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Internet-based physical activity (PA) and weight management programs have the potential to improve employees’ health in large occupational health settings. To be successful, the program must engage a wide range of employees, especially those at risk of weight gain or ill health.
The aim of the study was to assess the use and nonuse (user attrition) of a Web-based and monitoring device–based PA and weight management program in a range of employees and to determine if engagement with the program was related to the employees’ baseline characteristics or measured outcomes.
Longitudinal observational study of a cohort of employees having access to the MiLife Web-based automated behavior change system. Employees were recruited from manufacturing and office sites in the North West and the South of England. Baseline health data were collected, and participants were given devices to monitor their weight and PA via data upload to the website. Website use, PA, and weight data were collected throughout the 12-week program.
Overall, 12% of employees at the four sites (265/2302) agreed to participate in the program, with 130 men (49%) and 135 women (51%), and of these, 233 went on to start the program. During the program, the dropout rate was 5% (11/233). Of the remaining 222 Web program users, 173 (78%) were using the program at the end of the 12 weeks, with 69% (153/222) continuing after this period. Engagement with the program varied by site but was not significantly different between the office and factory sites. During the first 2 weeks, participants used the website, on average, 6 times per week, suggesting an initial learning period after which the frequency of website log-in was typically 2 visits per week and 7 minutes per visit. Employees who uploaded weight data had a significant reduction in weight (−2.6 kg, SD 3.2,
This Web-based PA and weight management program showed high levels of engagement across a wide range of employees, including overweight or obese workers, shift workers, and those who do not work with computers. Weight loss was observed at both office and manufacturing sites. The use of monitoring devices to capture and send data to the automated Web-based coaching program may have influenced the high levels of engagement observed in this study. When combined with objective monitoring devices for PA and weight, both use of the website and outcomes can be tracked, allowing the online coaching program to become more personalized to the individual.
Overweight and obesity are now major causes of preventable health problems across the world. Obesity has serious implications for the individual’s health as well as the population health and economy. A report by the UK National Audit Office (NAO) estimated that, in 1998 alone, the indirect costs of obesity to the UK economy (18 million sick days and 40,000 lost years of working life) were around four times greater (£2.1 billion/year) than the direct costs of treatment (£0.5 billion/year) [
Health behaviors are personal and complex, and the challenge lies in creating and deploying intervention programs that address this complexity in an engaging, easy to use, and yet effective way. Internet-based interventions serve as a feasible and acceptable delivery method for these programs, thereby providing scale, but evidence suggests that programs must go beyond providing advice and information alone. A review of Internet use for weight loss suggests that successful online programs include a structured approach to modifying energy balance, the use of cognitive-behavioral strategies such as self-monitoring, and individualized feedback and support [
Our previous research [
Following this work with objective PA data collection, we extended the program to include an online weight management module and automated data capture weighing scales. The objective of this study was to assess the level of program engagement of a wide range of employees. A key difference from our previous studies [
The study was designed to test the level of engagement with the Web program for employees recruited at 4 work sites in the United Kingdom over a 12-week period and the effects of this on the employees’ health. Work sites were chosen that were geographically and demographically different in order to evaluate if engagement with the program varied by location, baseline demographics, or level of interaction with computers during work hours. Two work sites were in the North West of England and 2 work sites were in the South of England in order to determine the influence of region upon uptake and use of the program. This could be important as, on average, 39% of UK households do not have Internet access. This varies by region, with the highest levels of Internet access observed in the South West and around London, compared to lower levels of access in households in the North West of the country [
In order to determine the baseline health profiles and characteristics of all employees who were attracted to the MiLife program, minimal exclusion criteria were applied. The program was offered to adults employed at any of the 4 work sites who had regular Internet and email access or who were willing to access Internet and email via computers installed in communal areas at their workplace. Exclusion criteria were pregnancy, any holiday of more than 2 weeks during the study period without computer access, previous instruction from a health professional not to engage in PA, being severely underweight (body mass index, BMI, < 16 kg/m2), or already taking part in a clinical trial.
