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Effectiveness of a healthcare-based mobile intervention on sedentary patterns, physical activity, mental well-being and clinical and productivity outcomes in office employees with type 2 diabetes: study protocol for a randomized controlled trial

Abstract

Background

Prolonged sedentary time is associated with an increased incidence of chronic disease including type 2 diabetes mellitus (DM2). Given that occupational sedentary time contributes significantly to the total amount of daily sedentariness, incorporating programmes to reduce occupational sedentary time in patients with chronic disease would allow for physical, mental and productivity benefits. The aim of this study is to evaluate the short-, medium- and long-term effectiveness of a mHealth programme for sitting less and moving more at work on habitual and occupational sedentary behaviour and physical activity in office staff with DM2. Secondary aims. To evaluate the effectiveness on glycaemic control and lipid profile at 6- and 12-month follow-up; anthropometric profile, blood pressure, mental well-being and work-related post-intervention outcomes at 3, 6 and 12 months.

Methods

Multicentre randomized controlled trial. A sample size of 220 patients will be randomly allocated into a control (n = 110) or intervention group (n = 110), with post-intervention follow-ups at 6 and 12 months. Health professionals from Spanish Primary Health Care units will randomly invite patients (18–65 years of age) diagnosed with DM2, who have sedentary office desk-based jobs. The control group will receive usual healthcare and information on the health benefits of sitting less and moving more. The intervention group will receive, through a smartphone app and website, strategies and real-time feedback for 13 weeks to change occupational sedentary behaviour. Variables: (1) Subjective and objective habitual and occupational sedentary behaviour and physical activity (Workforce Sitting Questionnaire, Brief Physical Activity Assessment Tool, activPAL3TM); 2) Glucose, HbA1c; 3) Weight, height, waist circumference; 4) Total, HDL and LDL cholesterol, triglycerides; (5) Systolic, diastolic blood pressure; (6) Mental well-being (Warwick-Edinburgh Mental Well-being); (7) Presenteeism (Work Limitations Questionnaire); (8) Impact of work on employees´ health, sickness absence (6th European Working Conditions Survey); (9) Job-related mental strain (Job Content Questionnaire). Differences between groups pre- and post- intervention on the average value of the variables will be analysed.

Discussion

If the mHealth intervention is effective in reducing sedentary time and increasing physical activity in office employees with DM2, health professionals would have a low-cost tool for the control of patients with chronic disease.

Trial Registration

ClinicalTrials.gov NCT04092738. Registered September 17, 2019.

Peer Review reports

Background

Sedentary behaviour and physical inactivity are associated with an increased risk of chronic disease [1,2,3] and all-cause mortality [4, 5]. Prolonged sitting is associated with weight gain and obesity [6], metabolic syndrome [7, 8], cardiovascular disease [5, 9], a higher incidence of DM2 [6, 7, 10] and some types of cancer [11,12,13]. Excessive sitting is also associated with mental health problems such as lower mental well-being, anxiety and increased risk of depression [14,15,16]. In modern societies, these unhealthy behaviours contribute to chronic diseases, which progress slowly, appear increasingly early and lead to a loss in quality of life, social labour and health costs [17, 18]. Sedentary behaviour is defined as any activity with a caloric expenditure ≤1.5 Metabolic Equivalent Tasks while remaining in a sitting or reclining posture [19]. Physical inactivity, in turn, is defined as an insufficient physical activity level to meet physical activity recommendations [20]. Sedentary behaviour is a health-related risk factor regardless of physical inactivity [21], which has become very prevalent worldwide due to changes in the physical, social and economic environment [22, 23].

Worldwide, Ding et al. [24] estimated that physical inactivity cost health systems $53.8 billion in 2013. In addition, deaths attributable to physical inactivity cost a further $13.7 billion in lost productivity and resulted in 13.4 million disability-adjusted life years. It was also estimated that sedentary behaviour cost the National Health Service of the United Kingdom (2016–2017) £0.8 billion, which included expenditures on cardiovascular diseases (£424 million), DM2 (£281 million) and colon, lung and endometrial cancers (£56 million) [25]. In spite of that, longitudinal data (2007–2016) on adults from the United States [26] did not observe a significant increase in the adherence rate (63.2 and 65.2% respectively) to the physical activity recommendations (e.g. at least 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity aerobic physical activity per week or some equivalent combination) [20] but a significant increase in sedentary behaviour. The COVID-19 pandemic has promoted physical inactivity and sedentary behaviour [27,28,29], which had been defined as another pandemic [27, 30] and a major public health problem that must be addressed jointly by healthcare professionals [31].

Although there is no international consensus with regards to healthy sedentary behaviour thresholds for adults, a meta-analysis suggested that adults’ sedentary time should be limited to 7-8 h/day [4]. Other public health guidelines also recommend breaking up sedentary behaviour every 30 minutes [32]. And the World Health Organization (WHO, 2020) [20] suggests that adults should limit the amount of time they spend sedentary.

Occupational sedentary behaviour, physical inactivity and health

Evidence on the specific health effects of physical activity and sedentary behaviour in the occupational domain are less conclusive [33, 34]. Many adults spend half or more of their day at work, and the hours they spend sitting there make up half of their total sedentary time [35]. Moreover, the COVID-19 pandemic has promoted teleworking and therefore an increase in occupational sedentary behaviour, such as office work at home [36, 37]. Consequently, it is estimated that sedentary time at work would be responsible for 50% of the negative health effects attributable to sedentary behaviour [38].

Additionally, sedentary work time is associated with indicators of productivity and well-being [39], suggesting that “sitting less and moving more” at work could effectively reduce an array of markers of lost productivity [40]. This is especially relevant in people with prevalent chronic disease, who show higher losses of work productivity [41]. “Sitting less and moving more” could also be effective to cope with the increasing levels of work-related stress [42], another current major challenge for people with chronic disease [42, 43]. For all the above, reducing sedentary behaviour at the workplace is a priority area in which to intervene.

Reducing occupational sedentary time by promoting incidental physical activity (such as climbing stairs or walking for short periods) is a key prevention strategy that could help people gradually increase their physical activity levels towards those recommended for optimum health [44, 45]. While numerous health and financial benefits for both employees and employers could be achieved [44, 45], the evidence on effective, long-term intervention strategies in the workplace to reduce sedentary behaviour is limited [46, 47].

Mobile Health (mHealth) interventions to reduce occupational sedentary behaviour and physical inactivity

Mobile health (mHealth) has been defined as medical and public health practice supported by mobile devices such as mobile phones, patient monitoring devices, and personal digital assistants [48]. mHealth programmes could help change time spent on sedentary behaviour [49,50,51]. mHealth interventions at work are feasible, acceptable and effective tools for promoting physical activity [49] and reducing sedentary behaviour, even outside working hours [52]. However, studies evaluating the impact of these long-term and specific mHealth programmes on occupational sedentary behaviour are low [49].

Furthermore, the use and efficacy of mHealth programmes mostly focus on healthy adults, without taking into account the impact they might have on specific population groups [53], such as workers with prevalent chronic diseases like obesity or DM2. There is a need for a better understanding of how to integrate mHealth programmes in the self-management of patients with chronic diseases, specifically in the self-management of DM2, and in health care in general [54]. Self-management is fundamental for the well-being of people with DM2 [55, 56] who are mostly treated and managed in primary health care. mHealth interventions as a means of self-management and health promotion offer a promising solution in primary health care to cope with the increasing demand for treatment and control of DM2 [55].

Healthcare-based mHealth interventions to reduce occupational sedentary behaviour and physical inactivity in people with chronic diseases

Primary health care plays a key role in promoting physical activity and reducing sedentary behaviour for chronic disease. Physical activity is a cost-effective drug that is universally prescribed as first-line treatment, especially to patients with chronic diseases like DM2 [57]. Patients’ physical activity levels and sedentary behaviour reductions can be achieved by using new strategies in clinical practice such as technology including wearables and mHealth programmes. These can be useful for improving treatment adherence and enabling the evaluation and registration of lifestyles in the medical records [57].

