Nearly half of American adults are projected to have obesity by 2030 (1). This is concerning given that obesity increases the risk for numerous chronic diseases (2–4). Regular moderate to vigorous physical activity (MVPA; i.e., any bodily movement that achieves a moderate to vigorous level of energy expenditure, whether this movement is incidental or planned exercise) helps to prevent at least 25 chronic diseases, such as diabetes and cardiovascular disease, and reduces the risk for premature mortality (5,6). Although typically not effective as a standalone weight loss strategy (7), MVPA can assist with weight loss, and high levels of MVPA are related to better weight loss maintenance (8–10). Accordingly, national guidelines recommend that all adults engage in ≥150 MVPA min·wk−1 and that adults with obesity engage in ≥250 min·wk−1 (11,12). However, many individuals, and especially those with obesity, fall short of these guidelines (13,14). Novel approaches to facilitate regular MVPA among inactive adults with obesity have tremendous potential to improve public health.
Emerging research suggests that the time of day that individuals exercise (i.e., engage in planned, purposeful activity intended to promote health or fitness) could be leveraged to promote regular MVPA (15–18). A consistent exercise time may both help to establish a cue for exercise, thus facilitating habit formation, and protect time for exercise, thus simplifying planning and reducing perceived lack of time as a barrier (16,19,20). Although it can be defined in multiple ways, most studies to date have defined consistent exercise timing as performing a majority (e.g., >50%) of one’s exercise sessions across the week within a specific, predetermined time window (e.g., in the afternoon, defined as 12:00–4:59 pm) (17,18,21). Two recent observational studies showed that adults with current or former obesity who reported exercising at a consistent time day to day performed more MVPA and had stronger exercise habits than those with variable exercise timing both cross-sectionally and at 1-yr follow-up (17,18). Additionally, consistent early morning exercise was related to greater stability in exercise routine and the strongest exercise habits (18). Several studies in nonweight management populations have also assessed the associations among exercise timing, MVPA, and exercise habit with mixed findings (22–25). In two studies, timing consistency was unrelated to habit strength among mothers of school-aged children and physically active adults (24,25). This raises the possibility that consistent timing may be less important for certain subgroups. Indeed, self-regulation and goal-setting theories posit that autonomy is key for promoting goal attainment and would argue for leaving exercise timing to individual discretion (26,27). However, empirical support for the importance of autonomy to MVPA goal attainment is mixed (28,29).
Thus, although encouraging individuals to exercise at a consistent time could be an effective and highly translatable strategy for facilitating regular MVPA among adults with obesity, additional research is needed to explore the viability and usefulness of this approach. There are also key limitations to previous research that need to be addressed. First, because previous studies were observational, it is possible that consistent exercise timing serves as a marker for another factor, like greater motivation, that underlies greater MVPA and a stronger exercise habit (17,18). Experimental trials that randomly assign consistent exercise timing are needed to rule out potential confounds. Additionally, it is unknown whether insufficiently active individuals (i.e., those not meeting national physical activity guidelines (11)) with obesity are able and willing to exercise at a prescribed time in free-living settings. A few studies investigating the effects of exercise timing on various health-related outcomes (e.g., weight, glycemic control) have shown good adherence to prescribed exercise timing in supervised exercise settings (30,31). However, by design, these studies do not allow for evaluation of feasibility and acceptability in a naturalistic setting or whether certain timing prescriptions predict greater MVPA because exercise dose is tightly controlled. Supervised studies may also select for individuals with high motivation given required on-site exercise. It is therefore critical to assess the feasibility and acceptability of prescribed exercise timing in a free-living setting to determine whether a larger randomized trial testing exercise timing as an MVPA promotion strategy among adults with obesity is warranted. These data can also inform larger trials testing the effects of exercise timing on non-MVPA outcomes when studies in these areas are translated from mechanistic, supervised exercise designs to interventional studies outside of the laboratory (32,33).
The current study aimed to test the feasibility and acceptability of prescribed exercise timing (morning, evening, or choice) in a free-living setting among inactive adults with obesity using a randomized, crossover design. Per the study’s preregistered protocol, feasibility was assessed via consent, study completion, and exercise timing adherence rates; acceptability was assessed via self-report. As secondary aims, we 1) compared MVPA between timing conditions, including the percent of days with bouted MVPA, weekly bouted MVPA minutes, and weekly total MVPA minutes, and 2) characterized barriers and facilitators to prescribed exercise timing. We hypothesized that prescribed exercise timing would be feasible and acceptable, as assessed with prespecified benchmarks (feasibility: consent rate, ≥50%; study completion rate, ≥80%; exercise timing adherence rate, ≥60 (morning and evening conditions only); acceptability: ≥3.5 out of 5). Based on previous findings (17,18), we also hypothesized that MVPA would be greatest in the morning condition followed by the evening and then the choice conditions based on effect size. The secondary aim characterizing barriers and facilitators was exploratory and descriptive in nature with no a priori hypothesis.
A within-subjects, mixed-methods design was used to efficiently assess feasibility and acceptability, while also allowing for the characterization of MVPA and barriers/facilitators by timing condition when controlling for person-level characteristics.
Participants were adults 35–65 yr old with a body mass index (BMI) of 30.0–50.0 kg·m−2 who were able to read and write English, had regular access to the Internet, reported ability to walk ≥10 consecutive minutes without assistance, and were currently performing <50 min·wk−1 of bouted MVPA. Participants were excluded if they were participating in a weight management or exercise intervention; were pregnant, breastfeeding, or planning to become pregnant during the study; or if MVPA without further medical clearance was contraindicated by a validated exercise readiness screening tool (the 2018 Physical Activity Readiness Questionnaire for Everyone (PAR-Q) (34)).