Employees were recruited via leaflet distribution during working hours, including during shift patterns (weekends, nights, etc) at the manufacturing sites. At screening, date of birth, gender, blood pressure (BP), resting heart rate, BMI, and medication were recorded and the Physical Activity Readiness Questionnaire(PAR-Q) [
The Web program was combined with a Bluetooth-enabled wrist-worn triaxial accelerometer to measure PA and with Bluetooth-enabled weighing scales to record body weight (see Multimedia Appendix). Both devices sent the captured data back to the user’s Web program. The data from these devices were reported back to the individual via their automated coaching Web program, allowing self-monitoring of their PA and body weight and also objective measurement of PA and weight throughout the program. The components of the Web program and the monitoring devices are described in more detail below.
Participants recruited to the program were shown, in a brief training session (approximately 20 minutes, group size of up to 15 participants per trainer), how to register on the website and how to create their own personal password-protected account. The trainer also demonstrated how to upload weight data from the scales to the PA monitor and how then to upload the weight and PA data from the PA monitor to the computer. Basic computer skills training was provided when necessary and included use of a mouse, computer startup, log-in, and website navigation.
The Internet, email, and mobile phone behavior change system was similar to that used in previous studies [
The system made recommendations to the user of the mode to follow (weight loss, weight maintenance, or PA) based on their baseline weight, height, waist circumference, and stated goals. The major difference between the modes lies in the frequency with which users are encouraged to monitor their energy intake. For weight loss, monitoring energy intake is frequently encouraged; for weight maintenance, monitoring energy intake is encouraged if weight increases; for PA only, monitoring energy intake plays less of a role. The user could follow the recommended mode, choose another mode, or enter a nonactive browse mode with no goal or target setting. Users could also switch between the modes during the 12-week study period. The tools that support each mode are based on best evidence and practice from the literature. For example, in the weight loss mode, the tools were developed based on strategies used within the Diabetes Prevention Program [
The design of the Web-based system was founded on key behavior change theories. For example, providing users with information on the typical PA levels of people like themselves is based on Festinger’s (1954) Social Comparison Theory [
Other studies have shown that asking people to form specific plans (implementation intentions) [
While pedometers are low cost, they are typically used to record walking and therefore are less appropriate for 24-hour monitoring of PA. Accelerometer-based devices tend to allow a wider range of movement and for total PA to be recorded [
We developed a water-resistant, Bluetooth, wrist-worn device that could be worn continuously, including while swimming/bathing. The device contained a miniature triaxial accelerometer unit that produced a signal as the wearer made physical movement, recording all movement up to acceleration levels of 6 g. The acceleration signal was measured and the resultant amplitude integrated. The data were then stored within the PA monitor memory ready for download and analysis. To establish validity of the PA monitor, a lab-based study was conducted in 22 adults (12 males, 10 females). Subjects undertook 10 different semistructured PA conditions: lying down, seated computer work, stacking shelves, washing dishes, sweeping, cleaning windows, and 4 treadmill-based activities (walking at 4 km/h and 6 km/h, running at 8 km/h and 10 km/h). These activities were chosen to represent a range of different physical intensities and metabolic equivalents (METs) and included many tasks that may be undertaken by the participants in their daily living. Oxygen uptake (VO2)and indirect calorimetry were measured continuously throughout the activities using a K4b2 portable metabolic gas analyzer with data telemetry (Cosmed, Italy) to determine metabolic rate at rest and METs [
Accelerometer data were analyzed by calculating the number of minutes spent within the range corresponding to moderate intensity (MET level 3) or above [
Data from the PA monitor were transmitted via a Bluetooth microprocessor to a personal computer (PC). Bluetooth-enabled personal weighing scales also sent data on the user’s weight to the PA monitor via Bluetooth. Each weight reading was held within the PA monitor with the PA data until transfer to the PC. The PA monitor memory could store many PA and weight readings before needing to upload the data to the PC. All data were sent via the PC to a central secure database managed by an industry standard commercial infrastructure supplier and held in accordance with the UK Data Protection Act and all relevant regulations. The integration of the PA monitor, weighing scales, and PC permitted direct acquisition of data from the wearer via the Internet throughout the study.
