- Email: [email protected]
Physical Activity Loyalty Cards for Behavior Change
Physical Activity Loyalty Cards for Behavior Change A Quasi-Experimental Study Ruth F. Hunter, PhD, Mark A. Tully, PhD, Michael Davis, MSc, Michael Stevenson, BSc, Frank Kee, MD Background: Financial incentives have been advocated by the UK and U.S. governments to encourage adoption of healthy lifestyles. However, evidence to support the use of incentives for changing physical activity (PA) behavior is sparse. Purpose: To investigate the effectiveness of financial incentives to increase PA in adults in the workplace. Design: Two-arm quasi-experimental design. Setting/participants: Employees (n¼406) in a workplace setting in Belfast, Northern Ireland, UK. Intervention: Using a loyalty card to collect points and earn rewards, participants (n¼199) in the Incentive Group monitored their PA levels and received financial incentives (retail vouchers) for minutes of PA completed over the course of a 12-week intervention period. Participants (n¼207) in the comparison group used their loyalty card to self-monitor their PA levels but were not able to earn points or obtain incentives (No Incentive Group). Main outcome measures: The primary outcome was minutes of PA objectively measured using a novel PA tracking system at baseline (April 2011); Week 6 (June 2011); and Week 12 (July 2011). Other outcomes, including a self-report measure of PA, were collected at baseline, Week 12, and 6 months (October 2011). Data were analyzed in June 2012. Results: No significant differences between groups were found for primary or secondary outcomes at the 12-week and 6-month assessments. Participants in the Incentive Group recorded 17.52 minutes of PA/week (95% CI¼12.49, 22.56) compared to 16.63 minutes/week (95% CI¼11.76, 21.51) in the No Incentive Group at Week 12 (p¼0.59). At 6 months, participants in the Incentive Group recorded 26.18 minutes of PA/week (95% CI¼20.06, 32.29) compared to 24.00 minutes/week (95% CI¼17.45, 30.54) in the No Incentive Group (p¼0.45). Conclusions: Financial incentives did not encourage participants to undertake more PA than selfmonitoring PA. This study contributes to the evidence base and has important implications for increasing participation in physical activity and fostering links with the business sector. (Am J Prev Med 2013;45(1):56–63) & 2013 American Journal of Preventive Medicine
Background From the Centre for Public Health (Hunter, Tully, Stevenson, Kee), the UKCRC Centre of Excellence for Public Health (Hunter, Tully, Kee), the Centre for Secure Information Technologies (Davis), Queen’s University Belfast, Institute of Clinical Sciences B, Royal Victoria Hospital, Belfast, Northern Ireland, United Kingdom Address correspondence to: Ruth F. Hunter, PhD, Centre for Public Health, Queen’s University Belfast, Institute of Clinical Sciences B, Royal Victoria Hospital, Grosvenor Road, Belfast, BT12 6BJ, Northern Ireland, United Kingdom. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2013.02.022
56 Am J Prev Med 2013;45(1):56–63
hysical inactivity has recently been described1 as a “pandemic” with 6%–10% of all deaths from noncommunicable diseases worldwide attributable to physical inactivity.2 Current recommendations on the subject in the UK3 and U.S.4 have once again underlined the problems caused by inactive lifestyles and the degree of change needed in the population to meet the minimum guidelines for PA. Given the modest effect of previous initiatives,5,6 more innovative approaches are required to halt the global rise in physical inactivity if these
P
& 2013 American Journal of Preventive Medicine • Published by Elsevier Inc.
Hunter et al / Am J Prev Med 2013;45(1):56–63
recommendations are to be realized. Public health specialists have adopted a broader approach that recognizes the role of supportive environments, such as the workplace,7 which have the potential to make healthy choices easier. Increasing PA levels in the workplace can have physical and mental health benefits for the employee and provide potential economic benefits for the organization through reduced absenteeism and increased productivity.8 However, current evidence to support the effectiveness of workplace PA interventions is mixed,9 with meta-analyses showing small positive effects (effect size: 0.21).10–12 Some promising examples of novel interventions for increasing PA include the use of incentives,13 including tax incentives14 and commitment contracts.15 The use of financial incentives has been advocated by the UK16 and U.S.17 governments to encourage the adoption of healthy lifestyles. Although there is evidence to support the use of such incentives for changing some behaviors (e.g., smoking, substance abuse),18,19 for other health behaviors the evidence is sparse. For PA, both financial20 and nonfinancial incentives21 have been shown to increase levels of PA, at least in the short term. However, many previous studies have focused mainly on structured exercise,21 with little evidence investigating the effectiveness of incentives for encouraging free-living PA. For financial incentive schemes to be worthwhile in the longer term, they must be based on a sustainable model. The UK Government, in the Public Health Responsibility Deal,16 and the U.S. government,17 have encouraged those involved in public health to work collaboratively with business. In the business sector, loyalty card schemes encourage repeated behavior (i.e., loyalty), such as shopping at a particular retailer, by rewarding participants for their repeated business by collecting points and subsequent rewards, for example, retail vouchers. This can be applied to the public health setting, for example, by participants earning points for minutes of PA, which are then reimbursed for rewards. Given the magnitude of expected benefits that can be gained if the sedentary workplace population engages in more PA, and the promising emerging literature on incentives for behavior change, the aim of this study was to investigate the effectiveness of financial incentives to encourage adults to undertake more PA, measured using a novel objective PA tracking system.
Methods Design To test the effectiveness of financial incentives to encourage PA, adults from a workplace setting (office-based) were recruited to participate in an assessor-blind quasi-experimental design with 6-month follow-up. This population was targeted because they July 2013
57
have a predominantly sedentary occupation and a high sickness rate.22 The study was approved by the Research Ethics Committee of the School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Northern Ireland.
Recruitment Participants were recruited from two buildings at Northern Ireland’s main government offices using e-mail invitation, posters, and a weblink on relevant Intranet sites. These recruitment methods directed potential participants to the Physical Activity Loyalty Card (PAL) Scheme website where they were able to access further information (including the Participant Information Sheet), register their interest to participate, and complete a screening questionnaire. Inclusion criteria were being aged 16–65 years, being based at the worksite ≥4 days/week and ≥6 hours/day; and being able to complete 15 minutes of moderate-paced walking (self-report). Exclusion criteria included having received specific advice by a primary care physician to not exercise. There was no racial or gender bias in the selection of participants. Those eligible confirmed that they had read and understood the Participant Information Sheet, provided informed consent, and were sent an “introduction pack” containing their PAL card and instructions on how to use the card. Following this, participants completed the baseline assessment. A computer-generated random allocation sequence was prepared by a statistician not involved in the administration of the trial. Random assignments were placed in individually numbered, sealed envelopes by the statistician to ensure concealment of allocation. All eligible participants in Building A were assigned to the Incentive Group, and those in Building B were assigned to the No Incentive Group.
Intervention The PAL Scheme integrated a novel PA tracking system with webbased monitoring and evidence-based behavior change tools,23 such as goal-setting24–28 (see Appendixes A and B, available online at www. ajpmonline.org, for details on components of the PAL Scheme and an explanation of how the technology generates minutes of PA). The PA tracking system used Near-Field Communication (NFC) technology and a loyalty card (PAL Card) that contained a passive Radio Frequency Identification (RFID) tag. This technology has previously been successfully piloted in a school-based setting16 and has been explained in detail in a previous publication.23 Sensors were placed along footpaths in the outdoor environment around the campus of the government offices (walking trails through parkland), and in an annexed gym and exercise studio (on the same campus), and participants scanned their PAL card at the sensors when undertaking PA (e.g., walking). The card fits into employees’ staff pass holder and therefore was convenient for participants to carry around. Participants logged onto their personal account on the study website and received real-time feedback on various aspects of their PA, including minutes of activity. The PAL card recorded only PA undertaken during work hours and only activity completed when participants scanned their PAL card at sensors placed around the workplace environment. Therefore, the PAL card did not monitor or reward any other activities undertaken outside of the workplace. Physical activity completed outside of the workplace was captured using the Global Physical Activity Questionnaire (GPAQ)
58
Hunter et al / Am J Prev Med 2013;45(1):56–63
at baseline, Week 12, and 6 months. However, this activity was not incentivized (i.e., participants could not receive rewards for this activity).
