The effect of a weight gain prevention intervention on moderate-vigorous physical activity among black women: the Shape Program

Physical activity promotion in rural health care settings: A rapid realist review

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Rural communities have a unique health care and physical activity context.

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We conducted a rapid realist review in partnership with knowledge users.

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Check-ins from health care providers may lead to intervention success and are valued by participants.

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A method for tracking progress is an important component of interventions delivered in rural health care settings.

Physical activity promotion in health care settings is poorly understood and has limited uptake among health care providers. The environmental and health care context of rural communities is unique from urban areas and may interact to influence intervention delivery and success. The aim of this rapid realist review was to synthesize knowledge related to the promotion of physical activity in rural health and social care settings. We searched Medline EBSCO, CINAHL, PsychINFO, and SPORTDiscus for relevant publications. We included qualitative or quantitative studies reporting on an intervention to promote physical activity in rural health (e.g., primary or community care) or social (e.g., elder support services) care settings. Studies without a rural focus or well-defined physical activity/exercise component were excluded. Populations of interest included adults and children in the general population or clinical sub-population. Intervention mechanisms from included studies were mapped to the Behaviour Change Wheel (capability, opportunity, motivation (COM-B)). Twenty studies were included in our review. Most interventions focused on older adults or people with chronic disease risk factors. The most successful intervention strategies leading to increased physical activity behaviour included wearable activity trackers, and check-ins or reminders from trusted sources. Interventions with mechanisms categorized as physical opportunity, automatic motivation, and psychological capability were more likely to be successful than other factors of the COM-B model. Successful intervention activities included a method for tracking progress, providing counselling, and follow-up reminders to prompt behaviour change. Cultivation of necessary community partnerships and adaptations for implementation of interventions in rural communities were not clearly described and may support successful outcomes in future studies.

Association Between Patient Factors and the Effectiveness of Wearable Trackers at Increasing the Number of Steps per Day Among Adults With Cardiometabolic Conditions: Meta-analysis of Individual Patient Data From Randomized Controlled Trials

BACKGROUND

Current evidence supports the use of wearable trackers by people with cardiometabolic conditions. However, as the health benefits are small and confounded by heterogeneity, there remains uncertainty as to which patient groups are most helped by wearable trackers.

OBJECTIVE

This study examined the effects of wearable trackers in patients with cardiometabolic conditions to identify subgroups of patients who most benefited and to understand interventional differences.

METHODS

We obtained individual participant data from randomized controlled trials of wearable trackers that were conducted before December 2020 and measured steps per day as the primary outcome in participants with cardiometabolic conditions including diabetes, overweight or obesity, and cardiovascular disease. We used statistical models to account for clustering of participants within trials and heterogeneity across trials to estimate mean differences with the 95% CI.

RESULTS

Individual participant data were obtained from 9 of 25 eligible randomized controlled trials, which included 1481 of 3178 (47%) total participants. The wearable trackers revealed that over the median duration of 12 weeks, steps per day increased by 1656 (95% CI 918-2395), a significant change. Greater increases in steps per day from interventions using wearable trackers were observed in men (interaction coefficient -668, 95% CI -1157 to -180), patients in age categories over 50 years (50-59 years: interaction coefficient 1175, 95% CI 377-1973; 60-69 years: interaction coefficient 981, 95% CI 222-1740; 70-90 years: interaction coefficient 1060, 95% CI 200-1920), White patients (interaction coefficient 995, 95% CI 360-1631), and patients with fewer comorbidities (interaction coefficient -517, 95% CI -1188 to -11) compared to women, those aged below 50, non-White patients, and patients with multimorbidity. In terms of interventional differences, only face-to-face delivery of the tracker impacted the effectiveness of the interventions by increasing steps per day.

CONCLUSIONS

In patients with cardiometabolic conditions, interventions using wearable trackers to improve steps per day mostly benefited older White men without multimorbidity.

TRIAL REGISTRATION

PROSPERO CRD42019143012; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=143012.

The effectiveness of digital interventions for increasing physical activity in individuals of low socioeconomic status: a systematic review and meta-analysis

BACKGROUND

Digital technologies such as wearables, websites and mobile applications are increasingly used in interventions targeting physical activity (PA). Increasing access to such technologies makes an attractive prospect for helping individuals of low socioeconomic status (SES) in becoming more active and healthier. However, little is known about their effectiveness in such populations. The aim of this systematic review was to explore whether digital interventions were effective in promoting PA in low SES populations, whether interventions are of equal benefit to higher SES individuals and whether the number or type of behaviour change techniques (BCTs) used in digital PA interventions was associated with intervention effects.

METHODS

A systematic search strategy was used to identify eligible studies from MEDLINE, Embase, PsycINFO, Web of Science, Scopus and The Cochrane Library, published between January 1990 and March 2020. Randomised controlled trials, using digital technology as the primary intervention tool, and a control group that did not receive any digital technology-based intervention were included, provided they had a measure of PA as an outcome. Lastly, studies that did not have any measure of SES were excluded from the review. Risk of Bias was assessed using the Cochrane Risk of Bias tool version 2.

