Participant characteristics
The number of participants enrolled in the study and participant flow from initial consent to completion of the final follow-up questionnaire are summarised in Figure 2. In brief, 190 police officers and staff completed the online screening and consent form; eight of these were ineligible and excluded for reasons shown in Figure 2. Of the 182 participants beginning the study, 180 provided baseline data. Seven participants officially withdrew through the 8-month study period, and an additional proportion did not complete the questionnaires at each data collection point, i.e. 19/178 (11%) at week 6, 25/176 (14%) at week 12, and 30/173 (17%) at month 8. The overall participant retention rate from beginning the study to 8-month follow-up was 143/182 (79%).
The socio-demographic and occupational characteristics of the 180 participants that provided baseline data are shown in Table 2. Of the total sample, 71% (n = 128) were based within the urban Plymouth Basic Command Unit (BCU) and 29% (n = 52) were at the more rural North Dorset sites (the higher numbers recruited from Plymouth BCU reflected the larger population size at this site in comparison with North Dorset, see Additional File 2). The age of participants ranged from 19 to 64 years, with a mean age of 39.3±9.6 years. The majority of participants were male, police officers, of White ethnicity, and were shift workers. The sample was diverse in terms of marital status and education. The majority of participants (58%, n = 105) reported their role as mainly sedentary compared with only 17% (n = 30) who reported being mainly active while on duty, affirming the need for the intervention. Baseline activity levels of officers and staff are shown in Table 3. While the mean daily step count was moderately high at 10,555 steps, there was a large range of activity levels for steps and self-reported PA outcomes. Mean self-reported sedentary time on a typical weekday was 6.41±2.94 hours.
The representativeness of the participant sample (number, occupation, gender, age and ethnicity) compared to the Plymouth BCU and North Dorset police populations as a whole is shown in Additional File 2. The characteristics of interviewees are given in Additional File 3.
Table 2 Participant characteristics: socio-demographic and occupational
Study variables
|
Participated in study
(n = 180)
|
Age in years, mean (SD)
|
39.3 (9.6)
|
Male, n (%)
|
107 (59 %)
|
Ethnicity, n (%)
White
|
177 (98 %)
|
Marital status, n (%)
Single (never married or civil partnered)
Married or civil partnership
Divorced, separated or widowed
Prefer not to say
|
40 (22 %)
112 (62 %)
26 (14 %)
2 (1 %)
|
Main residence, n (%)
Urban (city or town)
Suburban
Rural (including rural village, hamlet or isolated dwelling)
|
96 (54 %)
43 (24 %)
41 (23 %)
|
Highest level of education, n (%)
Lower secondary school (GCSE, CSE, O-level, Standard Grade, Intermediates)
Upper secondary school (AS or A-level, Scottish Highers)
Professional or technical qualification (below degree)
University / college degree
Postgraduate (masters / PhD)
|
38 (21 %)
44 (24 %)
41 (23 %)
48 (27 %)
9 (5 %)
|
Police force, n (%)
Devon & Cornwall Police (Plymouth Basic Command Unit)
Dorset Police (North Dorset)
|
128 (71 %)
52 (29 %)
|
Occupation, n (%)
Police officer
Police community support officer (PCSO) or special constable
Police staff
Rank, n (%) (officers only, n = 114)
Constable
Sergeant
Inspector, chief inspector or superintendent
|
114 (63 %)
30 (17 %)
36 (20 %)
87 (76 %)
23 (20 %)
4 (4 %)
|
Years of police force service, mean (SD)
|
12.1 (8.0)
|
Working 30 or more hours per week, n (%)
|
167 (93 %)
|
Shift work, n (%)
Type of shifta (shift workers only, n = 144)
Morning (early)
Afternoon (late)
Night
Rotating
|
144 (80 %)
95 (66 %)
96 (67 %)
30 (21 %)
59 (41 %)
|
How active is your role? n (%)
Mainly sedentary
Mainly active
Equally active and sedentary
|
105 (58 %)
30 (17 %)
45 (25 %)
|
a Note: Some participants worked more than one type of shift. SD = Standard Deviation
Table 3 Baseline steps, self-reported physical activity and sedentary time
Outcome
|
n
|
Mean (SD)
|
95% CI
|
Range
|
Step count
(mean steps/day)1
|
167
|
10,555 (3,259)
|
10,057 to 11,053
|
3,797 to 20,819
|
Total PA (minutes/week)
|
180
|
170.4 (106.2)
|
154.8 to 186.0
|
10.0 to 540.0
|
Total PA
(MET-minutes/week)
|
180
|
3,182.1 (2,527.8)
|
2,810.3 to 3,553.9
|
66.0 to 16,398.0
|
Moderate-to-vigorous PA (MVPA)
(MET-minutes/week)
|
180
|
1,718.6 (1,829.5)
|
1,449.5 to 1,987.6
|
0.0 to 12,240.0
|
Sedentary time
(hours on a typical weekday)
|
180
|
6.41 (2.94)
|
5.98 to 6.85
|
1.00 to 15.00
|
Note: n = number of observations; SD = Standard Deviation; 95% CI = 95% Confidence Interval
1 Mean daily steps were calculated for participants who had worn the Fitbit on ≥5 of the previous 7 days including at least one weekend day.
