Exploring Regular Exercisers’ Experiences with Readiness/Recovery Scores Produced by Wearable Devices: A Descriptive Qualitative Study

doi:10.21203/rs.3.rs-3888994/v1

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Exploring Regular Exercisers’ Experiences with Readiness/Recovery Scores Produced by Wearable Devices: A Descriptive Qualitative Study

https://doi.org/10.21203/rs.3.rs-3888994/v1

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published 25 Apr, 2024

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Meta-session autoregulation, a person-adaptive exercise programming approach, is characterized by individuals’ matching exercise demands specifically to their current readiness states. Some consumer wearables provide ‘recovery’ or ‘readiness’ scores, computed primarily based on heart rate variability. Despite the growing popularity of consumer wearables and interest in person-adaptive programming, limited research exists on how exercisers interact, interpret and use these scores. This study explores individuals' experiences with wearable devices and their associated readiness or recovery scores. Seventeen regular exercisers who owned and used a Whoop™ band or Oura™ ring for at least 3 months participated in a one-on-one virtual semi-structured interview. Interviews were analyzed using reflexive thematic analysis, with themes supported by 'in-vivo' quotes. This paper focuses on three key themes for a comprehensive demonstration. Theme 1, 'It's more about how I can make adjustments to optimize my programming,' (MPR) highlights users' intended use of wearables for guiding training decisions. Theme 2, 'So many things outside of training modifications have changed,' (Misty) reveals that users also modify non-exercise behaviors to manage and optimize their scores. Theme 3, ‘You can’t really capture the complexities of a human on a device’ (Letty) underscores users' recognition of the limitations and errors associated with these devices emphasizing self-reliance to further direct behavioral adjustments. While wearable devices provide a numeric approach to measuring readiness and recovery, users prioritize self-awareness, flexibility, and personal judgment for exercise decisions. Understanding these experiences, in addition to exploring the psycho-behavioral aspects of user interactions, can contribute to refining meta-session autoregulation.

Person-adaptive

exercise

autoregulation

heart rate variability

self-monitoring

health behavior

Despite the documented benefits of exercise as a form of physical activity, and the unfavorable consequences of a sedentary lifestyle, considerable parts of the world population have difficulties adopting and maintaining this complex behavior (Gjestvang et al., 2020). Recently, experts have suggested that complex behaviors should be presented with flexibility – allowing the program to be routinely tuned based on an individual’s varying circumstances (i.e., person-adaptive; Conroy et al., 2020). Meta-session autoregulation is an existing framework – originally proposed to guide sport-specific conditioning – for person-adaptive exercise programming that accounts for both training and non-training-related stressors (Greig et al., 2020). Specifically, exercise sessions are deliberately chosen that best match an individual’s ‘readiness’, a multidimensional construct that reflects their current availability of physiological, emotional, and cognitive resources (Strohacker et al., 2023). However, the existing literature on the application of meta-session autoregulation highlights significant limitations in its current development, particularly, in the varied approaches to measuring readiness that have lacked empirical and theoretical support (Ibrahim et al., in press). Further, sufficient descriptions are rarely provided regarding if and how exercise demand fluctuates based on readiness measures. Given the direct role of readiness in guiding behavioral decisions, this gap critically limits the translation and applicability of meta-session autoregulation in health promotion and disease prevention settings.

Heart rate variability (HRV) holds considerable face validity as a measure of readiness and has been utilized in research as a meta-session autoregulatory approach to guide adjustments to training (Kiviniemi et al., 2010). HRV represents the degree of variation in time between adjacent heartbeats, which is responsive to various behavioral (e.g., energy restriction), physiological (e.g., illness), environmental (e.g., loud noise), and psychological (e.g., emotional distress) stimuli (Shaffer & Ginsberg 2017). A low HRV conveys a monotonously regular heart rate and is associated with impaired regulatory and homeostatic autonomic nervous system functions, reducing the body’s ability to cope with internal and external stressors (Shaffer & Ginsberg 2017). While electrocardiographic tracing was historically required to assess HRV accurately, technological advancements and decreased production costs of consumer off-the-shelf wearables (COTSW) now provide individuals with HRV-derived scores that are easy to interpret. These scores, often labeled as percent ‘recovery’ or ‘readiness,’ are designed to guide wearer’s training (Bunn et al., 2018).

