|Year : 2021 | Volume
| Issue : 2 | Page : 93-97
Correlation of smartphone usage with functional capacity in young adults
Dipti B Geete, Aashi Sethiya, Jaimala V Shetye, Mohini N Kamat, Saraswati Iyer
Physiotherapy School and Centre, Seth GSMC and KEMH, Mumbai, Maharashtra, India
|Date of Submission||30-Jun-2021|
|Date of Decision||31-Dec-2021|
|Date of Acceptance||10-Jan-2022|
|Date of Web Publication||15-Feb-2022|
Dipti B Geete
Assistant Professor, PT School & Center, Seth GSMC & KEMH, Mumbai, Maharashtra
Source of Support: None, Conflict of Interest: None
BACKGROUND: Smartphone usage has increased in the young adult population. Excessive usage can negatively affect the various body systems, including pulmonary functions and functional capacity.
AIMS AND OBJECTIVES: This study aimed to correlate the functional capacity with Smartphone usage.
METHODS: Fifty participants were recruited and grouped into exercising and non-exercising individuals. Smartphone usage was assessed by SAPS (Smartphone Addiction Proneness Scale Score) and the number of hours of phone usage, which was categorized as being <4hrs and >4hrs of phone usage in a day. Functional capacity was assessed by 6 Minute Walk Test (6 Minute Walk Distance and Recovery Time) and Single Breath Count.
RESULTS: Smartphone usage is observed to be of longer duration in young adults, who are at moderate to no risk due to their addiction proneness. SAPS showed a weak inverse relationship with 6MWTD (P=0.053) but no significant correlation with either Recovery Time or SBC.
CONCLUSION: This study showed that exercise does contribute to functional capacity, and prolonged smartphone usage will negatively affect functional capacity.
Keywords: Smartphone usage, functional capacity, Six-Minute Walk Test
|How to cite this article:|
Geete DB, Sethiya A, Shetye JV, Kamat MN, Iyer S. Correlation of smartphone usage with functional capacity in young adults. Physiother - J Indian Assoc Physiother 2021;15:93-7
|How to cite this URL:|
Geete DB, Sethiya A, Shetye JV, Kamat MN, Iyer S. Correlation of smartphone usage with functional capacity in young adults. Physiother - J Indian Assoc Physiother [serial online] 2021 [cited 2022 May 21];15:93-7. Available from: https://www.pjiap.org/text.asp?2021/15/2/93/337717
| Introduction|| |
Smartphone usage today has increased exponentially. The predicted number of Indian users is expected to touch 760.5 million by 2022, with the usage highest in the young adult population, i.e., 25–34 years. Integrating digital technology into daily life is the reason for smartphone addiction. The likelihood of developing addiction is measured by the Smartphone Addiction Proneness Scale Score (SAPS). Increased sedentary behavior due to smartphone usage is associated with activities <1.5 Metabolic equivalents (METs), affecting metabolic functions, increasing oxidative stress, inflammation, and abnormal glycemic control. This is a precursor for comorbidities such as coronary artery disease, Type 2 diabetes mellitus, and obesity.,,,
Sustained postures during smartphone use cause deviations such as forward head and rounded shoulders., Faulty postures significantly alter the biomechanics of the upper quadrant., Sedentary behavior results in disuse of skeletal muscles, decreasing functional capacity.,
The 6-min walk test is a simple, practical tool measuring functional capacity., The 6-min walk distance (6MWD) correlates with the quality of life with good reproducibility., Being a submaximal test, it correlates with daily activities, that are performed at a submaximal level of exertion.,
Good pulmonary function is a prerequisite for aerobic activity. The Single Breath Count (SBC) is a tool for assessing pulmonary function. SBC measures how far an individual can count in a normal speaking voice after a maximal voluntary inhalation at the sound of a metronome at the beat of 2 counts/s. SBC correlates strongly with Forced and Slow Vital Capacity (FVC). It is indirectly a measure of functional capacity.
Here is a paucity in the literature associating smartphone usage with functional capacity. Hence, this study aimed to assess smartphone usage and addiction in young adults, using the SAPS Scale and correlating it with SBC and 6MWD.
| Subjects and Methods|| |
This was a one-time observational study that included 50 physiotherapy students (15 males and 35 females), 18–24 years. Individuals with musculoskeletal trauma, neurological, cardiovascular, and respiratory impairments were excluded. Ethics approval was acquired from the Institutional Ethics Committee, and the study was conducted in the physiotherapy outpatient department of a tertiary care hospital.