The primary outcome of the study was the assessment of use of the Web-based and monitoring device–based PA and weight management program in this range of employees and the relationship between program use and the secondary outcome measures. Use of the program is defined by nonusage attrition data (Is the subject continuing to use the program?), as discussed by Eysenbach [
Sample size was more than 200 employees in order to improve the accuracy of the confidence intervals around the expected level of engagement (eg, if 80% are engaged in a study size of 200, the confidence interval is 74-85, while reducing the study size to 100 would widen this confidence interval to 71-87). Allocation of each subject to a program mode (weight loss, weight maintenance, PA, or browse) for data analysis was completed by assessing the number of weeks spent in each mode then allocating the participant to the mode in which he or she had spent the greatest number of weeks. Statistical comparison between the program modes was not undertaken as participants were not randomized to modes and could switch between these modes at any time during the study period.
Secondary outcome measures included baseline characteristics (BMI, health profile, age, gender), weight (data captured from Bluetooth weighing scales), PA level (time spent in moderate and vigorous activity measured via the triaxial accelerometer throughout the 12 weeks), BP, resting heart rate, and sleep quality and quantity (Pittsburgh Sleep Quality Index, PSQI [
The health profile data collected at screening (height, weight, BMI, gender, age, BP) were aggregated for each site and compared to health risk appraisal (HRA) data collected around 3 months earlier at the same sites. As the HRA had included a greater number of employees (n = 992), this was undertaken to test if the employees participating in the Web program were similar to the larger group of employees on the same site who attended the HRA.
At the end of the study, process evaluation questionnaires were sent to managers and occupational health (OH) staff at each site to determine the impact of the study at that site, and feedback questionnaires were sent to all participants. All employees were permitted to keep the hardware and software and, if they wished, could continue to use the program.
Analysis of variance (ANOVA) was used for comparison of baseline data (age, gender, systolic BP, diastolic BP, BMI, resting heart rate) between the sites. Fisher exact test was used to compare the nonusage attrition rates between the sites. The probability of attrition in the first 12 weeks was modeled using multivariate logistic regression with the baseline independent variables (age, gender, baseline systolic BP, baseline diastolic BP, baseline BMI, baseline resting heart rate) and the dependent outcome variables (weight change using LOCF, mean daily recorded minutes of MET 3+ PA, BP change at 12 weeks) with site as a covariate. The association between the total interaction time with the website during the 12 weeks and the baseline independent variables and dependent outcome variables as listed above was modeled using multivariate linear regression, again with site as a covariate. The log of the total interaction time was used to preserve normality assumptions of the model. HRA data were compared with the baseline characteristics of MiLife participants using the Kolmogorov-Smirnov test. Weight change (using LOCF) and BP change at the end of the 12-week study were analyzed using analysis of covariance (ANCOVA), with baseline included as a covariate. Multivariate logistic regression with site, age, and gender as covariates was used to examine the relationship between baseline BP and the probability of attendance at the 12-week BP measure. Data were analyzed using SAS version 9.1 (SAS Institute Inc, Cary, NC, USA).