Group 1: Incentive Group. Participants used their PAL card to self-monitor PA levels, and minutes of PA were converted to points (1 minute¼1 point; capped at 30 points per day) over the 12-week intervention period. Points were redeemed for rewards at Week 6 and Week 12. Through successful engagement with the retail sector, rewards were various “in kind” vouchers from local businesses. Table 1 details the type and level of the rewards (financial incentives). Group 2: No Incentive Group. In the comparison group, participants used their PAL card to self-monitor their PA levels over the 12-week intervention period but did not collect points or earn rewards. Both groups were told that their PA was being recorded and received feedback on their PA levels via the study website. Therefore, both groups had a purpose to carry and use the PAL card. Michie and colleagues29 have shown that selfmonitoring was the most effective behavior change technique, whether used in isolation or as a component of a multipart intervention such as this. The intervention was based on principles of Learning Theory by providing an immediate reward for health behaviors that provide health gains in the future.30 The intrinsic motivation, through selfmonitoring of PA, was designed to improve self-efficacy, thereby enhancing the link between change in behavior and desired outcome, whereas extrinsic motivation was enabled by modest
financial incentives. Thus, financial incentives were embedded in a complex intervention and delivered as part of a behavior change program incorporating evidence-based behavior change tools, as advocated by Marteau and colleagues.18
Outcome Measures Demographic characteristics were collected at baseline, including age; gender; self-reported height and weight (to calculate BMI); highest level of education; and staff grade. The primary outcome was minutes of PA objectively measured using the PA tracking system at baseline (April 2011); Week 6 (June 2011); and Week 12 (July 2011). Other outcomes were collected at baseline; Week 12; and 6 months (October 2011); these included workplace PA (selfreported using the GPAQ)31; health (SF-8; www.sf-36.org/tools/ sf8.shtml); quality of life (EQ5D; www.euroqol.org); self-efficacy (PA Self-efficacy Scale)32; and work absenteeism (self-report in the previous 6 months). Participants were asked to rate their level of satisfaction with various aspects of the program on a 5-point Likert-type scale.
Data Analysis The sample required to detect an effect size of 0.3 using moderateto vigorous-intensity PA (MVPA) (minutes/week) from GPAQ, at 90% power assuming a one-sided hypothesis and a significance level of po0.05 was estimated as n¼406. MVPA was calculated using the method recommended by the instrument developers.31,33
Table 1. Type and level of incentives Incentive Free sandwich (buy-1-get-1-free)
PAL value (points)
Monetary value (£)
75
2.50
Free exercise class pass
120
4.00
Free cinema pass
150
5.00
Free session of bowling/skating or indoor playground
150
5.00
£5 spa and beauty voucher off any treatment (massage, facial, manicure)
150
5.00
£10 sports shop voucher
300
10.00
£10 5-a-side pitch hire voucher
300
10.00
Free cinema pass X 2
300
10.00
£10 spa and beauty voucher off any treatment (massage, facial, manicure)
300
10.00
Free exercise class pass X 5
600
20.00
£20 5-a-side pitch hire voucher
600
20.00
Free cinema pass X 4
600
20.00
£20 spa and beauty voucher off any treatment (massage, facial, manicure)
600
20.00
Free personal training session
750
25.00
Free exercise class pass X 10
960
32.00
Free 5-a-side pitch for 1 hour
1200
40.00
Free 1-month gym membership for you and a guest
1800
60.00
PAL, physical activity loyalty
www.ajpmonline.org
59
Hunter et al / Am J Prev Med 2013;45(1):56–63 Data were analyzed in June 2012 using the SPSS, version 17.0. Statistical analyses were based on the intention-to-treat principle. Groups were compared at baseline using the independent samples t-test for continuous data and the chi-square test for discrete data. An ANCOVA (using baseline PA as the covariate to account for regression to the mean and differences at baseline) was performed for investigating differences between groups using minutes of PA (minutes/week) at Week 6 and Week 12, recorded using the objective PA tracking system, in the primary analysis. Participants who did not use their PAL card (in either arm) were attributed 0 minutes/week, indicating that participants did not do any PA that used their PAL card that week. An ANCOVA (using baseline values as the covariate) was performed to investigate differences between groups for all other outcomes. Finally, independent t-tests were performed to examine differences between groups for number of bouts of PA recorded using the PAL card to ascertain if the card was carried with similar frequency by each group.
Results Baseline Characteristics A total of 406 participants were recruited and randomized into two groups: (1) Incentive Group (n=199); and (2) No Incentive Group (n=207). Follow-up data were obtained
from 84% of participants at 6-month follow-up. Table 2 shows the baseline characteristics stratified by group. The mean age of participants was 43.32⫾9.37 years (mean⫾SD), and 67% were female, which is representative of the government civil service population.34 At baseline, 53% of the population were categorized as having “low” PA levels. All participants (n¼406) received the intervention according to group allocation. Table 3 presents mean⫾95% CI for all outcome measures.
Physical Activity Figure 1 and Table 3 shows the mean (⫾95% CI) minutes of PA/week using the objective tracking system. At 12 weeks, participants in the Incentive Group recorded 17.52 minutes of PA/week (95% CI¼12.49, 22.56) compared to 16.63 minutes/week (95% CI¼11.76, 21.51) in the No Incentive Group (p¼0.59). At Week 6, participants in the Incentive Group recorded 26.18 minutes of PA/week (95% CI¼20.06, 32.29) compared to 24.00 minutes/week (95% CI¼17.45, 30.54) in the No Incentive Group (p¼0.45).
Table 2. Baseline characteristics of participants according to group (M⫾95% CI) unless otherwise stated Incentive group (n¼199)
No incentive group (n¼207)
p-value
Age (years)
43.30 (SD 9.58)
43.34 (SD 9.20)
0.96
Gender, % female
66
68
0.78
BMI
27.16 (26.34, 28.02)
26.92 (26.28, 27.54)
0.71
Staff grade, %
a
4.50 (Grade 5+)
3.90 (Grade 5+)
0.07
Education (highest qualification), % university degree or highera
40.20
38.20
0.97
Objective PA: minutes of PA (minutes/week)
44.79 (36.95, 52.62)
49.06 (41.42, 56.70)
0.44
GPAQ: minutes of work PA (minutes/week)
8.29 (0.15, 16.43)
34.49 (9.51, 59.47)
0.05
GPAQ: minutes of MVPA (minutes/week) GPAQ: physical activity category, %a
256.86 (208.25, 310.68)
343.14 (256.57, 411.39)
0.07
20.1 High
20.8 High
0.19
23.1 Moderate
30.4 Moderate
56.8 Low
48.8 Low
SF-8: Mental Component Score
49.48 (48.22, 50.75)
50.08 (48.83, 51.33)
0.29
SF-8: Physical Component Score
52.78 (51.85, 53.71)
53.30 (52.51, 54.10)
0.40
EQ5D: Health State
74.55 (72.19, 76.90)
77.37 (75.29, 79.46)
0.09
EQ5D: Weighted Health Index
0.89 (0.87, 0.92)
0.92 (0.90, 0.94)
0.09
Physical Activity Self-efficacy Scale
2.59 (2.48, 2.70)
2.53 (2.43, 2.63)
0.42
Work absenteeism: sick days (in past 6 months)
2.54 (1.23, 3.84)
3.24 (1.60, 4.88)
0.47
Note: Test used was an independent-samples t-test for normally distributed continuous variables, unless otherwise noted. Grade 5þ¼highest staff grade. a 2 χ test for discrete variables EQ5D, EuroQol, five dimensions; GPAQ, Global Physical Activity Questionnaire; MVPA, moderate- to vigorous-intensity physical activity; PA, physical activity; SF, short form
July 2013
60
Hunter et al / Am J Prev Med 2013;45(1):56–63
Table 3. Outcomes according to group (M⫾95% CI) for all unless otherwise noted Outcome Objective PA: minutes of PA (minutes/week)
GPAQ: minutes of work PA (minutes/week)
SF-8: Mental Component Score
SF-8: Physical Component Score
EQ5D: Health State
EQ5D: Weighted Health Index
Physical Activity Self-efficacy Scale
Work absenteeism: sick days (in past 6 months)
BMI
Group (n)
Week 12
Incentive (199)
17.52 (12.49, 22.56)
No incentive (207)
16.63 (11.76, 21.51)
Incentive (168)
23.45 (13.91, 32.98)
No incentive (175)
10.73 (1.02, 20.44)
Incentive (168)
50.63 (49.75, 51.52)
No incentive (175)
50.70 (49.80, 51.61)
Incentive (168)
51.63 (50.98, 52.28)
No incentive (175)
51.63 (50.96, 52.30)
Incentive (168)
79.55 (78.11, 80.99)
No incentive (175)
78.36 (76.88, 79.84)
Incentive (168)
0.92 (0.91, 0.93)
No incentive (175)
0.92 (0.91, 0.94)
Incentive (168)
2.61 (2.54, 2.67)
No incentive (175)
2.53 (2.47, 2.60)
Incentive (168)
0.53 (0.20, 0.85)
No incentive (175)
0.59 (0.26, 0.92)
Incentive (168)
27.02 (26.83, 27.21)
No incentive (175)
27.25 (27.06, 27.44)
p-value 0.59
p-value
6 months 26.18 (20.06, 32.29)
a
0.45
24.00 (17.45, 30.54)a 0.07
22.49 (–2.58, 47.55)
0.48
35.02 (10.64, 59.41) 0.91
49.60 (48.71, 50.49)
0.80
49.44 (48.57, 50.31) 0.99
52.74 (52.13, 53.35)
0.10
53.45 (52.85, 54.05) 0.26
80.03 (78.45, 81.60)
0.24
78.70 (77.15, 80.25) 0.81
0.93 (0.92, 0.94)
0.96
0.93 (0.92, 0.94) 0.13
2.60 (2.51, 2.69)
0.57
2.56 (2.47, 2.65) 0.78
0.64 (0.39, 1.66)
0.22
1.52 (0.56, 2.48) 0.09
26.77 (26.51, 27.04)
0.38
26.94 (26.68, 27.21)
a
Week-6 outcomes EQ5D, EuroQol, five dimensions; GPAQ, Global Physical Activity Questionnaire; MVPA, moderate- to vigorous-intensity physical activity; PA, physical activity; SF, short form
At 6 months, participants in the Incentive Group completed a mean of 22.49 minutes/week (95% CI¼2.58, 47.55) workplace PA compared to a mean of 35.02 minutes/week (95% CI¼10.64, 59.41) in the No Incentive Group (p¼0.48), measured using the GPAQ (work domain). This “reverse trend,” which is similar to that for the Week-12 results, may be due to self-report or some other factor. There was no significant difference between groups for number of bouts of PA recorded using the PAL card (p¼0.18) suggesting that rates of carrying the card were similar between groups.