Acceptability of the intervention
Usage data showed that engagement with the Fitbit® was high in both the short and longer term, compared with lower engagement with the Bupa Boost app which declined more rapidly over time. As shown in Table 4, 83% of participants reported wearing the Fitbit® at month 8 compared with only 27% who were still using Bupa Boost at the same time point. The mean wear time for the Fitbit® was 6.6±1.0 days per week for 22.0±3.7 hours per day at week 12, and 6.5±1.1 hours per week for 21.4±4.1 hours per day at month 8. Of the Bupa Boost app users, the majority logged in for one to five minutes per day.
Table 4 Self-reported Fitbit® wear and Bupa Boost use
Time point
|
Number of respondents
|
Number (%) of participants reporting wearing the Fitbit®
|
Number (%) of participants reporting using the Bupa Boost app
|
Week 6
|
159
|
156 (98%)
|
104 (65%)
|
Week 12
|
151
|
146 (97%)
|
91 (60%)
|
Month 8
|
143
|
119 (83%)
|
39 (27%)
|
Note: Participants who reported wearing the Fitbit® or using the Bupa Boost app for any amount of time are included.
Usability and usefulness ratings indicated that the Fitbit® was perceived as more user friendly and useful in promoting PA than the Bupa Boost app. Ratings at week 12 are shown in Table 5.
Table 5 Perceived usability and usefulness of the Fitbit® and Bupa Boost app at week 12 (post-intervention)
Intervention component
|
Usability rating
|
Usefulness rating
|
n
|
Mean (SD)
|
Range
|
n
|
Mean (SD)
|
Range
|
Fitbit® activity monitor
|
147
|
4.7 (0.5)
|
3 to 5
|
147
|
3.9 (1.0)
|
1 to 5
|
Bupa Boost app
|
118
|
3.6 (1.2)
|
1 to 5
|
117
|
3.2 (1.3)
|
1 to 5
|
Note: SD = Standard Deviation; n = number of responses
Participants were asked: “On a scale of 1 to 5, where 5 = strongly agree, 4 = agree, 3 = neutral, 2 = disagree, 1 = strongly disagree, how much do you agree that the Fitbit / Bupa Boost was easy to use [usability] / helped you to be more physically active [usefulness]?”
In accordance with the quantitative results, all of the interviewees stated that the Fitbit® was easy to use and navigate, and that the device had met or exceeded their expectations. In comparison, the Bupa Boost app was seen as more difficult to use and less useful in helping officers and staff to be more active. Participants reported problems linking the app to the Fitbit® and finding colleagues, and perceived that there were too many meaningless notifications and not enough automated tracking of activity within Bupa Boost. Rewards and competitions were also perceived as unfair. There was a clear link between perceived usability and usefulness and engagement with the intervention:
"The Bupa Boost app, I used very occasionally because I didn’t particularly find it a very user friendly or useful app."
(Police staff, female)
Many participants felt that there was duplication in function between the Fitbit® (and the Fitbit® app) and the Bupa Boost app (“It almost became like doing the same thing twice”), and so did not perceive a need for continued use of Bupa Boost.
The Fitbit® was seen as practical to wear with the police uniform as it was small and lightweight, but also durable. The most frequently suggested improvement was waterproofing:
"I don't like the fact that it's not waterproof. I go to the beach a few days in the summer, surfing, and playing on the beach and in the sea. I'm in the sea for maybe four or five hours. It seems ironic that you've got to take off an activity tracker. It seems like it's almost not fit for purpose.”
