Impact of a group-based intervention program on physical activity and health-related outcomes in worksite settings

Gu, Ming; Wang, Yejing; Shi, Yan; Yu, Jie; Xu, Jiying; Jia, Yingnan; Cheng, Minna

doi:10.1186/s12889-020-09036-2

Cited by 12 publications

(28 citation statements)

References 52 publications

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“…Of these 17 countries/territories, eight are classified as high‐income, six are upper middle‐income, and three are lower middle‐income countries/territories. In total, 52 studies were conducted in high‐income (65.0% 54,55,57–81,90–108,113–116,129,131 ), 23 in upper middle‐ (28.7% 82–84,86–89,109–112,117–124,128,132,133 ), and five in lower middle‐income countries/territories (6.3% 51,125–127,130 ). There were no studies from low‐income countries.…”

Section: Resultsmentioning

confidence: 99%

“…Over half (43/80, 53.8% 51,55,57–60,62–66,68,71,72,74,80,83–85,87,88,91,92,94,99,106,108,111,112,117–119,122–126,129,132,133 ) of studies did not report a theoretical framework for intervention development. Of the 37 studies (46.3% 54,61,67,69,70,73,75–79,82,86,89,90,93,95–98,100–105,107,109,110,113–116,121,127,128,130,131 ) that did, social‐cognitive theory was most commonly reported (8/80, 10.0% 54,76,86,97,104,105,107,121 ).…”

Section: Resultsmentioning

confidence: 99%

“…Fifty‐six interventions used a single mHealth component: delivered wholly via an app (23/80, 28.7% 68–70,79,82,83,85,88,89,91,92,95–97,103,106–109,113,114,124,126,128–130 ), pedometer (not app based; 19/80, 23.8% 55,60–68,87,94,112,115,116,119,120,123,132 ), or text messages (14/80, 17.5% 51,72–75,84,90,93,96,110,111,117,118,125 ). The remaining 24 interventions used a combination of an app and text messages (7/80, 8.8% 59,99,101,104,122,127,131 ), wearable plus associated app (e.g., Fitbit app; 7/80, 8.8% 57,77,80,81,98,100,121 ), pedometer (not app based) along with text messages (4/80, 5.0% 54,71,86,133 ), or wearable and associated app and text messages together (3/80, 3.8% 76,78,105 ). Two studies (2.5%) used an app plus a pedometer (not an app‐based pedometer) 58,103 and one used a wearable plus associated and additional app (1/80, 1.3% 102 ).…”

Section: Resultsmentioning

confidence: 99%

“…Most (64/80, 80.0%) studies included a measure of efficacy in relation to the targeted movement behavior(s). Of the 74 physical activity interventions, self‐reported methods were most common (i.e., questionnaire and time‐use diary; 31/74, 41.9% 51,61,66,72–75,77,78,86,89,90,93–97,99,105,110,113,116–119,122,123,125,127,131,133 ), followed by device measured (i.e., pedometer, accelerometer, and smartphone app; 24/74, 32.4% 57,60,62,64,65,67,68,70,79–81,83,88,91,96,98,100,102,103,116,120,124,129,130,132 ), and some used a combination of both (6/74, 8.1% 55,58,63,112,115,121 ). There were 14 studies that did not report a specific physical activity‐related outcome measure (14/74, 18.9% 71,76,84,85,91,92,101,104,106–108,114,126,128 ).…”

Section: Resultsmentioning

confidence: 99%

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mHealth interventions targeting movement behaviors in Asia: A scoping review

et al. 2021

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Summary mHealth interventions can promote healthy movement behaviors (physical activity, sedentary behavior, and sleep). However, recent reviews include few studies from Asia, despite it being home to over 60% of the world population. The aim is to map the current evidence for mHealth interventions targeting movement behaviors in Asia. Six databases were searched up until August 2021. Included studies described an mHealth intervention targeting one or more movement behaviors, delivered in a country/territory in Asia, to a general population. A total of 3986 unique records were screened for eligibility in duplicate. Eighty studies with 1,413,652 participants were included. Most were randomized (38.8%) or quasi‐experimental (27.5%) trials. Studies were from 17 countries/territories (out of 55); majority were high‐ (65.0%) or upper middle‐income (28.7%). Physical activity was targeted most often (93.8%), few targeted sedentary behavior (7.5%), or sleep (8.8%). Most targeted one movement behavior (90.0%), and none targeted all three together. Interventions typically incorporated a single mHealth component (70.0%; app, pedometer, text messages, wearable) and were delivered remotely (66.3%). The average intervention length was 121.8 (SD 127.6) days. mHealth interventions in Asia have primarily targeted physical activity in high‐ and upper middle‐income countries. There are few interventions targeting sedentary behavior or sleep, and no interventions in low‐income countries.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

mHealth interventions targeting movement behaviors in Asia: A scoping review

et al. 2021

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“…Developing interventions in targeting these reasons and solving these barriers for non-urban villagers will be important step for increase the proportion of non-urban villagers in engaging in recreational physical activity. For example, Gu et al [ 41 ] found change in physical activity among office workers after the implementation of a worksite intervention programs at 17 worksites in the urban city of Shanghai with pedometers for 100 days. The goal of using and developing physical activity interventions are to promote recreational physical activity levels among both urban villagers and non-urban villagers.…”

Section: Discussionmentioning

confidence: 99%

Level of engagement of recreational physical activity of urban villagers in Luohu, Shenzhen, China

Shi

Leung

Zheng³

et al. 2021

PLoS ONE

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Physical activity is important for health. However, there is a lack of literature related to the physical activity levels of adults living in urban villagers, which is a vulnerable population in China. The aim of this study is to compare the physical activity and sedentary behavior engagements between urban villagers and non-urban villagers using the 2019 Luohu Shenzhen, China Community Diagnosis Questionnaire. A total of 1205 adults living in urban villages and non-urban villages were included in the analysis. Unadjusted and multiple multivariate logistic regression were conducted for the dependent variable of engagement in recreational physical activity, frequency of recreational physical activity per week, and hours spent in sedentary behaviors per day. Descriptive analysis was conducted to identify the reasons for not engaging in physical activity among urban villagers and non-urban villagers. Across the included sample, 29.05% were urban villagers and 70.95% were non-urban villagers. The results suggested that urban villagers are more likely to engage in physical activity than non-urban villager (OR = 1.90, 95% CI [1.40, 2.59], p < 0.001). However, it was also found that urban village status had no significant association for frequency in engaging in physical activity and average hours spent in sedentary behaviors. Both urban villagers and non-urban villages indicated that lack of time, lack of safe and appropriate environment, and working in labor intensive occupations as some of the reasons for not engaging in physical activity. There is a need for tailed interventions and policies for promoting physical activity among urban villagers and non-urban villagers. Additional studies are needed to further our understanding of the physical activity behaviors among urban villagers in China.

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