Objectives

In this context, the main objective of this protocol for a randomized controlled trial is to assess the effectiveness in the short-medium and long term of a mHealth programme to “sit less and move more” at work on reducing sedentary behaviour and increasing habitual and work physical activity in office staff with DM2. The secondary objectives are to evaluate the effectiveness of the programme on i) clinical variables: glycaemic control and lipid profile; ii) anthropometric and blood pressure variables; iii) improvement in mental well-being, presenteeism, absenteeism and work-related stress.

This is important given the scarcity of studies that evaluate the effectiveness of mHealth programmes in reducing sedentary behaviour as a therapeutic approach for people with chronic pathology, specifically with DM2, which also evaluate the global health impact of the mHealth intervention on clinical parameters, mental well-being and productivity [46]. This study will make it possible to clarify whether people diagnosed with one of the most prevalent chronic diseases, DM2, are likely to benefit from the implementation of mHealth interventions by healthcare professionals in a wide range of health variables and through the use of objective measures of habitual and occupational sedentary behaviour [49].

Trial design

This is a prospective, multicentre, two-arm randomized controlled trial with an intervention and control group (i.e. usual care) that will evaluate the effectiveness of an intervention based on a 13-week mHealth programme that aims to replace sedentary work tasks with active ones in office workers with DM2.

Methods

This study protocol has been developed based on the Standard Protocol Items: Recommendation for Interventional Trials (SPIRIT) guidelines [58].

Study setting

Data will be collected from primary health care centres in the metropolitan area of Barcelona. This densely populated urban area has a high prevalence of patients with DM2 - in Spain, 7.8% of the adult population have DM2 from which 4.17% belong to the age range of 18–65 years old (5.35% males, 3.28% females) [59] – which ensures easy access to people with this medical condition.

Recruitment

First, project information will be disseminated to the directors of the primary health care centres in the aforementioned metropolitan area through the Foundation University Institute for Research in Primary Health Care Jordi Gol i Gurina. A meeting with the medical staff of each general practice that shows an interest in volunteering for the project will be organized to recruit physicians and nurses willing to participate in the project. Physicians and nurses that volunteer will sign a written informed consent agreeing to recruit and monitor 5–6 patients for each medical staff unit. Each unit will include one physician and one nurse.

General practices recruitment will be ongoing until enough medical units are reached to achieve the required sample size. In general practices where medical units will participate, a member of the research team will run a two-hour training course with the medical staff that will include: (i) Benefits of physical activity and reduction of sedentary behaviour in DM2; (ii) Information and objectives of the study; (iii) Schedule of the visits and tasks in each follow-up visit. Also, a certified medical professional will provide medical units with a full list of adult patients (18–65 years of age) within their own patients’ portfolio that are diagnosed with DM2 and could potentially participate in the study. Medical units will be advised to ring patients during the first week after the training session, following the list of names order and asking for the inclusion criteria. A specific interview guide for physicians and nurses will be developed to standardize recruitment procedures and identify patients that meet the inclusion criteria.

Patients that meet the inclusion criteria will be invited to participate. If the response is NO, reasons for not volunteering will be recorded in an on-line form (Survey Monkey Enterprise). If the response is YES, more detailed information about the study will be provided and a first appointment with the nurse will be organized in the general practice. In appointment 1, patients will be provided with written detailed information about the study, and if they volunteer they will sign a written informed consent to participate. This process will be repeated until each unit recruits the 5–6 patients agreed (Fig. 1).

Fig. 1
figure 1

Recruitment procedure for the Walk@Work-App study protocol of a randomized controlled trial

Eligibility criteria

Participating patients must be between 18 and 65 years old (working age) and have a mobile phone (smartphone). They must also be diagnosed with DM2 in accordance with international criteria [60], be office workers with a minimum of 55% of their daily working hours performing sedentary tasks according to the Occupational Sitting and Physical Activity Questionnaire (OSPAQ) [61] and have a work contract of at least 18.5 hours.

Furthermore, participants must not (i) have a diagnosis of musculoskeletal, cardiovascular, pulmonary or orthopaedic problems or any other physical condition that prevents them from being physically active; (ii) participate simultaneously in another study or programme of sedentary behaviour, physical activity, nutrition or weight control; (iii) be pregnant; or (iv) have a history of psychiatric problems or substance abuse that could interfere with adherence to the study protocol.

Allocation

After obtaining the informed consent of the patients to participate in the trial, they will be randomly assigned to the control and intervention group by the research team. The research team will use an Excel file to create a random sequence of numbers allocating patients into two blocks: the control and the intervention group. The sequence of numbers will be the result of a simple randomization process. The research team will randomly provide each medical unit with a series of six numbers to allocate to each patient. After finishing recruiting, the medical unit will allocate one number to each patient and will send this information to the research team.

Blinding

After the number assignment, (i) the research team will blindly provide “Walk@Work-App Kits” for each patient corresponding to either the control or intervention group to nurses. Both kits will have the same look (Fig. 2) but with different content depending on whether the patient belongs to the intervention or control group (Fig. 3). The physician, in charge of recording and monitoring clinical data, will be blinded to the patients’ group randomization. Patients will be blinded to the existence of other patients receiving the intervention or being in a control group. The independent researcher who evaluates the participants at the end of the intervention and at the 6- and 12-month follow-ups will be blinded to the participants’ treatment group assignment. In addition, the person who will carry out the data analysis will not participate in the data collection.

Fig. 2
figure 2

Walk@Work-AppKits provided to nurses of primary health care centres

Fig. 3
figure 3

Content of the control (a) and intervention (b) groups kit for Walk@Wor-kApp program

Intervention

The Walk@Work-Application (W@W-App) is an automated mobile phone and web-based intervention that focuses on decreasing and breaking up prolonged occupational sitting time in desk-based office employees. The W@W-App includes a self-monitoring tool that adds a commercially available sensor (MetaWearC; MbientLab Inc) [62] covered with a waterproof round case and attached via a band to the thigh. The sensor gathers employee’s postural and movement information during working hours. The W@W-App communicates with the MetaWearC external sensor by synchronizing the raw sensor data with the W@W-App software via a low-energy Bluetooth System. Postural and movement data are directly processed and displayed in real time by the app on the phone. Figure 4 depicts the W@W-App (login page) and the MetaWearC sensor.

Fig. 4
figure 4

(a) Walk@Work-App interface; (b) the MetaWearC sensor with the case and thigh band

The W@W-App+MetaWearC was developed from a previous version [63] to self-monitor and quantify occupational sitting, standing and stepping while offering real-time feedback on these behaviours (Fig. 5). Both are essential components for changing behaviours at the time and place where they occur, as well as for increasing individuals´ awareness and empowerment toward behaviour change [64]. The W@W-App+MetaWearC self-monitoring system has demonstrated a high level of accuracy and validity in determining postural position, providing a low-cost alternative tool for the examination of occupational sitting, standing, stationary and upright times in desk-based office employees [65].

Fig. 5
figure 5

Walk@Work-App interface displaying employees’ occupational stepping, sitting and standing time

Walk@Work-App registration, configuration and instalment

Participants will install and configure the W@W-App, following the guidance provided: (i) registration on the W@W web platform (http://walkatwork.uvic.cat/en/) [66]; (ii) user verification through email; (iii) W@W-App installation and initialization; (iv) recording day and time period configuration (i.e. between 3 and 8 working hours); and (v) recognition of the MetaWearC sensor via Bluetooth. Participants can also read the private policy of the W@W-App on the W@W website. Registration and employee guidance is available on a YouTube tutorial at https://youtu.be/cS0oJ9xPTbo. The W@W-App is available for downloading in Google Play and App Store. After registration and configuration, the complete W@W-App will be installed on the employees´ own mobile phones for 13 weeks, the period during which employees will wear the band with the attached MetaWearC, only during working hours (including the time taken to go to and back from work).

Description of how the W@W-App programme for changing occupational sedentary time works.

