Free Access
Mov Sport Sci/Sci Mot
Number 119, 2023
Page(s) 1 - 8
Published online 12 October 2022

© ACAPS, 2022

1 Introduction

The benefits of active living are widely documented and engaging in regular physical activity (PA) is important for physical and mental health for people of all ages (Warburton, Nicol, & Bredin, 2004). Numerous intervention programs, aimed to facilitate PA in non-active people, have been developed and tested (for a summary, see Bock, Jarczok, & Litaker, 2014; Conn, Adam, Hafdahl, & Mehr, 2011). Del Campo Vega, Tutte, Bermudez, and Parra (2017) found statistically significant increases in people who engaged in moderate to vigorous physical activity from baseline to follow-up in areas where outdoor gyms had been installed, and Cranney et al. (2016) also found the proportion of people engaging in moderate to vigorous physical activity in the outdoor gym area increased significantly from baseline (6%) to post-installation (36%) and to follow-up (40%). Also, intervention studies have suggested that open-air environments (e.g., outdoor fitness centre), placed in urban green areas, may have direct and positive impacts on mental health and promote autonomous motivation to PA (Johnson, Ivarsson, Parker, Andersen, & Svetoft, 2019).

The public health implications of health technology solutions can have important potentiating effects. Examples of the implications of technologies on public health within the recent digital revolution is the potential for telehealth to improve health care delivery (Dorsey & Topol, 2016). Moreover, examples of health technology solutions are activity-tracking devices such as Nike Fuelband (Bice, Ball, & McClaran, 2016). The use of activity-tracking devices has, for example, been found to increase PA and PA motivation (Bice et al., 2016; Bravata et al., 2007). Most activity-tracking devices offer immediate feedback tied to goals (e.g., 10,000 steps) and tracking changes in PA can motivate steady progress towards goals and increased self-efficacy. More research is needed to investigate the motivational influence of popular commercial activity monitors in relation to PA.

New innovative designs using health technology (e.g., PA apps for smartphones) applied to outdoor exercise might attract new users and promote sustainable health behaviours (Shane, Lowe, & Ólaighin, 2014). Earlier research on App-based initiatives, outside the public outdoor exercise zones, relate to carpooling (users share the same car) and bicycle sharing systems. Generally, these initiatives had positive effects on, for example, empowerment and back up technology-mediated activities combined with in-person collaboration activities (Christensen & Shaheen, 2014).

One framework, commonly used to understand why people engage in different behaviours (e.g., PA), is self-determination theory (SDT). According to SDT, individuals are most elective and persistent in pursuing healthy living when they are autonomously motivated (Ryan & Deci, 2002). Autonomous motivation (e.g., identified regulation) is largely internal and based on conscious values that are personally important to the individual. Such individuals engage in activities because they find them intrinsically satisfying or because they identify with and value the outcomes (Williams, Niemiec, Patrick, Ryan, & Deci, 2009). SDT posits that individuals will develop autonomous motivation for a particular behaviour when significant others adopt a need-supportive approach toward the person (Ryan & Deci, 2002). When basic psychological needs for autonomy (i.e., feeling volitional and self-endorsed), competence (i.e., feeling mastery and elective), and relatedness (i.e., feeling of belonging and being cared for) are supported, this will facilitate a process of internalization resulting in more autonomous forms of self-regulation (Williams et al., 2009). SDT has a considerable amount of research supporting its validity in health behaviour change settings and in the exercise field (Fortier, Duda, Guerin, & Teixeira, 2012).

Based on the SDT framework, one approach that has been effective to support behaviour change is motivational interviewing (MI) (Lundahl, Kunz, Brownell, Tollefson, & Burke, 2010). More specifically, MI targets the three key components in SDT (autonomy, competence, and relatedness). The purpose of the study was to investigate if participation in a three-month electronic tracking outdoor physical activity and a MI intervention compared to a control group without MI led to positive behavioural, psychological, and physiological outcomes based on a two-group pre-post experimental design. An expected result of the study is that participation in the intervention, that is MI, outdoor physical activity and guided by a smartphone application, will lead to higher autonomous motivation, elevated physical activity (more steps), improved physical and psychological health (reduced body weight) and cardiorespiratory fitness.