RESULTS

Of the 14,589 records initially identified, 19 studies were included in the final meta-analysis. Using random-effects models, in low SES there was a standardised mean difference (SMD (95%CI)) in PA between intervention and control groups of 0.06 (- 0.08,0.20). In high SES the SMD was 0.34 (0.22,0.45). Heterogeneity was modest in both low (I2 = 0.18) and high (I2 = 0) SES groups. The studies used a range of digital technologies and BCTs in their interventions, but the main findings were consistent across all of the sub-group analyses (digital interventions with a PA only focus, country, chronic disease, and duration of intervention) and there was no association with the number or type of BCTs.

DISCUSSION

Digital interventions targeting PA do not show equivalent efficacy for people of low and high SES. For people of low SES, there is no evidence that digital PA interventions are effective, irrespective of the behaviour change techniques used. In contrast, the same interventions in high SES participants do indicate effectiveness. To reduce inequalities and improve effectiveness, future development of digital interventions aimed at improving PA must make more effort to meet the needs of low SES people within the target population.

Effective behavior change techniques to promote physical activity in adults with overweight or obesity: A systematic review and meta-analysis

Multicomponent behavior change interventions are typically used in weight management, but results are largely heterogeneous and modest. Determining which techniques (behavior change technique [BCTs]) are more effective in changing behavior is thus required. This study aimed to identify the most effective BCTs for increasing physical activity (PA) in digital and face-to-face behavior change interventions in adults with overweight/obesity. Four databases were searched for eligible studies until October 2019. BCTs were coded using BCTTv1 and MBCT taxonomies. Sixty-two RCTs were included. Meta-regressions were performed to explore BCTs' moderating role. Five BCTs showed significant moderator effects on PA in digital interventions: goal setting behavior, goal setting outcome, graded tasks, social incentive, and self-monitoring of behavior (adjusted R² 's = 0.15-0.51). One BCT showed significant moderator effects on PA in face-to-face interventions, behavioral practice and rehearsal (adjusted R²  = 0.22). Multivariate and sensitivity analysis generally led to similar findings. Effective BCTs for increasing PA in adults with overweight/obesity in digital and face-to-face interventions seem to differ. Evidence suggests that using goal setting, social incentive, and graded tasks might help improve PA in digital interventions while avoiding inconsistent self-monitoring of behavior. In face-to-face interventions, prompting behavioral practice and rehearsal might lead to better PA outcomes. Still, further studies are needed. Implications of the current findings are discussed.

Interventions Using Wearable Physical Activity Trackers Among Adults With Cardiometabolic Conditions: A Systematic Review and Meta-analysis

Importance

Wearable physical activity (PA) trackers, such as accelerometers, fitness trackers, and pedometers, are accessible technologies that may encourage increased PA levels in line with current recommendations. However, whether their use is associated with improvements in PA levels in participants who experience 1 or more cardiometabolic conditions, such as diabetes, prediabetes, obesity, and cardiovascular disease, is unknown.

Objective

To assess the association of interventions using wearable PA trackers (accelerometers, fitness trackers, and pedometers) with PA levels and other health outcomes in adults with cardiometabolic conditions.

Data Sources

For this systematic review and meta-analysis, searches of MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and PsycINFO were performed from January 1, 2000, until December 31, 2020, with no language restriction. A combination of Medical Subject Heading terms and text words of diabetes, obesity, cardiovascular disease, pedometers, accelerometers, and Fitbits were used.

Study Selection

Randomized clinical trials or cluster randomized clinical trials that evaluated the use of wearable PA trackers, such as pedometers, accelerometers, or fitness trackers, were included. Trials were excluded if they assessed the trackers only as measuring tools of PA before and after another intervention, they required participants to be hospitalized, assessors were not blinded to the trackers, or they used a tracker to measure the effect of a pharmacological treatment on PA among individuals.

Data Extraction and Synthesis

The study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. A random-effects model was used for the meta-analysis.

Main Outcomes and Measures

The primary outcome was mean difference in PA levels. When the scale was different across studies, standardized mean differences were calculated. Heterogeneity was quantified using the I2 statistic and explored using mixed-effects metaregression.

Results

A total of 38 randomized clinical trials with 4203 participants were eligible in the systematic review; 29 trials evaluated pedometers, and 9 evaluated accelerometers or fitness trackers. Four studies did not provide amenable outcome data, leaving 34 trials (3793 participants) for the meta-analysis. Intervention vs comparator analysis showed a significant association of wearable tracker use with increased PA levels overall (standardized mean difference, 0.72; 95% CI, 0.46-0.97; I2 = 88%; 95% CI, 84.3%-90.8%; P < .001) in studies with short to medium follow-up for median of 15 (range, 12-52) weeks. Multivariable metaregression showed an association between increased PA levels and interventions that involved face-to-face consultations with facilitators (23 studies; β = -0.04; 95% CI, -0.11 to -0.01), included men (23 studies; β = 0.48; 95% CI, 0.01-0.96), and assessed pedometer-based interventions (26 studies; β = 0.20; 95% CI, 0.02-0.32).