Despite the surging popularity of COSTWs, there remains a notable lack of scientific inquiry exploring the determinates influencing user’s adoption and utilization of metric purported to enhance training (Shei et al., 2022). A comprehensive understanding of individuals’ engagement with these HRV-derived scores is imperative, given that the intended, person-adaptive outcomes (e.g., rest or restorative activity in response to low readiness/recovery score) does depend on the user’s buy-in to and understanding of the metric. Thus, utilizing a semi-structured interview guided by a descriptive qualitative approach (Sandelowski 2010), the purpose of the current study was to understand individuals’ lived experiences using and interacting with COTSWs that provide HRV-derived indicators of readiness or recovery. Following established guidelines for designing and refining behavioral interventions and strategies (Czajkowski et al., 2015), qualitative research designs represent key pre-efficacy approaches that yield richer descriptions of the target phenomena that should inform continued optimization of behavioral treatments.

Design Overview

The University’s Institutional Review Board approved all described procedures. Using one-on-one semi-structured interviews, a descriptive qualitative approach was employed to explore participants’ perceptions and experiences related to their HRV-derived scores a suitable approach when prior knowledge is sparse and participant language and terminology is vital for analysis (Sandelowski 2010). A constructivist paradigm, recognizing the diversity of valid realities and individual constructions of reality was adopted (Denzin and Lincoln 2011). Furthermore, participants' demographics, exercise behaviors, and motivation were gauged through validated online surveys. Due to rich data and word limit constraints, this paper focuses on three primary themes (from six total) to ensure depth and clarity in line with our research goals.

Procedure

Reflexivity of the Researchers. The lead author, a doctoral student with background in qualitative research and athletics, conducted the interviews. His experience includes graduate course work geared towards a qualitative research certificate and practical insights from his time as a collegiate basketball player, competitive powerlifter, and coach. While new to leading a qualitative investigation, he received mentorship by seasoned researchers. The second author (CB), an assistant professor, contributed with extensive qualitative research experiences, familiarity with COTSW devices, and insights such as the used the Whoop™ band. The senior author (KS) an associate professor in kinesiology whose research agenda includes empirically defining readiness and replicable procedures for meta-session autoregulation.

When utilizing descriptive qualitative research methodology, it should be acknowledged that as a researcher team, we apply our own interpretation to participant's experiences based on prior knowledge of theory (Bradshaw et al., 2017; Kahlke 2014; Sandelowski 2010). Pertaining to this study, prior knowledge of several theories include: affective-reflective theory of physical inactivity and exercise (Brand and Ekkekakis 2017), self-determination theory (Deci and Ryan 1985), goal-setting theory (Locke and Latham 2006), and periodization theory (Cunanan et al., 2018). Throughout this investigation we aim to demonstrate the application of our qualitative methodological knowledge through detailed explanations of the developmental process, data collection and analysis, and the criteria to evaluate the investigation’s quality.

Participants and Recruitment. Individuals were recruited through social media flier advertisements, and snowball sampling. The inclusion criteria included: adults aged 18–45 years old, access to a computer with a camera and microphone (for virtual interview purposes), owned and self-reported utilizing a Whoop™ band or Oura™ Ring for at least 3 months for a minimum of 4 days per week. To allow for maximum variation sampling of exercise modalities (Sandelowski 2010), individuals were further eligible if they reported engaging in muscle-strengthening exercise, aerobic exercise, or both. A “regular exerciser” was defined as meeting the national recommendations for muscle-strengthening physical activity (targeting all major muscle groups at least twice per week; (Piercy et al., 2018) or engaging in at least moderate aerobic exercise on three or more days per week for the prior three months, following the American College of Sports Medicine's pre-participation screening criteria (Liguori 2020).

Interview Guide Development and Piloting. Interviews were designed to be semi-structured allowing flexibility through key questions. This design facilitates exploration by permitting the interviewee and interviewer to diverge and pursue an idea or response in more detail enabling the discovery of information that is important to participants and may not have been previously thought of by the research team (Kallio et al., 2016). Pilot interviews by the first author refined the guide, tested for bias, and confirmed question effectiveness (Sampson 2004). The final interview guide can be viewed in the supplementary files.