Other Outcomes An ANCOVA showed that there were no significant differences between the groups for the other outcomes at Week 12 and 6 months (p40.05; Table 3).
422.50) over the 12 weeks, and 82% (n¼121) earned sufficient points to collect a reward (minimum: 75 points). Only 6% (n¼7) of participants redeemed their points at Week 6, and 57% (n¼69) at the end of the intervention. Subsequently, 63% (n¼48) of those who collected vouchers exchanged them at the relevant retailers.
Participant Feedback More than 90% (n¼331) of participants were satisfied with taking part in the scheme, with 89% (n¼322) of participants stating that the PAL card was “very helpful” in encouraging them to undertake more PA. Almost 70% (n¼135) felt the incentives were of benefit in helping to increase PA levels.
Discussion Summary of Findings
Incentives Of those who used their PAL card on at least one occasion in the Incentive Group (75%; n¼148), participants earned a mean of 374 points (95% CI¼325.00,
Using a novel loyalty card scheme that included financial incentives did not significantly increase PA in adults compared to self-monitoring PA alone. The study also showed a high uptake and participation rate (63%), www.ajpmonline.org
Minutes/week of physical activity
Hunter et al / Am J Prev Med 2013;45(1):56–63 140 120 100 80 60 40
*
20 0
1
2
3
4
5
6
7
8
9
10
11
12
Week Incentive
No incentive
Figure 1. Minutes of physical activity/week (M⫾95% CI) recorded by the PAL card *The decline in physical activity in Week 2 is due to a holiday period. PAL, physical activity loyalty.
particularly among the sedentary population who accounted for more than 50% of the sample. These results are encouraging, as habits developed in a workplace environment could help create a sustainable behavior change in PA. However, it is important to emphasize that this was a workplace intervention with a largely homogenous group of participants that do not represent the general population.
Changes in Health Behaviors Results showed that modest, extrinsic financial incentives did not lead to a long-term behavior change in PA. This finding is similar to other PA interventions20,21,35,36 and smoking cessation studies.37,38 Rewards can help align an individual’s behaviors more closely with their true preferences, thereby enhancing, rather than restricting autonomy.18 Nevertheless, some researchers have expressed caution about using extrinsic incentives as they may have the undesired effects of actually inhibiting or “crowding out” intrinsic motivation, thereby reducing the likelihood of the desired change in behavior in the long term.37,39,40 Indeed, there is little evidence to support the use of financial incentives for longterm, sustainable behavior change. An additional factor to consider may be that the extrinsic financial incentive was too modest. The study showed that 57% of participants waited longer to redeem “points” they had earned, seemingly aiming for highervalue rewards. Kane et al.41 and Lussier et al.42 suggested that incentives have a rank-ordering effect, with greater incentives triggering better responses. Indeed, some smoking-cessation research would suggest that much larger financial incentives are required (up to $750).38,43 However, a recent systematic review demonstrated that this rarely resulted in a sustained behavior change.38 Little is known about the level of financial incentives required for health behavior change. Paul-Ebhohimhen July 2013
61
and Avenell suggested that a financial incentive based on more than 1.2% of an individual’s income may be effective in a systematic review of weight loss studies. However, the long-term sustainability of the “business model” on which these large incentive schemes are based is questionable, particularly in the current economic climate. If financial incentive interventions are to be implemented on a large scale, it is imperative that they be based on a sustainable “business model.” The ready “buy-in” of the retail partners in this study suggests that a sustainable model could be achievable. Such schemes could provide a “win–win” situation for both public health and businesses by offering modest financial incentives, such as for retail vouchers, in return for an increased number of customers for local retailers. However, further research is required to determine the minimum level for an effective incentive. 44
Improving Uptake to Physical Activity Programs The findings resonate with those reported by Curry et al.37 in that the extrinsic financial incentive was successful for enhancing uptake and participation. This finding is encouraging, as recruitment has been cited as a critical factor limiting the effectiveness of PA interventions.6,45 Previous research in a similar population demonstrated an uptake rate of 5.6%.46 Given that strategies to recruit participants into PA interventions have thus far had only limited success, the level of participation in the current study is an important and promising finding.
Collaborating with Business In collaboration with retail partners, an existing “proven” mechanism that delivers behavior change in the private sector was applied to a public health setting using a sustainable “Physical Activity Loyalty Card.” The Public Health Responsibility Deal, supported by the UK government,16 has encouraged public health to work collaboratively with business, fostering links between researchers, government, and industry. This study provides an example of how researchers can successfully engage with the business sector in public health.
Strengths and Weaknesses This study incorporated a novel objective measure of PA that captured PA specifically related to the intervention. Financial incentives were delivered as part of an evidence-based behavior change program, such as incorporating feedback and goal-setting tools. Such approaches have been encouraged by others.18 Minutes of MVPA using GPAQ were used as a measure of PA at 6 months. A parallel validation substudy using accelerometry suggested that GPAQ was a reliable measure
62
Hunter et al / Am J Prev Med 2013;45(1):56–63
of change in PA (r¼0.44; C Cleland, Queen’s University Belfast, unpublished observations, 2012). Differences at baseline and regression to the mean were accounted for by using ANCOVA with baseline PA as a covariate. However, results should be interpreted with caution as the analyses of secondary outcomes were not adjusted for multiple comparisons, and owing to having only one unit per cluster, it was not possible to account for clustering effects in the analysis.
Future Research Trials of a larger number of randomized worksites are needed to provide more definitive evidence of the effectiveness of the intervention. There are some unanswered questions concerning the use of financial incentives for behavior change. The scope for introducing financial incentives in public health is extensive, but there is much to learn. Research is needed to investigate the optimal level and timing of incentives. Further, there is little research to date on the potential cost effectiveness of such an intervention, if any. Additionally, there may be subgroups of the population who are more or less responsive to financial incentives depending on their personal rates of time preference,47–49 so an important ancillary question is whether differential incentives need to be offered. Lastly, aside from these empirical challenges, the use of financial incentives raises a range of potential ethical issues,18,50 which will eventually require resolution if there is ever to be more widespread public "buy-in" to initiatives such as this.