During Week 0, the W@W-App provides self-monitoring features to get baseline measurements for occupational stepping, sitting and standing time and set up a programme baseline with individual targets for each employee. During Weeks 1–12, the app keeps self-monitoring and displaying employees’ occupational activity in real-time, including an emoji of an animated chair at the bottom of the screen. According to the time spent in sitting bouts, the chair changes from a green chair (< 20 min) to a yellow chair (20–40 min) and then to a red chair (40–60 min) (Fig. 6). When sitting time is prolonged for more than one hour, a vibration feature of the mobile phone is activated.

Fig. 6
figure 6

Chair images with feedback displayed on the Walk@Work-App mobile screen

At the end of the working day, the mobile application sends data to the web server and it returns a daily summary message with the support of a chair image reflecting how well the employee has done in reaching the previously set-up individualized goals. In this message, participants can see the chair that represents their daily goals achievement (Fig. 7).

Fig. 7
figure 7

Chair images with feedback on employees’ summary of daily activity displayed on Walk@Work-App

When one or both targets have been achieved during most days of the week, a congratulations message is sent at the end of the working week with the support of a blue chair image. These weekly motivational messages report the progress made, appearing only when the goals are completed. Additionally, daily graphs provide feedback on employees´ occupational sitting and moving time (Fig. 8). While in the App these graphs can be checked weekly, in the W@W-App website (Fig. 9) the graphs show continuous feedback from week 0 to week 12 in relation to the goals that should be achieved throughout the programme.

Fig. 8
figure 8

Graphs with employees’ feedback on sitting and moving time displayed on W@W-App mobile screen

Fig. 9
figure 9

Graphs with employees’ feedback on sitting and moving time displayed on W@W-App website

Most importantly, during Weeks 1–12 employees have access to automated strategies to sit less and move more at work during an 8-week ramping phase and a 4-week maintenance phase. During this period, fortnight and weekly messages inform employees about the strategies and goals that can be used to change their occupational sedentary behaviour. During the ramping phase, every two weeks employees are challenged to progressively increase their movement by replacing occupational sitting time with 10 minutes (Weeks 1–2), 20 minutes (Weeks 3–4), 30 minutes (Weeks 5–6) or 37 minutes (Weeks 7–8) of moving or stepping above the average baseline (Week 0).

Description of the W@W-App strategies

Strategies to achieve the programme goals initially focus on reducing and breaking occupational sitting time by performing work tasks actively, such as active email, breaking up meetings time, getting off the chair briefly to move the joints, walking while talking on the phone and getting up to throw documents in the trash, drink water or print documents (Weeks 1–2). These progress to include short 5-minute walks, for example active parking, active relaxation at work, holding meetings while walking with one or two people or using the farthest bathroom (Weeks 3–4) and then longer walks of 10 minutes by targeting active transport, active breakfasts or including walks in the itinerary to get to work and back home (Weeks 5–6). During weeks 7–8, workers are encouraged to walk with greater intensity by using the stairs, walking briskly or adding three periods of 5–10 minutes of walking per day. Additionally, employees are encouraged to check resources on Twitter that explain in detail the benefits of replacing sitting time for light or moderate physical activity, share with users the barriers they find to reducing sitting time during the work day and show solutions to overcome these barriers. Finally, employees are encouraged to find their own strategies to sit less and move more at work and share them in the W@W-App Twitter. Over these 8 weeks, employees are also encouraged to share their active work experiences on the W@W-App Twitter and share the 5 to 10-minute walks added to their work routine to help others discover various routes and times to walk at work.

During the maintenance phase (weeks 9–12), employees are challenged to keep reducing daily sitting time and increase movement or walking time by 40–60 minutes a day. Strategies to achieve this goal focus on reducing sitting time at weekends, adding commuting or walks in the employees´ immediate environment where they live or work, staying active by helping others and being active or sharing knowledge with others about the benefits experienced from sitting less and being more active. The W@W-App strategies and intervention have been developed from previous versions [67, 68].

Description of the W@W-App behaviour change techniques

W@W-App provides a range of effective behaviour change techniques to reduce and break up sitting time and increase physical activity at work [69]. Underpinned by the Behaviour Change Wheel, a theoretically driven framework that incorporates multiple theories of behaviour change [70], the behaviour change techniques included in W@W-App are: (i) Feedback on behaviour and outcomes of behaviour (e.g. providing real-time feedback on the behaviours at the time when they occur); (ii) Self-monitoring of behaviour and outcomes of behaviour (e.g. providing graphics and messages with individual feedback on progress); (iii) Information about health consequences (e.g. providing articles on the health benefits of replacing sitting time with physical activity); (iv) Goal setting (i.e. setting goals every 2 weeks for reducing occupational sitting time and increasing moving); (v) Action Planning (e.g. providing groups of strategies that are progressively planned to increasing the length of the breaks in sitting time); (vi) Social support (i.e. providing a social network for sharing experiences and strategies using twitter or the blog); (vii) Instructions on how to perform the behaviour (e.g. providing detailed instructions of the tasks that can be done to reduce occupational sitting); and (H) Prompts (e.g. using coloured chairs).

Outcomes

The following outcomes will be assessed on the effectiveness of W@W-App: (i) Objective and subjective habitual and occupational sedentary behaviour, measured by the activPAL3TM device (PAL Technologies Ltd., Glasgow) and the Workforce Sitting Questionnaire (WSQ) [71, 72]; (ii) Objective and subjective habitual and occupational physical activity, measured with the activPAL3TM device and the Spanish Brief Physical Activity Assessment Tool (SBPAAT) [73, 74]; (iii) Glycaemic control variables using glucose, glycosylated haemoglobin A1c (HbA1c); (iv) Anthropometric variables by weight, height, body mass index (BMI), and waist circumference; (v) Lipid profile by total cholesterol, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) and triglycerides; (vi) Systolic and diastolic blood pressure (BP); (vii) Mental well-being measured with the Warwick-Edinburgh Mental Well-being Scale (WEMWS) [75]; and (viii) Work-related outcomes including presenteeism measured with the Work Limitations Questionnaire (WLQ) [76], sickness absence [77], work-related stress (Job Content Questionnaire, JCQ) [78] and impact of work on employees’ health [77].

Subjective habitual and occupational sedentary behaviour and physical activity

A seven-day total and domain-specific sitting time questionnaire (Workforce Sitting Questionnaire, WSQ) will assess weekly sitting time (minutes/day) at work and while travelling to and from work [71]. These domains will be targeted since Walk@Work-App aims to reduce sitting time (i) at work and (ii) while commuting. This questionnaire has high validity and reliability in the adult population for weekday sitting time at work (r = 0.69–0.74), while it is lower for weekend days across all domains (r = 0.23–0.74) [78]. Forward-backward translation into Catalan and Spanish identified linguistic equivalence [79]. The Spanish version of a Brief Physical Activity Assessment Tool (SBPAAT) will identify participants who are insufficiently active versus those who follow the health recommendations for physical activity [73]. The SBPAAT has showed moderate validity (k = 0.454, 95% CI: 0.402–0.505) and a specificity and sensitivity of 74.3 and 74.6%, respectively. Validity is fair for identifying daily minutes engaged in moderate (r = 0.215, 95% CI: 0.156 to 0.272) and vigorous PA (r = 0.282, 95% CI: 0.165 to 0.391) [73].

Objective habitual and occupational physical activity

The activPAL3TM will be used to measure and quantify the sedentary behaviour patterns and physical activity of employees across weekdays (working and non-working time) and weekends. Working and non-working times will be established by using participants’ daily records. The activPAL3TM device is a valid measure to quantify body posture and activity patterns during free-living conditions [72]. The device will be attached to participants’ right thigh using a flexible nitrile sleeve and a transparent film (10 × 10 cm of hypoallergenic Tegaderm™ Foam Adhesive Dressing). The waterproof dressing of the activPAL3TM allows participants to wear the monitor continuously for 24 hours per day for 7 complete days. Participants will receive additional dressings and instructions on how to reattach the device if needed. Additionally, participants will be asked to record their daily wake-up time, bedtime, working hours, and any monitor removal time.