Interview Schedule. Interested individuals attended a Zoom session detailing the study and criteria and, if eligible gave verbal consent before completing a series of questionnaires described below. Interviews were scheduled within two weeks, participants could speak freely, skip any uncomfortable questions, and chose pseudonyms for anonymity. Conducted between December 2022 and March 2023, interview durations ranged from 36–92 minutes (no incentive or compensation was offered). Interviews were transcribed verbatim and member checking confirmed transcript accuracy without conflicts.

Instrumentation

Participant demographics. Participants provided demographic information, including age, duration using their wearable, gender identity, total annual income, education, and race.

Exercise behavior. The researchers created items that allowed participants to describe their typical exercise behavior. Aerobic frequency (days per week), duration (minutes per session), and intensity during these sessions; the latter item was constructed on a three-point scale with descriptive anchors based on rating criteria for the ‘talk test’ (i.e., intensity inferred based on how easy or difficult it is to hold a conversation while under physical exertion) (Reed and Pipe 2014) as well as physical sensations (i.e., heart rate, muscle strain, breathing rate). For muscle-strengthening exercise, items captured frequency, volume (average repetitions and working sets), and perceptions of intensity based on subjective heaviness or strain (Category 10 Ratio Scale; Borg 1998).

Behavioral Regulation. The Behavioral Regulation in Exercise Questionnaire version 3 assess motivation across six subscales, ranging from amotivation to intrinsic motivation, using a 5-point scale to calculate a relative autonomy index (Wilson et al., 2006). The index ranges from − 19 to + 24, with higher scores reflecting more autonomous motivation on the Self-Determination Theory continuum.

Data Analyses

Upon approval of interview transcripts from participants, a reflexive thematic analysis was conducted (Braun and Clarke 2019). This approach emphasizes transparency, awareness of theoretical assumptions, and avoids using predetermined codes or coding reliability. Data analysis was conducted inductively through an iterative process of repeat reading, reviewing, and refining of codes and interpretations of themes, without being restricted by a prior theory or framework.

The first author (AI) immersed himself in the data by reading and re-reading thoroughly through transcripts, taking notes on initial patterns and meanings of each participant’s thoughts, feelings, and experiences. Codes were then generated from meaningful points in the data. For the six Whoop™ band participants’ data, the second (CB), third authors (KS) provided feedback as ‘critical friends’ to encourage exploration and reflection of alternative explanations and interpretations that could further refine subsequent codes and themes (Smith and McGannon 2018). Similar and contrasting experiences across the remaining eleven Oura™ Ring participants were coded to identify central patterns of meaning. Codes were then combined into potential themes and sub-themes, which were continuously reviewed and combined to generate larger initial subthemes. Sub-themes were collapsed into higher order themes or subthemes representing patterns of shared meaning reflecting participant experiences (Braun and Clarke 2019).

Criteria for Determining Rigor. In line with our methodological, ontological, and epistemological choices, we adopted specific criteria to guide the quality of this research project, including the worthiness of the research topic, rich rigor, meaningful coherence, integrity, and significant contributions (Bradshaw et al., 2017; Smith and McGannon 2018; Tracy 2010).

The current topic can be considered ‘worthy’ as it contributes to the refinement process of behavioral intervention strategies (i.e., meta-session autoregulation) to actively foster exercise adherence among a variety of populations, which is timely with the recent desire for exercise programs to be person adaptive. ‘Rich rigor’ was demonstrated by engaging with critical friends and an external auditor throughout the developmental processes, data collection, and analysis procedures. ‘Meaningful coherence’ was achieved by utilizing methods consistent with the stated purpose and effectively connecting the rationale, methods, and findings. By including the reflexivity of our research team, theoretical assumptions, member checking, and being fully transparent of the developmental process of the current investigation, ‘integrity’ has been demonstrated. Finally, the current investigation provides ‘significant contribution’ by providing novel insight into the practical and methodological considerations that need to be made as further refinement of HRV-based meta-session autoregulation is attempted to allow for the improvement of exercise adherence in real-world settings.

Findings

Initially, 25 individuals expressed interest in participating; however, two failed to meet the wearable device criteria, and six declined participation. Data saturation was reached with the remaining 17 individuals, as defined by Fusch and Ness (2015). Participants characteristics can be found in Table 1. Overall, 53% of participants reported having an advanced degree, and 71% reported a personal annual income greater than $80,000 per year. The average RAI score was 20 ± 2.

