Effectiveness of the multi-component dynamic work intervention to reduce sitting time in office workers – Results from a pragmatic cluster randomised controlled trial

Renaud, Lidewij R.; Jelsma, Judith G. M.; Huysmans, Maaike A.; Nassau, Femke van; Lakerveld, Jeroen; Speklé, Erwin M.; Bosmans, Judith E.; Stijnman, Dominique P.M.; Loyen, Anne; Beek, Allard J. van der; Ploeg, Hidde P. van der

doi:10.1016/j.apergo.2019.103027

Cited by 32 publications

(61 citation statements)

References 51 publications

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“…Although this increase was not significant, this indicates that Activator feedback is more likely to affect the number of steps than the sedentary time. This finding is in line with the results of previous Activator studies [ 21 , 35 ] and suggests that more efforts should be made to clarify the difference between sedentary behavior and physical inactivity and to emphasize the importance of standing and light-intensity physical activity.…”

Section: Discussionsupporting

confidence: 91%

“…The Activator has recently been validated by Gill et al [ 20 ]. The Activator is worn on the front of the thigh, either in a pants pocket or attached with an elastic band to clothing covering the upper thigh (eg, trousers, jeans, shorts, leggings, tights, or dresses), and provides visual and tactile feedback [ 21 ]. Visual feedback is presented through a smartphone app via Bluetooth connection.…”

Section: Methodsmentioning

confidence: 99%

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Engagement, Acceptability, Usability, and Preliminary Efficacy of a Self-Monitoring Mobile Health Intervention to Reduce Sedentary Behavior in Belgian Older Adults: Mixed Methods Study

Compernolle¹,

Cardon²,

Ploeg³

et al. 2020

JMIR Mhealth Uhealth

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Background Although healthy aging can be stimulated by the reduction of sedentary behavior, few interventions are available for older adults. Previous studies suggest that self-monitoring might be a promising behavior change technique to reduce older adults’ sedentary behavior. However, little is known about older adults’ experiences with a self-monitoring–based intervention aimed at the reduction of sedentary behavior. Objective The aim of this study is to evaluate engagement, acceptability, usability, and preliminary efficacy of a self-monitoring–based mHealth intervention developed to reduce older adults’ sedentary behavior. Methods A mixed methods study was performed among 28 community-dwelling older adults living in Flanders, Belgium. The 3-week intervention consisted of general sedentary behavior information as well as visual and tactile feedback on participants’ sedentary behavior. Semistructured interviews were conducted to explore engagement with, and acceptability and usability of, the intervention. Sitting time was measured using the thigh-worn activPAL (PAL Technologies) accelerometer before and after the intervention. System usage data of the app were recorded. Quantitative data were analyzed using descriptive statistics and paired-samples t tests; qualitative data were thematically analyzed and presented using pen profiles. Results Participants mainly reported positive feelings regarding the intervention, referring to it as motivating, surprising, and interesting. They commonly reported that the intervention changed their thinking (ie, they became more aware of their sedentary behavior) but not their actual behavior. There were mixed opinions on the kind of feedback (ie, tactile vs visual) that they preferred. The intervention was considered easy to use, and the design was described as clear. Some problems were noticed regarding attaching and wearing the self-monitoring device. System usage data showed that the median frequency of consulting the app widely differed among participants, ranging from 0 to 20 times a day. No significant reductions were found in objectively measured sitting time. Conclusions Although the intervention was well perceived by the majority of older adults, no reductions in sitting time were found. Possible explanations for the lack of reductions might be the short intervention duration or the fact that only bringing the habitual sedentary behavior into conscious awareness might not be sufficient to achieve behavior change. Trial Registration ClinicalTrials.gov NCT04003324; https://tinyurl.com/y2p4g8hx

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Section: Discussionsupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Engagement, Acceptability, Usability, and Preliminary Efficacy of a Self-Monitoring Mobile Health Intervention to Reduce Sedentary Behavior in Belgian Older Adults: Mixed Methods Study

Compernolle¹,

Cardon²,

Ploeg³

et al. 2020

JMIR Mhealth Uhealth

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“…Most did not have specific risk factors, but included those at risk of developing Type 2 Diabetes Mellitus (T2DM), overweight with a body mass index (BMI) of ≥25 kg/m 2 , and those not meeting PA recommended guidelines. Thirty-nine [ 10 – 12 , 24 , 26 , 33 – 39 , 41 , 42 , 44 , 48 – 50 , 52 , 54 – 60 , 62 , 64 – 70 , 72 – 74 , 76 , 77 ] of the 57 RCTs used pedometers; the remaining 18 [ 25 , 27 – 32 , 40 , 43 , 45 – 47 , 51 , 53 , 61 , 63 , 71 , 75 ] used other step-count monitoring interventions such as other body-worn trackers and smartphone applications. The intensity of interventions ranged from the provision of a pedometer or other step-count monitor, often with PA electronic/print resources, written exercise programmes or on-line PA updates, to those that additionally provided individual PA consultations, group counselling or financial incentives [ 10 – 12 , 24 – 77 ].…”

Section: Resultsmentioning

confidence: 99%

“…Hobbs et al found no relationship between intervention effectiveness and the number of intervention contacts or mode of delivery and found only limited PA data beyond 12 months [95]. Our review therefore builds on previous work on step-count monitors, by focusing solely on objective physical activity measures in the adult general population, and including a number of recently published larger trials with longer follow-up periods [10][11][12] and trials of body-worn fitness trackers [25, 27-29, 40, 43, 45, 47, 51, 53, 61, 63, 71, 75] or smartphone only applications [30][31][32]46] or combined [25,28,40,47,53,63,71].…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