2 Methods

2.1 Participants and inclusion criteria

Altogether 20 participants, working within the municipality of Halmstad, Sweden, were selected for the study. The inclusion criteria were: (a) having a primarily inactive job, (b) limited exercise activity in the past year, and (c) employed within Halmstad Municipal Council. Based on the pool of 66 participants who met the inclusion criteria, a random selection of participants, where a weighting for gender was carried out due to an overbalance of women, resulted in two groups (experimental and control) of 10 participants including six women and four men in each group. None of the participants knew each other at the start of the study as they worked at completely different institutions within Halmstad Municipality Council, which indicates no biased association between employees. That is, all participants were randomly drawn from the total number of interested participants for the study and, thus, randomly divided into two groups with the same number of participants. At the end of the intervention period, one man from both the experimental and control groups dropped-out, mainly due to changed work routines or an exit from employment. Consequently, the final group of participants for the experimental group consisted of six women and three men with a mean age of 51.9 years ± 4.8, and the control group consisted of six women and three men with a mean age of 48.9 years ± 10.9.

2.2 Physical activity

PA data were gathered by wrist-worn activity sensors (Apple Watch1, software version and iPhone) that collect information about each day’s physical activity (steps taken). All participants were, at the start of the study, given one of these activity sensors. Data were first stored locally on the participants’ smartphones and then downloaded from the Health Data App using the QS Access application (Quantified Self Labs, California, USA).

2.3 Psychological measurements

In the study, two psychological constructs were measured. The two constructs were motivation regulations (i.e., amotivation, external motivation, introjected motivation, identified motivation, intrinsic motivation) collected using the Behavioural Regulation in Exercise Questionnaire-2 (BREQ-2) (Markland & Tobin, 2004), and psychological health (i.e., well-being, illness) collected using General Health Questionnaire-12 (GHQ-12) (Goldberg et al., 1997). McDonald’s ω ranged between 0.77 and 0.91 for the BREQ-2 and between 0.72 and 0.93 for the GHQ-12.

2.4 Physiological measurements

A bioimpedance analysis of body mass (weight kg), total body fat mass, and total body muscle mass were measured and the modified Bruce Treadmill Test (time to exhaustion) was used to measure cardiovascular fitness. All body-composition measurements were performed in the morning, and each participant abstained from eating and drinking for at least six hours prior to the testing.

2.5 Exercise intervention

The participants took part in the two-group pre-post experimental design aimed to increase PA and well-being (see Fig. 1). Both the experimental and control groups were instructed on how to use the basic functions on their wrist-worn activity sensors (steps, active calories, time, and synchronization with iPhone). The control group participants received no other support to increase their PA and were asked to continue their normal life activities during the three-month control period. For the experimental group, PA was supported through individual MI coaching sessions and resistance-training programs specially designed for use in an outdoor gym. In the beginning and at the end of the intervention, the individual MI coaching was conducted with about 30 minutes of conversation for each participant. When the intervention started, the participants were introduced to an outdoor gym and instructed on how to use it (instructors were present at the start of the intervention for each participant) to further promote PA. Also, the participants were advised to track PA through the default functions on their watches.

For detailed information about the method used to measure physical activity, psychological questionnaires, physical measurements, as well as the exercise intervention see Johnson et al. (2019).

thumbnail Fig. 1

The two-group pre-post experimental design.

2.6 Procedures

Table 1 outlines the time plan for the study procedures from the first contact with the participants until the final testing session three-months later. Ethical approval for the study was granted by the regional ethics committee (reference number 2016/843). However, we did not pre-register our study in open science.

Table 1

Time plan for the study.

