I Did 10,000 Steps so I Earned This Treat: Problematic Smartwatch Use and Exercise Tracking Associations with Compensatory Eating and Sedentary Activity

Abstract

Smartwatches are digital devices, similar to smartphones, and come with the possibility of problematic use. Problematic technology use is the experience of psychological distress or reduced daily functioning in response to excessive or addictive technology use. The purpose of this study was to explore whether problematic use of smartwatch devices for exercise tracking influences user’s health behaviors such as eating habits and sedentary activity. An online survey was conducted among college-aged smartwatch users (n = 221). Results showed that using smartwatches for exercise tracking has a positive relationship with compensatory eating behavior (i.e., increasing caloric intake after exercise) when the use is higher in problematic use. This study concludes that although smartwatch devices are promoted to aid healthy behaviors, their impact on positive health outcomes may be limited for some users, to the extent that compensatory eating can derail fitness goals and may exacerbate eating behaviors. Future research should aim to develop health messaging for smartwatch users to make them aware of the potential for compensatory eating behavior to undermine their goals in the face of excessive smartwatch use for fitness purposes.

Introduction

Smartwatches are wearable gadgets equipped with sensors such as electrocardiogram, blood oxygen saturation, and accelerometer, which allow real-time monitoring of body movements, blood pressure, heart rates, and physical activities.^1–3 There were 148.74 million smartwatch users worldwide in 2019, and it is predicted to be approximately 230.85 million by the end of 2028.⁴ A significant number of smartwatch users use these health tracking systems for fitness and weight loss purposes.^5,6 Some smartwatch devices also offer goal achievement features such as collecting rewards during physical activity.⁷ This gamification of exercise using digital technology has the potential to lead to problematic and excessive use, similar to problematic smartphone use and problematic gaming.

Problematic use can be defined as negative use or addiction to information technology such as the Internet or social media sites including dependency on Internet-enabled devices (e.g., smartphones).^8–10 Problematic use of technology usually goes beyond time spent, to a point where individuals experience psychological distress and/or an impairment in daily functioning,¹¹ similar to those with substance use disorders (e.g., mood modification, withdrawal symptoms, loss of interest in hobbies and other experiences).¹² Problematic technology use is often associated with negative health outcomes such as poor sleep, depression, anxiety, and loneliness.^13–20

Frequent exposure to health data is associated with self-quantification behavior, goal-pursuit motivation, and may trigger emotional discomfort such as annoyance.²¹ Smartwatch-provided health data may motivate some users, as they would follow health advice and feedback from fitness coaches, resulting in adjusting caloric intake and increasing or decreasing physical activity. Users who follow smartwatch-provided health data without consulting dietitians or health professionals may perform behaviors in a way that is detrimental to their health and well-being and can put them at risk of developing future health problems.

Compensatory eating

Compensatory behaviors related to disordered eating can take multiple forms, including excessive physical exercise to “make up” for excessive caloric intake, or alternatively, allowing oneself to binge eat or increasing caloric intake after exercise.²² These behaviors may often result in a cycle of intense exercise and exercise fixation along with binge or other unhealthy eating choices. However, compensatory eating does not need to be at the level of pathologically disordered eating but more of an everyday decision. For example, whether to have dessert based on whether one exercised that day, or to choose a higher calorie food over a lower calorie one.^23,24 Most often, such increased caloric intake overcompensates for the calories burned during exercise, as people overestimate how many calories exercise uses; thus, compensatory eating is a behavioral component implicated in weight loss failure from exercise regimens.^25,26 In this study, we describe compensatory eating as a type of behavior among participants who may not be diagnosed with clinically disordered eating but who may be tracking their exercise regimen to maintain or gain a healthier lifestyle, including being “in shape” or “losing weight.” These individuals might then overcompensate for previous energy expenditure during exercise by increasing their energy intake at the next meal.^24,26

One study shows that fitness tracking devices are unique predictors of disordered eating behavior.²⁷ Another study demonstrated a significant relationship between exercise and compensatory eating where participants ate more calories upon post-exercise than pre-exercise.²⁸ Smartwatch use can be problematic when frequently checked for health data in order to reach targeted fitness goals.²⁹ And people who wear fitness devices tend to engage in more physical exercise and goal-motivated behavior than those who do not use them.³⁰ Smartwatches when used for tracking physical exercise may therefore act as a potential moderator and influence the relationship between exercise tracking and compensatory eating behaviors.

H1:

Increased smartwatch exercise tracking will be associated with greater compensatory eating.

H2:

Problematic smartwatch use will moderate the relationship between smartwatch exercise tracking and compensatory eating such that when problematic smartwatch use increases, the positive relationship between exercise tracking and compensatory eating will strengthen.