Data will be processed using activPAL Professional Software™ (version 7.2.32), Microsoft Excel 2010 (Redmond, WA, USA), and MATLAB v8.4 (MathWorks®, Natick, MA, USA), following previously published protocols [80]. Resulting from the activPAL3TM software output, the following outcomes will be determined: total sitting time, total standing time, total number of sitting bouts and number of sitting bouts with three different lengths (< 20 min, 20–40 min, 40–60 min and > 60 min). Additionally, total time spent in light-intensity physical activity and moderate-to-vigorous physical activity will be determined by using previously validated count-to-activity thresholds [81].

Positive mental well-being.

The Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS) will assess positive mental well-being over the previous 2 weeks [75]. The 14-item scale has five response categories: 1 (“None”) to 5 (“All the time”). Responses are added to identify the final score, 14–70, and indicating low to high positive mental well-being. WEMWBS shows high internal reliability (Cronbach’s alpha = 0.93) and 1 week test-retest reliability (r = 0.97) in the Spanish population [82].

Presenteeism

The Work Limitations Questionnaire (WLQ) will assess performance and the degree to which health problems interfere with the ability to perform job roles [76]. Spanish [83] and Catalan [84] versions of the WLQ have been developed and validated. In the WLQ, respondents self-report levels of difficulty in performing 25 specific job roles across four scales, with scores expressed as an average of responses. The 5-item “Time Scale” addresses difficulty in scheduling demands. For the “Mental-Interpersonal Scale” six items cover difficulty performing cognitive tasks involving the processing of sensory information and interacting with others on the job. The “Output Scale” has five items exploring limitations in meeting demands for quantity, quality and timeliness of completed work. The nine-item “Physical Scale” assesses ability to perform job tasks that involve bodily strength, movement, endurance, coordination and flexibility.

Sub-scales scores are transformed to a 0–100 continuum to represent the amount of time in the previous 2 weeks affected by limited on-the-job performance (from a low to high rate of difficulty). These scales estimate work loss, known as the WLQ index [76], which is the weighted sum of the scores from the WLQ scales. In the present study, the WLQ index will be calculated by adding the scores of three WLQ scales; the “Physical Scale” will be excluded from the current analyses as it was not relevant to these job roles.

Job-related mental strain or work-related stress

The Job Content Questionnaire (JCQ) will measure work stressors [78]. This questionnaire follows the demand-control model and includes two dimensions: psychological demands of work and control over work. Work stress is expressed as the result of the interaction between both dimensions. The scale measures a third dimension – social support from co-workers, managers and supervisors – that can act as a moderator of the relationship between the demands of and control over one’s work [78].

The minimum reduced version of the questionnaire includes three dimensions: psychological demands (9 items), control over work (9 items) and support at work (11 items). The possible response categories for each of the items are: totally disagree (1), disagree (2), agree (3) and totally agree (4). The psychological demands dimension assesses workload, the intellectual demands and the time pressure. The control over work dimension assesses the possibility of making decisions, creativity and the application and development of one’s own skills. The support at work dimension assesses the support received from co-workers and supervisors.

The Spanish version of the minimum reduced version shows high reliability and content validity and moderate concept validity for each of the three dimensions [85]. Work stress is expressed as a ratio between psychological demands and control at work, with lower scores indicating less stress. The range of scores for the psychological demands dimensions varies between 12 and 48, while the score for the control at work dimension varies between 24 and 97 [74]. For the social support dimension, co-worker support will be measured by using a 5-item scale, while supervisor support will be measured with a 6-item scale on a 4-point Likert scale (1 = agree; 2 = somewhat agree; 3 = somewhat disagree; and 4 = disagree). Total social support is the sum of co-worker and supervisor support, with higher values reflecting greater perceived social support [78].

Employees’ work-related health problems

Six questions from the 6th European Working Conditions Survey will measure work-related health problems [77]. Patients will be asked if they have suffered, in the last 3 months, any of the following health problems: backache, muscle pain in the shoulders, neck and/or upper limbs muscle pain in the lower limbs anxiety, general tiredness or general health problems [77]. The results will be given in percentages.

Employees´ sickness absence

Two questions will be asked [77] in relation to the number of work days the patient has missed, in the last 3 months, due to sick leave or health reasons, and how many of these days were caused by work-related health problems (excluding accidents) [77]. The results will be reported as a percentage of patients that have missed a day of work and the average number of days of temporary disability.

Participant timeline

The intervention group will have access to the automated W@W-App for 13 weeks. The control group will be asked to continue their routine daily activities receiving usual health care. All participants will receive the usual health recommendations and general information on the health benefits of sitting less and moving more through an infographic (Additional file 1). Figure 10 shows the planned follow-up visits to be carried out by the healthcare professionals, the variables to be measured and the tasks to be performed in each follow-up visit.

Fig. 10
figure 10

Participants’ timeline for the Walk@Work-App study protocol of a randomized controlled trial

Data collection methods

The data collection will be carried out at the beginning and at 3, 6 and 12 months using the activPAL3TM device, data from the computerized medical history and a survey. At 6 and 12 months, each patient will have a blood test done for the clinical variables (Table 1). In each measurement, the participants will be summoned in consultation to record the subjective and objective habitual and occupational sedentary behaviour and the subjective and objective habitual and occupational level of physical activity.

Table 1 Data collection methods for the Walk@Work-App study protocol of a randomized controlled trial

The anthropometric variables will be measured with an approved Seca 770 scale and a height measuring rod Seca 222. The BMI will be calculated by dividing the body weight by the square of the height in metres (kg/m2). Measurement of the waist circumference, in centimetres, will be completed using a stretch-resistant tape measure and recorded as the midpoint between the lower margin of the lowest palpable rib and the top of the iliac crest. The measurement will be repeated twice. If the measurements are within 1 cm of each other, the average will be calculated; and if the difference exceeds 1 cm, the two measurements will be repeated.

For systolic and diastolic blood pressure (BP), three measurements will be made, using the mean of the last two, with a validated OMRON M3 sphygmomanometer and following the recommendations of the European Society of Hypertension (https://www.eshonline.org/). The degree of metabolic control according to the percentage of HbA1c and basal glucose, lipid profile (total cholesterol total, HDL, LDL, triglycerides) will be measured by a preliminary blood test, and at the 6- and 12-month follow-ups.

At baseline, data from participants’ personal, social and work profile will be collected. Age, sex and social class will be recorded based on the clinical history and data from a questionnaire. A validated scale of occupation as an indicator of social class, according to the British Registrar General classification [86], will be used to record the highest level of completed studies, type of work, length of time working in the company, type of work contract, number of work hours and usual days of work.

Changes in glucose-lowering therapy within the last 3 months, the type of oral antidiabetic drug or insulin, type of insulin, if participants follow a special diet or have modified their diet in the last 3 months will be also recorded. These variables will describe participants and, when the data is processed and if some outlier is observed, to justify possible unusual behaviours. Table 1 shows the variables and its type recorded at each instant: continuous or qualitative variables.

SBPAAT: Brief Physical Activity Assessment Tool; WSQ: Workforce Sitting Questionnaire; BMI: Body mass index; HbA1c: glycosylated haemoglobin A1c; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; WEMWS: Warwick-Edinburgh Mental Well-being Scale; WLQ: Work Limitations Questionnaire; JCQ: Job-related mental strain; Continuous variable (CV), Qualitative variable (QV).

Statistical methods

A descriptive study of all the measured variables will contain the mean, standard deviation, minimum and maximum value and dispersion coefficient. The homogeneity of the intervention and control group at the initial moment – that is, that there are no statistically significant differences between the two groups in any of the variables measured – will be verified by performing a two-tailed test of means for independent samples for each variable (Additional file 2).

The effect of the mHealth W@W-App programme will be measured by continuous variables (Table 1) which will be used to evaluate the differences between before and after implementing the mHealth programme. In some variables, the programme will be effective if the mean increases in the intervention group while for other variables effectiveness will happen if the mean decreases. For example, the programme will be effective if physical activity increases and total cholesterol decreases. Therefore a one-tailed test of means will be performed. For these continuous variables, the difference of the variable between moment t of the measurement and the initial moment will be calculated, (XDt = Xt − X0), which quantifies whether there have been changes, after t months, associated with the intervention. The intervention has a positive effect if the results of the intervention group are significantly better than those of the control group; that is, if the values of the variable XDt are better in the intervention group. The qualitative variables (see Table 1) will be used to check if any patients display unusual behaviour that are due to changes in their medication or diet [86]. Intention-to treat analysis will be used. The statistical tests to be carried out in the event that homogeneity between the groups is not accepted while the procedure is outlined in Additional file 3.