2.7 Data analysis

Non-clinical magnitude based inference (MBI) was calculated using an online published spread sheet (Hopkins, 2003), and inferences were based on the disposition of the confidence limit for the mean difference to the smallest worthwhile change (0.2 between-subject SD). The probability that a change in testing score was beneficial, harmful or trivial was identified according to the magnitude-based inferences approach (Batterham & Hopkins, 2006). Descriptors were assigned using the following scales: 0–4.9% very unlikely; 5–24.9% unlikely; 25–74.9% possibly; 75–94.9% likely; 95–99.49% very likely; >99.5% most likely (Hopkins, 2017). Pre-test pooled standard deviations were calculated using pre-test values from the sample as a whole (both experimental group and control group). Within-group standardized mean difference effect sizes (ESw) were calculated by using the mean change of the group (Δ experimental or Δ control) in the numerator of the equation and using the pre-test pooled standard deviation in the denominator. Between-group standardized mean difference effect sizes (ES) were calculated by using the difference between experimental ESw and control ESw. Effect sizes of 0.20–0.50 are considered small in magnitude; those between 0.50–0.80 are medium, and those above 0.80 are large by Cohen’s conventions for the behavioural sciences (Hopkins, 2017). An expected outcome of the study is that participation in MI and outdoor physical activity will lead to higher autonomous motivation and elevated physical health (more steps and lower body fat). Given the common method biases associated with the use of self-report measures we used an ES = 0.50 as a threshold for the smallest important effect, rather than using Cohen’s threshold of 0.2, which is the effect size generally recommended for MBI by Hopkins (2017). Using Hopkins’ guidelines for calculating sample (Hopkins, 2017) and Cohen’s threshold of 0.5 for a standard difference as the smallest important effect, the chance for a type 1 error was set at 0.5% and type 2 error at 25%, based on physical activity (steps) as the main outcome measure, a minimum sample size of 15 is recommended.

3 Results

In this study, PA (steps), psychological well-being and motivation, as well as anthropometrics and physical tests were measured before and after the intervention (see Tab. 2).

Table 2

Physical activity behaviour (steps), psychological well-being and motivation, anthropometrics and physical measures pre- and post-intervention period.

3.1 Baseline comparison

Baseline measurements showed a statistically significant (P = 0.03) difference in body fat between groups, but no other differences were obtained. Effect size statistics together with MBI confirmed the large (ES = 1.0) very likely (MBI = 97%) difference in fat mass between groups and showed a medium (ES = 0.69) likely (MBI = 91%) difference in body weight between groups.

3.2 Intervention effects

The between group changes for the BREQ-2 were less clear, but there was a possibly trivial (<99%) reduction in identified regulation (ESbetween = 0.72) in the control group. After the three-month intervention, there was a likely (84%) small (ESbetween = 0.40) beneficial increase in PA in the experimental group compared to the control group (see Tab. 2). There was no missing data in this study and the internal dropout was 0%. Inspection of the interaction between time and PA for both groups showed a negative interaction between PA and time for the experimental group (R2 = 0.17) and almost no interaction between PA and time for the control group (R2 = 0.03) (see Fig. 2).

thumbnail Fig. 2

Average daily steps over the 90-day intervention period for both experimental (solid blue) and control (solid orange) groups. Note: dotted blue line: trendline for the experimental group; dotted orange line: trendline for the control group..