This study was approved by the Miriam Hospital Institutional Review Board (protocol no. 1545853) and registered at clinicaltrials.gov (NCT05073042). All participants provided written informed consent for participation in the study. The study was performed in accordance with the Declaration of Helsinki. Participants were recruited from September 2021 to December 2021 using printed flyers that were posted in the community (e.g., on bulletin boards in local businesses) and online advertisements (e.g., on Facebook, on the website for the center where the study took place, in a post in a local weight loss clinic Facebook support group). Participants excluded from other studies at the research center where the study was conducted were also screened if interested. Advertisements stated that a research study was being conducted that sought to learn about people’s experiences with performing exercise at a certain time of day and whether having a specific time to exercise can help individuals become more physically active. Advertisements also outlined basic eligibility criteria and provided instructions for initial screening.
Interested participants completed an online or phone screen followed by a baseline visit during which they provided informed consent, had their height and weight measured, completed the exercise readiness screening tool, and were provided an accelerometer to wear for 1 wk to confirm eligibility. For the accelerometry eligibility screening, participants were told that the purpose was to ensure that the study and its activity goals were an appropriate fit. Participants had also been informed of the study’s purpose and had self-reported their MVPA levels during the initial screen; participants were not told what specific MVPA cutoff would be used to determine eligibility to maximize data validity. Participants were instructed to not alter their usual physical activity and were informed they may be able to participate in other studies if deemed ineligible. The same accelerometer valid wear criteria described below were used for the screening period.
Eligible participants attended a 45-min psychoeducational session with a member of the research team to discuss the benefits of MVPA, exercise safety considerations, exercise intensity assessment, and goal setting. A single investigator (L.M.S.) with a doctoral degree in clinical psychology and experience delivering health behavior change interventions among adults with obesity led all sessions to ensure consistency given the small sample size. When discussing benefits, participants were informed that ≥150 min·wk−1 of MVPA offers the greatest benefits, but that benefits begin to accrue with any amount of MVPA (35). Participants also were trained in greater detail on using the accelerometer and completing the nightly surveys, completed a baseline questionnaire, and were randomized. The order of the timing conditions was counterbalanced using an incomplete Latin square design. Participants were randomly assigned to an order using a random number generator with blocking (block size = 6). Participants and staff were unmasked to condition order.
Participants completed three 3-wk exercise conditions separated by 2-wk washout periods (13 wk total). During the exercise conditions, participants were instructed to exercise ≥15 consecutive minutes per day (with more MVPA encouraged) at a moderate intensity (65%–75% of age-adjusted estimated maximum heart rate), with walking recommended given that it is generally safe and free. A daily exercise goal was selected to maximize data collection opportunities. A minimum of 15 min·d−1 was selected by the research team as a feasible target for a daily goal based on previous experience with this population, as well as previous research showing that higher MVPA goals can help low active individuals achieve lower target MVPA (i.e., shooting for a higher yet realistic goal such as 15 min·d−1 can help participants achieve 10 min·d−1) (28,36). Participants were instructed to exercise from 5:00 to 10:00 am and from 5:00 to 10:00 pm during the morning and evening conditions, respectively; no timing prescription was provided during the choice condition. Participants were asked to wear an ActiGraph GT9X accelerometer (Tabtronics, Dayton, OH) on their nondominant hip during all waking hours throughout the study to assess MVPA and timing. Participants used the same accelerometer during the screening week and the 13-wk study period unless the device was lost or broken, in which case a replacement was provided. Participants synced their accelerometer data to a secure Web-based platform (ActiGraph’s CentrePoint) weekly using a free, secure app (ActiGraph’s CentrePoint Sync). Participants did not have access to the Web-based platform, and no data were visible in the app. During timing conditions, participants were e-mailed a survey link at 9:00 pm each night to report on whether they exercised that day, the timing of their exercise if any, and exercise barriers/facilitators (see below). The survey was designed to take <1–2 min to complete, and participants were asked to complete it within ~12 h. During the washout period, participants continued to wear the accelerometer but had no exercise goal and did not complete nightly surveys. Participants received a templated e-mail each Sunday reporting their adherence with the study protocol, reminding them of the upcoming week’s goal, and providing general tips for adhering to MVPA.
At the end of the study, participants completed an online acceptability survey and were compensated (up to $223: $10 per week with ≥4 valid days of accelerometry, $1 per nightly survey, $30 for postintervention survey).
Feasibility metrics included consent rates, defined as the percent of eligible individuals enrolling; study completion rates, defined as the percent of consented individuals who completed the postintervention survey; and exercise timing adherence rates, defined as the percent of days participants exercised on which exercise occurred during the assigned time window. For the timing adherence feasibility metric, we were primarily interested in whether the exercise that participants performed was at the assigned time (vs whether participants met daily exercise goals) because the timing prescription was the novel component being tested. The exercise timing adherence feasibility metric was only relevant to the morning and evening conditions because participants did not have an assigned time window for the choice condition for assessing adherence. Timing adherence was assessed on both the sample and person levels and using both accelerometer data (percent of days with accelerometer-detected MVPA bout(s) where ≥1 bout was in the assigned window) and self-reported exercise (percent of self-reported exercise sessions in the assigned window). Both measurements were used because some participants reported difficulty achieving the prescribed exercise intensity or engaging in ≥15 sustained exercise minutes, which could interfere with exercise being classified as bouted MVPA per accelerometry. Thus, self-reported data captured intentional exercise attempts.