The study was approved by 2 independent research ethics committees, one in the North West and one in the South of England. All research was conducted in accordance with the Helsinki Declaration [
Of the 2302 employees at the work sites, 265 (12%) agreed to take part in the program. The numbers for each site and the characteristics of these participants are shown in
Baseline demographic data for participants at each site
Office North | Factory North | Office South | Factory South | All Sites | |
Total number of eligible employees at work site, no. | 852 | 252 | 705 | 493 | 2302 |
Volunteered for trial, no. | 71 | 44 | 93 | 57 | 265 |
Percentage of employees that volunteered for trial, % | 8 | 17 | 13 | 12 | 12 |
Referred to physician, % | 32 | 47 | 40 | 49 | 41 |
Age (years), mean (SD) | 39.5 (7.2) | 43.4 (8.2) | 39.1a (7.6) | 43.7b (8.8) | 40.9 (8.1) |
Men, % | 42 | 65 | 31 | 75 | 49 |
BMI (kg/m2), mean (SD) | 25.7c (4.0) | 29.7 (5.6) | 26.4a (4.4) | 28.4 (4.7) | 27.1 (4.8) |
Systolic blood pressure (mmHg), mean (SD) | 124 (14) | 135d (13) | 130 (16) | 135a (16) | 130 (16) |
Diastolic blood pressure (mmHg), mean (SD) | 81e (10) | 87 (8) | 87 (11) | 91 (12) | 86 (11) |
Resting heart rate (beats/min), mean (SD) | 67.5 (13.6) | 73.7 (10.1) | 75.2d (11.7) | 73.1 (9.8) | 72.4 (12) |
aSignificantly different to Factory North (at
bSignificantly different to both office sites (at
cSignificantly different to both factory sites (at
dSignificantly different to Office North (at
eSignificantly different to all other sites (at
Of the 265 participants who agreed to take part, 233 started the program (
Flow chart of enrollment, withdrawal, and follow-up (*For LOCF analysis of weight change, the 5 subjects withdrawing after the program start were also included; noncompliant participants were not included in the weight LOCF analysis as no follow-up weight was recorded)
The office sites had a higher total number of employees compared to the factory sites, hence the larger number of participants from these office sites. There were significant differences between the sites at baseline for age, BMI, BP, and heart rate (
We compared the baseline health profiles of the 264 employees participating in the MiLife Web program to HRA data collected previously from a larger group of employees at the same sites (n = 992 total). Employees who participated in the Web program were of a similar age as those attending the HRA but had a higher mean baseline BMI (HRA: 25.0 kg/m2[SD 4.1]; MiLife: 27.1 kg/m2[SD 4.8];
Of the 233 participants starting the program, 6 withdrew and 5 were noncompliant (no data upload or log-in throughout the 12-week period). In the remaining 222 subjects, website use remained high, with 78% (173/222) of the participants still using the website at the end of the 12-week study and 69% (153/222) continuing to use the website after the 12 weeks (
Employee website use during and following the 12-week study, by work site
Office North | Factory North | Office South | Factory South | All Sites | |
Starting program, no. | 67 | 37 | 79 | 50 | 233 |
Withdrawal during 12 weeks, no. | 1 | 0 | 3 | 2 | 6 |
Noncompliance, no. | 0 | 1 | 1 | 3 | 5 |
Website users at week 1, no. | 66 | 36 | 75 | 45 | 222 |
Website users at week 12, no. | 50 | 19 | 72 | 32 | 173 |
Website users at week 12, % | 76 | 53 | 96 | 71 | 78 |
Website use following the 12 weeks, no. | 44 | 14 | 67 | 28 | 153 |
Website use following the 12 weeks, % | 67 | 39 | 89 | 62 | 69 |
Nonusage attrition data (the proportion of participants who stopped using the program and the proportion who remain) are presented in
Nonusage attrition curves [
Log-in data are shown in
Participant’s log-in data throughout and following the study period (Each dot represents a user’s visit and log-in to the website. Green boxes are the website visits recorded during the 12-week study period. Data within the red box were recorded over the 2-week Christmas holiday period when the work sites were closed.)
During the first 2 weeks of the study, participants were spending more time on the website per log-in compared to the subsequent weeks (mean week 1: 11.6 minutes; mean week 2: 8.6 minutes;
Mean website log-in duration and frequency throughout the study period (Data points are means with standard error and are presented for all employees as there was no significant difference between the sites based on ANOVA.)
Mean interaction time with the website throughout the study period (Data points are means with standard error and are presented for all employees as there was no significant difference between the sites based on ANOVA.)