(Police inspector, male)
It was also suggested that the algorithm for capturing activity data could be adapted for night shift workers. As the current cut-off for measuring daily steps is midnight to midnight, this is designed for those with a typical 9 to 5 work pattern. This might result in feelings of discouragement when monitoring steps on a daily basis. This may be an important consideration for tailoring of mHealth technologies:
"Sometimes… my body clock isn't midnight to midnight. My body is seven in the morning until seven in the morning. If I do a night shift, I might sleep most of the day, then do a night shift, it will read for that day only 3,000 steps. Of course, I'm going to be awake another 12 hours yet. If you're a night worker, the data gives you a midnight cut-off, even though you're going to be awake for another 10 hours.”
(Police inspector, male)
There were large individual differences in levels of engagement with the intervention over time. For many, engagement was consistently high through the eight months of the study, while others reported fluctuations in their engagement over time. For example, one police officer stopped using the Bupa Boost app but then experienced a motivational pull to use it again:
“I really missed not going on the app, updating and getting my points up. I find it quite a good motivational tool. So I went back to it about two weeks after stopping.”
(Police constable, male)
Another officer had stopped wearing the Fitbit® as she felt that it had already helped her to be more active and so was no longer needed, but reported that she would use the device again in the event of a relapse in behaviour:
"If I slip again, I'd probably put [the Fitbit] on and wear it every day again."
(Police sergeant, female)
Although experiences of the intervention were positive overall, the qualitative data highlighted some potential negative consequences of mHealth and fitness technology use for a small number of individuals. Adverse physical effects included skin irritation as a result of Fitbit® wear, which was reported by five participants (approximately 3%). Negative psychological consequences were also reported by a small number of participants, and led two to withdraw from the study. These included feelings of failure and guilt when not meeting goals, and anxiety and cognitive rumination resulting from tracking activity and sleep. For example:
“The trouble is you look at it and then you get overly anxious about how bad your sleep is. And then that actually can have quite a negative effect because then you’re thinking, ‘Oh, God, I’m not going to get much sleep tonight.’ Or you look at it and go, ‘Oh, I haven’t got much sleep, so therefore, I feel tired.’ I think fitness watches are great, but sometimes it can have quite, I think, a negative impact when you look at your results because you’re overthinking it.”
(Police constable, female)
Impact of the intervention
As shown in Table 6, there were no significant changes in mean daily step count from baseline to mid-intervention (week 6) or post-intervention (week 12). There was an apparent significant reduction in mean daily step count from baseline to 8-month follow-up (mean decrease 888 steps/day, 95% CI: -1,518 to -258; p = 0.006). However, with a sensitivity analysis including only participants who had reported no events affecting their PA level (such as illness or annual leave) in the previous seven days, this change became non-significant (mean decrease 765 steps/day, 95% CI: -1,755 to 225; p = 0.126) (see Additional File 4).
There were significant increases in the self-reported PA outcomes in the short term (see Table 6). From baseline to week 6, total PA increased by a mean of 27.8 minutes/week (95% CI: 10.9 to 44.7; p = 0.001) or 460.3 MET-minutes/week (95% CI: 71.3 to 849.3; p = 0.021). Moderate-to-vigorous physical activity (MVPA) increased by a mean of 271.9 MET-minutes/week (95% CI: 6.3 to 537.6; p = 0.045) during this period. From baseline to week 12, the mean increase in total PA was 22.7 minutes/week (95% CI: 4.8 to 40.6; p = 0.013) or 465.4 MET-minutes/week (95% CI: 106.7 to 824.1; p = 0.011). MVPA increased by a mean of 402.9 MET-minutes/week (95% CI: 129.9 to 676.0; p = 0.004). These increases were largely maintained at month 8; at this time point there was a near significant increase in total PA (mean increase 18.6 minutes/week, 95% CI: -0.1 to 37.2; p = 0.052) and a significant increase in MVPA (mean increase 420.5 MET-minutes/week, 95% CI: 56.4 to 784.6; p = 0.024).
The interviews helped to explain why self-reported PA increased but there were no significant changes in steps. Many participants reported making changes to their usual activity type, which would not have been reflected in step count data. For example, some individuals had begun boxing or water-based activities (where it was not practical to wear the Fitbit®), and others reported more gym activity and strength training:
“One of the complaints that people say is, look, I go in the gym, I work really hard, but it doesn’t record that as a step. I can see that; you’re not really stepping. But it almost looks like you’re not doing any exercise. Some people are doing 5,000 steps a day, but they go in the gym for two hours and that’s not recorded.”