Conclusion This study contributes to the much needed evidence base and has important implications for public health and the development of future strategies for promoting PA, particularly in regard to PA participation. The business sector can help make this a sustainable “business model” by providing modest financial incentives, in return for increased footfall for local retailers; also, and more importantly, it received “buy-in” from the workforce. However, much work remains to be done before such interventions can be designed successfully and implemented at a population level. This research was supported by funding from the National Prevention Research Initiative (NPRI) (grant number G0802045) and their funding partners (Alzheimer's Research Trust; Alzheimer's Society; Biotechnology and Biological Sciences Research Council; British Heart Foundation; Cancer Research UK; Chief Scientist Office, Scottish Government Health Directorate; Department of Health; Diabetes UK;
Economic and Social Research Council; Engineering and Physical Sciences Research Council; Health and Social Care Research and Development Division of the Public Health Agency (HSC R&D Division); Medical Research Council; The Stroke Association; Welsh Assembly Government; and World Cancer Research Fund (www.npri.org.uk), and the Department for Employment and Learning, Northern Ireland (grant number M6003CPH). The authors thank Intelligent Health Ltd. for their contribution to the technology used in the Physical Activity Loyalty Card Scheme, Prof. Ken Addley, Patricia McQuillan, and Mary McGrath, Occupational Health, Northern Ireland Civil Service for their invaluable support, and the various businesses that sponsored the retail vouchers. No financial disclosures were reported by the authors of this paper.
References 1. Kohl HW 3rd, Craig CL, Lambert EV, et al. for the Lancet Physical Activity Series Working Group. The pandemic of physical inactivity: global action for public health. Lancet 2012;380:294–305. 2. Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT; for the Lancet Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 2012;380:219–29. 3. Department of Health. UK physical activity guidelines. 2011. www.dh. gov.uk/en/Publicationsandstatistics/Publications/PublicationsPoli cyAndGuidance/DH_128209. 4. Ewing Garber C, Blissmer B, Deschenes MR, et al. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc 2011;43(7): 1334–59. 5. Conn VS, Hafdahl AR, Mehr DR. Interventions to increase physical activity among healthy adults: meta-analysis of outcomes. Am J Public Health 2011;101(4):751–8. 6. Foster C, Hillsdon M, Thorogood M, Kaur A, Wedatilake T. Interventions for promoting physical activity. Cochrane Database Syst Rev 2012(1): CD003180. 7. Barr-Anderson DJ, AuYoung M, Whitt-Glover MC, Glenn BA, Yancey AK. Integration of short bouts of physical activity into organizational routine: a systematic review of the literature. Am J Prev Med 2011;40 (1):76–93. 8. McEachan RR, Lawton RJ, Jackson C, Conner M, Meads DM, West RM. Testing a workplace physical activity intervention: a cluster randomized controlled trial. Int J Behav Nutr Phys Act 2011;11(8):29. 9. Hutchinson AD, Wilson C. Improving nutrition and physical activity in the workplace: a meta-analysis of intervention studies. Health Promot Int 2012;27(2):238–49. 10. Abraham C, Graham-Rowe E. Are worksite interventions effective in increasing physical activity? A systematic review and meta-analysis. Health Psychol Rev 2009;3(1):108–44. 11. Conn VS, Hafdahl AR, Cooper PS, Brown LM, Lusk SL. Meta-analysis of workplace physical activity interventions. Am J Prev Med 2009;37 (4):330–9. 12. Rongen A, Robroek SJ, van Lenthe FJ, Burdorf A. Workplace health promotion: a meta-analysis of effectiveness. Am J Prev Med 2013;44 (4):406–15. 13. Schmidt H. Wellness incentives, equity, and the 5 groups problem. Am J Public Health 2012;102(1):49–54.
www.ajpmonline.org
Hunter et al / Am J Prev Med 2013;45(1):56–63 14. Von Tigerstrom B, Larre T, Sauder JA. Using the tax system to promote physical activity: critical analysis of Canadian initiatives. Am J Public Health 2011;101(8):e10–e16. 15. Halpern SD, Asch DA, Volpp KG. Commitment contracts as a way to health. BMJ 2012;344:e522. 16. Department of Health. Healthy lives, healthy people. Our strategy for public health in England. 2010. www.dh.gov.uk/en/Publicationsandsta tistics/Publications/PublicationsPolicyAndGuidance/DH_121941. 17. NIH. NIH science of behavior change: meeting summary. NIH, 2009. commonfund.nih.gov/documents/SOBC_Meeting_Summary_2009.pdf. 18. Marteau TM, Ashcroft RE, Oliver A. Using financial incentives to achieve behaviour change. BMJ 2009;338:b1415. 19. Higgins ST, Silverman K, Sigmon SC, Naito NA. Incentives and health: an introduction. Prev Med 2012;55(S):S2–S6. 20. Charness G, Gneezy U. Incentives to exercise. Econometrica 2009;77 (3):909–31. 21. Johannesson M, Ostling R, Ranehill E. The effect of competition on physical activity: a randomized trial. B E J Econom Anal Policy 2010;10 (1):1–29. 22. BBC News Northern Ireland. Civil service sick days cost taxpayer £22m. 2011. www.bbc.co.uk/news/uk-northern-ireland-13557270. 23. Hunter RF, Davies M, Tully MA, Kee F. The Physical Activity Loyalty Card Scheme: development and application of a novel system for incentivising behaviour change. In: Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Series. New York: Springer, 2012:0091. 24. Leslie E, Marshall AL, Owen N, Bauman A. Engagement and retention of participants in a physical activity website. Prev Med 2005;40(1):54–9. 25. Ferney SL, Marshall AL. Website physical activity interventions: preferences of potential users. Health Educ Res 2006;21(4):560–6. 26. Vandelanotte C, Spathonis KM, Eakin EG, Owen N. Website-delivered physical activity interventions: a review of the literature. Am J Prev Med 2007;33(1):54–64. 27. Ferney SL, Marshall AL, Eakin EG, Owen N. Randomized trial of a neighbourhood environment-focused physical activity website intervention. Prev Med 2009;48(2):144–50. 28. Pressler A, Knebel U, Esch S, et al. An internet-delivered exercise intervention for workplace health promotion in overweight sedentary employees: a randomised trial. Prev Med 2010;51(3–4):234–9. 29. Michie S, Abraham C, Whittington C, McAteer J, Gupta S. Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol 2009;28(6):690–701. 30. Largarde M, Haines A, Palmer N. Conditional cash transfers for improving uptake of health interventions in low- and middle-income countries: a systematic review. JAMA 2007;298(16):1900–10. 31. Bull FC, Maslin TS, Armstrong T. Global Physical Activity Questionnaire (GPAQ): nine country reliability and validity study. J Phys Act Health 2009;6(6):790–804. 32. Marcus BH, Selby VC, Niaura RS, Rossi JS. Self-efficacy and the stages of exercise behavior change. Res Q Exerc Sport 1992;63(1):60–6. 33. WHO. Global physical activity questionnaire (GPAQ) analysis guide. Geneva, Switzerland: WHO. www.who.int/chp/steps/resources/GPA Q_Analysis_Guide.pdf.
July 2013
63
34. Department of Finance and Personnel. Personnel statistics for the 11 NI departments. 2010. 35. Jeffery RW, Wing RR, Thorson C, Burton LR. Use of personal trainers and financial incentives to increase exercise in a behavioral weight-loss program. J Consult Clin Psychol 1998;66(5):777–83. 36. Finkelstein EA, Brown DS, Brown DR, Buchner DM. A randomized study of financial incentives to increase physical activity among sedentary older adults. Prev Med 2008;47(2):182–7. 37. Curry SJ, Wagner EH, Grothaus LC. Evaluation of intrinsic and extrinsic motivation interventions with a self-help smoking cessation program. J Consult Clin Psychol 1991;59(2):318–24. 38. Cahill K, Perera R. Competitions and incentives for smoking cessation. Cochrane Database Syst Rev 2011;(4):CD004307. 39. Marteau TM, Ogilvie D, Roland M, Suhrcke M, Kelly M. Judging nudging: can nudging improve population health? BMJ 2011;342: 263–5. 40. Deci R, Ryan R. Intrinsic motivation and self-determination in human behaviour. New York: Plenum Press, 1985. 41. Kane RL, Johnson PE, Town RJ, Butler M. A structured review of the effect of economic incentives on consumers’ preventive behavior. Am J Prev Med 2004;27(4):327–52. 42. Lussier JP, Heil SH, Mongeon JA, Badger GJ, Higgins ST. A metaanalysis of voucher-based reinforcement therapy for substance use disorders. Addiction 2006;101(2):192–203. 43. Volpp KG, Troxel AB, Pauly MV, et al. A randomized, controlled trial of financial incentives for smoking cessation. N Engl J Med 2009;360 (7):699–709. 44. Paul-Ebhohimhen V, Avenell A. Systematic review of the use of financial incentives in treatments for obesity and overweight. Obes Rev 2008;9(4):355–67. 45. Foster CE, Brennan G, Matthews A, McAdam C, Fitzsimons C, Mutrie N. Recruiting participants to walking intervention studies: a systematic review. Int J Behav Nutr Phys Act 2011;8:137. 46. Tully MA, Cupples ME, Hart ND, et al. Randomised controlled trial of home-based walking programmes at and below current recommended levels of exercise in sedentary adults. J Epidemiol Community Health 2007;61(9):778–83. 47. Bickel W, Marsch L. Toward a behavioural economic understanding of drug dependence: delay discounting. Addiction 2001;96(1):73–86. 48. Reynolds B. A review of delay-discounting research with humans: relation to drug use and gambling. Behav Pharmacol 2006;17(8):651–67. 49. Chabris F, Laibson D, Morris C, Schuldt J, Taubinsky D. Individual laboratory-measured discount rates predict field behaviour. J Risk Uncertain 2008;37(2–3):237–69. 50. Schmidt H, Asch DA, Halpern SD. Fairness and wellness incentives: what is the relevance of the process-outcome distinction? Prev Med 2012;55(S):S118–S123.