The SAPS scale, a 4-point Likert scale of 15 items to measure smartphone addiction, was administered through a written questionnaire. The severity of addiction was classified as high risk, potential risk, and no risk, where a higher score correlates with a greater degree of smartphone addiction. Participants were categorized into smartphone users for >4 h and <4 h. The sample was further categorized into exercising and nonexercising according to the American College of Sports Medicine (ACSM) guidelines.
The functional capacity was assessed using the 6-min walking distance and SBC.
The 6-min walk test was conducted according to the American Thoracic Society (ATS) guidelines. Participants were asked to walk a distance of 30 m back and forth for 6 min and the distance covered was documented. SBC was demonstrated using Metronome Beats® App, version 3.5.0, compatible with the mobile-based Android operating system, version 6.0, Marshmallow, and higher. Metronome was set at the frequency of 2 bps before recording SBC, which was recorded from the best of 3 trials.
| Results|| |
Statistical Analysis was performed using R studio Software (R Version 3.6.2), Boston, MA, USA. The data were assessed for normality using the Shapiro − Wilk normality test. The normality test yielded P values to be <5% for general recovery time and SBC; hence, the Pearson correlation coefficient was used for these and Spearman's for the other correlations.
In the population sample, mean age was 21 years (±standard deviation [SD] = 1.58), mean body mass index was 21.93 kg/m2 (±SD = 2.87), mean SAPS score was 33.42 (±SD = 5.54), while the mean 6MWD was 532.5 m (±SD = 73.51). The mean SBC and recovery time was 39.32 (±SD = 10.07 and 6.68 (±SD = 1.96) sec respectively [Table 1]. Out of total 50 participants, 23 subjects were exercising and 27 subjects were not exercising according to ACSM guidelines. Out of 23 exercising individuals, 13 exercising individuals used their phones for <4 h and 10 of them for over 4 h. In the nonexercising group, only 9 were using it for <4 h and 18 individuals were using it for over 4 h a day [Table 2]. The SAPS score was correlated with the 6MWD, recovery time, and SBC [Table 3] of vital parameters of the participants. The SAPS correlation with all parameters was compared in the exercising and nonexercising individuals.
|Table 3: Correlation of Smartphone Addiction Proneness Scale with six minute walk distance, recovery time and single breath count|
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The study highlights a weak inverse relationship between 6MWD and SAPS scores in the total sample size and the exercising population. No significant correlation could be found for the same in the nonexercising group. There was no significant correlation between SAPS and general recovery time for the exercising group, but we found a significant correlation in the nonexercising group.
There was no significant correlation between SAPS and SBC in any group.
| Discussion|| |
The primary aim of our study was to correlate SAPS scores with functional capacity. There was a weak negative correlation between SAPS and 6MWD, suggesting that increased smartphone usage can reduce submaximal functional capacity.
Smartphone usage was further studied among exercising and nonexercising individuals, the sedentary behavior associated is defined as any waking behavior with an energy expenditure of 1.5 METs or fewer.
There is reduced activity of lipoprotein lipase (LPL) with prolonged sitting, a protein responsible for maintaining low high-density lipoprotein levels. LPL is a critical enzyme for triglyceride (TG) catabolism-rich lipoproteins in the vascular endothelium. LPL is highly sensitive to physical inactivity, leading to decreased TG uptake by the nonexercising large muscles and increased levels of plasma TGs, leading to the development of atherosclerosis.,
Physical inactivity is associated with a sympathetic response, evoking an inflammatory cascade that elicits oxidative stresses and cellular damage. Prolonged sitting has also been associated with alterations in the circulating sex hormone levels, often causing tumors and malignancies.
Chronic inflammation increases the reactive oxygen species (ROS) that has a prime role in cellular signaling and damages cellular proteins and genetic materials. The damage is mediated through a combination of increased ROS and decreased protective proteins and antioxidant defense mechanisms. This pathway is seen most in disuse atrophy, a side effect of sedentary attitudes. Along with the cellular damage, increased fatigue is present, mediated by the ROS pathway.