Table 1

Demographic Information and Exercise Behavior Results
Participant	Age	Duration w. Wearable	Gender Identity	Race	Aerobic Exercise		Muscle Strengthening Exercise
Participant	Age	Duration w. Wearable	Gender Identity	Race	Intensity^#	Duration (mins/day)	Avg. Rep Range	Avg. Working Sets	Average Strain
Bob^*	36	0.9	Woman	White	1	10	8–14	3	7
MPR^*	28	0.5	Woman	White	2	40	8–14	3	5
Powerade^†	31	1.5	Man	White	1	20	4–7	4	8
DN^*	37	1.3	Woman	White	2	30	8–14	3	6
Detroit^†	40	3.9	Man	White	2	60	8–14	3	5
Ginny^*	39	1.8	Woman	White	2	30	8–14	3	7
Brian^†	33	2.9	Man	Mixed	2	60	8–14	3	6
LC^*	30	1.5	Woman	White	1	20	8–14	3	5
Susan^*	37	2.7	Woman	White	2	40	8–14	3	7
Brad^†	27	1.5	Male	Greek	2	30	8–14	3	5
Misty^†	30	0.3	Woman	White	3	40	4–7	3	7
Sarah^*	32	0.3	Woman	White	2	40	8–14	3	4
Sean^†	29	1.3	Man	White	1	80	8–14	3	5
Michelle^*	37	3.3	Woman	Mi'kmaq	1	30	8–14	4	7
Letty^*	30	3.3	Woman	White	2	20	4–7	4	8
Rose^*	43	1.3	Woman	White	2	30	8–14	3	7
Heath^*	27	0.3	Man	White	2	40	1–3	3	4
*= Oura Ring User, † = Whoop user
^#1 = I can easily hold a conversation- there is no noticeable change in my breathing rate
2 = I notice physical sensations (heart rate, muscle strain), but it is not exhausting- I breathe somewhat harder, but I can still comfortably talk.
3 = My rapid breathing, and heart rate is hard to ignore- I would not be able to carry on a conversation without stopping to catch my breath.

While our reflexive analysis yielded six overarching themes, we have deliberately narrowed our focus to three key themes that pertain to respondents’ lived experiences with their COTSWs; remianing themes will be covered in a second manuscript. This ensures that each theme, within each paper, receives the necessary attention and space for a comprehensive exploration and demonstration. Theme 1 highlights users using their COTSW for guiding training purposes, while theme 2 indicates the concurrent utilization of the metric provided to guide behaviors that enhance recovery/readiness. Within theme 3, respondents acknowledge the limitations and errors associated with these devices, necessitating self-reliance to further direct behavioral adjustments. The following section presents titles of these themes and related subthemes in detail utilizing ‘in-vivo’ quotes from our sample (*signifies Oura™ users, † signifies Whoop™ band users).

Theme 1. ‘It’s more so how can I make adjustments to optimize my programming.’ (MPR*)

Subtheme 1.1 ‘They’re on spectrums and I can choose where I want to fall.’ (Michelle*) In this subtheme, participants expressed that their scores provided valuable insights into their physiological state and assisted them with gauging their body’s ability to handle training intensity for the day. As Misty^† described, ‘The readiness [score] helps me understand physiologically where my body is at and whether I can go at a certain level and be in a recovery state or I’m primed to push.’ The data served as ‘valuable intel to just be better’ enabling participants in planning daily exercise sessions using the scores to determine ‘whether I should push myself harder or take it easy.’ Moreover, participants highlighted the pragmatic value of their score, which were not employed to decide whether to exercise, but rather to customize their intended programming according to their current state.

Brad^†: If it's a good recovery score and I have a long-distance run planned obviously I am going to stick to it. If it's a bad recovery score, then I’m finding out a way of how I can just get my heart rate up a little bit without something that's too damaging [air quotes] on the body. So, instead of going outside for a 10-mile run instead I’m gonna go on a stationary bike for 20 minutes. Weightlifting, same thing. If I have a good recovery score, I'm going in. If I have a bad recovery score, and I’m trying to hit max squat not the greatest day to hit a max squat, but I still want to get the legs moving maybe I’m doing something higher reps, and a goblet squat.

Furthermore, participants acknowledged the mental challenge of ‘suppressing the urge to push harder’ due to competitive tendencies. However, they realized that optimizing performance for the future required a balance of effort. Overcoming the perception of reduced effort as ‘failure,’ they embraced the concept of ‘sacrifice for future progress.’