Appendix Supplementary data Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.amepre.2013.02.022.
Background From the Centre for Public Health (Hunter, Tully, Stevenson, Kee), the UKCRC Centre of Excellence for Public Health (Hunter, Tully, Kee), the Centre for Secure Information Technologies (Davis), Queen’s University Belfast, Institute of Clinical Sciences B, Royal Victoria Hospital, Belfast, Northern Ireland, United Kingdom Address correspondence to: Ruth F. Hunter, PhD, Centre for Public Health, Queen’s University Belfast, Institute of Clinical Sciences B, Royal Victoria Hospital, Grosvenor Road, Belfast, BT12 6BJ, Northern Ireland, United Kingdom. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2013.02.022
56 Am J Prev Med 2013;45(1):56–63
hysical inactivity has recently been described1 as a “pandemic” with 6%–10% of all deaths from noncommunicable diseases worldwide attributable to physical inactivity.2 Current recommendations on the subject in the UK3 and U.S.4 have once again underlined the problems caused by inactive lifestyles and the degree of change needed in the population to meet the minimum guidelines for PA. Given the modest effect of previous initiatives,5,6 more innovative approaches are required to halt the global rise in physical inactivity if these
P
& 2013 American Journal of Preventive Medicine • Published by Elsevier Inc.
Hunter et al / Am J Prev Med 2013;45(1):56–63
recommendations are to be realized. Public health specialists have adopted a broader approach that recognizes the role of supportive environments, such as the workplace,7 which have the potential to make healthy choices easier. Increasing PA levels in the workplace can have physical and mental health benefits for the employee and provide potential economic benefits for the organization through reduced absenteeism and increased productivity.8 However, current evidence to support the effectiveness of workplace PA interventions is mixed,9 with meta-analyses showing small positive effects (effect size: 0.21).10–12 Some promising examples of novel interventions for increasing PA include the use of incentives,13 including tax incentives14 and commitment contracts.15 The use of financial incentives has been advocated by the UK16 and U.S.17 governments to encourage the adoption of healthy lifestyles. Although there is evidence to support the use of such incentives for changing some behaviors (e.g., smoking, substance abuse),18,19 for other health behaviors the evidence is sparse. For PA, both financial20 and nonfinancial incentives21 have been shown to increase levels of PA, at least in the short term. However, many previous studies have focused mainly on structured exercise,21 with little evidence investigating the effectiveness of incentives for encouraging free-living PA. For financial incentive schemes to be worthwhile in the longer term, they must be based on a sustainable model. The UK Government, in the Public Health Responsibility Deal,16 and the U.S. government,17 have encouraged those involved in public health to work collaboratively with business. In the business sector, loyalty card schemes encourage repeated behavior (i.e., loyalty), such as shopping at a particular retailer, by rewarding participants for their repeated business by collecting points and subsequent rewards, for example, retail vouchers. This can be applied to the public health setting, for example, by participants earning points for minutes of PA, which are then reimbursed for rewards. Given the magnitude of expected benefits that can be gained if the sedentary workplace population engages in more PA, and the promising emerging literature on incentives for behavior change, the aim of this study was to investigate the effectiveness of financial incentives to encourage adults to undertake more PA, measured using a novel objective PA tracking system.
Methods Design To test the effectiveness of financial incentives to encourage PA, adults from a workplace setting (office-based) were recruited to participate in an assessor-blind quasi-experimental design with 6-month follow-up. This population was targeted because they July 2013
57
have a predominantly sedentary occupation and a high sickness rate.22 The study was approved by the Research Ethics Committee of the School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Northern Ireland.
Recruitment Participants were recruited from two buildings at Northern Ireland’s main government offices using e-mail invitation, posters, and a weblink on relevant Intranet sites. These recruitment methods directed potential participants to the Physical Activity Loyalty Card (PAL) Scheme website where they were able to access further information (including the Participant Information Sheet), register their interest to participate, and complete a screening questionnaire. Inclusion criteria were being aged 16–65 years, being based at the worksite ≥4 days/week and ≥6 hours/day; and being able to complete 15 minutes of moderate-paced walking (self-report). Exclusion criteria included having received specific advice by a primary care physician to not exercise. There was no racial or gender bias in the selection of participants. Those eligible confirmed that they had read and understood the Participant Information Sheet, provided informed consent, and were sent an “introduction pack” containing their PAL card and instructions on how to use the card. Following this, participants completed the baseline assessment. A computer-generated random allocation sequence was prepared by a statistician not involved in the administration of the trial. Random assignments were placed in individually numbered, sealed envelopes by the statistician to ensure concealment of allocation. All eligible participants in Building A were assigned to the Incentive Group, and those in Building B were assigned to the No Incentive Group.
Intervention The PAL Scheme integrated a novel PA tracking system with webbased monitoring and evidence-based behavior change tools,23 such as goal-setting24–28 (see Appendixes A and B, available online at www. ajpmonline.org, for details on components of the PAL Scheme and an explanation of how the technology generates minutes of PA). The PA tracking system used Near-Field Communication (NFC) technology and a loyalty card (PAL Card) that contained a passive Radio Frequency Identification (RFID) tag. This technology has previously been successfully piloted in a school-based setting16 and has been explained in detail in a previous publication.23 Sensors were placed along footpaths in the outdoor environment around the campus of the government offices (walking trails through parkland), and in an annexed gym and exercise studio (on the same campus), and participants scanned their PAL card at the sensors when undertaking PA (e.g., walking). The card fits into employees’ staff pass holder and therefore was convenient for participants to carry around. Participants logged onto their personal account on the study website and received real-time feedback on various aspects of their PA, including minutes of activity. The PAL card recorded only PA undertaken during work hours and only activity completed when participants scanned their PAL card at sensors placed around the workplace environment. Therefore, the PAL card did not monitor or reward any other activities undertaken outside of the workplace. Physical activity completed outside of the workplace was captured using the Global Physical Activity Questionnaire (GPAQ)
58
Hunter et al / Am J Prev Med 2013;45(1):56–63
at baseline, Week 12, and 6 months. However, this activity was not incentivized (i.e., participants could not receive rewards for this activity).
Group 1: Incentive Group. Participants used their PAL card to self-monitor PA levels, and minutes of PA were converted to points (1 minute¼1 point; capped at 30 points per day) over the 12-week intervention period. Points were redeemed for rewards at Week 6 and Week 12. Through successful engagement with the retail sector, rewards were various “in kind” vouchers from local businesses. Table 1 details the type and level of the rewards (financial incentives). Group 2: No Incentive Group. In the comparison group, participants used their PAL card to self-monitor their PA levels over the 12-week intervention period but did not collect points or earn rewards. Both groups were told that their PA was being recorded and received feedback on their PA levels via the study website. Therefore, both groups had a purpose to carry and use the PAL card. Michie and colleagues29 have shown that selfmonitoring was the most effective behavior change technique, whether used in isolation or as a component of a multipart intervention such as this. The intervention was based on principles of Learning Theory by providing an immediate reward for health behaviors that provide health gains in the future.30 The intrinsic motivation, through selfmonitoring of PA, was designed to improve self-efficacy, thereby enhancing the link between change in behavior and desired outcome, whereas extrinsic motivation was enabled by modest
financial incentives. Thus, financial incentives were embedded in a complex intervention and delivered as part of a behavior change program incorporating evidence-based behavior change tools, as advocated by Marteau and colleagues.18
Outcome Measures Demographic characteristics were collected at baseline, including age; gender; self-reported height and weight (to calculate BMI); highest level of education; and staff grade. The primary outcome was minutes of PA objectively measured using the PA tracking system at baseline (April 2011); Week 6 (June 2011); and Week 12 (July 2011). Other outcomes were collected at baseline; Week 12; and 6 months (October 2011); these included workplace PA (selfreported using the GPAQ)31; health (SF-8; www.sf-36.org/tools/ sf8.shtml); quality of life (EQ5D; www.euroqol.org); self-efficacy (PA Self-efficacy Scale)32; and work absenteeism (self-report in the previous 6 months). Participants were asked to rate their level of satisfaction with various aspects of the program on a 5-point Likert-type scale.