The effects of step-count monitoring interventions on physical activity: systematic review and meta-analysis of community-based randomised controlled trials in adults

Chaudhry

Wahlich

Fortescue

et al. 2020

Int J Behav Nutr Phys Act

123

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Background Step-count monitors (pedometers, body-worn trackers and smartphone applications) can increase walking, helping to tackle physical inactivity. We aimed to assess the effect of step-count monitors on physical activity (PA) in randomised controlled trials (RCTs) amongst community-dwelling adults; including longer-term effects, differences between step-count monitors, and between intervention components. Methods Systematic literature searches in seven databases identified RCTs in healthy adults, or those at risk of disease, published between January 2000–April 2020. Two reviewers independently selected studies, extracted data and assessed risk of bias. Outcome was mean differences (MD) with 95% confidence intervals (CI) in steps at follow-up between treatment and control groups. Our preferred outcome measure was from studies with follow-up steps adjusted for baseline steps (change studies); but we also included studies reporting follow-up differences only (end-point studies). Multivariate-meta-analysis used random-effect estimates at different time-points for change studies only. Meta-regression compared effects of different step-count monitors and intervention components amongst all studies at ≤4 months. Results Of 12,491 records identified, 70 RCTs (at generally low risk of bias) were included, with 57 trials (16,355 participants) included in meta-analyses: 32 provided change from baseline data; 25 provided end-point only. Multivariate meta-analysis of the 32 change studies demonstrated step-counts favoured intervention groups: MD of 1126 steps/day 95%CI [787, 1466] at ≤4 months, 1050 steps/day [602, 1498] at 6 months, 464 steps/day [301, 626] at 1 year, 121 steps/day [− 64, 306] at 2 years and 434 steps/day [191, 676] at 3–4 years. Meta-regression of the 57 trials at ≤4 months demonstrated in mutually-adjusted analyses that: end-point were similar to change studies (+ 257 steps/day [− 417, 931]); body-worn trackers/smartphone applications were less effective than pedometers (− 834 steps/day [− 1542, − 126]); and interventions providing additional counselling/incentives were not better than those without (− 812 steps/day [− 1503, − 122]). Conclusions Step-count monitoring leads to short and long-term step-count increases, with no evidence that either body-worn trackers/smartphone applications, or additional counselling/incentives offer further benefit over simpler pedometer-based interventions. Simple step-count monitoring interventions should be prioritised to address the public health physical inactivity challenge. Systematic review registration PROSPERO number CRD42017075810.

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“…After the implementation of sit-stand workstations, long-term evaluations have shown that adherence to the use of the standing option decreased over time [28][29][30] or remained absent on a group level [31]. In general, participation levels in workplace health promotion programs, could be lower than 50% [32].…”

Section: Introductionmentioning

confidence: 99%

Natural Patterns of Sitting, Standing and Stepping During and Outside Work—Differences between Habitual Users and Non-Users of Sit–Stand Workstations

Renaud

Huysmans

Ploeg

et al. 2020

IJERPH

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Sit–stand workstations have shown to reduce sitting time in office workers on a group level. However, movement behaviour patterns might differ between subgroups of workers. Therefore, the objective of this study was to examine sitting, standing and stepping outcomes between habitual users and non-users of sit–stand workstations. From an international office population based in the Netherlands, 24 users and 25 non-users of sit–stand workstations were included (all had long-term access to these workstations). Using the ActivPAL, sitting, standing and stepping were objectively measured during and outside working hours. Differences in outcomes between users and non-users were analysed using linear regression. During working hours, users sat less (−1.64; 95% IC= −2.27–−1.01 h/8 h workday) and stood more (1.51; 95% IC= 0.92–2.10 h/8 h workday) than non-users. Attenuated but similar differences were also found for total sitting time over the whole week. Furthermore, time in static standing bouts was relatively high for users during working hours (median= 0.56; IQR = 0.19−1.08 h/8 h workday). During non-working hours on workdays and during non-working days, no differences were found between users and non-users. During working hours, habitual users of their sit–stand workstation sat substantially less and stood proportionally more than non-users. No differences were observed outside working hours, leading to attenuated but similar differences in total sitting and standing time between users and non-users for total days. This indicated that the users of sit–stand workstations reduced their sitting time at work, but this seemed not to be accompanied by major carry-over or compensatory effects outside working hours.

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Effectiveness of the multi-component dynamic work intervention to reduce sitting time in office workers – Results from a pragmatic cluster randomised controlled trial

Cited by 32 publications

References 51 publications

Engagement, Acceptability, Usability, and Preliminary Efficacy of a Self-Monitoring Mobile Health Intervention to Reduce Sedentary Behavior in Belgian Older Adults: Mixed Methods Study

Engagement, Acceptability, Usability, and Preliminary Efficacy of a Self-Monitoring Mobile Health Intervention to Reduce Sedentary Behavior in Belgian Older Adults: Mixed Methods Study

The effects of step-count monitoring interventions on physical activity: systematic review and meta-analysis of community-based randomised controlled trials in adults

Natural Patterns of Sitting, Standing and Stepping During and Outside Work—Differences between Habitual Users and Non-Users of Sit–Stand Workstations

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