Brian^†: It's mental, almost like a voice in my head that's telling me you can squat more, you can bench more, you can do an extra set. It's feeling capable of doing more work but having to suppress those urges of wanting to do more.

After this initial mental challenge, a significant shift in participant’s exercise approach was experienced, moving away from ‘numbers-driven goals’ to focus on ‘progress and enjoyment.’

Bob ^†: It's been more freeing and making adjustments has made my relationship with training insanely positive. I truly look forward to challenging my body, but challenging my body doesn't necessarily mean putting more weight on the bar. I'm not held down by always needing to put weight on the bar. I am happy with the progress in making the movement look better, in making the movement feel better, and still finding physical progress there as well. Because before I felt very locked into the numbers like, I would have to hit these numbers. Now I am not tied to numbers necessarily, I’m tied to progress, and the fact that progress can look different.

As the scores allowed participants to be more flexible with their training, a sense of ‘freedom’ improving mental well-being and reducing ‘anxieties’ was reported, learning to understand the value of lower effort days without feeling ‘guilty.’

Ginny ^†: I don't struggle anymore with the recovery days because I know it's helping me get stronger in the long run. So, it feels good, because knowing that it's okay to take a down day mentally lets me know that I’m doing the right thing for myself and my health.

Subtheme 1.2.‘It’s a way to assess and set mental expectations before I do exercise.’ (Sean ^† ) The wearable scores emerged as a valuable tool for mentally preparing users before engaging in exercise. Specifically, a lower score prompted Sean^† to adjust their workout expectation for the day stating, ‘Sometimes it’s well my score is lower… maybe this workout today isn’t going to go as well, and I should just keep that in the back of my mind.’ Misty^† acknowledged her ‘mental game is improving’ sharing how the data helped her overcome resistance to working out stating: ‘I’ll look down, and I’m like Misty you gotta go, you have a good recovery score, your body is ready. Let’s go and do it!’

Some participants emphasized ‘the more thought I put into this, the worse it actually becomes’ when attempting to align their mindset with their scores revealing how adhering to a training plan would ultimately ‘ease the day-to-day execution’ towards their goals. Heath^* echoed this sentiment, stating, ‘I don't want to say it's not an option, but sometimes it's not an option. Whether you have a low readiness or not part of the training plan is you gotta go run today. So, it's almost a mental thing where it's like I'll do it anyway.’ Sarah^* noted the impact the score had on her focus within her session, ‘If I wake up, and I’m feeling awesome the readiness score agrees, and I'm working out I might be thinking about the day ahead while I’m counting my 10 reps versus if I’m feeling sluggish or my readiness score is bad then all I’m doing is really focusing on those muscles and those 10 reps so I don’t hurt myself.’

Theme 2. ‘So many things outside of training modifications have changed.’ (Misty†)

Subtheme 2.1.‘I gotta drink water… can’t drink alcohol… stop drinking coffee before 2pm.’ (Misty ^† ) In this subtheme, participants demonstrated a heighted awareness of the impact of various lifestyle factors on their scores. Several participants highlighted the influence of alcohol consumption on their scores.

Powerade ^†: I noticed pretty early on that drinking small amounts of alcohol has a noticeable impact on me and my recovery. It just shows me that I don't really recover well when I drink alcohol [laughter]. So, now I’m able to plan some of those things around my training, so I’m not going into my sessions feeling like [expletive deleted]. [laughter].

Additionally, participants made conscious decisions about caffeine intake to improve their scores.

Sean ^†: I have actually become really mindful about how frequently and it's more of a rarity to have caffeine past noon, because I noticed having that caffeine will influence how I sleep, which will then influence my recovery score.

Participants also noted the impact of nutrition on their scores and performance paying attention to factors such as hydration, meal timing, and macronutrient content.

LC ^*: I want to make sure that I'm having enough protein, that I’m getting electrolytes and water, getting enough calories, and carbohydrates at certain times.

Subtheme 2.2.‘If I’m not sleeping right that number is going to be lower’ (Brian ^† ). Participants recognized that various behaviors and lifestyle choices impacted their sleep quality and scores and actively made changes to optimize their sleep patterns and readiness/recovery metrics. Several participants realized the importance of ‘consistent sleep habits’ and a set bedtime.

Brian ^†: "To me, the only way that I really optimize recovery nowadays is through increased sleep. Having a set bedtime and a set waking time definitely adds to the increased recovery metrics through Whoop, and I also feel better just from a mental and physical standpoint."