Of the 228 employees using the program (222 starting the program plus 6 withdrawals during the 12-week study), 211 (93%) uploaded weight data that could be used to determine weight change during the study period using the LOCF. The mean weight change in this group was −2.6 kg (SD 3.2;
Weight change from baseline using the LOCF and the mode in which the most time was spent for each employee uploading weight data during the 12-week program
Mean weight loss was higher in those employees who spent most of the 12-week period in the weight loss mode (132/212, 63%; mean weight change −3.5 kg, SD 3.6). No statistical comparison was conducted between modes since subjects could switch modes during the study period. There was no significant difference in weight change between the sites, but there was a significant inverse association between baseline BMI and the amount of weight lost over the 12-week period (−0.284,
Baseline BMI and mode in which the most time was spent for each employee uploading weight data during the 12-week program
The accelerometer-recorded levels of PA were highly variable between individuals in the group, with values ranging from 12 to 714 minutes of moderate or above PA per person per week. The average recorded level for the group was 173 minutes (SE 12.8) of moderate or above PA per week.
At all sites, weight loss was the most popular mode. Of the 228 website users (including those who withdrew during the 12-week study), 138 (61%) spent the most time in weight loss mode, 46 (20%) spent the most time in weight maintenance mode, 39 (17%) spent the most time in the PA only mode, and 4 (2%) spent the most time in the nonactive browse mode of monitoring without goal and target setting.
At the start of the program, each participant was asked to select one or more goals that he or she would most want from a list on the website. Research has shown that people with a strongly desirable goal are more likely to enact their intentions to perform a health behavior [
Frequency of choice of the listed goals prior to beginning the 12-week Web program (F = women, M = men)
At the 12-week BP assessment, some employees at each site were lost to follow-up (Office South, 28%; Office North, 35%; Factory South, 35%; Factory North, 51%). Logistic regression with site, age, and gender as covariates showed that participants with a higher baseline BP were more likely to attend the follow-up (P= .047).
The high level of participants lost to follow-up was possibly influenced by the proximity of these measures to the Christmas holiday period. As a result, data have been aggregated for all employees who returned for a 12-week BP measure (n = 135, excluding those on hypertensive medication). The mean baseline BP in this group was 129/86 mmHg (SD 15/10, range 94/64 to 181/119), and the mean 12-week BP was 128/80 mmHg (SD 15/10, range 95/59 to 164/100). There was a significant reduction in diastolic BP (−5.9 mmHg, SD 9.9;P< .001).
Data from the Pittsburgh Sleep Quality Index (PSQI) questionnaire collected at baseline and 12 weeks (n = 93 completed) suggested an increase in sleep quality overall, corresponding to a decrease in the global PSQI score (
Data collected from the exit questionnaires (n = 130) showed that 101 employees (78%) found the website very easy to use, with the most useful tools listed as those providing PA analysis, planning, and information. Many employees liked wearing the PA monitor and found that having it on served as a constant reminder to keep to the program. The site was seen as informative, motivating, and helpful. The PA and weight charts were thought particularly helpful as they enabled participants to monitor their progress and played an important role in providing feedback and motivation. The low response rate (130/222 website users, 59%) to the exit questionnaire may have been influenced by the proximity to the holiday period.
The OH staff at the work sites who responded to the survey (n = 6) agreed that “the study had been a supportive program in the company objective”of vitalizing employee health. Several of the OH staff commented that the study had been a positive initiative that participants had found enjoyable and rewarding and which should be encouraged. Employee participation in the program resulted in some extra work for OH staff in answering participants queries, although OH staff were generally happy with the resources they had received from the study team to support participants.
Feedback from managers at the work sites (n = 6) was overall very positive, and managers received positive feedback from participants. Managers noted that the study appeared to have been beneficial in the workplace, and participation may even have led to increased job satisfaction in some instances. Managers were also in agreement that they would encourage future staff participation in the Web program and would recommend participation in the program to other sites.