4 Discussion

4.1 Main findings and comparisons within existing literature

The beneficial increase in PA (steps) for the experimental group could be due to motivation, and the combination of MI and novel health technology equipment. Because both the experimental and control groups were given the wrist-worn activity sensors at the same time (see Tab. 1), it is likely that a combination of factors, as outlined above, together influenced the increase in PA behaviour at the end of the intervention. More specifically, the possibility for the participants to take part in individual MI coaching sessions might have been a central part of the increases in PA behaviour (steps). Previous studies have also shown that MI can strengthen a person’s self-efficacy for behaviour change to increase PA (Hardcastle, Taylor, Bailey, Harley, & Hagger, 2013). Also, in this case, the potential mechanisms for the link between MI and PA may perhaps increase levels of basic psychological needs as well as extend the level of motivation for an already autonomously motivated person. Successful internalization involves the integration of formerly external regulations into one’s sense of self, typically in the form of important personal values. This might be particularly relevant in relation to changes in motivation and behavior on individual MI coaching since it is a function of intervention content and the interpersonal style in which the present content was delivered (see also Hardcastle, Fortier, Blake, & Hagger, 2017). The results from our study indicate that the experimental group maintained a similar level of identified regulation, as opposed to a decreased level in the control group at the post-measure. For the control group, it is suggested that since the individual MI coaching has not yet started, combined with the fact that the participants have not yet started their physical training in relation to the study, this could potentially explain the decrease in identified regulation. For the experimental group, this result may indicate that the participants continued to engage in an activity that they deemed personally valuable and important. In a partly similar way, Silva et al. (2008) also reported that need-supportive interventions to enhance autonomous motivation and competence for PA resulted in important improvements in cardiorespiratory fitness as well as positive changes in other health factors. In this context, we speculate that the difference in PA (steps) for the experimental group at the post-measurement also reflects the effect that the MI dialogue probably had, and not least in relation to the last process (planning), which involves both developing commitment to change and formulating an action plan for the on-going intervention. In a pre-study to the current study, a six-week intervention programme with sedentary adults showed promising results regarding PA changes and motivation, along with decreases in body weight and stress symptoms (Johnson et al., 2019). Similar results have been found in sedentary and middle-age samples, based on a 12-week exercise training and lifestyle intervention (Kozey-Keadle et al., 2014). Some studies have also found significant improved physical and mental health status compared to controls after a three-month MI-based health coaching intervention (Butterworth, Linden, McClay, & Leo, 2006).

4.2 Study limitations

One potential limitation could be that the participants may not have benefited from MI as much as we thought because they were already motivated to change, which highlights the importance of pre-treatment assessment. There was a statistical difference in the pre-test observed in body fat between groups, but no other differences were obtained. It is possible that the group with greater pre-test body fat might be more prone to a reduction in body fat and this may have the influenced the between-group change in body fat. Due to the lack of change in muscle mass in the current study, we speculate that muscular strength training did not greatly influence the outcomes between groups. Still another study limitation has to do with the limited number of participants in the intervention, which places challenging demands on statistical analysis. In our case, we selected MBI analysis because conventional null hypothesis significance testing often has high type II error rates for small sample sizes, and publication bias associated with these errors are a weakness, which MBI has been reported not to have (Hopkins & Batterham, 2016). Many of the issues with MBI are common to all statistical analysis and may not be a problem when analyses are performed with these weaknesses in mind. MBI analysis is, however, content-rich and allows for relatively meaningful interpretation. One of the strengths of the study is the combination of both physical and psychological measurements, allowing a multifactorial assessment of the intervention program and the usefulness of the results.

5 Conclusion

One possible implication of the study is that more studies that elucidate the feasibility and accuracy of smartphone applications that motivate PA should be conducted. As for now, limited research exists with adequately constructed designs. In line with previous recommendations, we argue that large-scaled, experimental, and long-term randomized control trials should be conducted to explore the effects of exercise app-based interventions. Another practical implication is that following the 10,000 steps per day goal over three months may not induce enough PA to improve health and well-being in a middle-aged sedentary population.

Future research should ensure that fitness technology continues to include theoretically derived behaviour change techniques, perhaps based on a SDT framework, to promote and potentially increase motivation, mental health and well-being. Strategies such as social support and coaching seem to be especially helpful in increasing activity and healthy behaviours although there are many questions that remain unanswered, the public health implications of using fitness technology to promote behaviour change seem worthy of future study.

Funding source

The study was funded by a grant from The Knowledge Foundation, Sweden [grant number 20160097].


MBI: magnitude based inference

MI: motivational interviewing

PA: physical activity

SDT: self-determination theory


A special thanks to Erik Blomberg and Camilla Schough at Eleiko Sport AB, Sweden, Erik Viberg, Anton Bärwald and Pelle Wiberg at Swedish Adrenaline, Sweden, and Sofia Warpman at Halmstad Municipality, Sweden for helping to make the research project possible.