Participants proactively improved their 'sleep hygiene' by consciously minimizing exposure to disruptive factors such as, blue light from screens and implemented specific bedtime routines to create a conducive environment for restful sleep.

Rose ^*: I do set up a wind down time like, okay it's time to start turning off bright lights, wear blue blockers, don't look at your phone in the middle of the night, cool off your bedroom, so it made be cognizant of all of those finer points of things that add up to quality sleep.

Subtheme 2.3.‘I feel way better when I’m doing those things that take care of my body’(Detroit^†) Participants emphasized the importance of various techniques intended to improve body integrity to enhance well-being and their metrics such as cryotherapy, sauna, hot-cold showers, and acupuncture. Additionally, foam rolling, and self-release work were considered effective techniques while acknowledging the importance of mobility and stretching.

Rose^*: I like to make sure I stretch because I spent a lot of years not doing that, and I paid for it [laughter]. So, if I can attribute the low score to my hip hurting, I’ll take some ibuprofen before I go to bed, or I'll take a hot bath.

Theme 3. ‘You can’t really capture the complexities of a human on a device’ (Letty)

Subtheme 3.1.‘It’s just a wearable. It’s not God! It doesn’t know exactly what is happening’ (Susan ^* ) While these devices provide valuable insight, participants observed what they capture ‘is quantitative, not qualitative’ lacking the ability of ‘tracking my thoughts and the way my brain is working.’ Ginny* explained that ‘it doesn’t ask me how I’m feeling,’ where Sean^† highlights ‘perhaps just mentally I'm fatigued, which may not show up with a physiological metric. It could be something emotionally heavy that's happened at work especially when it comes to my job, I am responsible for the health of student athletes.’ Moreover, participants drew attention to discrepancies in the tracking of various activities. The device appeared to overlook resistance training and misinterpret different activities. LC^* humorously portrayed this scenario, pointing out, ‘one issue that I have with the Oura Ring. I’ll train for an hour, and it's like nothing, and then I’ll wash my hair, it's like strength training 15 min, and I’m like no, but I mean, okay? [laughter].’

Subtheme 3.2.‘The metrics indicated my body is recovered, but I just didn’t feel that way’ (Sean ^† ) Despite favorable scores given by participants devices, participants recounted instances where incongruency occurred due to perceived fatigued or discomfort. Powerade^† explained that during his recent preparation for a powerlifting meet ‘I just noticed that the aches and things started to get to me, but it hadn't changed the actual readiness or recovery score. It was more my own experience and my own perceptions of how I was feeling.’ The incongruence was evident in sleep assessments as well. DN^* stated, ‘This morning I felt like I got decent sleep, and it said I slept like [expletive deleted]. Usually my scores [are] around the 70s and 80s and this morning it was 56. So, I should have felt like trash, but I really don’t.’ Sarah^* highlighted the impact of specific events on her scores, explaining, ‘Last night I had a nightmare so, it might say that my score is 87, but I don't feel as well rested because I was awake for a little while with that nightmare, and my heart rate goes up in the trends because of that nightmare.’ Like others she emphasized the importance of considering her actual state of readiness rather than solely relying on the numerical score, stating, ‘I'm not just gonna sit on the couch and be a vegetable, because my readiness score.’

Subtheme 3.3 ‘It’s almost like having something with you that’s guiding you.’ (Brad ^† ) While recognizing the devices utility, participants emphasized that it was not meant to dictate their actions, but rather complement their decision-making process; ‘I recognize that this is just a tool, it's not the guiding force of my life. It's something to benefit, not to detract from (Sean^†). Similarly, LC^* expressed her utilization of the wearable as ‘another piece of data to look at the puzzle of everything.’ Participants adopted an integrative approach, combining the device's insights with their internal cues and self-awareness. Heath^* explained, ‘it's a tool I can use and look at, but I also look internally at myself. So, I make the decision using both." Sarah^* emphasized the importance of personal judgment, stating, ‘I'm the person with the brain. So, I’m going to decide what activities I’m going to do, based on how I’m feeling, not necessarily what a little computer tells me.’