(Police Community Support Officer, male)
The interview findings also explained how the intervention worked, and revealed differences in its impact according to baseline activity levels. The main behaviour change mechanisms that were apparent from the interviews included goal-setting, self-monitoring, awareness, feedback and self-regulation. For example:
"The goal-setting, I think it made me think about what I wanted to do each week. I would often look at it and go, "I need to run one more time this week."
(Police sergeant, female)
"I think the Fitbit raises my awareness to the fact that on certain parts of my shift, I don't do very much. I can have days where I'm only doing 4,000 steps, 4,500 steps."
(Police inspector, male)
"I liked that it all went green when you hit your target. It sounds really silly, but it does make you want to do it because you want to hit it to go green, and it also - when you hit your 10,000 steps, it says, “Congratulations” or “Wow”. It’s just a little message to say well done. I thought that was good.”
(Police staff, female)
These mechanisms were most pronounced for participants who were less active at baseline (i.e. baseline steps <10,000/day and IPAQ classification ‘low’ or ‘moderate’). This group perceived the greatest impact of the intervention on their motivation and behaviour. In contrast, officers and staff who were highly active and intrinsically motivated at baseline perceived that the technology helped them to maintain, rather than significantly increase, their activity:
“I’ve always done physical activity. So for me, it’s been a good recording tool but it’s not really made me do any more because I do it anyway and I’ll probably always do it.”
(Police Community Support Officer, male)
Longer-term behaviour change was also apparent, particularly for the less active officers and staff. Some participants reported changes in mind set regarding PA, and others showed improved confidence and self-efficacy:
“It certainly helped in getting me motivated and getting back into being fit again. I’ve got back into that mind set now.”
(Police sergeant, female)
“Because of the goals that I’ve achieved since I’ve worn it [the Fitbit], I do feel more confident.”
(Police sergeant, male)
There was also evidence of habit formation including both wearing the Fitbit® and adopting and maintaining new PA routines:
“I see no reason why I would not wear it [the Fitbit]. It’s just a bit of a habit now to always check and see how much I’ve slept and I like to know what my heart rate is when I’m out running and so on.”
(Police constable, male)
“With walking, I wouldn’t tend to go out walking but now I find myself after an hour just getting out and going for a walk around and checking my steps quite a lot so I still do it.”
(Police sergeant, male)
While there was a slight reduction in sedentary time during the study, the changes were not statistically significant (see Table 6). The interviews explained that this was mainly due to perceived pressure of work and organisational culture or social norms where breaks were not perceived as appropriate. Officers and staff wished for more opportunities to take breaks during the working day, in addition to encouragement from managers or supervisors:
“I don’t really have a choice. If I’m sat at a computer, working at a computer, I don’t feel that I’ve got the ability to say... I guess, I do have the ability to say I’m going to leave this now and do something else. But that something else wouldn’t necessarily be walking, it would be driving to another police station.”
(Police inspector, male)
“Well, because of the job I’ve got, it’s quite difficult to-, if I said to my supervisor, “My Fitbit tells me I’ve got to get up and do 250 paces”, I don’t know how well that would go down.”