The study was designed to test the level of Web program engagement over a 12-week period for a wide range of employees recruited at four work sites in the United Kingdom. Work sites were chosen that were geographically and demographically different in order to evaluate if engagement and outcomes varied by location, baseline demographic and health measures, or level of interaction with computers during work hours.
This study showed that a Web-based PA and weight management program designed to include components known to be effective (a structured approach to modifying energy balance, the use of cognitive-behavioral strategies such as self-monitoring, and individualized feedback and support [
A key difference in this study compared with our previous studies [
Also, rather than an RCT, the current study was conducted in a more naturalistic setting in which a branded commercial program (MiLife) was offered to employees as a benefit in collaboration with their company’s OH professionals. One limitation of this approach was the lack of a control group. Subsequent use of the program in an employee wellness setting may provide the opportunity to test engagement with the program against alternative weight management and PA initiatives available to those employees. This would build on the insights generated in this research by using an RCT efficacy design and allowing a full intent-to-treat and per protocol statistical analysis.
Employees who participated in the Web program had a higher average diastolic BP and BMI compared to employees previously taking part in an HRA at the same site. This was most noticeable at the factory sites. This does not mean that the Web program enrolled all high-risk individuals, as not all employees take part in HRAs [
Further comparison of the baseline demographic data in this adult employee population with the profiles of participants using a Dutch Web-based health promotion program available to the Dutch general public at no cost [
Nonusage attrition rates were much lower with MiLife than with other Web-based eHealth interventions that have reported this data [
Recent reviews including more than 50 studies of Internet-based programs for PA and dietary behavior change [
Norman et al [
Reinforcement and positive feedback are also an important part of the program, and the PA and weight graphs and charts were the most liked part of the program by users. Where positive feedback is not received for effort, users may be less likely to engage with the program. In a pedometer-based walking program, those users who did not receive positive feedback for all of their effort were 5 times more likely to fail to wear the pedometer compared with a group for whom total effort was recognized [
Therefore, in this program, the use of simple monitoring devices to continuously record PA data and weight data combined with automated data upload to the website and positive feedback may have had a number of effects. It is likely that this approach influenced the high levels of data capture, the utilization and engagement rates observed, and also the likelihood of achieving the behavior change. The combination of monitoring devices with a Web-based program is not without challenges, and we did experience some initial hardware reliability issues. This was anticipated to an extent, and part of the study design was to test the robustness of the hardware in such a large group of subjects. While replacement devices were issued to any employee experiencing technical problems, feedback from the exit questionnaires indicated that hardware problems did influence the user experience for some participants. However, Web program use remained high in these employees.
Analysis of the mean interaction time with the website suggests that there was a learning period in the first few weeks, with users spending more time on the website, finding the tools, and navigating the site. Typically by the fourth week of the program, subjects were interacting with the website for 10-20 minutes per week, and the total time spent on the website over the 12 weeks was associated with the amount of weight lost.
The time spent on the website per week is similar to our previously reported work [
This study suggests that the MiLife Web-based program designed to support PA and weight management and utilizing simple monitoring devices for weight and PA can be successfully deployed in both office and manufacturing sites. The program received positive feedback from both OH staff and managers at each site. Most importantly, the program appealed to and engaged those employees who would most benefit from changes in PA and weight management, with many employees enjoying the experience, improving their health parameters, and returning to follow a second 3-month program with no financial incentive.
This study was supported by Unilever. The authors would like to thank Louise du-Plessis and Natasha Soureti for their contributions to the design and content of the program and Dale Esliger of the University of Exeter for the activity monitor validation work.
MiLife Coaching Ltd is a subsidiary company of Unilever.
Image of the interactive website and of the monitoring devices
body mass index
blood pressure
health risk appraisal
last observation carried forward
metabolic equivalent
occupational health
physical activity
Physical Activity Readiness Questionnaire
personal computer
Pittsburgh Sleep Quality Index
randomized controlled trial