Sedentary behavior

Another compensatory behavior relevant to exercise tracking is sedentary activity. Individuals may allow themselves to engage in greater sedentary behavior based on their exercise tracking.²⁵ Further evidence shows that smartwatches allow access to the Internet and, therefore, may influence screen-based sedentary behavior among young users.³¹ Recent studies suggest that tracking exercise data using smartwatches or fitness devices and receiving real-time feedback can increase sedentary behavior.^32,33

The current study draws on these findings and aims to investigate the relationship between exercise tracking and sedentary behaviors of smartwatch users and also, how excessive smartwatch use may moderate that relationship. RQ1:

Will smartwatch exercise tracking be associated with sedentary behavior?

RQ2:

Will problematic smartwatch use moderate the relationship between smartwatch exercise tracking and sedentary behavior?

Method

Participants

An online survey was conducted among students at a large mid-Atlantic university who are current smartwatch users (n = 221) and participated for course credit. Participants’ average age was 20.50 years (SD = 3.71); 23 percent identified as male and 76 percent identified as female. Participants were White (79 percent), Asian American (6 percent), Hispanic or Latino (5 percent), and Black or African American (4 percent), other (3 percent), and 3 percent did not respond. Two participants (0.93 percent) identified as nonbinary; however, they were not included in the analysis because their number was too low to draw accurate statistical conclusions.

Procedure

A questionnaire asked participants about their smartwatch use, physical exercise, and daily activities, eating habits, app tracking information, and demographic questions. Each measures within the questionnaire were randomized to reduce order effects.³⁴ Smartwatch use and problematic smartwatch use were asked at the end of the survey based on previous research suggesting that asking questions about media usage at the beginning of survey can influence participant’s responses to other questions.³⁵ The study was determined exempt (category 2) by the Institutional Review Board (IRB) at the University of Delaware and data were collected in 2023.

Measures

Problematic smartwatch use

Participants’ problematic smartwatch use was measured on a 6-item scale modified from Smartphone Application-Based Addiction Scale.³⁶ The scale includes items such as “If I cannot use or access my smartwatch when I feel like, I feel sad, moody, or irritable,” response options ranged from 1 (strongly disagree) to 6 (strongly agree) (M = 2.11, SD = 0.98, Cronbach α = 0.85).

General smartwatch use

Participants were asked how often they use their smartwatches in an average week, first by asking how many days in an average week they wear their smartwatch (0–7) and then asking on the days they wear the smartwatches how many hours they wear it (0–24). The two answers were multiplied together to create an average use per week measure (M = 72.88 hours, SD = 42.76, range: 0–168).

Exercise tracking

A scale was developed for this study to ask participants about specific uses of their smartwatch device with the prompt “Which of the following activities do you use your smartwatch for?” using six items (“tracking steps,” “tracking a workout/exercise,” “tracking body mass index,” and “tracking other exercise”). Response options range from 1 (never) to 7 (all the time) (M = 4.08, SD = 1.40, Cronbach α = 0.82).

Compensatory eating

Compensatory eating was measured using a 15-item scale.³⁷ The scale includes items such as “I am rewarding myself for the effort I put into exercise.” Response options were from 1 (strongly disagree) to 7 (strongly agree) (M = 4.51, SD = 0.89, Cronbach α = 0.85).

Sedentary behavior

Sedentary behavior was measured by asking participants how many days in the past week they spent at least 10 consecutive minutes sitting (0–7), and on those days how many hours and minutes they spent sitting on average (0–24 hours; portions of hours were converted to hours, e.g., 15 minutes = 0.25 hours). The two were multiplied together for a measure of sedentary activity in the past week (M = 36.84, SD = 25.14, range: 0–150).³⁸

Results

See Table 1 for correlations among all the variables included in the analysis.

Table 1.

Descriptive Statistics and Correlations for Tracking Physical Activity, Problematic Smartwatch Use, Compensatory Eating, Sedentary Behavior, Gender, and Weekly Smartwatch Use

Variables	M	SD	1	2	3	4	5	6
1. Problematic smartwatch use	2.11	0.98	—
2. General smartwatch use	72.88	42.76	−0.15^*	—
3. Exercise tracking	4.08	1.40	0.17^*	0.30^*	—
4. Compensatory eating	4.51	0.89	0.24^*	−0.07	0.13	—
5. Sedentary behavior	36.84	25.14	−0.06	0.24^*	−0.09	0.16^*	—
6. Gender	0.76	0.43	−0.06	0.07	0.15^*	0.13	0.02	—

p < 0.05, n = 221.

Two separate moderation analyses were performed using PROCESS in SPSS, Model 1.³⁹ The first tested whether exercise tracking was associated with compensatory eating, moderated by problematic smartwatch use, and the second replaced compensatory eating with sedentary activity as the dependent variable. Gender (men = 0, women = 1) and general smartwatch use per week were used as covariates in both analyses. Full statistical results can be found in Table 2.

Table 2.