A prolonged sitting posture with smartphone usage causes changes in the upper quadrant of the body. Slouching and rounded shoulder posture decreases thoracic mobility, affecting the optimum function of diaphragm. Such postures are associated with low forced expiratory volume in 1 forced vital capacity (FEV1/FVC) ratios.,
Logically it would be expected, sedentary time and light intensity activity time were highly negatively correlated (r = 0.96); more time spent in light-intensity activity is associated with less time spent sedentary. This suggests that it may be a feasible approach to promote light-intensity activities as a way of ameliorating the deleterious health consequences of sedentary time.,
Altered length-tension relationship of the accessory muscles of inspiration, decreased thoracic mobility contribute to impaired respiratory mechanics. Prolonged slouching causes crowding of ribs, affecting lung expansion. It restricts the descent of the diaphragm, affecting lung volumes and hence functional capacity.
Although smartphones disrupt physical activity, it may also serve to motivate physical activity by health apps that connect and monitor physical activity, measuring and comparing progress with peers. In our study, we observed that nonexercising individuals had higher phone usage than those who were exercising. Nearly half the total population was exercising, which could have influenced the correlation of 6MWD and smartphone usage.
In previous studies, a negative significant correlation has been implied between smartphone addiction and physical activity. Around half of the sample size included in a study by Haripriya S et al. had low physical activity and were categorized as potential risk members on the SAPS scale.
The altered length-tension relationship of the accessory muscles of inspiration that also control neck movements and posture related decreased thoracic mobility contribute to impaired respiratory mechanics. A prolonged sitting posture with smartphone usage causes changes in the upper quadrant of the body. Slouching and rounded shoulder posture decreases thoracic mobility, affecting the optimum function of the diaphragm. Such postures are associated with low FEV1/FVC ratios., Prolonged slouching causes crowding of ribs, affecting lung expansion. It restricts the descent of the diaphragm, affecting lung volumes and hence functional capacity. Additionally, our study aimed to correlate SAPS and SBC. Several studies have correlated SBC with pulmonary functions. A positive correlation of Single Breath Count was seen with peak expiratory flow rate, FEV1, FVC, and forced expiratory flow 25% to 75%. However, we did not find any significant correlation between SAPS and single breath count in any group.
The secondary objective of the study was to correlate the general recovery time of vitals with SAPS [Table 3]. Recovery time is the time taken by all the three vital parameters, namely heart rate (HR), respiratory rate, blood pressure (BP) to come to baseline after completing the 6MWT. HR recovery (HRR) is mainly thought to be because of parasympathetic reactivation and is an excellent reflection of an individual's health status. A delayed HRR is associated with an increased risk of cardiovascular mortality, autonomic dysfunction, and metabolic syndrome. Similarly, HRR is associated with cardiovascular fitness indices such as maximum oxygen uptake, endurance capacity, and central hemodynamic variables like resting HR and resting BP. Increased endurance capacity is one of the indices of cardiovascular fitness. Studies have shown that exercise endurance capacity is linearly related to HRR.,
Participants who did not exercise had reduced 6MWD and hence an attenuated hemodynamic response. Due to this, their recovery time was less than the exercising group, who walked more during the test (exerted more), had significantly more change in their vital parameters and hence longer time for recovery. There was a nonsignificant correlation for SAPS and general recovery time for the exercising group.
In addition, our study aimed to correlate SAPS and SBC. Several studies have correlated SBC with pulmonary functions. A positive correlation of SBC was seen with peak expiratory flow rate, FEV1, FVC and forced expiratory flow 25%–75%. As our study sample comprised of young adults with no comorbidities, it could play a huge role in not letting the sedentary behavior on the smartphone affect the SBC, i.e., the lung functions. As 6MWT is a submaximal test, a young adult could have done it easily even if they had a sedentary lifestyle that affected their functional capacity.,,
| Conclusion|| |
Smartphone usage is observed to be of longer duration in young adults, who are at moderate to no risk of reduced functional capacity due to their addiction proneness. The study highlights that smartphone usage has a weak correlation with a functional capacity in young adults. The exercising population that used their smartphones for a less duration had better functional capacity than the nonexercising population who had marginally higher smartphone usage and lowered functional capacity.
The authors acknowledge Mrs. Reia Natu, Data Scientist, University of Queensland, Australia, for their valuable contributions along with the participants who volunteered for this study.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3]