Sample size

Using the free software G*Power [87], the sample size was calculated based on the power of the test and the effect size. Table 2 shows the results for powers of 0.95 and 0.80 and three effect sizes (i.e. small, medium and large). If the intervention and control group are homogenous, the sample size to be taken for a mean effect is 176 in total; 88 patients to the intervention group and 88 to the control group. With a total sample size of 176, the power of the homogeneity test is 0.91 (Table 2).

Table 2 Power function to test homogeneity of the control and intervention group as a function of the effect size

It is estimated that on average there is a loss of 20% of patients in clinical trials. If the sample size is increased by this percentage, 220 patients will have to be recruited; 110 in the intervention and 110 in the control group. If the losses do not exceed 20%, a test power of 0.95 would be guaranteed for a mean size effect. If the sample size is not increased at baseline, a loss of 20% would mean having a sample size of 140 patients, which would mean a test power of 0.90. The effect size capable of detecting the test performed with a power of 0.95 or 0.8 shows in additional file 4.

Data monitoring

Operating procedures will be documented in a Standard Operating Procedure manual to standardize the administration of trial conditions, data collection methods, tracking procedures, and checking programming into REDCap (e.g. randomization and administration of tools within specified timeline) [88].

Ethics and dissemination

This trial will follow the standards of Good Clinical Practice and the principles of the Declaration of Helsinki. The project has been approved by the Clinical Research Ethics Committee of the Primary Care Research Institute Jordi Gol i Gurina with the registration code P18/102. The trial will be reported and patients that volunteer will be asked to sign a written informed consent prior to program participation. Each medical unit participant will be asked for their commitment to the research. The confidentiality of the subjects will be in accordance with regulations of the Organic law of Protection of Personal Data (15/1999 of December 13, LOPD) as well as the EU General Data Protection Regulation (2016/679, GDPR). Study results will be reported according to Consolidated Standards of Reporting Trials (CONSORT) recommendations [88] and will also be reported in the ClinicalTrials.gov registry.

Discussion

Over the last few decades, adults have increased the number of daily hours dedicated to sedentary activities in all areas of life [4]: travel, leisure time, and domestic and work activities. This increase is due to environmental, social, political and cultural factors [23], which result in harmful effects on people’s health, well-being and productivity, and especially people with chronic disease [6]. Given the high prevalence and complexity of chronic diseases associated with sedentary behaviour and physical inactivity such as DM2, it is essential to develop and evaluate new intervention strategies (mHealth) that promote self-care and the sustainability of the health system through the integration of programmes that promote physical activity and the reduction of sedentary behaviour in healthcare. This would contribute to improving the quality of care for people with DM2.

The W@W-App will be applied by healthcare professionals to office workers with DM2, but it could also be applied to other groups of patients with office jobs and chronic diseases associated with sedentary behaviour. The randomized controlled trial of this study will strengthen current evidence on the prescription of mHealth interventions by healthcare professionals to reduce sedentary behaviour and increase habitual and occupational physical activity applied to people with chronic disease. Further, the randomized control trial will examine whether improvement in physical activity and sedentary behaviour leads to both clinical improvement and improvement in well-being and productivity. Therefore, it can provide a practical, cost-effective and accessible intervention that improves adherence to interventions in the lifestyle of people with DM2. The main limitation will be not studying the effect that the intervention could have in the longer term (> 12 months follow-up), which would require a longer study. The main strengths are its evaluation of the impact of a mHealth programme at the clinical level, on mental well-being and on work outcomes and prescribed by healthcare professionals. In addition, it has been designed using behavioural change techniques to reduce sedentary behaviour, and it is the first research of this type to be carried out at the healthcare level, therefore it provides a base for future research in this field.

Abbreviations

DM2:

Diabetes Mellitus type 2

WHO:

World Health Organization

mHealth:

mobile Health

OSPAQ:

Occupational Sitting and Physical Activity Questionnaire

W@W-App:

Walk@Work-Application

WSQ:

Workforce Sitting Questionnaire

SBPAAT:

Spanish Brief Physical Activity Assessment Tool

HbA1c:

Glycosylated Hemoglobin A1c

BMI:

Body Mass Index

HDL:

High-Density Lipoprotein

LDL:

Low-Density Lipoprotein

BP:

Blood Pressure

WEMWS:

Warwick-Edinburgh Mental Well-being Scale

WLQ:

Work Limitations Questionnaire

JCQ:

Job Content Questionnaire

CV:

Continuous Variables

QV:

Qualitative Variable

CONSORT:

Consolidated Standards of Reporting Trials

References

  1. Lavie CJ, Ozemek C, Carbone S, Katzmarzyk PT, Blair SN. Sedentary Behavior, Exercise, and Cardiovascular Health. Circ Res. 2019;124(5):799–815. https://doi.org/10.1161/CIRCRESAHA.118.312669.

    CAS  Article  PubMed  Google Scholar 

  2. Booth FW, Roberts CK, Thyfault JP, Ruegsegger GN, Toedebusch RG. Role of Inactivity in Chronic Diseases: Evolutionary Insight and Pathophysiological Mechanisms. Physiol Rev. 2017;97(4):1351–402. https://doi.org/10.1152/physrev.00019.2016.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Bauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet. 2014;384(9937):45–52. https://doi.org/10.1016/S0140-6736(14)60648-6.

    Article  PubMed  Google Scholar 

  4. Chau JY, Grunseit AC, Chey T, Stamatakis E, Brown WJ, Matthews CE, et al. Daily sitting time and all-cause mortality: a meta-analysis. PLoS One. 2013;8(11):e80000. https://doi.org/10.1371/journal.pone.0080000.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Wilmot EG, Edwardson CL, Achana FA, Davies MJ, Gorely T, Gray LJ, et al. Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia. 2012;55(11):2895–905. https://doi.org/10.1007/s00125-012-2677-z.

    CAS  Article  PubMed  Google Scholar 

  6. Guo C, Zhou Q, Zhang D, QinP LQ, Tian G, et al. Association of total sedentary behaviour and television viewing with risk of overweight/obesity, type 2 diabetes and hypertension: A dose-response meta-analysis. Diabetes Obes Metab. 2020;22(1):79–90. https://doi.org/10.1111/dom.13867.

    CAS  Article  PubMed  Google Scholar 

  7. van der Berg JD, Stehouwer CD, Bosma H, van der Velde JH, Willems PJ, Savelberg HH, et al. Associations of total amount and patterns of sedentary behaviour with type 2 diabetes and the metabolic syndrome: The Maastricht Study. Diabetologia. 2016;59(4):709–18. https://doi.org/10.1007/s00125-015-3861-8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Greer AE, Sui X, Maslow AL, Greer BK, Blair SN. The effects of sedentary behavior on metabolic syndrome independent of physical activity and cardiorespiratory fitness. J Phys Act Health. 2015;12(1):68–73. https://doi.org/10.1123/jpah.2013-0186.

    Article  PubMed  Google Scholar 

  9. Al Tunaiji H, Davis JC, Mansournia MA, Khan KM. Population attributable fraction of leading non-communicable cardiovascular diseases due to leisure-time physical inactivity: a systematic review. BMJ Open Sport Exerc Med. 2019;5(1):e000512. https://doi.org/10.1136/bmjsem-2019-000512.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Åsvold BO, Midthjell K, Krokstad S, Rangul V, Bauman A. Prolonged sitting may increase diabetes risk in physically inactive individuals: an 11 year follow-up of the HUNT Study, Norway Diabetologia 2017;60(5):830–835. doi:https://doi.org/10.1007/s00125-016-4193-z.