Acceptability was assessed with a 15-item de novo questionnaire that was e-mailed to participants on the final day of the intervention. Five items inquired about satisfaction with and perceived helpfulness of prescribed exercise timing overall (e.g., How helpful was it to have a specific time of day to exercise?), and five items inquired about satisfaction with/helpfulness of prescribed exercise timing in the morning and evening in particular (e.g., How satisfied were you with exercising consistently during the morning?). Responses were rated on a 5-point scale (1 = not at all, 5 = very) and averaged. Participants were asked whether they preferred assigned or choice exercise timing and reported their preferred condition. Participants were asked to share their experience trying to follow the exercise prescriptions using a free-response format.
MVPA Timing and Levels
MVPA timing and levels were assessed with GT9X accelerometers and nightly surveys. For accelerometry, days with ≥10 h of wear were considered valid, and participants were required to have ≥4 valid wear days in ≥2 out of the 3 wk per timing condition to be included in analyses (37); a ≥2-wk threshold was selected to ensure there were adequate data to accurately characterize MVPA while not unnecessarily excluding participants who may have been missing data in a select week (e.g., due to a lost device). MVPA was classified using validated cut points (38). The average number of ≥10-min MVPA bouts per week and both bouted and total MVPA min per week were calculated. Bouts were defined as ≥10 min (vs 15 min) to align with the broader literature and to allow for use of established scoring algorithms (38). MVPA timing was captured via the accelerometer; a buffer of ±30 min was used when coding bouts as in versus outside the assigned window to allow sessions straddling the window to fall within the window. For surveys, participants self-reported whether they exercised for ≥15 min that day per the program goal and, if so, the time of exercise initiation. The average number of exercise sessions per week was calculated. The same ±30-min buffer was applied.
Barriers and Facilitators
Exercise barriers and facilitators were assessed in the nightly surveys using a de novo questionnaire informed by previous literature on factors affecting exercise (e.g., enjoyment, weather) and hypothesized mechanisms of action of consistent exercise timing’s potential effect (e.g., habit) (19,20,39). If participants reported no exercise that day, they responded to the stem “Please select which of the following barrier(s) interfered with walking today” by selecting from the following barrier(s): oversleeping, feeling too tired, known scheduling conflict, unanticipated conflict, stress, did not feel like it, sickness, injury, bad weather, and other (specify). The percentage of nonexercise days that each barrier was endorsed was calculated for each participant.
If participants reported they had exercised, they responded to the stem “Please rate the extent to which you experienced each of the following before or during your walking” by rating five potential exercise facilitators: level of enjoyment, perceived effort, extent to which decision felt automatic, extent deliberated about decision to exercise, and perceived helpfulness of planned time. Responses were rated using a 5-point scale (1 = not at all, 5 = very much). The mean level of endorsement for each facilitator was calculated for each participant.
During the morning and evening conditions only, if participants reported exercising at a different time than prescribed, they reported why using a free response to “You reported exercising at a different time of day than the current timing prescription. For what reason(s) did you exercise at a different time today?” Reasons were grouped thematically by the research team.
Sociodemographic characteristics were self-reported on a survey, and BMI was calculated based on weight (to the nearest 0.1 kg) and height (to the nearest 0.25 cm) measured in duplicate using a calibrated scale (Tanita Corporation of America Inc., Arlington Heights, IL) and wall-mounted stadiometer (Harpenden; Holtain Ltd., Crymyh, UK).
Because this was primarily a feasibility/acceptability study, a power analysis was not conducted. Instead, a sample size of 15 was judged by the research team as adequate—given the within-subjects design—for obtaining the data on feasibility and acceptability needed to inform a larger efficacy trial while also making most appropriate use of the resources available for the project. This sample size is commensurate with similar feasibility studies (30). Feasibility was assessed by calculating the consent, study completion, and timing adherence rates as percentages and comparing these values to the prespecified targets (≥50%, ≥80%, and ≥60%, respectively) (30). Acceptability was assessed by computing the mean and standard deviation (SD) of the five items assessing satisfaction with/perceived helpfulness of consistent timing overall, in the morning, and in the evening and comparing these values to the prespecified target of ≥3.5. Preferred timing prescription was characterized with counts and percentages. Repeated-measures analysis of variance (ANOVA) was used to compare accelerometry-measured MVPA and self-reported exercise session frequency between conditions. Planned pairwise comparisons compared the timing conditions. The magnitude and clinical significance of effects was of primary interest given our n, with expected medium-sized effects (i.e., partial η2 ≥ 0.06). Repeated-measures ANOVA was also used to compare accelerometer wear and survey completion between conditions to identify any covariates for inclusion in analyses. The average percentage of nonexercise days participants endorsed each barrier and the extent to which they experienced each facilitator were characterized separately for each timing condition with mean and SD; barriers were also rank ordered. Data were analyzed in SPSS version 28 (IBM Corp., Armonk, NY).
Sociodemographic Characteristics and BMI
Participants (n = 15) were 51.3±7.8 yr old and had BMI of 38.6±5.2 kg·m−2 (mean ± SD). Fourteen participants (93.3%) identified as female, all identified as White, and 14 (93.3%) identified as non-Hispanic. One participant (6.7%) had a high school education, 4 (26.7%) had some college (<4 yr), 4 (26.7%) had a college or university degree, and 6 (40.0%) had graduate or professional training. Nine participants (60.0%) were married, 4 (26.7%) were divorced, 1 (6.7%) was unmarried but living with a significant other, and 1 (6.7%) was unmarried and not living with a significant other.
