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Review Special Issues

Association between problematic TikTok use and mental health: A systematic review and meta-analysis

  • Received: 26 December 2024 Revised: 24 March 2025 Accepted: 02 April 2025 Published: 16 April 2025
  • Background 

    TikTok is a significant part of social media usage, since 25.6% of the total global population has a TikTok account, and, thus, scholars should pay attention to its association with users' mental health.

    Objective 

    To synthesize and evaluate the association between problematic TikTok use and mental health.

    Methods 

    We applied the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines in our review. The review protocol was registered with PROSPERO (CRD42024582054). We searched PubMed, Scopus, Web of Science, PsycINFO, ProQuest, and CINAHL until September 02, 2024.

    Results 

    We identified 16 studies with 15,821 individuals. All studies were cross-sectional and were conducted after 2019. Quality was moderate in 10 studies, good in three studies, and poor in three studies. Our random effects models showed a positive association between TikTok use and depression (β = 0.321, 95% confidence interval: 0.261 to 0.381, p < 0.001, I2 = 78.0%, n = 6 studies), and anxiety (β = 0.406, 95% confidence interval: 0.279 to 0.533, p < 0.001, I2 = 94.8%, n = 4 studies). Data to perform meta-analysis with the other mental health variables were limited. However, our descriptive data showed a positive association between TikTok use and body image issues, poor sleep, anger, distress intolerance, narcissism, and stress.

    Conclusion 

    Our findings suggest that problematic TikTok use has a negative association with several mental health issues. Given the high levels of TikTok use, especially among young adults, our findings are essential to further enhance our understanding of the association between TikTok use and mental health. Finally, there is a need for further studies of better quality to assess the association between problematic TikTok use and mental health in a more valid way.

    Citation: Petros Galanis, Aglaia Katsiroumpa, Zoe Katsiroumpa, Polyxeni Mangoulia, Parisis Gallos, Ioannis Moisoglou, Evmorfia Koukia. Association between problematic TikTok use and mental health: A systematic review and meta-analysis[J]. AIMS Public Health, 2025, 12(2): 491-519. doi: 10.3934/publichealth.2025027

    Related Papers:

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  • Background 

    TikTok is a significant part of social media usage, since 25.6% of the total global population has a TikTok account, and, thus, scholars should pay attention to its association with users' mental health.

    Objective 

    To synthesize and evaluate the association between problematic TikTok use and mental health.

    Methods 

    We applied the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines in our review. The review protocol was registered with PROSPERO (CRD42024582054). We searched PubMed, Scopus, Web of Science, PsycINFO, ProQuest, and CINAHL until September 02, 2024.

    Results 

    We identified 16 studies with 15,821 individuals. All studies were cross-sectional and were conducted after 2019. Quality was moderate in 10 studies, good in three studies, and poor in three studies. Our random effects models showed a positive association between TikTok use and depression (β = 0.321, 95% confidence interval: 0.261 to 0.381, p < 0.001, I2 = 78.0%, n = 6 studies), and anxiety (β = 0.406, 95% confidence interval: 0.279 to 0.533, p < 0.001, I2 = 94.8%, n = 4 studies). Data to perform meta-analysis with the other mental health variables were limited. However, our descriptive data showed a positive association between TikTok use and body image issues, poor sleep, anger, distress intolerance, narcissism, and stress.

    Conclusion 

    Our findings suggest that problematic TikTok use has a negative association with several mental health issues. Given the high levels of TikTok use, especially among young adults, our findings are essential to further enhance our understanding of the association between TikTok use and mental health. Finally, there is a need for further studies of better quality to assess the association between problematic TikTok use and mental health in a more valid way.



    Currently, fractional-order calculus (FOC) plays a dynamic role in the development and applications of modern science and technology [1]. The FOC is a field of mathematics that deals with differentiation and integration under an arbitrary order of the operation; the order is not limited to the integer one but can also be any real or even complex number [2,3,4]. For a long time, it was ignored due to the integral complexity and freedom of integer order calculus, as well as the reality that FOC does not have entirely conventional physical or geometrical understandings [5]. Currently, there is a strong interest in generalising integer-order NPDEs to model complex engineering problems such as bio-engineering, electronics, viscoelasticity, control theory, thermal sciences, electrical networking, chemical, and fluid dynamics [6,7].