Data Analysis The sample required to detect an effect size of 0.3 using moderateto vigorous-intensity PA (MVPA) (minutes/week) from GPAQ, at 90% power assuming a one-sided hypothesis and a significance level of po0.05 was estimated as n¼406. MVPA was calculated using the method recommended by the instrument developers.31,33
Table 1. Type and level of incentives Incentive Free sandwich (buy-1-get-1-free)
PAL value (points)
Monetary value (£)
75
2.50
Free exercise class pass
120
4.00
Free cinema pass
150
5.00
Free session of bowling/skating or indoor playground
150
5.00
£5 spa and beauty voucher off any treatment (massage, facial, manicure)
150
5.00
£10 sports shop voucher
300
10.00
£10 5-a-side pitch hire voucher
300
10.00
Free cinema pass X 2
300
10.00
£10 spa and beauty voucher off any treatment (massage, facial, manicure)
300
10.00
Free exercise class pass X 5
600
20.00
£20 5-a-side pitch hire voucher
600
20.00
Free cinema pass X 4
600
20.00
£20 spa and beauty voucher off any treatment (massage, facial, manicure)
600
20.00
Free personal training session
750
25.00
Free exercise class pass X 10
960
32.00
Free 5-a-side pitch for 1 hour
1200
40.00
Free 1-month gym membership for you and a guest
1800
60.00
PAL, physical activity loyalty
www.ajpmonline.org
59
Hunter et al / Am J Prev Med 2013;45(1):56–63 Data were analyzed in June 2012 using the SPSS, version 17.0. Statistical analyses were based on the intention-to-treat principle. Groups were compared at baseline using the independent samples t-test for continuous data and the chi-square test for discrete data. An ANCOVA (using baseline PA as the covariate to account for regression to the mean and differences at baseline) was performed for investigating differences between groups using minutes of PA (minutes/week) at Week 6 and Week 12, recorded using the objective PA tracking system, in the primary analysis. Participants who did not use their PAL card (in either arm) were attributed 0 minutes/week, indicating that participants did not do any PA that used their PAL card that week. An ANCOVA (using baseline values as the covariate) was performed to investigate differences between groups for all other outcomes. Finally, independent t-tests were performed to examine differences between groups for number of bouts of PA recorded using the PAL card to ascertain if the card was carried with similar frequency by each group.
Results Baseline Characteristics A total of 406 participants were recruited and randomized into two groups: (1) Incentive Group (n=199); and (2) No Incentive Group (n=207). Follow-up data were obtained
from 84% of participants at 6-month follow-up. Table 2 shows the baseline characteristics stratified by group. The mean age of participants was 43.32⫾9.37 years (mean⫾SD), and 67% were female, which is representative of the government civil service population.34 At baseline, 53% of the population were categorized as having “low” PA levels. All participants (n¼406) received the intervention according to group allocation. Table 3 presents mean⫾95% CI for all outcome measures.
Physical Activity Figure 1 and Table 3 shows the mean (⫾95% CI) minutes of PA/week using the objective tracking system. At 12 weeks, participants in the Incentive Group recorded 17.52 minutes of PA/week (95% CI¼12.49, 22.56) compared to 16.63 minutes/week (95% CI¼11.76, 21.51) in the No Incentive Group (p¼0.59). At Week 6, participants in the Incentive Group recorded 26.18 minutes of PA/week (95% CI¼20.06, 32.29) compared to 24.00 minutes/week (95% CI¼17.45, 30.54) in the No Incentive Group (p¼0.45).
Table 2. Baseline characteristics of participants according to group (M⫾95% CI) unless otherwise stated Incentive group (n¼199)
No incentive group (n¼207)
p-value
Age (years)
43.30 (SD 9.58)
43.34 (SD 9.20)
0.96
Gender, % female
66
68
0.78
BMI
27.16 (26.34, 28.02)
26.92 (26.28, 27.54)
0.71
Staff grade, %
a
4.50 (Grade 5+)
3.90 (Grade 5+)
0.07
Education (highest qualification), % university degree or highera
40.20
38.20
0.97
Objective PA: minutes of PA (minutes/week)
44.79 (36.95, 52.62)
49.06 (41.42, 56.70)
0.44
GPAQ: minutes of work PA (minutes/week)
8.29 (0.15, 16.43)
34.49 (9.51, 59.47)
0.05
GPAQ: minutes of MVPA (minutes/week) GPAQ: physical activity category, %a
256.86 (208.25, 310.68)
343.14 (256.57, 411.39)
0.07
20.1 High
20.8 High
0.19
23.1 Moderate
30.4 Moderate
56.8 Low
48.8 Low
SF-8: Mental Component Score
49.48 (48.22, 50.75)
50.08 (48.83, 51.33)
0.29
SF-8: Physical Component Score
52.78 (51.85, 53.71)
53.30 (52.51, 54.10)
0.40
EQ5D: Health State
74.55 (72.19, 76.90)
77.37 (75.29, 79.46)
0.09
EQ5D: Weighted Health Index
0.89 (0.87, 0.92)
0.92 (0.90, 0.94)
0.09
Physical Activity Self-efficacy Scale
2.59 (2.48, 2.70)
2.53 (2.43, 2.63)
0.42
Work absenteeism: sick days (in past 6 months)
2.54 (1.23, 3.84)
3.24 (1.60, 4.88)
0.47
Note: Test used was an independent-samples t-test for normally distributed continuous variables, unless otherwise noted. Grade 5þ¼highest staff grade. a 2 χ test for discrete variables EQ5D, EuroQol, five dimensions; GPAQ, Global Physical Activity Questionnaire; MVPA, moderate- to vigorous-intensity physical activity; PA, physical activity; SF, short form
July 2013
60
Hunter et al / Am J Prev Med 2013;45(1):56–63
Table 3. Outcomes according to group (M⫾95% CI) for all unless otherwise noted Outcome Objective PA: minutes of PA (minutes/week)
GPAQ: minutes of work PA (minutes/week)
SF-8: Mental Component Score
SF-8: Physical Component Score
EQ5D: Health State
EQ5D: Weighted Health Index
Physical Activity Self-efficacy Scale
Work absenteeism: sick days (in past 6 months)
BMI
Group (n)
Week 12
Incentive (199)
17.52 (12.49, 22.56)
No incentive (207)
16.63 (11.76, 21.51)
Incentive (168)
23.45 (13.91, 32.98)
No incentive (175)
10.73 (1.02, 20.44)
Incentive (168)
50.63 (49.75, 51.52)
No incentive (175)
50.70 (49.80, 51.61)
Incentive (168)
51.63 (50.98, 52.28)
No incentive (175)
51.63 (50.96, 52.30)
Incentive (168)
79.55 (78.11, 80.99)
No incentive (175)
78.36 (76.88, 79.84)
Incentive (168)
0.92 (0.91, 0.93)
No incentive (175)
0.92 (0.91, 0.94)
Incentive (168)
2.61 (2.54, 2.67)
No incentive (175)
2.53 (2.47, 2.60)
Incentive (168)
0.53 (0.20, 0.85)
No incentive (175)
0.59 (0.26, 0.92)
Incentive (168)
27.02 (26.83, 27.21)
No incentive (175)
27.25 (27.06, 27.44)
p-value 0.59
p-value
6 months 26.18 (20.06, 32.29)
a
0.45
24.00 (17.45, 30.54)a 0.07
22.49 (–2.58, 47.55)
0.48
35.02 (10.64, 59.41) 0.91
49.60 (48.71, 50.49)
0.80
49.44 (48.57, 50.31) 0.99
52.74 (52.13, 53.35)
0.10
53.45 (52.85, 54.05) 0.26
80.03 (78.45, 81.60)
0.24
78.70 (77.15, 80.25) 0.81
0.93 (0.92, 0.94)
0.96
0.93 (0.92, 0.94) 0.13
2.60 (2.51, 2.69)
0.57
2.56 (2.47, 2.65) 0.78
0.64 (0.39, 1.66)
0.22
1.52 (0.56, 2.48) 0.09
26.77 (26.51, 27.04)
0.38
26.94 (26.68, 27.21)
a
Week-6 outcomes EQ5D, EuroQol, five dimensions; GPAQ, Global Physical Activity Questionnaire; MVPA, moderate- to vigorous-intensity physical activity; PA, physical activity; SF, short form
At 6 months, participants in the Incentive Group completed a mean of 22.49 minutes/week (95% CI¼2.58, 47.55) workplace PA compared to a mean of 35.02 minutes/week (95% CI¼10.64, 59.41) in the No Incentive Group (p¼0.48), measured using the GPAQ (work domain). This “reverse trend,” which is similar to that for the Week-12 results, may be due to self-report or some other factor. There was no significant difference between groups for number of bouts of PA recorded using the PAL card (p¼0.18) suggesting that rates of carrying the card were similar between groups.