All available data were used to assess the primary aims in accordance with the intention-to-treat principle. Data from all 15 participants were used to evaluate consent rate, study completion rate, and self-reported exercise timing adherence (feasibility metrics). One participant was lost to follow-up and did not return the accelerometer or complete the postintervention survey. Data from 14 participants were thus used to assess accelerometer-measured exercise timing adherence (feasibility metric), acceptability, and timing preferences. Only participants who contributed valid data for all three timing conditions were included in the secondary analyses comparing MVPA timing conditions (completer-only analyses). The completer sample consisted of n = 11 for accelerometry data (one study completer discontinued accelerometer wear midstudy due to discomfort but continued with all study components) and n = 12 for self-report analyses. Accelerometer data were unavailable for 3 wk total across two participants because of technical issues or a lost device. All available data were used when characterizing barriers and facilitators (n = 15).
Participants wore the accelerometer for 44.4 ± 11.9 valid days (mean ± SD) across the three timing conditions (range = 26–62 d, 621 total days available for the full sample) and for 805.1 ± 61.1 min·d−1 (range = 719.3–910.4 min·d−1). Participants completed 52.6 ± 15.1 nightly surveys (range = 17–63 surveys, 789 surveys total available for the full sample); >96% of nightly surveys were completed within 12 h (i.e., by 9:00 am) and >98% were completed by noon the next day. As shown in Supplemental Content 1 (table, https://links.lww.com/TJACSM/A217), the mean number of valid wear days, daily wear time, and number of completed surveys did not differ between the three timing conditions in either intention-to-treat or completer-only models. Accelerometer wear (min·d−1 and valid number of wear days) and nightly survey completion rates were also similar across randomization sequences (see Supplemental Content 2, table, https://links.lww.com/TJACSM/A218).
Feasibility: Do Participants Enroll in the Study, Do They Complete the Study, and When Participants Exercise during the Morning and Evening Conditions, Is It at the Assigned Time?
As shown in Fig. 1, 19 (65.5%) of the 29 individuals appearing eligible consented to participate (target, ≥50%). Four individuals were deemed screen failures after in-person screening and were not randomized; Fig. 1 provides reasons for screen failure. Fifteen individuals were randomized and started the study.
Study Completion Rate
Of the 15 randomized individuals, 12 (80.0%) completed all timing conditions and 14 (93.3%) completed the postintervention survey (target, ≥80%). Fig. 1 outlines reasons for attrition.
Exercise Timing Adherence Rates
Twelve participants had at least 1 d during the morning condition with accelerometer-measured bouted MVPA (mean ± SD = 5.4 ± 3.2 d, range = 1–9 d), and all 15 participants had at least 1 d with self-reported exercise per the nightly surveys (mean ± SD = 11.9 ± 4.5 d, range = 1–21 d). On average, participants performed bouted MVPA during the morning window as assigned on 62.4% ± 41.4% of these days per accelerometry and exercised during the morning window as assigned on 83.8% ± 27.7% of these days per self-report (target, ≥60%).
Eleven participants had at least 1 d during the evening condition with bouted MVPA as measured by accelerometry (mean ± SD = 5.2 ± 3.8 d, range = 1–13 d), and 14 participants had at least 1 d with reported exercise per the nightly surveys (mean ± SD = 13.6 ± 5.4 d, range = 1–20 d). On average, participants performed bouted MVPA during the evening window as assigned on 75.6% ± 29.6% of these days per accelerometry and exercised during the morning window as assigned on 83.3% ± 21.6% of these days per self-report (target, ≥60%).
When evaluating adherence to a consistent exercise timing prescription in general (i.e., morning plus evening conditions combined), 14 participants had at least 1 d during the combined timeframe with bouted MVPA as measured by accelerometry (mean ± SD = 8.6 ± 6.0 d, range = 1–17 d), and 15 participants had at least 1 d with reported exercise per the nightly surveys (mean ± SD = 24.6 ± 7.1 d, range = 13–41 d). On average, participants performed bouted MVPA during the assigned window on 69.9% ± 33.3% of days with accelerometer-measured bouted MPVA and self-reported exercising during the assigned window on 87.4% ± 14.0% of days.
Similar patterns were observed when evaluating timing adherence at the sample level (see Supplemental Content 3, results, https://links.lww.com/TJACSM/A219).
Acceptability and Preferred Timing: Do Participants Like or Find Having a Prescribed Consistent Exercise Time Useful, and What Timing Prescription Do They Prefer?
Mean acceptability ratings were as follows: consistent timing overall = 3.7±0.8, consistent morning exercise = 3.2±1.5, and consistent evening exercise = 2.9±1.4 (target, ≥3.5). Nine participants (64.3%) preferred picking their exercise time, whereas five participants (35.7%) preferred having a specific time. When asked about their preferred prescription, seven participants (50.0%) preferred choice, four participants (28.6%) preferred morning, and three participants (21.4%) preferred evening. Table 1 displays participants’ free-text responses about their experiences.
