent status was examined using multinomial logistic regression.
Participants (N=212) were predominantly middle-aged females with obesity, and 67 (31.6%) self-identified as a racial/ethnic minority. Initially, 73 (34.4%) participants were categorized as adherent to step-based goals, with 110 51.9% suboptimally adherent and 29 13.7% nonadherent, and there was a high probability of either remaining suboptimally adherent from week to week or transitioning to a nonadherent status. However, 149 (70.3%) participants started out adherent to minute-based goals (34 16% suboptimally adherent and 29 13.7% nonadherent), with suboptimally adherent seen as the most variable status. During the graded goal phase, participants were more likely to transition to a less adherent status for minute-based goals (odds ratio OR 1.39, 95% CI 1.31-1.48) compared to step-based goals (OR 1.24, 95% CI 1.17-1.30); however, no differences were seen during the fixed goal phase (minute-based goals: OR 1.06, 95% CI 1.05-1.08; step-based goals: OR 1.07, 95% CI 1.05-1.08).
States of vulnerability to poor PA adherence can emerge rapidly and early in obesity treatment. There is a window of opportunity within the initial 2 months to bring more people toward adherent behavior, especially those who fail to meet the prescribed goals but engage in self-monitoring. Although this study describes the probability of adhering to step- and minute-based targets, it will be prudent to determine how individual characteristics and contextual states relate to these behavioral patterns, which can inform how best to adapt interventions.
ClinicalTrials.gov NCT02688621; https://clinicaltrials.gov/ct2/show/NCT02688621.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The purpose of this study was to compare a technology‐based system, an in‐person behavioral weight loss intervention, and a combination of both over a 6‐month period in overweight adults. Fifty‐one ...subjects (age: 44.2 ± 8.7 years, BMI: 33.7 ± 3.6 kg/m2) participated in a 6‐month behavioral weight loss program and were randomized to one of three groups: standard behavioral weight loss (SBWL), SBWL plus technology‐based system (SBWL+TECH), or technology‐based system only (TECH). All groups reduced caloric intake and progressively increased moderate intensity physical activity. SBWL and SBWL+TECH attended weekly meetings. SBWL+TECH also received a TECH that included an energy monitoring armband and website to monitor energy intake and expenditure. TECH used the technology system and received monthly telephone calls. Body weight and physical activity were assessed at 0 and 6 months. Retention at 6 months was significantly different (P = 0.005) between groups (SBWL: 53%, SBWL+TECH: 100%, and TECH: 77%). Intent‐to‐treat (ITT) analysis revealed significant weight losses at 6 months in SBWL+TECH (−8.8 ± 5.0 kg, −8.7 ± 4.7%), SBWL (−3.7 ± 5.7 kg, −4.1 ± 6.3%), and TECH (−5.8 ± 6.6 kg, −6.3 ± 7.1%) (P < 0.001). Self‐report physical activity increased significantly in SBWL (473.9 ± 800.7 kcal/week), SBWL+TECH (713.9 ± 1,278.8 kcal/week), and TECH (1,066.2 ± 1,371 kcal/week) (P < 0.001), with no differences between groups (P = 0.25). The TECH used in conjunction with monthly telephone calls, produced similar, if not greater weight losses and changes in physical activity than the standard in‐person behavioral program at 6 months. The use of this technology may provide an effective short‐term clinical alternative to standard in‐person behavioral weight loss interventions, with the longer term effects warranting investigation.
We sought to identify patient-reported barriers and facilitators to healthy eating and physical activity among patients before or after knee arthroplasty.
Twenty patients with knee osteoarthritis ...aged 40-79 years who had knee arthroplasty surgery scheduled or completed within 3 months were interviewed. Interview topics included perceived barriers and facilitators to healthy eating and activity before or after surgery. Interviews were coded and analyzed using constant comparative analysis.
Interviews were completed with 11 pre-operative (67.1 ± 7.6 years, 45.5% female, BMI 31.2 ± 6.3) and nine post-operative patients (61.7 ± 11.7 years, 44.4% female, BMI 30.2 ± 4.7 kg/m
). The most commonly identified personal barriers to healthy eating identified were desire for high-fat/high-calorie foods, managing overconsumption and mood. Factors related to planning, portion control and motivation to improve health were identified as healthy eating facilitators. Identified personal barriers for activity included pain, physical limitations and lack of motivation, whereas facilitators included having motivation to improve knee symptoms/outcomes, personal commitment to activity and monitoring activity levels.
Identifying specific eating and activity barriers and facilitators, such as mood and motivation to improve outcomes, provides critical insight from the patient perspective, which will aid in developing weight management programs during rehabilitation for knee arthroplasty patients. Implications for rehabilitation This study provides insight into the identified barriers and facilitators to healthy eating and physical activity in knee arthroplasty patients, both before and after surgery. Intrapersonal barriers that may hinder engagement in physical activity and rehabilitation include pain, physical limitations and lack of motivation; factors that may help to improve activity and the rehabilitation process include being motivated to improve knee outcomes, having a personal commitment to activity and tracking activity levels. Barriers that may interfere with healthy eating behaviors and knee arthroplasty rehabilitation include the desire for high-fat/high-calorie foods, overeating and mood; whereas planning and portion control may help to facilitate healthy eating. Understanding barriers and facilitators to healthy eating and physical activity can help guide rehabilitation professionals with their discussions on weight management with patients who had or are contemplating knee arthroplasty.
A challenge in intensive obesity treatment is making care scalable. Little is known about whether the outcome of physician-directed weight loss treatment can be improved by adding mobile technology.
...We conducted a 2-arm, 12-month study (October 1, 2007, through September 31, 2010). Seventy adults (body mass index >25 and ≤40 calculated as weight in kilograms divided by height in meters squared) were randomly assigned either to standard-of-care group treatment alone (standard group) or to the standard and connective mobile technology system (+mobile group). Participants attended biweekly weight loss groups held by the Veterans Affairs outpatient clinic. The +mobile group was provided personal digital assistants to self-monitor diet and physical activity; they also received biweekly coaching calls for 6 months. Weight was measured at baseline and at 3-, 6-, 9-, and 12-month follow-up.
Sixty-nine adults received intervention (mean age, 57.7 years; 85.5% were men). A longitudinal intent-to-treat analysis indicated that the +mobile group lost a mean of 3.9 kg more (representing 3.1% more weight loss relative to the control group; 95% CI, 2.2-5.5 kg) than the standard group at each postbaseline time point. Compared with the standard group, the +mobile group had significantly greater odds of having lost 5% or more of their baseline weight at each postbaseline time point (odds ratio, 6.5; 95% CI, 2.5-18.6).
The addition of a personal digital assistant and telephone coaching can enhance short-term weight loss in combination with an existing system of care. Mobile connective technology holds promise as a scalable mechanism for augmenting the effect of physician-directed weight loss treatment.
clinicaltrials.gov Identifier: NCT00371462.
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