  11. Gilchrist SC, Howard VJ, Akinyemiju T, Judd SE, Cushman M, Hooker SP, et al. Association of Sedentary Behavior with Cancer Mortality in Middle-aged and Older US Adults. JAMA Oncol. 2020;6(8):1210–7. https://doi.org/10.1001/jamaoncol.2020.2045.

    Article  PubMed  Google Scholar 

  12. Jochem C, Wallmann-Sperlich B, Leitzmann MF. The Influence of Sedentary Behavior on Cancer Risk: Epidemiologic Evidence and Potential Molecular Mechanisms. Curr Nutr Rep. 2019;8(3):167–74. https://doi.org/10.1007/s13668-019-0263-4.

    Article  PubMed  Google Scholar 

  13. Schmid D, Leitzmann MF. Television viewing and time spent sedentary in relation to cancer risk: a meta-analysis. J Natl Cancer Inst. 2014;106(11):dju303. https://doi.org/10.1093/jnci/dju303.

    CAS  Article  Google Scholar 

  14. Teychenne M, Costigan SA, Parker K. The association between sedentary behaviour and risk of anxiety: a systematic review. BMC Public Health. 2015;15:513. https://doi.org/10.1186/s12889-015-1843-x.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Zhai L, Zhang Y, Zhang D. Sedentary behaviour and the risk of depression: a meta-analysis. Br J Sports Med. 2015;49(11):705–9. https://doi.org/10.1136/bjsports-2014-093613.

    Article  PubMed  Google Scholar 

  16. Atkin AJ, Adams E, Bull FC, Biddle SJ. Non-occupational sitting and mental well-being in employed adults. Ann Behav Med. 2012;43(2):181–8. https://doi.org/10.1007/s12160-011-9320-y.

    Article  PubMed  Google Scholar 

  17. World Health Organization. Noncommunicable Dise 2021. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. Accessed 15 Aug 2021.

  18. Hoffman C, Rice D, Sung HY. Persons with chronic conditions Their prevalence and costs. JAMA. 1996;276(18):1473–9.

    CAS  Article  Google Scholar 

  19. Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, et al. Sedentary Behaviour Research Network (SBRN) - Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017;14(1):75. https://doi.org/10.1186/s12966-017-0525-8.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451–62. https://doi.org/10.1136/bjsports-2020-102955.

    Article  PubMed  Google Scholar 

  21. Bailey DP, Hewson DJ, Champion RB, Sayegh SM. Sitting Time and Risk of Cardiovascular Disease and Diabetes: A Systematic Review and Meta-Analysis. Am J Prev Med. 2019;57(3):408–16. https://doi.org/10.1016/j.amepre.2019.04.015.

    Article  PubMed  Google Scholar 

  22. Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018;6(10):e1077–86. https://doi.org/10.1016/S2214-109X(18)30357-7.

    Article  PubMed  Google Scholar 

  23. Owen N, Healy GN, Matthews CE, Dunstan DW. Too much sitting: the population health science of sedentary behavior. Exerc Sport Sci Rev. 2010;38(3):105–13. https://doi.org/10.1097/JES.0b013e3181e373a2.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ding D, Lawson KD, Kolbe-Alexander TL, Finkelstein EA, Katzmarzyk PT, van Mechelen W, et al. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. 2016;388(10051):1311–24. https://doi.org/10.1016/S0140-6736(16)30383-X.

    Article  PubMed  Google Scholar 

  25. Heron L, O'Neill C, McAneney H, Kee F, Tully MA. Direct healthcare costs of sedentary behaviour in the UK. J Epidemiol Community Health. 2019;73(7):625–9. https://doi.org/10.1136/jech-2018-211758.

    Article  PubMed  Google Scholar 

  26. Du Y, Liu B, Sun Y, Snetselaar LG, Wallace RB, Bao W. Trends in Adherence to the Physical Activity Guidelines for Americans for Aerobic Activity and Time Spent on Sedentary Behavior Among US Adults, 2007 to 2016. JAMA Netw Open. 2019;2(7):e197597. https://doi.org/10.1001/jamanetworkopen.2019.7597.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Meyer J, McDowell C, Lansing J, et al. Changes in Physical Activity and Sedentary Behavior in Response to COVID-19 and Their Associations with Mental Health in 3052 US Adults. Int J Environ Res Public Health. 2020;17(18):6469. https://doi.org/10.3390/ijerph17186469.

    CAS  Article  PubMed Central  Google Scholar 

  28. Amini H, Habibi S, Islamoglu AH, Isanejad E, Uz C, Daniyari H. COVID-19 pandemic-induced physical inactivity: the necessity of updating the Global Action Plan on Physical Activity 2018-2030. Environ Health Prev Med 2021;26(1):32. Published 2021 Mar 7. doi:https://doi.org/10.1186/s12199-021-00955-z.

  29. Park AH, Zhong S, Yang H, Jeong J, Lee C. Impact of COVID-19 on physical activity: A rapid review. J Glob Health. 2022;12:05003. Published 2022 Apr 30. https://doi.org/10.7189/jogh.12.05003.

  30. Pratt M, Ramirez Varela A, Salvo D, Kohl Iii HW, Ding D. Attacking the pandemic of physical inactivity: what is holding us back? Br J Sports Med. 2020;54(13):760–2. https://doi.org/10.1136/bjsports-2019-101392.

    Article  PubMed  Google Scholar 

  31. González K, Fuentes J, Márquez JL. Physical Inactivity, Sedentary Behavior and Chronic Diseases. Korean J Fam Med. 2017;38(3):111–5. https://doi.org/10.4082/kjfm.2017.38.3.111.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ross R, Chaput JP, Giangregorio LM, Janssen I, Saunders TJ, Kho ME, et al. Canadian 24-Hour Movement Guidelines for Adults aged 18-64 years and Adults aged 65 years or older: an integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab. 2020;45(10):S57–S102. https://doi.org/10.1139/apnm-2020-0467.

    Article  PubMed  Google Scholar 

  33. Cillekens B, Lang M, van Mechelen W, Verhangen E, Huysmans MA, Holtermann A, et al. How does occupational physical activity influence health? An umbrella review of 23 health outcomes across 158 observational studies. Br J Sports Med. 2020;54(24):1474–81. https://doi.org/10.1136/bjsports-2020-102587.

    Article  PubMed  Google Scholar 

  34. Straker L, Coenen P, Gilson N, Healy GN. Sedentary Work—Evidence on an Emergent Work Health and Safety Issue. Safe Work Australia: Canberra, Australia; 2016.

    Google Scholar 

  35. Parry S, Straker L, Gilson ND, Smith AJ. Participatory workplace interventions can reduce sedentary time for office workers--a randomised controlled trial. PLoS One. 2013;8(11):e78957. https://doi.org/10.1371/journal.pone.0078957.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. Fukushima N, Machida M, Kikuchi H, Amagasa S, Hayashi T, Odagiri Y, et al. Associations of working from home with occupational physical activity and sedentary behavior under the COVID-19 pandemic. J Occup Health. 2021;63(1):e12212. https://doi.org/10.1002/1348-9585.12212.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. McDowell CP, Herring MP, Lansing J, Brower C, Meyer JD. Working From Home and Job Loss Due to the COVID-19 Pandemic Are Associated With Greater Time in Sedentary Behaviors. Front Public Health. 2020;8:597619. https://doi.org/10.3389/fpubh.2020.597619.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Straker L, Healy GN, Atherton R, Dunstan DW. Excessive occupational sitting is not a "safe system of work": time for doctors to get chatting with patients. Med J Aust. 2014;201(3):138–40. https://doi.org/10.5694/mja13.00037.

    Article  PubMed  Google Scholar 

  39. Puig-Ribera A, Martínez-Lemos I, Giné-Garriga M, González-Suárez AM, Bort-Roig J, Fortuño J, et al. Self-reported sitting time and physical activity: interactive associations with mental well-being and productivity in office employees. BMC Public Health. 2015;15:72. https://doi.org/10.1186/s12889-015-1447-5.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Puig-Ribera A, Bort-Roig J, Giné-Garriga M, González-Suárez AM, Martínez-Lemos I, Fortuño J, et al. Impact of a workplace 'sit less, move more' program on efficiency-related outcomes of office employees. BMC Public Health. 2017;17(1):455. https://doi.org/10.1186/s12889-017-4367-8.