The primary objective of this study was to explore individual’s experiences with COTSW providing HRV-derived readiness/recovery scores. The findings from our investigation offer a foundation to better understand the practical and methodological considerations required for refining person-adaptive programming guided by readiness/recovery states, a previously unexplored area of research in this domain. First, our findings empirically document the occurrence of person-adaptive exercise programming being performed by adults under their own volition. Additionally, participants incorporated lifestyle changes to ultimately improve these scores overtime. Despite the apparent utility of receiving daily readiness/recovery scores users importantly placed significant emphasis on the importance of self-awareness, flexibility, and personal judgment when making exercise-related decisions. Participants viewed their wearables as valuable tools but not infallible authorities. In the following sections, we discuss how these findings can be leveraged for person-adaptive intervention development targeting exercise behavior.

The current study begins to fill an important gap in exercise science literature to understand occurrence of and process for intra-individualization of exercise programming.

Exercise prescriptions for health and sport are typically personalized based on relatively less dynamic characteristics (e.g., disease state, fitness level, expertise, player position, time relative to competition, or injury status; Cunanan et al. 2018; Garber et al. 2011). While these personalization practices account for crucial between-person that impact program efficacy and safety, there is growing recognition that physical, perceptual, cognitive, and affective states – which impact decisions to and experience of performing exercise - can fluctuate more frequently over time (hours, days) and across varying contexts within individuals (Dunton 2017).

Theme one provides evidence that individuals who are regular exercisers not only recognize shifts in perceptions of readiness or recovery, but actively modify their programming based on this information. Research does indicate that perceived effort and affective experiences are key determinants of physical activity engagement (Brand and Ekkekakis 2017; Trost et al. 2002). Individuals may not adhere to a rigid exercise program if it yields too many instances where the scheduled effort exceeds what they can physically and/or emotionally manage in the moment. Conversely, an exercise program may foster more behavioral instigation and engagement when individuals are provided with flexible goals that can meaningfully and consistently account for variation in resource levels (Kwasnicka et al., 2016 & 2021), as unanticipated and unfavorable shifts in mental and physical states can make an otherwise regularly-performed dose of exercise seem unachievable or unappealing in the moment. While research towards developing evidence-based and replicable person-adaptive programs for exercise and physical activity is in its infancy, there is evidence to suggest that intra-individual goal-setting improves success in meeting daily step goals compared to static prescriptions (Korinek et al. 2018; Phatak et al. 2018). The descriptions of real-life processes for intra-individualization in the current study reflect the goals of just-in-time-adaptive interventions, where tailored treatments is provided in the optional amount, at the right time, and only when needed to support behavior change and maintenance (Hardeman et al. 2019). Further examination of person-adaptive exercise programming in response to deviations in perceptions of readiness/recovery is warranted.

Interestingly, the current findings identify a potentially complementary focus for person-adaptive interventions – enhancing general levels of readiness or recovery to positively influences exercise-related behavior. Theme two demonstrates that a common practice of participants was to modify other behaviors (e.g., sleep routines, nutrition, restorative practices) – not necessarily as an unintended spillover effect (Dolan and Galizzi 2015; Tucker and Reicks 2002), but specifically to increase their scores so that they could confidently enact on their desired training plan. Such behavioral targets reflect factors cited in prior studies underlying perceptions of readiness to engage in exercise (e.g., vitality, body integrity, physical fatigue, nourishment; Strohacker and Zakrajsek 2016; Strohacker et al., 2019). This understanding of how various actions interact with one another aligns with the application of multiple health behavior change (Prochaska et al., 2008), which refers to efforts to promote two or more health behaviors, ‘bundled’ within a single intervention.

The consideration that sufficient mental and physical readiness may be prerequisites for regular exercise aligns with Antonovsky’s concept that salutogenesis (i.e., development of health) emerges from one’s self of coherence (Antonovsky 1993). This sense of coherence reflects the confidence that: (1) experiences and demands of life are predictable; (2) resources can be made available to meet demands; and (3) meeting these demands merit investment and commitment (Sudeck et al., 2023). This perspective has implications for intervention strategies. That is, rather than assume that individuals with consistently poor readiness or recovery will behave rationally (“I need to exercise to become healthy”; (Zenko et al., 2016)) or based on misguided optimism (“exercise will make me feel better”; (Backhouse et al., 2007), a more behaviorally-sensitive approach should recognize that unfavorable pre-exercise states may weaken (or counter) intended effects. However, simultaneous implementations of person-adaptive exercise programming with additional strategies to improve sleep, nutrition, and/or other related behaviors could be perceived as overwhelming and incite higher dropout rates among participants (Prochaska et al., 2008). In line with traditional forms of periodization (Stone et al., 1999), individuals seeking to increase exercise behavior may benefit from a ‘preparatory’ phase that aims to enhance general perceptions of readiness before enacting specific forms of exercise training. To date, however, evidence is mixed regarding superiority of sequential versus simultaneous delivery of multiple behavior change interventions (James et al., 2016).