    Korteweg-de Vries (KdV) equation has been extensively investigated in physics to describe the interaction and evaluation of non-linear waves. It was originally derived by D. J. Korteweg and G. de Vries, in an investigation to re-generate the solitary waves that govern a small amplitude in one dimension with lengthy propagating gravity waves spreading in a shallow channel of water [8]. Apart from solitary waves, the KdV equation has also been studied for numerous physical phenomena, including hydro-magnetic waves, lattice dynamics, physics of plasma such as electron-acoustic (EA) and ion-acoustic (IA) waves [9]. It is also used in aerodynamics, fluid dynamics, transportation of mass, boundary layer behavior, and modeling the mechanical behavior of materials modeled as a continuous mass for shock wave formations. The concept of excitation of the electron-acoustic (EA) mode was initially proposed by Fried and Gould in the reference [10]. They have noted that the Landau's dampening effect on the EA potentials weakens with increasing wave number. In further experiments, weak dampening of the EA in plasma containing both high- and low-temperature electrons was found to be caused by [11]. Such plasma circumstances had previously been noted in a number of contexts, including [12]. Iwamoto [13] has studied the development of the high frequency Langmuir mode and the electron-acoustic wave evolution in nonrelativistic electron-positron plasma. It has been demonstrated that, in compared to the Langmuir wave, the low frequency EA excitation Landau damped with a substantially higher growth rate. The propagation of electron acoustic waves (EAWs) in plasmas and in space environments has drawn a lot of interest due to its value in comprehending a variety of collective processes in laboratory equipment [14,15]. Irfan et al. [16] studied fast (Langmuir) and slow (electron-acoustic) modes in dense electron-positron-ions (EPI) plasma. They observed that there is a chance that the fast electron-acoustic (FEA) mode could occur. The slow electron-acoustic (SEA) solitons are not allowed to propagate because they have a negative phase dispersion impact. The EPI plasma may allow for the evolution of electron-holes at a relatively low positron concentration, leading to the formation of compressive FEA solitons. Broadband electrostatic noise (BEN) emissions in the auroral and other regions of the magnetosphere, such as the plasma sheet boundary layer (PSBL) and the polar cusp, have been detected by satellite data. The Korteweg-de Vries (KdV) equation is obtained from studies of small-amplitude EAWs in unmagnetized plasma using first-order reductive perturbation theory [17]. More than 20 percent of the solitary waves' amplitude is understated by the first order soliton solution. Consequently, higher-order fixes may be used to help solve this issue. Shewy et al. [18,19] investigated the contributions of higher-order corrections to the properties of electrostatic EAWs as well as the impacts of non-thermal distribution of hot electrons. The higher-order correction contribution is given by the time-fractional modified Korteweg-de Vries (TF-mKdV) which modulates the amplitude of the solitary wave.

    The time-fractional modified KdV (TF-mKdV) equation is well-known for its contribution in the creation of the Lax pair and an unlimited quantity of conservation laws for the KdV equation [20]. The finite-gap integration approach and Whitham modulation theory are used to investigate the comprehensive classification of solutions to the defocusing complex modified KdV equation with step-like initial condition [21]. Wang et al. [22] employed the nonlinear steepest descent method of Deift and Zhou, the long-time asymptotics of the focusing Kundu-Eckhaus equation with nonzero boundary conditions at infinity. The TF-mKdV equation is obtained using perturbation expansions and the theory that the soliton width is modest in comparison to the plasma inhomogeneity scale length. In this case, the soliton retains its whole identity, as well as its amplitude, breadth, and speed. Further, Breathers and the unique soliton behavior of the TF-mKdV equation are well-known. Here, we study the localized solutions for time-fractional Korteweg-de Vries (TF-KdV) and time-fractional modified Korteweg-de Vries (TF-mKdV) with help of an analytical method called Aboodh transform decomposition method (ADM) given in [23].

    From the conventional Fourier integral, the Aboodh transform is derived. The Aboodh transform was first proposed by Khalid Aboodh [24] to show how to solve various ordinary differential equations in the time domain. This was done because of the Aboodh transform's mathematical features and simplicity. Differential equations are typically solved using Fourier, Laplace, Elzaki, and Sumudu transforms as the primary mathematical methods. Motivated from these study we are interesting to apply the Aboodh transform together with decomposition method to find an approximate solution of our proposed model. The proposed models have been extensively used in modeling complex physical systems such as thermal as well as current flows in electric circuits. It also plays an important role in modeling particles vibrating in their lattices. It should be noted that the physical phenomena in the KdV equation will be considered as non-conservative to describe fractional differential equations. Recently, the time-fractional KdVB equation together with nonlinearities has been extensively investigated using various methods [25]. We consider the integer order of the time derivative term of the mKdV model given in [26] be in the arbitrary order of the following form

    αutα+aumux+b3ux3=0,0<α1, (1.1)

    with

    u(x,0)=f(x), (1.2)

    the numbers a,bR with a is the non-linearity coefficient and b is the dispersive coefficient of Eq (1.1) and the time fractional-order (0<α1) derivative is supposed to be in Caputo's form. Equation (1.1) is known as time-fractional modified Korteweg-de Vries (TF-mKdV) equation and is used vastly in plasma physics to study solitary and shock waves [27]. When m=1, the TF-mKdV reduce to simple time fractional Korteweg-de Vries (TF-KdV) equation.