(Police staff, female)
Table 6 Changes in mean daily step count and self-reported physical activity (PA) and sedentary time – all participants
Outcome
|
Mean change from baseline (week 0)
|
Mid-intervention
(end of individual phase, week 6)
|
Post-intervention
(end of social phase, week 12)
|
Follow-up (month 8)
|
Mean change (SD)
n = number of observations
|
95% CI
|
p-value
|
Mean change (SD)
n = number of observations
|
95% CI
|
p-value
|
Mean change (SD)
n = number of observations
|
95% CI
|
p-value
|
Mean daily steps
|
-214 (2,830)
n = 118
|
-730 to 302
|
0.413
|
78 (2,599)
n = 114
|
-404 to 561
|
0.748
|
-888 (2,956)
n = 87
|
-1,518 to -258
|
0.006*
|
Total PA (minutes/week)
|
27.8 (107.1)
n = 157
|
10.9 to 44.7
|
0.001
|
22.7 (111.2)
n = 151
|
4.8 to 40.6
|
0.013
|
18.6 (113.0)
n = 143
|
-0.1 to 37.2
|
0.052
|
Total PA
(MET-minutes/week)
|
460.3 (2,467.5)
n = 157
|
71.3 to 849.3
|
0.021
|
465.4 (2,230.9)
n = 151
|
106.7 to 824.1
|
0.011
|
317.7 (2,522.0)
n = 143
|
-99.2 to 734.6
|
0.134
|
MVPA
(MET-minutes/week)
|
271.9 (1,685.1)
n = 157
|
6.3 to 537.6
|
0.045
|
402.9 (1,698.0)
n = 151
|
129.9 to 676.0
|
0.004
|
420.5 (2,202.6)
n = 143
|
56.4 to 784.6
|
0.024
|
Sedentary time (hours on a typical weekday)
|
-0.12 (3.36)
n = 157
|
-0.65 to 0.41
|
0.651
|
-0.03 (3.45)
n = 151
|
-0.59 to 0.52
|
0.906
|
-0.24 (2.99)
n = 143
|
-0.73 to 0.26
|
0.344
|
Note: SD = Standard Deviation; 95% CI = 95% Confidence Interval. Pairwise deletion used.
p-values where significant (i.e. <0.05) are highlighted in bold.
* The change became non-significant (p = 0.126) with a sensitivity analysis controlling for self-reported events affecting PA level (e.g. illness, annual leave) – see Additional File 4
Overall, as shown in Table 7, there were no statistically significant changes in physical or mental health-related quality of life, perceived stress, or any of the HPQ outcomes (absenteeism, presenteeism and combined productivity score) from baseline to week 6 (mid-intervention) or baseline to week 12 (post-intervention). From baseline to 8-month follow-up, there was a significant improvement in mental health-related quality of life (mean increase in SF-12 Mental Component Score or MCS 1.75 points, 95% CI: 0.28 to 3.23; p = 0.020) (see Table 7). Changes in physical health-related quality of life, perceived stress and the HPQ outcomes were non-significant (all p-values >0.05).
However, the majority of interviewees reported that they perceived wider benefits of using the Fitbit® and Bupa Boost app and/or increasing their PA level, in both the short and longer term. These included weight loss, improved sleep, feeling fitter (including reduced resting heart rate), feeling healthier and having more energy, improved mood, feeling less stressed, and improved resilience.
Some interviewees noticed improved morale and a sense of camaraderie within the organisation, which was reported to result from the social aspects of the intervention (e.g. social support and competitions). For example:
“I feel there are benefits to having them [Fitbits]... that camaraderie and competitiveness between the team, to outstep each other, do that actual run, or do that extra time of physical activity. I think it’s all useful and increases morale.”
(Police constable, male)
Table 7 Change in self-reported secondary outcomes baseline to mid-intervention (week 6), baseline to post-intervention (week 12) and baseline to follow-up (month 8) – all participants
Outcome
|
Mean change from baseline (week 0)
|
Mid-intervention
(end of individual phase, week 6)
|
Post-intervention
(end of social phase, week 12)
|
Follow-up (month 8)
|
Mean change (SD)
n = number of observations
|
95% CI
|
p-value
|
Mean change (SD)
n = number of observations
|
95% CI
|
p-value
|
Mean change (SD)
n = number of observations
|
95% CI
|
p-value
|
SF-12 Physical Component Score (PCS)1
|
-0.02 (6.75)
n = 145
|
-1.13 to 1.09
|
0.966
|
0.15 (6.76)
n = 144
|
-0.96 to 1.27
|
0.786
|
-0.23 (7.30)
n = 137
|
-1.47 to 1.00
|
0.708
|
SF-12 Mental Component Score (MCS)1
|
0.08 (7.21)
n = 145
|
-1.11 to 1.26
|
0.897
|
0.26 (8.91)
n = 144
|
-1.21 to 1.72
|
0.730
|
1.75 (8.73)
n = 137
|
0.28 to 3.23
|
0.020
|
Perceived Stress Scale (PSS-4) score2
|
0.02 (2.59)
n = 144
|
-0.41 to 0.45
|
0.923
|
-0.11 (2.80)
n = 142
|
-0.57 to 0.36
|
0.654
|
-0.41 (3.12)
n = 137
|