Given the rapid expansion of and reliance on technological advances in health-related settings, a particularly meaningful takeaway from the current study is that participants explicitly recognized but were not necessarily deterred by the limitations of their COTSW. Participants acknowledged the potential for incongruency between the readiness/recovery score and their personal perceptions. This is perhaps unsurprising, as minimal correlation has been observed between subjective and objective measures of athlete well-being and the noted discrepancies between medical measurements and individual health perceptions (Kurlansik and Maffei 2016; Saw et al., 2015). Recognizing this ‘orthogonal health concept’, wherein objective findings and the subjective conditions are considered two dimensions of health (Franke 2012; Sudeck et al., 2023) may be a critical target of the educational component of an intervention that incorporates meta-session autoregulation for exercise programming. Beyond teaching individuals, the risks, benefits, and behavioral goals regarding exercise, additional lessons can clarify how COTSW readiness/recovery scores are derived and the importance of self-reflection to consider meaningful subjective states before making exercise decisions. Building explicit expectations in this manner should support continued user engagement and strengthen perceptions that the behavioral strategy is acceptable and socially valid (Perski et al., 2017; Sekhon et al., 2017). Furthermore, allowing autonomy in decision-making based on personally-relevant factors, experiences, and situations with feedback from easy-to-interpret readiness/recovery scores – rather than relegating individuals as passive recipients of algorithm-derived directives – stands to support more self-determined forms of motivation (Deci and Ryan 2000).

Limitations

While this study offers new insights into individuals’ utilization and experiences with their COTSW, we note several important limitations. Primarily, the reliance of qualitative research as the exclusive method in this investigation is exploratory, aiming to offer a rich understanding of a specific phenomenon while restricting generalizability (Anderson 2010). Recruitment unfolded opportunistically predominantly leveraging social media channels for dissemination. While this method ensured a fair and discreet recruitment strategy, it raises concerns about potential participant bias, as demonstrated within our sample, who are highly motivated, active, predominantly white, and affluent. Thus, future research should attempt to replicate or build upon these findings in a more targeted manner with a more diverse population. Furthermore, the investigation of only two wearables in the study prompts additional exploration of alternative options, providing a broader range of perspectives on HRV-derived scores. Despite these limitations, the investigation's novelty provides crucial insights into user experiences on COTSW, which offer a quantitative approach to readiness/recovery monitoring. Future research should delve into the psycho-behavioral aspects of user interactions to refine meta-session autoregulation as a behavioral strategy, enhancing exercise adherence.

In conclusion, the findings from the current study provide novel insights into how individuals process and utilize COTSW-derived measurements to guide exercise training, including their potential limitations. Based upon this work, additional empirical evidence is needed to inform strategies for exercise programming that rely on the monitoring and management of, as well as responsiveness to, individuals’ states of readiness or recovery. Mixed methods research could bridge the gap between quantitative outcomes and qualitative experiences, offering a more comprehensive understanding of user engagement and the practical application of wearable technology in exercise contexts (Kass 2016).

Author Contribution

All authors contributed to the study conceptions, design, and analysis. Material preparation and data collection were performed by AHI. The draft of the manuscript was written by AHI and critically reviewed by CTB and KS. All authors read and approved the final manuscript.

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No competing interests reported.

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Exploring Regular Exercisers’ Experiences with Readiness/Recovery Scores Produced by Wearable Devices: A Descriptive Qualitative Study

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Abstract

Introduction

Methods

Design Overview

Procedure

Instrumentation

Data Analyses

Findings

Theme 1. ‘It’s more so how can I make adjustments to optimize my programming.’ (MPR*)

Theme 2. ‘So many things outside of training modifications have changed.’ (Misty†)

Theme 3. ‘You can’t really capture the complexities of a human on a device’ (Letty)

Discussion

Limitations

Declarations

Author Contribution

References

Additional Declarations

Supplementary Files

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