TABLE 1 -
Participant Responses to the Free-Text Question about Their Experiences in the Study, with Identified Themes and Participants’ Reported Preferred Timing Condition.
|I learned a lot about myself. Planning a time is helpful. My schedule often changes. Morning/afternoon is usually the best. Having to walk after 5:00 pm did not work for me.
||Certain time preferred, certain time disliked
|Just the fact I needed to exercise daily was a huge motivator.
|This experience showed me that I’m more likely to exercise in the morning or early afternoon hours. I got more out of the exercise I was doing and I enjoyed it more.
||Certain time preferred
|I found that I need to reorganize my life more to get back on track. I need to work on quality of exercise and not just how many steps I do during the day.
|I think exercising in the evening when the time change happens will be easily attainable.
|Having an exact time to exercise does not always fit with my schedule. My weekend schedule differs from my weekday schedule, so it is hard for me to work out at a consistent time across all days of the week. It was helpful to feel accountable, though, and having to do the surveys nightly helped with that.
||Choice preferred, scheduling conflicts, accountability
|Morning is just always better for me. My kids have a ton of activities in the afternoon and I had to travel over this last weekend that was evening exercise and I wasn’t able to do it. By the end of the day, I’m tired.
||Certain time preferred, certain time disliked, scheduling conflicts
|This was a fascinating study to participate in. I was certain the morning hours would be easier than the evening, but it turned out to be the opposite. As I am not always a motivated exerciser, one suggestion would be to use a text message system to encourage participants to complete their daily exercise. I think getting some reinforcement from an external source would be very helpful.
||Certain time preferred, surprise preference
|I found that the most important thing was having someone/something to be accountable to
|I think it was definitely more challenging due to my personal work schedule. My workday starts at 6:45, so the morning timeframe was difficult. By the time the evening time slot rolled around it was difficult for me to get up the energy and motivation after winding down, since my workday ends at 2:00 pm.
||Certain time disliked, scheduling conflicts
|It was a good program. I liked having a daily goal. With my hectic schedule and kids in multiple sports, it was tough meeting some of the targets.
||Accountability, scheduling conflicts
|Time of year definitely would change my mind on when I could walk. I prefer walking outside so the weather and daylight matters. I would have preferred having a goal of the 15 minutes when I wanted and would have wanted to log it when I did it. Sometimes I would walk or exercise when it wasn’t my time slot and feel like it didn’t count
||Environmental factors, choice preferred
|Gym hours were a challenge. Morning was easier because I could go to the gym, but evening my gym often closed before I was done with work and closes ridiculously early on the weekends. For me, a 6:00–11:00 am time frame would have led to perfect adherence. Before this study, I thought I was a later day exercise person but I found I liked doing the morning times.
||Environmental factors, specific time preferred, surprise preference
One participant did not provide a response. Some responses were edited lightly for clarity and/or shortened.
Differences in Physical Activity Levels by Timing Condition: Does the Amount of Exercise That Individuals Perform Differ Based on Exercise Timing Prescription?
Across all three conditions, participants performed 2.1 ± 1.9 MVPA bouts per week (range = 0.1–7.0 bouts per week), 36.0 ± 31.5 bouted MVPA min·wk−1 (range = 1.6–108.7 min·wk−1), and 97.9 ± 64.0 total MVPA min·wk−1 (range = 26.1–262.3min·wk−1) per accelerometry. Participants self-reported exercise 4.4 ± 1.3 d·wk−1 (range = 2.5–6.8 d·wk−1).
As shown in Table 2, there were between-condition differences for several aspects of MVPA. Specifically, there was a large-sized effect of condition on average weekly number of accelerometer-measured MVPA bouts and bouted MVPA minutes. The average number of MVPA bouts and bouted minutes per week was approximately three to four times greater during the morning and evening conditions versus the choice condition. One participant engaged in a very high amount of MVPA during the evening condition, contributing to large variance. Results from sensitivity analyses excluding this participant (see Table 2) revealed more pronounced, reliable differences aligning with the pattern described above. There was a medium-sized effect of condition on average weekly total MVPA minutes as measured via accelerometry and on number of self-reported exercise days.
TABLE 2 -
Comparisons of MVPA between Exercise Timing Conditions.
||Pairwise Comparisons, Mean Difference between Conditions (95% CI)
|Accelerometer data (n = 11)
|No. MVPA bouts per week
||0.07 ± 0.23
||2.41 ± 1.44
||3.48 ± 5.82
||0.84 ± 0.84
am vs pm: −1.07 (−4.98 to 2.83); am vs choice: 1.57 (0.59 to 2.56); pm vs choice: 2.65 (−0.84 to 6.14)
|Bouted MVPA minutes per week
||0.78 ± 2.58
||42.35 ± 31.03
||56.76 ± 88.63
||15.29 ± 17.01
am vs pm: −14.41 (−75.78 to 46.96); am vs choice: 27.06 (4.84 to 49.28); pm vs choice: 14.41 (−46.96 to 75.78)
|Total MVPA minutes per week
||75.11 ± 45.50
||104.41 ± 50.73
||124.82 ± 140.40
||73.42 ± 50.10
am vs pm: −20.41 (−100.56 to 59.74); am vs choice: 30.99 (−3.59 to 65.57); pm vs choice: 51.40 (−21.22 to 124.02)
|Survey data (n = 12)
|No. MVPA days per week
||4.51 ± 1.10
||4.48 ± 1.74
||3.98 ± 1.57
am vs pm: 0.04 (−0.97 to 1.04); am vs choice: 0.54 (−0.22 to 1.29); pm vs choice: 0.50 (−0.39 to 1.39)
Sensitivity analysis excluding participant with outlier data
|Accelerometer data (n = 10)
|No. MVPA bouts per week
||0.08 ± 0.25
||2.40 ± 1.52
||1.80 ± 1.69
||0.66 ± 0.62
am vs pm: 0.61 (−0.62 to 1.84); am vs choice: 1.75 (0.73 to 2.77); pm vs choice: 1.14 (0.10 to 2.18)
|Bouted MVPA minutes per week
||0.86 ± 2.71
||43.29 ± 32.54
||32.14 ± 36.36
||11.26 ± 11.09
am vs pm: 11.15 (−14.56 to 36.85); am vs choice: 32.03 (10.41 to 53.66); pm vs choice: 20.89 (−0.97 to 42.74)
|Total MVPA minutes per week
||68.22 ± 41.48
||97.89 ± 48.36
||85.44 ± 54.28
||62.30 ± 37.12
am vs pm: 12.45 (−24.16 to 49.05); am vs choice: 35.59 (−1.49 to 72.66); pm vs choice: 23.14 (−17.46 to 63.74)
|Survey data (n = 11)
|No. MVPA days per week
||4.58 ± 1.13
||4.38 ± 1.79
||3.91 ± 1.62
am vs pm: 0.20 (−0.85 to 1.24); am vs choice: 0.67 (−0.10 to 1.44); pm vs choice: 0.47 (−0.51 to 1.46)
Effect size is partial η2. Mean ± SD values are shown for MVPA at baseline and by timing condition. Baseline values are provided for descriptive purposes only; baseline data were not included in the models. CI, confidence interval.