Moderation Regression Results, Exercise Tracking, and Problematic Smartwatch Use Predicting Compensatory Eating and Sedentary Behavior, General Smartwatch Use, and Gender

	Compensatory eating				Sedentary behavior
	B	SE	95 percent CI	p	B	SE	95 percent CI	p
Problematic smartwatch use	−0.15	0.18	−0.5, 0.21	0.41	−8.26	5.14	−18.40, 1.88	0.10
Exercise tracking	−0.11	0.09	−0.29, 0.07	0.23	−7.10	2.62	−12.27, −1.91	0.01*
Interaction	0.08	0.04	0.04, 0.16	0.04*	2.05	1.14	−0.19, 4.30	0.07
General smartwatch use	−0.02	0.01	−0.04, 0.01	0.23	0.15	0.04	0.06, 0.23	0.01*
Gender	0.33	0.14	0.05, 0.61	0.01*	1.15	4.13	−7.00, 9.31	0.78

p < 0.05, n = 221.

Note: Standard Error (SE); Confidence Interval (CI).

A significant interaction was found between problematic smartwatch use and using the smartwatch for exercise tracking on compensatory eating (Figure 1). Probing the interaction using the Johnson-Neyman technique finds that when problematic smartwatch use is low (<2.53, or 73.4 percent of the sample) there is no significant association between exercise tracking and compensatory eating. However, as problematic smartwatch use increases (starting at problematic smartwatch use ≥2.53, 26.6 percent), the relationship between exercise tracking and compensatory eating becomes significant and positive. In addition, the relationship between problematic smartwatch uses and compensatory eating is not significant when exercise tracking is low (<3.41, or 23.1 percent of the sample), but as exercise tracking increases (starting at exercise tracking ≥3.41, or 76.9 percent) the relationship becomes significant and positive. Gender identity was also significantly associated with compensatory eating, such that women were higher in compensatory eating compared with men. There was no significant relationship between compensatory eating and general smartwatch use.

FIG. 1.

Interaction Between Exercise Tracking and Problematic Smartwatch Use on Compensatory Eating.

When sedentary activity is the focal outcome, there was not a significant interaction between problematic smartwatch use and exercise tracking. Gender was not significantly associated with sedentary activity, but general smartwatch use was, such that the more smartwatch use reported, the more sedentary activity reported.

Discussion

Results suggested that problematic use of smartwatches significantly moderates the influence of exercise tracking on compensatory eating, such that increasing the level of smartwatch use strengthens the positive relationship between exercise tracking and compensatory eating. Specifically, when smartwatch use is relatively low, there was no statistically significant association between exercise tracking and compensatory eating behaviors, but as smartwatch use increased, the positive relationship between exercise tracking and compensatory eating behavior became significant. This finding unveils the potential issue with the problematic use of smartwatches; they are part of a healthy exercise regimen but may have hidden consequences such as developing compensatory eating behaviors. Previous research finds that increased caloric intake after exercise is one reason why exercise regimens fail to help people lose weight,^25,26 and this study suggests that smartwatch use might further exacerbate this issue. Similar findings have been reported in studies indicating a potential link between wearing fitness devices and developing compensatory behavior and exercise dependence.⁴⁰

Additionally, women showed higher compensatory eating behavior than men. Compensatory eating behaviors are linked to persistent disordered eating patterns, which can lead to a range of potential negative health outcomes, including greater feelings of body dissatisfaction, lower self-esteem, and mental health problems.^41–45 Moreover, eating disorders are associated with poor nutritional intake, posing serious risks to women’s health including osteopenia, osteoporosis, amenorrhea, and other health complications.^46–48 Results also showed that general smartwatch use was significantly positively related to sedentary behaviors. This finding undermines the overall effectiveness of smartwatches for fitness purposes.

This study recruited a convenience sample of undergraduates, which limits generalizability. However, its findings mirror other research on compensatory eating and gender,^49,50 indicating some consistency with other work that increases confidence in the findings. Also, younger people are more likely to use a smartwatch compared with older people,^5,6 meaning college students are within the age range most likely to be influenced by smartwatch use.

Conclusions

Although smartwatch devices are promoted to aid healthy behaviors, their positive health impact may be limited for some users, to the extent that compensatory eating can derail fitness goals. Many users, including young women, are at risk of developing compensatory eating behavior for excessively relying on smartwatch devices when used for exercise and fitness purposes. Future research should aim to develop health messaging for smartwatch users to make them aware of the potential for compensatory eating to undermine their goals in the face of excessive smartwatch use for fitness purposes.

Footnotes

Authors’ Contributions

All authors contributed to and have approved the final article. A.S.: Conceptualization, methodology, and writing original draft. M.E.E.: Formal analysis, writing–review and editing, and supervision.

Author Disclosure Statement

The authors report there are no competing interests to declare.

Funding Information

No funding was received for this article.

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