    Article  PubMed  PubMed Central  Google Scholar 

  41. McCarthy M, Vorderstrasse A, Yan J, Portillo A, Dickson VV. Managing Diabetes in the Workplace. Workplace Health Saf. 2021;69(5):216–23. https://doi.org/10.1177/2165079920965538.

    Article  PubMed  Google Scholar 

  42. Taylor WC, Das BM, Paxton RJ, Shegog R, Suminski RR, Johnson SR, et al. Development and implementation of a logic model: Occupational stress, physical activity, and sedentary behavior in the workplace. Work. 2021;68(2):521–2. https://doi.org/10.3233/WOR-203352.

    Article  PubMed  Google Scholar 

  43. Nyberg ST, Fransson EI, Heikkilä K, Ahola K, Afredsson L, Bjorner JB, et al. Job strain as a risk factor for type 2 diabetes: a pooled analysis of 124,808 men and women. Diabetes Care. 2014;37(8):2268–75. https://doi.org/10.2337/dc13-2936.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Munir F, Biddle SJH, Davies MJ, Dunstan D, Esliger D, Gray LJ, et al. Stand More AT Work (SMArT Work): using the behaviour change wheel to develop an intervention to reduce sitting time in the workplace. BMC Public Health. 2018;18(1):319. https://doi.org/10.1186/s12889-018-5187-1.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hendriksen IJ, Snoijer M, de Kok BP, van Vilsteren J, Hofstetter H. Effectiveness of a Multilevel Workplace Health Promotion Program on Vitality, Health, and Work-Related Outcomes. J Occup Environ Med. 2016;58(6):575–83. https://doi.org/10.1097/JOM.0000000000000747.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Martin A, Fitzsimons C, Jepson R, Saunders DH, van der Ploeg HP, Teixeira PJ, et al. Interventions with potential to reduce sedentary time in adults: systematic review and meta-analysis. Br J Sports Med. 2015;49(16):1056–63. https://doi.org/10.1136/bjsports-2014-094524.

    Article  PubMed  Google Scholar 

  47. Bailey DP. Sedentary behaviour in the workplace: prevalence, health implications and interventions. Br Med Bull. 2021;137(1):42–50. https://doi.org/10.1093/bmb/ldaa039.

    Article  PubMed  Google Scholar 

  48. World Health Organization. mHealth: New horizons for health through mobile technologies: second global survey on eHealth Geneva 27, Switzerland: World Health Organization 2011 [cited 2016 April 4]. Available from: //www.who.int/goe/publications/goe_mhealth_web.pdf

  49. Buckingham SA, Williams AJ, Morrissey K, Price L, Harrison J. Mobile health interventions to promote physical activity and reduce sedentary behaviour in the workplace: A systematic review. Digit Health. 2019;5:2055207619839883. https://doi.org/10.1177/2055207619839883.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Marcolino MS, Oliveira JAQ, D'Agostino M, Ribeiro AL, Alkmim MBM, Novillo-Ortiz D. The Impact of mHealth Interventions: Systematic Review of Systematic Reviews. JMIR Mhealth Uhealth. 2018;6(1):e23. https://doi.org/10.2196/mhealth.8873.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Greenwood DA, Gee PM, Fatkin KJ, Peeples M. A Systematic Review of Reviews Evaluating Technology-Enabled Diabetes Self-Management Education and Support. J Diabetes Sci Technol. 2017;11(5):1015–27. https://doi.org/10.1177/1932296817713506.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Bort-Roig J, Chirveches-Pérez E, Giné-Garriga M, Navarro-Blasco L, Bausà-Peris R, Iturrioz-Rosell P, et al. An mHealth Workplace-Based "Sit Less, Move More" Program: Impact on Employees' Sedentary and Physical Activity Patterns at Work and Away from Work. Int J Environ Res Public Health. 2020;17(23):8844. https://doi.org/10.3390/ijerph17238844.

    Article  PubMed Central  Google Scholar 

  53. Schoeppe S, Alley S, Van Lippevelde W, Bray NA, Williams SL, Duncan MJ, et al. Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review. Int J Behav Nutr Phys Act. 2016;13(1):127. https://doi.org/10.1186/s12966-016-0454-y.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Iyengar V, Wolf A, Brown A, Close K. Challenges in Diabetes Care: Can Digital Health Help Address Them? Clin Diabetes. 2016;34(3):133–41. https://doi.org/10.2337/diaclin.34.3.133.

    Article  PubMed  PubMed Central  Google Scholar 

  55. So CF, Chung JW. Telehealth for diabetes self-management in primary healthcare: A systematic review and meta-analysis. J Telemed Telecare. 2018;24(5):356–64. https://doi.org/10.1177/1357633X17700552.

    Article  PubMed  Google Scholar 

  56. Wang Y, Xue H, Huang Y, Huang L, Zhang D. A Systematic Review of Application and Effectiveness of mHealth Interventions for Obesity and Diabetes Treatment and Self-Management. Adv Nutr. 2017;8(3):449–62. https://doi.org/10.3945/an.116.014100.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Sallis R, Franklin B, Joy L, Ross R, Sabgir D, Stone J. Strategies for promoting physical activity in clinical practice. Prog Cardiovasc Dis. 2015;57(4):375–86. https://doi.org/10.1016/j.pcad.2014.10.003.

    Article  PubMed  Google Scholar 

  58. Chan AW, Tetzlaff JM, Gøtzsche PC, Altman DG, Mann H, Berlin JA, et al. SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials. BMJ. 2013;346:e7586. https://doi.org/10.1136/bmj.e7586.

    Article  PubMed  PubMed Central  Google Scholar 

  59. INE. National Statistics Institute. Spanish Statistical. Office. 2017. https://www.ine.es/jaxi/Datos.htm?path=/t20/e245/p08/l0/&file=01003.px.

  60. American Diabetes Association. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes. Diabetes Care. 2020;43(Suppl 1):S14–31. https://doi.org/10.2337/dc20-S002.

    Article  Google Scholar 

  61. Chau JY, Van Der Ploeg HP, Dunn S, Kurko J, Bauman AE. Validity of the occupational sitting and physical activity questionnaire. Med Sci Sports Exerc. 2012;44(1):118–25. https://doi.org/10.1249/MSS.0b013e3182251060.

    Article  PubMed  Google Scholar 

  62. MetwearC Product Specification v1. MbientLab Inc. https://mbientlab.com/documents/MetaWearC-CPRO-PS.pdf. Accessed 22 July 2021.

  63. Bort-Roig J, Puig-Ribera A, Contreras RS, Chirveches-Pérez E, Martori JC, Gilson ND, et al. Monitoring sedentary patterns in office employees: validity of an m-health tool (Walk@Work-App) for occupational health. Gac Sanit. 2018;32(6):563–6.

    Article  Google Scholar 

  64. Gal R, May AM, van Overmeeren EJ, Simons M, Monninkhof EM. The Effect of Physical Activity Interventions Comprising Wearables and Smartphone Applications on Physical Activity: a Systematic Review and Meta-analysis. Sports Med Open. 2018;4(1):42.

    Article  Google Scholar 

  65. Bort-Roig J, Chirveches-Pérez E, Garcia-Cuyàs F, Dowd KP, Puig-Ribera A Monitoring Occupational Sitting, Standing, and Stepping in Office Employees With the W@W-App and the MetaWearC Sensor: Validation Study. JMIR Mhealth Uhealth 2020;8(8):e15338. doi:https://doi.org/10.3390/ijerph17238844.

  66. Walk@Work: Sit less, move more. University of Vic-Central University of Catalonia. http://walkatwork.uvic.cat/en/. Accessed 7 July 2021.