Exercise Barriers and Facilitators: What Factors Affect Exercise When Trying to Follow Different Exercise Timing Prescriptions?
On average, the top 5 most frequent barriers for each condition were as follows: morning—scheduling conflict, unanticipated conflict, too tired, overslept, other; evening—other, bad weather, unanticipated conflict, sick, scheduling conflict; and choice—unanticipated conflict, too tired, did not feel like it, other, scheduling conflict (see Supplemental Content 4, figure, https://links.lww.com/TJACSM/A220). Examples of “other” barriers included work, travel, holidays, and caregiving responsibilities. On average, participants endorsed moderate levels (i.e., ~2.25–3.00) of the five facilitators (see Supplemental Content 5, figure, https://links.lww.com/TJACSM/A221). Participants exercised at a time other than that prescribed on 7.7% of exercise days during the morning condition and 14.6% of exercise days during the evening condition. As shown in Supplemental Content 6 (figure, https://links.lww.com/TJACSM/A222), in approximately one-third of the time in both the morning and evening conditions, this off-time exercise was due to 1) perceived lack of time during the assigned window or viewing a different time as the only option or 2) a scheduling conflict. Participants reported off-time exercise due to sleeping in approximately one-quarter of the time in the morning condition. Other off-time exercise was due to a different time being more convenient or more liked.
Previous observational research suggests that exercising at a consistent time of day might be a useful strategy for promoting regular exercise participation (17,18), although the real-world utility of prescribing this strategy for individuals with initial low exercise levels is unknown. The current study assesses whether prescribing consistent exercise timing is feasible and acceptable in a free-living setting among individuals with obesity who are inactive. Additionally, we explored whether the amount of objectively measured and self-reported MVPA varied by prescribed exercise timing.
Overall, the results suggest that prescribing consistent exercise timing is feasible as evidenced by achievement of prespecified benchmarks. One-quarter of the target population who contacted the study and nearly two-thirds (65%) of those appearing eligible after initial screening completed informed consent. Only one participant did not complete the follow-up assessment and two elected to discontinue the study early. Additionally, participants demonstrated adequate adherence to the timing prescription. Participants exercised at the assigned time on at least 60% of days (our prespecified feasibility benchmark); this was true when evaluating timing adherence at both the person and sample levels and using both accelerometry and self-reported data. Thus, although overall MVPA levels remained limited across the study period, it appears that, on days participants exercised, they exercised primarily at the assigned time. Taken together, these data support the feasibility of recruiting and retaining individuals for a larger efficacy trial and suggest that participants generally appear willing and able to exercise at a set time.
Participants rated consistent exercise timing in general as acceptable. However, the acceptability of consistent morning and consistent evening exercise in particular was slightly lower than our prespecified target. These differences in acceptability ratings may reflect some individuals finding consistent exercise timing acceptable but wanting it to be at a different time than the morning or evening (e.g., lunch time). Free-response data also revealed that some individuals strongly preferred one time over the other, which could have resulted in favorable ratings of consistent timing in general and poorer ratings of specific times. Although consistent timing was acceptable, most participants preferred being able to pick their exercise time, and choice was the most popular timing prescription, as would be predicted by self-regulation and goal-setting theories (26,27). These data are interesting when considering that MVPA levels were higher in the morning and evening. Thus, as in some previous research (40), there was a disconnect between what participants preferred and what appeared to be most helpful for behavior change. This finding adds to evidence suggesting that choice may not be as important for adherence as some theories suggest (28,29). It will be important to investigate whether this discrepancy replicates and whether preference for a consistent exercise time increases over time in future trials.
On average, participants fell short of meeting the standing intervention goal of exercising each day for ≥15 min (accruing ≥105 bouted MVPA min·wk−1) during all timing conditions and across the full study period. It may be that the low-intensity intervention used in this study did not provide participants with sufficient support or resources needed to achieve more robust increases in MVPA. This possibility is supported by other literature suggesting that programs with more comprehensive behavior change techniques yield greater changes in MVPA and aligns with some qualitative feedback provided by participants (41). Despite the modest amount of MVPA that participants performed, both consistent evening and consistent morning exercise timing yielded greater MVPA than choice timing, particularly for MVPA that was accumulated in ≥10-min bouts. Thus, results suggest that both consistent timing prescriptions were more effective in facilitating change in the intended behavioral target of bouted MVPA than the choice condition. Pending replication with larger samples and testing over longer time periods, consistent exercise timing could be a highly translatable strategy to encourage MVPA. For example, practitioners could easily recommend this strategy (along with other evidence-based techniques) to patients seeking to become more active. However, the primary purpose of this feasibility study was not to assess differences in MVPA and definitive conclusions about the causal effect of exercise timing on MVPA levels cannot be drawn.