  • Batterham, A. & Hopkins, W. (2006). Making meaningful inferences about magnitudes. International Journal of Sports Physiology and Performance, 1(1), 50–57. [CrossRef] [PubMed] [Google Scholar]
  • Bice, M.R., Ball, J.W., & McClaran, S. (2016). Technology and physical activity motivation. International Journal of Sport and Exercise Psycholology, 14(4), 295–304. [CrossRef] [Google Scholar]
  • Bock, C., Jarczok, M.N., & Litaker, D. (2014). Community-based efforts to promote physical activity: A systematic review of interventions considering mode of delivery, study quality and populations subgroups. Journal of Science and Medicine in Sport, 17(3), 276–282. [CrossRef] [PubMed] [Google Scholar]
  • Bravata, D.M., Smith-Spangler, C., Sundaram, V., Gienger, A.L., Lin, N., Lewis, R., Stave, C.D., Ingram Olkin, I., & Sirard, J.R. (2007). Using pedometers to increase physical activity and improve health: A systematic review. JAMA, 298(19), 2296–2304. [CrossRef] [PubMed] [Google Scholar]
  • Butterworth, S., Linden, A., McClay, W., & Leo, M.C. (2006). Effect of motivational interviewing-based health coaching on employees’ physical and mental health status. Journal of Occupational Health Psychology, 11(4), 358–365. [CrossRef] [PubMed] [Google Scholar]
  • Christensen, M., & Shaheen, S.A. (2014). Is the future of urban mobility multi-modal and digitized transportation access? In Cities on the Move. Geneva: New Cities Foundation. [Google Scholar]
  • Conn, V.S., Hafdahl, A.R., & Mehr, D.R. (2011). Interventions to Increase Physical Activity Among Healthy Adults: Meta-Analysis of Outcomes. American Journal of Public Health, 101(4), 751–758. [CrossRef] [PubMed] [Google Scholar]
  • Cranney L., Phongsavan P., Kariuki M., Stride, V., Scott, A., Hua, M., & Bauman, A. (2016). Impact of an outdoor gym on park users’ physical activity: a natural experiment. Health Place, 37, 26–34. [CrossRef] [PubMed] [Google Scholar]
  • Del Campo Vega C., Tutte V., Bermudez, G., & Parra, D.C. (2017). Impact on area-level physical activity following the implementation of a fitness zone in Montevideo, Uruguay. Journal of Physical Activity and Health , 14(11), 883–887. [CrossRef] [PubMed] [Google Scholar]
  • Dorsey, E.R., & Topol, E.J. (2016). State of Telehealth. New England Journal of Medicine, 375, 154–161. [CrossRef] [PubMed] [Google Scholar]
  • Fortier, M.S., Duda, J.L., Guerin, E., & Teixeira, P. (2012). Promoting physical activity: development and testing of self-determination theory-based interventions. International Journal of Behavioral Nutrition and Physical Activity, 9(20), 9e20. [CrossRef] [Google Scholar]
  • Goldberg, D.P., Gater, R., Sartorius, N., Ustun, T.B., Piccinelli, M., Gureje, O., & Rutter, C. (1997). The validity of two versions of the GHQ in the WHO study of mental illness in general health care. Psychological Medicine, 2(1), 191–197. [CrossRef] [PubMed] [Google Scholar]
  • Hardcastle, S.J., Taylor, A.H., Bailey, M.P., Harley, R.S., & Hagger, M.S. (2013). Effectiveness of a motivational interviewing intervention on weight loss, physical activity and cardiovascular disease risk factors: a randomized controlled trail with a 12-month post-intervention follow-up. International Journal of Behavioral Nutrition and Physical Activity, 10(40), e40. [CrossRef] [Google Scholar]
  • Hardcastle, S.J., Fortier, M., Blake, N., & Hagger, M.S. (2017). Identifying content-based and relational techniques to change behaviour in motivational interviewing. Health Psychology Review, 11(1), 1–16. [Google Scholar]