Other Outcomes An ANCOVA showed that there were no significant differences between the groups for the other outcomes at Week 12 and 6 months (p40.05; Table 3).
422.50) over the 12 weeks, and 82% (n¼121) earned sufficient points to collect a reward (minimum: 75 points). Only 6% (n¼7) of participants redeemed their points at Week 6, and 57% (n¼69) at the end of the intervention. Subsequently, 63% (n¼48) of those who collected vouchers exchanged them at the relevant retailers.
Participant Feedback More than 90% (n¼331) of participants were satisfied with taking part in the scheme, with 89% (n¼322) of participants stating that the PAL card was “very helpful” in encouraging them to undertake more PA. Almost 70% (n¼135) felt the incentives were of benefit in helping to increase PA levels.
Discussion Summary of Findings
Incentives Of those who used their PAL card on at least one occasion in the Incentive Group (75%; n¼148), participants earned a mean of 374 points (95% CI¼325.00,
Using a novel loyalty card scheme that included financial incentives did not significantly increase PA in adults compared to self-monitoring PA alone. The study also showed a high uptake and participation rate (63%), www.ajpmonline.org
Minutes/week of physical activity
Hunter et al / Am J Prev Med 2013;45(1):56–63 140 120 100 80 60 40
*
20 0
1
2
3
4
5
6
7
8
9
10
11
12
Week Incentive
No incentive
Figure 1. Minutes of physical activity/week (M⫾95% CI) recorded by the PAL card *The decline in physical activity in Week 2 is due to a holiday period. PAL, physical activity loyalty.
particularly among the sedentary population who accounted for more than 50% of the sample. These results are encouraging, as habits developed in a workplace environment could help create a sustainable behavior change in PA. However, it is important to emphasize that this was a workplace intervention with a largely homogenous group of participants that do not represent the general population.
Changes in Health Behaviors Results showed that modest, extrinsic financial incentives did not lead to a long-term behavior change in PA. This finding is similar to other PA interventions20,21,35,36 and smoking cessation studies.37,38 Rewards can help align an individual’s behaviors more closely with their true preferences, thereby enhancing, rather than restricting autonomy.18 Nevertheless, some researchers have expressed caution about using extrinsic incentives as they may have the undesired effects of actually inhibiting or “crowding out” intrinsic motivation, thereby reducing the likelihood of the desired change in behavior in the long term.37,39,40 Indeed, there is little evidence to support the use of financial incentives for longterm, sustainable behavior change. An additional factor to consider may be that the extrinsic financial incentive was too modest. The study showed that 57% of participants waited longer to redeem “points” they had earned, seemingly aiming for highervalue rewards. Kane et al.41 and Lussier et al.42 suggested that incentives have a rank-ordering effect, with greater incentives triggering better responses. Indeed, some smoking-cessation research would suggest that much larger financial incentives are required (up to $750).38,43 However, a recent systematic review demonstrated that this rarely resulted in a sustained behavior change.38 Little is known about the level of financial incentives required for health behavior change. Paul-Ebhohimhen July 2013
61
and Avenell suggested that a financial incentive based on more than 1.2% of an individual’s income may be effective in a systematic review of weight loss studies. However, the long-term sustainability of the “business model” on which these large incentive schemes are based is questionable, particularly in the current economic climate. If financial incentive interventions are to be implemented on a large scale, it is imperative that they be based on a sustainable “business model.” The ready “buy-in” of the retail partners in this study suggests that a sustainable model could be achievable. Such schemes could provide a “win–win” situation for both public health and businesses by offering modest financial incentives, such as for retail vouchers, in return for an increased number of customers for local retailers. However, further research is required to determine the minimum level for an effective incentive. 44
Improving Uptake to Physical Activity Programs The findings resonate with those reported by Curry et al.37 in that the extrinsic financial incentive was successful for enhancing uptake and participation. This finding is encouraging, as recruitment has been cited as a critical factor limiting the effectiveness of PA interventions.6,45 Previous research in a similar population demonstrated an uptake rate of 5.6%.46 Given that strategies to recruit participants into PA interventions have thus far had only limited success, the level of participation in the current study is an important and promising finding.
Collaborating with Business In collaboration with retail partners, an existing “proven” mechanism that delivers behavior change in the private sector was applied to a public health setting using a sustainable “Physical Activity Loyalty Card.” The Public Health Responsibility Deal, supported by the UK government,16 has encouraged public health to work collaboratively with business, fostering links between researchers, government, and industry. This study provides an example of how researchers can successfully engage with the business sector in public health.
Strengths and Weaknesses This study incorporated a novel objective measure of PA that captured PA specifically related to the intervention. Financial incentives were delivered as part of an evidence-based behavior change program, such as incorporating feedback and goal-setting tools. Such approaches have been encouraged by others.18 Minutes of MVPA using GPAQ were used as a measure of PA at 6 months. A parallel validation substudy using accelerometry suggested that GPAQ was a reliable measure
62
Hunter et al / Am J Prev Med 2013;45(1):56–63
of change in PA (r¼0.44; C Cleland, Queen’s University Belfast, unpublished observations, 2012). Differences at baseline and regression to the mean were accounted for by using ANCOVA with baseline PA as a covariate. However, results should be interpreted with caution as the analyses of secondary outcomes were not adjusted for multiple comparisons, and owing to having only one unit per cluster, it was not possible to account for clustering effects in the analysis.
Future Research Trials of a larger number of randomized worksites are needed to provide more definitive evidence of the effectiveness of the intervention. There are some unanswered questions concerning the use of financial incentives for behavior change. The scope for introducing financial incentives in public health is extensive, but there is much to learn. Research is needed to investigate the optimal level and timing of incentives. Further, there is little research to date on the potential cost effectiveness of such an intervention, if any. Additionally, there may be subgroups of the population who are more or less responsive to financial incentives depending on their personal rates of time preference,47–49 so an important ancillary question is whether differential incentives need to be offered. Lastly, aside from these empirical challenges, the use of financial incentives raises a range of potential ethical issues,18,50 which will eventually require resolution if there is ever to be more widespread public "buy-in" to initiatives such as this.
Conclusion This study contributes to the much needed evidence base and has important implications for public health and the development of future strategies for promoting PA, particularly in regard to PA participation. The business sector can help make this a sustainable “business model” by providing modest financial incentives, in return for increased footfall for local retailers; also, and more importantly, it received “buy-in” from the workforce. However, much work remains to be done before such interventions can be designed successfully and implemented at a population level. This research was supported by funding from the National Prevention Research Initiative (NPRI) (grant number G0802045) and their funding partners (Alzheimer's Research Trust; Alzheimer's Society; Biotechnology and Biological Sciences Research Council; British Heart Foundation; Cancer Research UK; Chief Scientist Office, Scottish Government Health Directorate; Department of Health; Diabetes UK;
Economic and Social Research Council; Engineering and Physical Sciences Research Council; Health and Social Care Research and Development Division of the Public Health Agency (HSC R&D Division); Medical Research Council; The Stroke Association; Welsh Assembly Government; and World Cancer Research Fund (www.npri.org.uk), and the Department for Employment and Learning, Northern Ireland (grant number M6003CPH). The authors thank Intelligent Health Ltd. for their contribution to the technology used in the Physical Activity Loyalty Card Scheme, Prof. Ken Addley, Patricia McQuillan, and Mary McGrath, Occupational Health, Northern Ireland Civil Service for their invaluable support, and the various businesses that sponsored the retail vouchers. No financial disclosures were reported by the authors of this paper.