Lastly, although perceived facilitators to exercise were similar and many barriers were shared across conditions, some barriers appeared more condition specific. For example, oversleeping appeared to be more common in the morning condition, bad weather and other appeared more common in the evening condition, and unanticipated conflicts and “not feeling it” appeared more common in the choice condition. These data highlight factors that might undermine adherence to specific exercise timing prescriptions. Future trials should proactively address potential barriers with participants to promote adherence. As suggested by one participant, it may be helpful to use additional intervention components like text message reminders across conditions to address common exercise barriers.
Strengths of the current study include a within-subjects design that facilitates comparisons between conditions while controlling for person-level variables; assessment of MVPA with objective and self-report measures; use of nightly surveys, which limit recall bias; high observed adherence to the accelerometer wear and nightly survey protocols; and testing of exercise timing using a very low-intensity intervention, which enhances scalability. One major limitation is that the sample identified as primarily non-Hispanic White, highly educated, and female (with all completers being female and the male participant withdrawing due to injury). Relatedly, recruitment methods were passive rather than community based, which may have contributed to a less representative sample. Other limitations include potential seasonality effects because the study was conducted in winter in the Northeastern United States, the short duration of each condition, the use of several de novo questionnaires with unknown psychometrics, and potential social desirability biases (e.g., for self-reported exercise timing), although concerns about the latter are partially offset through similar patterns of findings across accelerometer and survey data. The sample size was also small, and there are thus limitations with use of inferential statistics. Concerns about power were partially offset by the within-subjects design and the focus on effect size, and these analyses were also secondary; the primary study purpose was to assess feasibility and acceptability. Additionally, some participants reported difficulty meeting the ≥15-min daily exercise goal and the nightly surveys did not systematically assess all potential barriers or facilitators (e.g., social support, gym access, employment, and socioeconomic status). Although the within-subject counterbalanced design likely reduced the influence of some of these factors (e.g., social support) in between-conditions comparisons, other factors may have been more condition specific and may not have been adequately captured if participants did not voluntarily report them (e.g., via the other barrier option in the nightly surveys or postintervention survey).
A future trial can address many of these limitations by using a more far-reaching community-based recruitment plan to enroll a larger and more diverse sample, enrolling individuals at staggered intervals throughout the year, including more comprehensive assessments of factors that promote or hinder MVPA using validated measures, and assigning individuals to a single timing prescription for a longer duration. The latter approach would also allow for assessment of potential mechanisms of action, like habit formation. Additionally, it could be beneficial to provide access to more exercise resources (e.g., 24-h gym, online workout programs) to ensure greater equity in overcoming environmental barriers and to include more empirically supported behavior change strategies to achieve greater increases in MVPA.
In conclusion, prescribed exercise timing was found to be feasible and acceptable. Aligning with previous observational work, comparisons of MVPA by condition also suggested that consistent timing may yield greater MVPA. A larger trial testing consistent exercise timing is warranted, and lessons learned from this study (e.g., about barriers like seasonal changes) can be used to optimize adherence.
This study was supported by award no. 20-01292 from the Paffenbarger-Blair Fund for Epidemiological Research on Physical Activity and the American College of Sports Medicine (recipient: L.M.S.), as well as a National Heart, Lung, and Blood Institute (NHLBI)–supported Loan Repayment award (1L30HL154167-01; recipient: L.M.S.). The authors thank the participants for their contributions.
L.M.S. reports grant funding from the American College of Sports Medicine and a Loan Repayment award from NHLBI. J.G.T. reports receipt of consulting fees from Lumme Health Inc. and Medifast Inc., as well as stock options for Lumme Health Inc. The other authors declare no conflicts of interest.
The results of the study do not constitute endorsement by the American College of Sports Medicine.