Conclusions and Relevance

In this systematic review and meta-analysis, interventions that combined wearable activity trackers with health professional consultations were associated with significant improvements in PA levels among people with cardiometabolic conditions.

Rural physical activity interventions in the United States: a systematic review and RE-AIM evaluation

Background

Previous reviews of rural physical activity interventions were focused on intervention effectiveness and had reported overall mixed findings. The purpose of this systematic review was to apply the Reach, Efficacy, Adoption, Implementation and Maintenance (RE-AIM) framework to evaluate the extent to which rural physical activity interventions in the U.S. have reported on dimensions of internal and external validity and to offer suggestions for future physical activity interventions for rural U.S. populations.

Methods

Pubmed, PsychINFO, CINAHL, PAIS, and Web of Science were searched through February 2019 to identify physical activity intervention studies conducted in rural regions in the U.S. with adult populations. Titles, abstracts, and full texts of articles were reviewed against inclusion and exclusion criteria. Data extraction from included articles included a summary of study details, rural classification system used, and the presence or absence of a total 61 RE-AIM indicators, including reach (n = 13), efficacy/effectiveness (n = 10), adoption (n = 21), implementation (n = 9), and maintenance (n = 8).

Results

A total of 40 full-text articles representing 29 unique studies were included. Classifications of rurality included self-statements by authors (n = 19, 65.5%), population/census-based definitions (n = 3, 10.3%), Rural Urban Continuum Codes (n = 3, 10.3%), Rural Urban Commuting Area codes (n = 2, 6.9%), the 2014 Alabama Rural Health Association classification system (n = 1, 3.4%) and the U.S. Office of Management and Budget classification system (n = 1, 3.4%). Individual studies reported between 14.8 to 52.5% of total RE-AIM indicators. Studies reported 15.4 to 84.6% indicators for reach; 20.0 to 70.0% indicators for efficacy/effectiveness; 4.8 to 47.6% indicators for adoption; 11.1 to 88.9% indicators for implementation; and 0 to 25.0% indicators for maintenance.

Conclusions

We found an overall poor reporting of components related to external validity, which hinders the generalizability of intervention findings, and a lack of consistency in the definition of rurality. Future research should focus on balancing factors of internal and external validity, and should aim to develop a greater understanding of how rurality influences health and behavior to provide contextual knowledge needed to advance the translation of physical activity interventions into practice in rural communities and reduce rural health disparities.

Trial registration

The review protocol was registered with PROSPERO: CRD42019116308.

Accelerometer- and Pedometer-Based Physical Activity Interventions Among Adults With Cardiometabolic Conditions: A Systematic Review and Meta-analysis

Importance

Accelerometers and pedometers are accessible technologies that could have a role in encouraging physical activity (PA) in line with current recommendations. However, there is no solid evidence of their association with PA in participants with 1 or more cardiometabolic conditions such as diabetes, prediabetes, obesity, and cardiovascular disease.

Objectives

To assess the association of accelerometer- and pedometer-based interventions with increased activity and other improved health outcomes in adults with cardiometabolic conditions and to examine characteristics of the studies that could influence the association of both interventions in improving PA.

Data Sources

Records from MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Cumulative Index to Nursing and Allied Health, and PsycINFO were searched from inception until August 2018 with no language restriction.

Study Selection

Randomized clinical trials or cluster randomized clinical trials evaluating the use of wearable technology devices such as pedometers and accelerometers as motivating and monitoring tools for increasing PA were included. After removing duplicates, the searches retrieved 5762 references. Following abstract and title screening of 1439 references and full-text screening of 107 studies, 36 studies met inclusion criteria.

Data Extraction and Synthesis

Mean difference in PA was assessed by random-effects meta-analysis. Where the scale was different across studies, the standardized mean difference was used instead. Heterogeneity was quantified using the I2 statistic and explored using mixed-effects metaregression. This study was registered with PROSPERO and followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline.

Main Outcomes and Measures

The primary outcome was objectively measured PA in the short to medium term (postintervention to 8 months' follow-up).

Results

Thirty-six randomized clinical trials (20 using accelerometers and 16 using pedometers) involving 5208 participants were eligible for review. Meta-analysis involving 32 of these trials (4856 participants) showed medium improvements in PA: accelerometers and pedometers combined vs comparator showed a small significant increase in PA overall (standardized mean difference, 0.39 [95% CI, 0.28-0.51]; I2 = 60% [95% CI, 41%-73%]) in studies of short to medium follow-up over a mean (SD) of 32 (28.6) weeks. Multivariable metaregression showed improved association with PA for complex interventions that involved face-to-face consultation sessions with facilitators (β = 0.36; 95% CI, 0.17-0.55; P < .001) and pedometer-based interventions (β = 0.30; 95% CI, 0.08-0.52; P = .002).

Conclusions and Relevance

In this study, complex accelerometer- and pedometer-based interventions led to significant small to medium improvements in PA levels of people with cardiometabolic conditions. However, longer-term trials are needed to assess their performance over time. This study found no evidence that simple self-monitored interventions using either pedometers or accelerometers are associated with improvements in PA.