This was a registry- and survey-based longitudinal study with membership registry data from December 1st 2016 to May 31st 2018, and survey data collected in June 2018. The “Strengthening the Reporting of Observational Studies in Epidemiology” guidelines were consulted for the reporting of the study .
The study was conducted within the setting of a fitness center chain (www.3T.no) in Central Norway. Twelve of 3T’s fitness centers are located in the city of Trondheim, which had approximately 205,000 inhabitants in 2020, and these centers had around 40,000 members at the time of data collection. All centers have a primary workout area, and most of the centers have group classes, fitness trainers, personal trainers, saunas, and member lounges with simple café services. Members can book a free-of-charge session with a fitness trainer to get help and guidance and a personalized exercise program, or choose to pay for a personal trainer for closer follow-up and guidance. Group activities in this chain are also numerous and various, including yoga, stretching, water gymnastics, spinning, strength training and aerobics, lasting from 20 to 90 min.
Participants and procedures
The inclusion criteria were all adult members, aged 18 years or older at the time of survey distribution, who were registered with an e-mail address, been a paying ordinary member for a minimum of the two previous years and who allowed linkage to their membership data on use of the fitness center. Members with free or employee contracts, who were younger than 18 years of age, or whose memberships had not lasted 2 years were excluded.
3 T’s head office sent an email to all 15,273 eligible members with information about the study and a link to the survey. The landing page for the survey included additional information about the study. Members who were willing to participate could tick two boxes: one confirming that they agreed to participate, the other confirming that they consented to having their membership data on the use of fitness centers be linked to their survey record. A reminder was sent to those who had not answered four days after the first request.
Fitness center use
Data on the use of fitness centers was collected from the membership registry, including timestamps for visits, bookings of group activity, and fitness trainer bookings. The number of days with visits during 18 months were categorized into thirds (low (1–57), medium (58–117), and high (118–543)), as well as a separate category for those with zero visits. Similarly, participants were categorized into thirds based on number of group activity bookings (low (1–26), medium (27–80), and high (81–550)), as well as a fourth category with zero bookings. Fitness trainer bookings were categorized into none, one, and two or more bookings. To measure regularity in fitness center visits during the 18-month period, members were classified into three groups based on the number of three-month periods in which they had visited the fitness center at least once: ((1) all six periods, (2) four and five periods, and (3) less than four periods).
Self-reported goal achievement
Self-reported goal achievement was measured on a visual analogue scale (VAS) using the question “On a scale from 0 to 100, to what degree do you experience reaching your exercise goals at the fitness center?” (0 [zero] = to a very little extent, 100 = to a very large extent). The distribution of score values was left-skewed, with most people reporting high goal achievement values (median = 71, interquartile range 57–80). For the purpose of the linear regression analyses that compared mean score values between categories of the use of fitness centers, the goal achievement variable was log-transformed after inverting the measurement scale (i.e., higher score value on the inverted VAS indicates lower goal achievement), so that a VAS score of 80 was inverted to 20. To avoid missing data when log-transforming zero, 1 (one) was added to all score values. For logistic regression analyses, we constructed a binary variable where the participants were classified into low and high goal achievement based on the 20th percentile of the distribution of score values. Participants below the 20th percentile (i.e., VAS 0–50) were classified as having low goal achievement, and those on the 20th percentile and above (i.e., VAS 51–100) were classified as having high goal achievement.
The survey provided information on sex (male or female), age in years (< 20, 20–29, 30–39, 40–49, 50–59, 60–69 or ≥ 70), highest completed level of education (compulsory, middle, and higher) and employment (full-time work, part-time work, student, or not working due to occupational rehabilitation, unemployment or being laid off, disability benefits, being retired or other).
Descriptive characteristics of the study population are presented as proportions within the four categories of days with visits. To obtain inferential statistics, we first used linear regression to compare mean goal achievement score between categories of the four variables representing long-term use of the fitness center (i.e., days with visits, three-month periods with visits, group activity bookings, and fitness trainer bookings). For all comparisons, participants with most frequent visits/bookings constituted the reference group. As described above, the scores for goal achievement were first inverted and then log-transformed. The results of the linear regression analyses are therefore presented as the ratio of geometric means with 95% confidence interval (CI) for each category, relative to the refence group. Secondly, we used logistic regression to estimate odds ratio (OR) with 95% CI of poor goal achievement. Similar to the linear regression analyses, participants with most frequent visits/bookings constituted the reference group for all comparisons. All associations were adjusted for age (< 30, 30–39, 40–49, 50–59, 60–69 and ≥ 70 years), higher education (yes or no), and sex (male, female). We used Stata version 17 (StataCorp LLC, College Station, TX, USA) for all analyses.
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