-0.94 to 0.12
|
0.128
|
Health and Work Performance Questionnaire (HPQ) scores:
|
|
|
|
|
|
|
|
|
|
Absolute absenteeism (hours lost/month)
|
3.72 (54.84)
n = 147
|
-5.21 to 12.66
|
0.412
|
8.51 (67.31)
n = 146
|
-2.50 to 19.52
|
0.129
|
-0.07 (49.38)
n = 139
|
-8.35 to 8.21
|
0.987
|
Relative absenteeism3
|
0.03 (0.39)
n = 147
|
-0.04 to 0.09
|
0.431
|
0.00 (0.79)
n = 146
|
-0.13 to 0.13
|
0.997
|
-0.04 (0.69)
n = 139
|
-0.15 to 0.08
|
0.510
|
Absolute presenteeism4
|
-1.16 (12.36)
n = 147
|
-3.17 to 0.86
|
0.259
|
-0.48 (15.19)
n = 146
|
-2.96 to 2.01
|
0.704
|
-0.43 (14.74)
n = 139
|
-2.90 to 2.04
|
0.730
|
Relative presenteeism5
|
-0.02 (0.33)
n = 147
|
-0.07 to 0.03
|
0.423
|
-0.03 (0.31)
n = 146
|
-0.09 to 0.02
|
0.171
|
-0.03 (0.39)
n = 139
|
-0.09 to 0.04
|
0.401
|
Combined productivity score6 (relative absenteeism and relative presenteeism)
|
-0.06 (0.52)
n = 147
|
-0.14 to 0.03
|
0.185
|
-0.19 (0.96)
n = 146
|
-0.18 to 0.14
|
0.816
|
0.01 (0.77)
|
-0.12 to 0.14
|
0.896
|
Notes: SD = Standard Deviation; 95% CI = 95% Confidence Interval.
Pairwise deletion used.
p-values where significant (i.e. <0.05) are highlighted in bold.
1 Higher scores indicate higher quality of life
2 Higher scores indicate higher perceived stress
3 Higher score = higher absenteeism, relative to expected hours
4 Higher score = lower lost performance, i.e. higher productivity
5 Higher score = higher performance or productivity relative to colleagues
6 Higher score = higher productivity
Individual versus social phases
The study found no differential impact of the ‘individual’ and ‘social’ phases on steps, self-reported PA or sedentary time, in either the short or longer term (see Additional File 5). No significant subgroup differences were observed between the two phases of the intervention by gender, age group, baseline activity level or occupation. According to the post-intervention survey (see Table 8), the majority of participants preferred the ‘individual’ (56%) phase of the study to the ‘social’ (7%) phase. The remaining 37% reported having no preference.
Table 8 Preferred phase (individual vs. social) as reported by participants, n = 91
Preferred phase
|
n (%) of participants
|
Individual Goal-setting; self-monitoring; feedback; earning virtual rewards (wellness points and badges); Bupa library/self-help information
|
51 (56)
|
Social The above individual components in addition to:
Social feed (social comparison); messaging colleagues (social support); competing with colleagues and taking part in company/team challenges
|
6 (7)
|
No preference / liked both phases equally
|
34 (37)
|
Note: Only participants who had used the Bupa Boost app during both the individual and social phases were asked to report their preference
The interviews confirmed that the individual components were generally perceived as more acceptable and most impactful. Overall, participants reported that goal-setting, self-monitoring and awareness were the most powerful components in increasing motivation and changing their behaviour. Some participants reported having concerns regarding privacy and sharing of their PA and health data with their colleagues within the social phase. Others stated that they would prefer to compare and compete with those of a similar age and activity level to themselves:
“I think a lot of them that do the fitness stuff, they’re lot younger than me and are probably a lot more competitive. It’s probably a bit of an age thing. I couldn’t really be bothered with competing with somebody who’s 25, who’s done 30,000 steps and you know, who thinks it’s really exciting. It just doesn’t do anything for me.”
(Police constable, female)
However, there were large individual differences in preferences and perceived impact of the individual and social components. These appeared to be due to personal preferences and personality differences, rather than associated with any identifiable characteristics such as occupation or baseline activity level:
“I am not into the social aspect of it. It suits certain people... it certainly doesn’t really suit me that much.”
(Police constable, male)
“That’s my nature. I’m very competitive. When I’m at work, I get very competitive [laughs]. When it came to the competing against each other... there was that stage where I pushed myself.”
(Police Community Support Officer, female)