  67. Puig-Ribera A, Bort-Roig J, González-Suárez AM, Martínez-Lemos I, Giné-Garriga M, Fortuño J, et al. Patterns of impact resulting from a 'sit less, move more' web-based program in sedentary office employees. PLoS One. 2015;10(4):e0122474. https://doi.org/10.1371/journal.pone.0122474.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  68. Bort-Roig J, Martin M, Puig-Ribera A, González-Suárez ÁM, Martínez-Lemos I, Martori JC, et al. Uptake and factors that influence the use of 'sit less, move more' occupational intervention strategies in Spanish office employees. Int J Behav Nutr Phys Act. 2014;11:152. https://doi.org/10.1186/s12966-014-0152-6.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Howlett N, Trivedi D, Troop NA, Chater AM. Are physical activity interventions for healthy inactive adults effective in promoting behaviour change and maintenance, and which behaviour change techniques are effective? A systematic review and meta-analysis. Transl. Behav Med. 2019;9(1):147 157. https://doi.org/10.1093/tbm/iby010.

    Article  Google Scholar 

  70. Michie S, Van Stralen MM, West R. The Behaviour Change Wheel: A new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6(1):42.

    Article  Google Scholar 

  71. Marshall AL, Miller YD, Burton NW, Brown WJ. Measuring total and domain-specific sitting: a study of reliability and validity. Med Sci Sports Exerc. 2010;42(6):1094–102. https://doi.org/10.1249/MSS.0b013e3181c5ec18.

    Article  PubMed  Google Scholar 

  72. Lyden K, Keadle SK, Staudenmayer J, Freedson PS. The activPALTM Accurately Classifies Activity Intensity Categories in Healthy Adults. Med Sci Sports Exerc. 2017;49(5):1022–8. https://doi.org/10.1249/MSS.0000000000001177.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Puig-Ribera A, Martín-Cantera C, Puigdomenech E, Real J, Romaguera M, Magdalena-Belio JF, et al. Screening Physical Activity in Family Practice: Validity of the Spanish Version of a Brief Physical Activity Questionnaire. PLoS One. 2015;10(9):e0136870. https://doi.org/10.1371/journal.pone.0136870.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  74. Puig Ribera A, Peña Chimenis O, Romaguera Bosch M, Duran Bellido E, Heras Tebar A, Solà Gonfaus M, et al. How to identify physical inactivity in primary care: validation of the Catalan and Spanish versions of 2 short questionnaires. Aten Primaria. 2012;44(8):485–93. https://doi.org/10.1016/j.aprim.2012.01.005.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Tennant R, Hiller L, Fishwick R, Stephen P, Joseph S, Weich S, et al. The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): development and UK validation. Health Qual Life Outcomes. 2007;5:63. https://doi.org/10.1186/1477-7525-5-63.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Lerner D, Amick BC 3rd, Rogers WH, Malspeis S, Bungay K, Cynn D. The Work Limitations Questionnaire. Med Care. 2001;39(1):72–85. https://doi.org/10.1097/00005650-200101000-00009.

    CAS  Article  PubMed  Google Scholar 

  77. Eurofound. Sixth European Working Conditions Survey – Overview report (2017 update). Luxembourg: Publications Office of the European Union; 2017.

  78. Karasek R, Brisson C, Kawakami N, Houtman I, Bongers P, Amick B. The Job Content Questionnaire (JCQ): an instrument for internationally comparative assessments of psychosocial job characteristics. J Occup Health Psychol. 1998;3(4):322–55. https://doi.org/10.1037//1076-8998.3.4.322.

    CAS  Article  PubMed  Google Scholar 

  79. Acquadro C, Conway K, Giroudet C. Linguistic validation manual for health outcome assessments. Lyon: Mapi Institute; 2004.

    Google Scholar 

  80. Dowd KP, Harrington DM, Bourke AK, Nelson J, Donnelly AE. The measurement of sedentary patterns and behaviors using the activPAL™ Professional physical activity monitor. Physiol Meas. 2012;33(11):1887–99. https://doi.org/10.1088/0967-3334/33/11/1887.

    Article  PubMed  Google Scholar 

  81. Powell C, Carson BP, Dowd KP, Donnelly AE. Simultaneous validation of five activity monitors for use in adult populations. Scand J Med Sci Sports. 2017;27(12):1881–92. https://doi.org/10.1111/sms.12813.

    CAS  Article  PubMed  Google Scholar 

  82. Castellví P, Forero CG, Codony M, Vilagut G, Brugulat P, Medina A, et al. The Spanish version of the Warwick-Edinburgh mental well-being scale (WEMWBS) is valid for use in the general population. Qual Life Res. 2014;23(3):857–68. https://doi.org/10.1007/s11136-013-0513-7.

    Article  PubMed  Google Scholar 

  83. Mapi Research Institute. Linguistic validation certificate: work limitations questionnaire. Lyon: Mapi Research Institute; 2006.

    Google Scholar 

  84. Puig-Ribera A, McKenna J, Gilson N, Brown WJ. Measuring presenteesim in Catalan employees: linguistic adaptation and validation. Int J Work Health Manag. 2008;1:198–208. https://doi.org/10.1108/17538350810926543.

    Article  Google Scholar 

  85. Escribà-Agüir V, Más Pons R, Flores RE. Validation of the Job Content Questionnaire in hospital nursing staff. Gac Sanit. 2001;15(2):142–9. https://doi.org/10.1016/s0213-9111(01)71533-6.

    Article  PubMed  Google Scholar 

  86. Alonso J, Pérez P, Sáez M, Murillo C. Validity of the occupation as an indicator of social class, according to the British Registrar General classification. Gac Sanit. 1997;11(5):205–13. https://doi.org/10.1016/s0213-9111(97)71299-8.

    CAS  Article  PubMed  Google Scholar 

  87. Faul F, Erdfelder E, Lang AG, Buchner A. G. Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 2007;39(2):175–191. doi:https://doi.org/10.3758/bf03193146.

  88. Schulz KF, Altman DG, Moher D. CONSORT Group. CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. J Clin Epidemiol. 2010;63(8):834–40. https://doi.org/10.1016/j.jclinepi.2010.02.005.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank the Foundation University Institute for Primary Health Care Research Jordi Gol i Gurina for supporting this work.

Availability of data and material

Not applicable.

Funding

The study was funded by Fondo de Investigación Sanitaria, Instituto de Salud Carlos III (PI17/01788) and the predoctoral research grant Isabel Fernández 2020 from the Spanish Society of Family and Community Medicine (semFYC). The funders had no role in the design, analysis, data interpretation or writing of the manuscript.

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Authors and Affiliations

Authors

Contributions

FA, CMC, MSM, IS, ECP, MSG, JMA and APR were responsible for the conception and design of the study. AC was responsible for the sample size calculation and conceived the statistical methods. FA, CMC, JBR, IS and APR conceived and participated in the design of the questionnaires. FA, AP, IS, ECP MSG and JMS participated in the development of the study. FA, AC and APR wrote the firsts drafts and final version of the study protocol. All authors have performed a critical revision of this manuscript and the final version.

Corresponding author

Correspondence to Francesc Alòs.

Ethics declarations

Ethics approval and consent to participate

This study protocol was approved by the Clinical Research Ethics Committee of the Primary Care Research Institute Jordi Gol with the registration code P18/102, where all of patients that volunteer will be asked to sign a written informed consent prior to program participation.

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1.

Infographic with general information on the health benefits of “sitting less and moving more” provided to the control and intervention groups.

Additional file 2.

Statistical tests to be performed to test for independent samples for each variable.

Additional file 3.

Statistical tests to be performed in the event that homogeneity between groups is not accepted.

Additional file 4.

Relationship between sample size and effect size in a mean comparison t test for independent data, one-tailed test. Test power 0.95 and 0.80.

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Alòs, F., Colomer, M.À., Martin-Cantera, C. et al. Effectiveness of a healthcare-based mobile intervention on sedentary patterns, physical activity, mental well-being and clinical and productivity outcomes in office employees with type 2 diabetes: study protocol for a randomized controlled trial. BMC Public Health 22, 1269 (2022). https://doi.org/10.1186/s12889-022-13676-x

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Keywords

  • Mobile applications
  • Smartphone
  • Sedentary behaviour
  • Physical activity
  • Office employees
  • Primary healthcare
  • Diabetes mellitus type 2
  • Workplace