  • Hopkins, W.G., & Batterham, A.M. (2016). Error rates, decisive outcomes and publication bias with several inferential methods. Sports Medicine, 46, 1563–1573. [CrossRef] [PubMed] [Google Scholar]
  • Hopkins, W.G. (2017). Estimating sample size for magnitude-based inferences. Sportscience, 21, 63–72. [Google Scholar]
  • Hopkins, W.G. (2003). A spreadsheet for analysis of straightforward controlled trials [Online], Sport Science, Accessed 20 July 2016. [Google Scholar]
  • Johnson, U., Ivarsson, A., Parker, J., Andersen, M.B., & Svetoft, I. (2019). Connecting in the fresh air: A study on the benefits of participation in an electronic tracking outdoor gym exercise programme, Montenegrian Journal of Sports Science Medicine, 8, 61–67. [CrossRef] [Google Scholar]
  • Kozey-Keadle, S., Staudenmayer, J., Libertine, A., Mavilia, M., Lyden, K., Braun, B., & Freedson, P. (2014). Changes in sedentary time and physical activity in response to a exercise training and/or lifestyle intervention. Journal of Physical Activity and Health, 1(7), 1324–1333. [CrossRef] [PubMed] [Google Scholar]
  • Lundahl, B.W., Kunz, C. Brownell, C., Tollefson, D., & Burke, B.L. (2010). A Meta-Analysis of Motivational Interviewing: Twenty-Five Years of Empirical Studies Research on Social Work Practice, Research on Social Work Practice, 20(2), 137–160. [CrossRef] [Google Scholar]
  • Markland, D., & Tobin, V. (2004). A modification to the Behavioral Regulation in Exercise Questionnaire to include an assessment of amotivation. Journal of Sport and Exercise Psychology, 26(2), 191–196. [CrossRef] [Google Scholar]
  • Ryan, R.M., & Deci, E.L. (2002). Handbook of self-determination research. New York, USA: The University of Rochester Press. [Google Scholar]
  • Shane, A., Lowe, S.H., & Ólaighin, G. (2014). Monitoring human health behaviour in one’s living environment: A technological review. Medical Engineering & Physics, 36(2), 147–168. [CrossRef] [PubMed] [Google Scholar]
  • Silva, M.N., Markland, M., Minderico, C.S., Vieira, P.N., Castro, M.M., Coutinho, S.R., Santos, T.C., Matos, M.G., Sardinha, L.B., & Teixeira, P.J. (2008). A randomized controlled trial to evaluate self-determination theory for exercise adherence and weight control: rationale and intervention description. BMC Public Health, 8(234), 1–13. [CrossRef] [PubMed] [Google Scholar]
  • Warburton, D.E.R., Nicol, C.W., & Bredin, S.S.D. (2004). Health benefits of physical activity: the evidence. CMAJ, 2(174), 801–809. [Google Scholar]
  • Williams, G.C., Niemiec, C.P, Patrick, H., Ryan, R.M., & Deci, E.L. (2009). The importance of supporting autonomy and perceived competence in facilitating long-term tobacco abstinence. Annual Behavioral Medicine, 37(3), 315–324. [CrossRef] [PubMed] [Google Scholar]

Cite this article as: Johnson U, Ivarsson A, Parker J, Svetoft I, & Andersen MB (2023) A study on the benefits of participation in an electronic tracking physical activity program and motivational interviewing during a three-month period. Mov Sport Sci/Sci Mot, 119, 1–8

All Tables

Table 1

Time plan for the study.

Table 2

Physical activity behaviour (steps), psychological well-being and motivation, anthropometrics and physical measures pre- and post-intervention period.

All Figures

thumbnail Fig. 1

The two-group pre-post experimental design.

In the text
thumbnail Fig. 2

Average daily steps over the 90-day intervention period for both experimental (solid blue) and control (solid orange) groups. Note: dotted blue line: trendline for the experimental group; dotted orange line: trendline for the control group..

In the text

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