References 1. Kohl HW 3rd, Craig CL, Lambert EV, et al. for the Lancet Physical Activity Series Working Group. The pandemic of physical inactivity: global action for public health. Lancet 2012;380:294–305. 2. Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT; for the Lancet Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 2012;380:219–29. 3. Department of Health. UK physical activity guidelines. 2011. www.dh. gov.uk/en/Publicationsandstatistics/Publications/PublicationsPoli cyAndGuidance/DH_128209. 4. Ewing Garber C, Blissmer B, Deschenes MR, et al. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc 2011;43(7): 1334–59. 5. Conn VS, Hafdahl AR, Mehr DR. Interventions to increase physical activity among healthy adults: meta-analysis of outcomes. Am J Public Health 2011;101(4):751–8. 6. Foster C, Hillsdon M, Thorogood M, Kaur A, Wedatilake T. Interventions for promoting physical activity. Cochrane Database Syst Rev 2012(1): CD003180. 7. Barr-Anderson DJ, AuYoung M, Whitt-Glover MC, Glenn BA, Yancey AK. Integration of short bouts of physical activity into organizational routine: a systematic review of the literature. Am J Prev Med 2011;40 (1):76–93. 8. McEachan RR, Lawton RJ, Jackson C, Conner M, Meads DM, West RM. Testing a workplace physical activity intervention: a cluster randomized controlled trial. Int J Behav Nutr Phys Act 2011;11(8):29. 9. Hutchinson AD, Wilson C. Improving nutrition and physical activity in the workplace: a meta-analysis of intervention studies. Health Promot Int 2012;27(2):238–49. 10. Abraham C, Graham-Rowe E. Are worksite interventions effective in increasing physical activity? A systematic review and meta-analysis. Health Psychol Rev 2009;3(1):108–44. 11. Conn VS, Hafdahl AR, Cooper PS, Brown LM, Lusk SL. Meta-analysis of workplace physical activity interventions. Am J Prev Med 2009;37 (4):330–9. 12. Rongen A, Robroek SJ, van Lenthe FJ, Burdorf A. Workplace health promotion: a meta-analysis of effectiveness. Am J Prev Med 2013;44 (4):406–15. 13. Schmidt H. Wellness incentives, equity, and the 5 groups problem. Am J Public Health 2012;102(1):49–54.
www.ajpmonline.org
Hunter et al / Am J Prev Med 2013;45(1):56–63 14. Von Tigerstrom B, Larre T, Sauder JA. Using the tax system to promote physical activity: critical analysis of Canadian initiatives. Am J Public Health 2011;101(8):e10–e16. 15. Halpern SD, Asch DA, Volpp KG. Commitment contracts as a way to health. BMJ 2012;344:e522. 16. Department of Health. Healthy lives, healthy people. Our strategy for public health in England. 2010. www.dh.gov.uk/en/Publicationsandsta tistics/Publications/PublicationsPolicyAndGuidance/DH_121941. 17. NIH. NIH science of behavior change: meeting summary. NIH, 2009. commonfund.nih.gov/documents/SOBC_Meeting_Summary_2009.pdf. 18. Marteau TM, Ashcroft RE, Oliver A. Using financial incentives to achieve behaviour change. BMJ 2009;338:b1415. 19. Higgins ST, Silverman K, Sigmon SC, Naito NA. Incentives and health: an introduction. Prev Med 2012;55(S):S2–S6. 20. Charness G, Gneezy U. Incentives to exercise. Econometrica 2009;77 (3):909–31. 21. Johannesson M, Ostling R, Ranehill E. The effect of competition on physical activity: a randomized trial. B E J Econom Anal Policy 2010;10 (1):1–29. 22. BBC News Northern Ireland. Civil service sick days cost taxpayer £22m. 2011. www.bbc.co.uk/news/uk-northern-ireland-13557270. 23. Hunter RF, Davies M, Tully MA, Kee F. The Physical Activity Loyalty Card Scheme: development and application of a novel system for incentivising behaviour change. In: Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Series. New York: Springer, 2012:0091. 24. Leslie E, Marshall AL, Owen N, Bauman A. Engagement and retention of participants in a physical activity website. Prev Med 2005;40(1):54–9. 25. Ferney SL, Marshall AL. Website physical activity interventions: preferences of potential users. Health Educ Res 2006;21(4):560–6. 26. Vandelanotte C, Spathonis KM, Eakin EG, Owen N. Website-delivered physical activity interventions: a review of the literature. Am J Prev Med 2007;33(1):54–64. 27. Ferney SL, Marshall AL, Eakin EG, Owen N. Randomized trial of a neighbourhood environment-focused physical activity website intervention. Prev Med 2009;48(2):144–50. 28. Pressler A, Knebel U, Esch S, et al. An internet-delivered exercise intervention for workplace health promotion in overweight sedentary employees: a randomised trial. Prev Med 2010;51(3–4):234–9. 29. Michie S, Abraham C, Whittington C, McAteer J, Gupta S. Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol 2009;28(6):690–701. 30. Largarde M, Haines A, Palmer N. Conditional cash transfers for improving uptake of health interventions in low- and middle-income countries: a systematic review. JAMA 2007;298(16):1900–10. 31. Bull FC, Maslin TS, Armstrong T. Global Physical Activity Questionnaire (GPAQ): nine country reliability and validity study. J Phys Act Health 2009;6(6):790–804. 32. Marcus BH, Selby VC, Niaura RS, Rossi JS. Self-efficacy and the stages of exercise behavior change. Res Q Exerc Sport 1992;63(1):60–6. 33. WHO. Global physical activity questionnaire (GPAQ) analysis guide. Geneva, Switzerland: WHO. www.who.int/chp/steps/resources/GPA Q_Analysis_Guide.pdf.
July 2013
63
34. Department of Finance and Personnel. Personnel statistics for the 11 NI departments. 2010. 35. Jeffery RW, Wing RR, Thorson C, Burton LR. Use of personal trainers and financial incentives to increase exercise in a behavioral weight-loss program. J Consult Clin Psychol 1998;66(5):777–83. 36. Finkelstein EA, Brown DS, Brown DR, Buchner DM. A randomized study of financial incentives to increase physical activity among sedentary older adults. Prev Med 2008;47(2):182–7. 37. Curry SJ, Wagner EH, Grothaus LC. Evaluation of intrinsic and extrinsic motivation interventions with a self-help smoking cessation program. J Consult Clin Psychol 1991;59(2):318–24. 38. Cahill K, Perera R. Competitions and incentives for smoking cessation. Cochrane Database Syst Rev 2011;(4):CD004307. 39. Marteau TM, Ogilvie D, Roland M, Suhrcke M, Kelly M. Judging nudging: can nudging improve population health? BMJ 2011;342: 263–5. 40. Deci R, Ryan R. Intrinsic motivation and self-determination in human behaviour. New York: Plenum Press, 1985. 41. Kane RL, Johnson PE, Town RJ, Butler M. A structured review of the effect of economic incentives on consumers’ preventive behavior. Am J Prev Med 2004;27(4):327–52. 42. Lussier JP, Heil SH, Mongeon JA, Badger GJ, Higgins ST. A metaanalysis of voucher-based reinforcement therapy for substance use disorders. Addiction 2006;101(2):192–203. 43. Volpp KG, Troxel AB, Pauly MV, et al. A randomized, controlled trial of financial incentives for smoking cessation. N Engl J Med 2009;360 (7):699–709. 44. Paul-Ebhohimhen V, Avenell A. Systematic review of the use of financial incentives in treatments for obesity and overweight. Obes Rev 2008;9(4):355–67. 45. Foster CE, Brennan G, Matthews A, McAdam C, Fitzsimons C, Mutrie N. Recruiting participants to walking intervention studies: a systematic review. Int J Behav Nutr Phys Act 2011;8:137. 46. Tully MA, Cupples ME, Hart ND, et al. Randomised controlled trial of home-based walking programmes at and below current recommended levels of exercise in sedentary adults. J Epidemiol Community Health 2007;61(9):778–83. 47. Bickel W, Marsch L. Toward a behavioural economic understanding of drug dependence: delay discounting. Addiction 2001;96(1):73–86. 48. Reynolds B. A review of delay-discounting research with humans: relation to drug use and gambling. Behav Pharmacol 2006;17(8):651–67. 49. Chabris F, Laibson D, Morris C, Schuldt J, Taubinsky D. Individual laboratory-measured discount rates predict field behaviour. J Risk Uncertain 2008;37(2–3):237–69. 50. Schmidt H, Asch DA, Halpern SD. Fairness and wellness incentives: what is the relevance of the process-outcome distinction? Prev Med 2012;55(S):S118–S123.
Appendix Supplementary data Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.amepre.2013.02.022.