1. Ward ZJ, Bleich SN, Cradock AL, et al. Projected U.S. state-level prevalence of adult obesity and severe obesity. N Engl J Med
2. Dwivedi AK, Dubey P, Cistola DP, Reddy SY. Association between obesity and cardiovascular outcomes: updated evidence from meta-analysis studies. Curr Cardiol Rep
3. Abdullah A, Peeters A, de Courten M, Stoelwinder J. The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract
4. Avgerinos KI, Spyrou N, Mantzoros CS, Dalamaga M. Obesity and cancer risk: emerging biological mechanisms and perspectives. Metabolism
5. Warburton DER, Bredin SSD. Health benefits of physical activity: a systematic review of current systematic reviews. Curr Opin Cardiol
6. Lee DC, Sui X, Church TS, et al. Changes in fitness and fatness on the development of cardiovascular disease risk factors hypertension, metabolic syndrome, and hypercholesterolemia. J Am Coll Cardiol
7. Dhurandhar EJ, Kaiser KA, Dawson JA, et al. Predicting adult weight change in the real world: a systematic review and meta-analysis accounting for compensatory changes in energy intake or expenditure. Int J Obes (Lond)
8. Swift DL, McGee JE, Earnest CP, et al. The effects of exercise and physical activity on weight loss and maintenance. Prog Cardiovasc Dis
9. Johns DJ, Hartmann-Boyce J, Jebb SA, et al. Diet or exercise interventions vs combined behavioral weight management programs: a systematic review and meta-analysis of direct comparisons. J Acad Nutr Diet
10. Jakicic JM, Marcus BH, Lang W, Janney C. Effect of exercise on 24-month weight loss maintenance in overweight women. Arch Intern Med
11. Piercy KL, Troiano RP, Ballard RM, et al. The physical activity guidelines for Americans. JAMA
12. Jensen MD, Ryan DH, Apovian CM, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. J Am Coll Cardiol
13. Zenko Z, Willis EA, White DA. Proportion of adults meeting the 2018 Physical Activity Guidelines for Americans according to accelerometers. Front Public Health
14. Tucker JM, Welk GJ, Beyler NK. Physical activity in U.S.: adults compliance with the physical activity guidelines for Americans. Am J Prev Med
15. Ainsworth BE, Ekelund U, Freedson PS, et al. Guide to the assessment of physical activity: clinical and research applications. Circulation
16. Schumacher LM, Thomas JG, Raynor HA, et al. Consistent morning exercise may be beneficial for individuals with obesity. Exerc Sport Sci Rev
17. Schumacher LM, Thomas JG, Raynor HA, et al. Relationship of consistency in timing of exercise performance and exercise levels among successful weight loss maintainers. Obesity
18. Schumacher LM, Thomas JG, Wing RR, Raynor HA, Rhodes RE, Bond DS. Sustaining regular exercise during weight loss maintenance: the role of consistent exercise timing. J Phys Act Health
19. Kaushal N, Rhodes RE, Spence JC, Meldrum JT. Increasing physical activity through principles of habit formation in new gym members: a randomized controlled trial. Ann Behav Med
20. Pimm R, Vandelanotte C, Rhodes RE, et al. Cue consistency associated with physical activity automaticity and behavior. Behav Med
21. Qian J, Walkup MP, Chen SH, et al. Association of objectively measured timing of physical activity bouts with cardiovascular health in type 2 diabetes. Diabetes Care
22. Maher JP, Rebar AL, Dunton GF. The influence of context stability on physical activity and sedentary behaviour habit and behaviour: an ecological momentary assessment study. Br J Health Psychol
23. Kaushal N, Rhodes RE. Exercise habit formation in new gym members: a longitudinal study. J Behav Med
24. Maher JP, Wang WL, Hedeker D, Dunton GF. Temporal stability of behavior, temporal cue-behavior associations, and physical activity habit strength among mothers with school-aged children. Psychol Health
25. Brooker PG, Jung ME, Kelly-Bowers D, et al. Does the time-of-day of exercise influence the total volume of exercise? A cross-sectional analysis of objectively monitored physical activity among active individuals. J Phys Act Health
26. Deci EL, Ryan RM. The “what” and “why” of goal pursuits: human needs and the self-determination of behavior. Psychol Inq
27. Locke EA, Latham GP. A Theory of Goal Setting & Task Performance
. Englewood Cliffs (NJ): Prentice-Hall, Inc; 1990.
28. McEwan D, Harden SM, Zumbo BD, et al. The effectiveness of multi-component goal setting interventions for changing physical activity behaviour: a systematic review and meta-analysis. Health Psychol Rev
29. Teixeira PJ, Carraça EV, Markland D, et al. Exercise, physical activity, and self-determination theory: a systematic review. Int J Behav Nutr Phys Act
30. Brooker PG, Gomersall SR, King NA, Leveritt MD. The feasibility and acceptability of morning versus evening exercise for overweight and obese adults: a randomized controlled trial. Contemp Clin Trials Commun
31. Creasy SA, Wayland L, Panter SL, et al. Effect of morning and evening exercise on energy balance: a pilot study. Nutrients
32. Teo SYM, Kanaley JA, Guelfi KJ, et al. The effect of exercise timing on glycemic control: a randomized clinical trial. Med Sci Sports Exerc
33. Blankenship JM, Rosenberg RC, Rynders CA, et al. Examining the role of exercise timing in weight management: a review. Int J Sports Med
34. Warburton DE, Jamnik V, Bredin SS, et al. The 2018 Physical Activity Readiness Questionnaire for Everyone (PAR-Q+) and electronic Physical Activity Readiness Medical Examination (ePARmed-X+): 2018 PAR-Q+. Health Fit J Can
35. Powell KE, King AC, Buchner DM, et al. The scientific foundation for the physical activity guidelines for Americans. J Phys Act Health
36. Jennings EG, Dunsiger SI, Bock BC, et al. Setting larger session duration goals is associated with greater future physical activity. PloS One
37. Tudor-Locke C, Camhi SM, Troiano RP. A catalog of rules, variables, and definitions applied to accelerometer data in the National Health and Nutrition Examination Survey, 2003–2006. Prev Chronic Dis
38. Troiano RP, Berrigan D, Dodd KW, et al. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc
39. Korkiakangas EE, Alahuhta MA, Laitinen JH. Barriers to regular exercise among adults at high risk or diagnosed with type 2 diabetes: a systematic review. Health Promot Int
40. Bond DS, Thomas JG, Raynor HA, et al. B-MOBILE—a smartphone-based intervention to reduce sedentary time in overweight/obese individuals: a within-subjects experimental trial. PloS One
41. Howlett N, Trivedi D, Troop NA, Chater AM. Are physical activity interventions for healthy inactive adults effective in promoting behavior change and maintenance, and which behavior change techniques are effective? A systematic review and meta-analysis. Transl Behav Med