    Recently, various descriptions for fractional-order operators are also broadly studied, for example, Caputo-Fabrizio (C-F), Liouville-Caputo, and Riemann-Liouville (R-L) [28]. The R-L derivatives involve the complexity of the assumed function with the power-law kernel, while, the LC derivatives include the complexity of the confined derivative of a considered problem with power-law. Recently, the fractional KdV type equations together with several other nonlinear systems involving the time-fractional order derivatives along various methods for their analytical solutions have been investigated. To investigate analytical as well as numerical solutions to FNPDEs/TF-mKdV equations, various techniques have been studied [29,30,31]. Here, we apply the Aboodh decomposition method (ADM) [23] to find an approximate solution of Eq (1.1) in Caputo's sense. The rest of the article is prepared as follows. The preliminaries section 2 includes the main definitions, remarks, and some important results regarding the proposed method. In section 3, the Aboodh transform and Aboodh decomposition method are discussed. In section 4, uniqueness and convergence analysis of the method is discussed. In section 5, we consider two examples in Caputo's form of TF-KdV and TF-mKdV equations with the application of the proposed method. This section also includes numerical solutions and discussion. In section 5, the accuracy and effectiveness of the ADM are confirmed by using the error analysis. We include the summary in section 6, while in section 7 we have given the future work and extension of our present work.

    In this section, we provide some basic definitions, theorems, lemmas, and remarks, which are essential for our proposed method. We also define some basic rules and definitions related to the Aboodh transform and decomposition methods with some related properties. The Aboodh transform (see the reference [32]) is defined for the functions of exponential order in the following set

    A={u(t):M,k1,k2,|u(t)|Mevt, k1vk2}, (2.1)

    the constant M must be a finite value for a given function u(t) in the set A, while k1,k2R. The Aboodh transform A u(t) is given by

    A(u(t))(v)=K(v)=1v0u(t)evtdt,t0, (2.2)

    where v[k1,k2] is the variable utilised to factor the variable t in the function u(.).

    Definition 1. [33] The Aboodh transform for two variables function x0, t0 is defined by

    A(u(x,t))(u,v)=K(u,v)=1uv00eutevtu(x,t)dxdt, (2.3)

    while the inverse Aboodh transform is give by

    u(x,t)=A1{K(u,v)}=12πiβ+iβiueut[12πiγ+iγivevxK(u,v)dv]du,

    where, K(u,v), is a function such that u and v defined such that Re (u)β and Re (v)γ. It should be noted that β,γR to be considered accordingly.

    Definition 2. [34] The Caputo's derivative for α>0 for u(t) is defined by function u:(0,)IR is given by

    cDαu(t)=1Γ(nα)t0(ts)nα1un(s)ds, (2.4)

    where α(n1,n], with n=[α]+1,[α] represent the integer part of a real number α, providing that the right-hand side of the above integral is continuously well-defined on (0,).

    Definition 3. [35] Applying Aboodh transform on function u(x,t) in the Caputo's sense is given by

    A{cDαu(x,t)}=vαK(u,v)n1k=0u(k)(x,0)v2α+k. (2.5)

    Definition 4. [35] The Aboodh transform of some other functions and derivatives are given as

    A{tn}=n!vn+2,nN,A{tα}=Γ(α+1)vα+2,α1.

    Here, we introduce ADM applied to attain a series of solutions of NODEs and NPDEs. It is a very much effective technique to find an approximate solution of dynamical systems. Writing Eq (1.1) in Caputo's sense, we obtain

    cDαtu+aumux+b3ux3=0, (3.1)

    0<α1 and

    u(x,0)=f(x). (3.2)

    Using ADM and the definitions given in the previous section, we obtain

    A{cDαtu}+aA{umux}+bA{3ux3}=0. (3.3)

    Applying the technique given in section 2 for ADM on fractional order, we obtain

    A{u(x,t)}=1v2A{u(x,0)}a1vαA{umux}b1vαA{3ux3}, (3.4)

    where Eq (3.2) becomes

    A{u(x,0)}=K(u). (3.5)

    By considering the series solution

    u=n=0un. (3.6)

    The non-linearity can be obtained as

    umux(x,t)=i=0An.

    The value of An for the functions ui,i=1,2,3,, is given by [36]

    An=1n!dndλn[nk=0λkumk(x,t)nk=0λkukx(x,t)]λ=0. (3.7)

    Applying an inverse Aboodh transform to above equation, we obtain

    u0=A1{1v2K(u,0)}=u(x,0).u1=aA1{1vαA{A0}}bA1{1vαA{u0xxx}},u2=aA1{1vαA{A1}}bA1{1vαA{u1xxx}},u3=aA1{1vαA{A2}}bA1{1vαA{u2xxx}},un+1=aA1{1vαA{An}}bA1{1vαA{unxxx}}. (3.8)

    The general solution can be written as

    u(x,t)=u0(x,t)aA1{1vαA{An}}bA1{1vαA{unxxx}}. (3.9)

    The necessary condition that ensures the presence of a unique solution is studied in this section, and the convergence of the solutions is discussed, we follow these theorems applied to the series solutions in [37,38].

    Theorem 1. (Uniqueness theorem) For 0<γ<1, the solution (3.9) of Eq (3.1) is a unique solution. where

    γ=(1+2+3)tα+1Γ(α), (4.1)

    where 1 and 2, are Lipschitz constants, 0<α1 and Γ is the well known Gamma function can be defined as

    Γ(α)=0ettα1dt,R>0.

    Proof. Define a mapping D: B B with B = (C[J],.) is the Banach space on J=[0,T]. We can write Eq (3.9) as

    un+1=u(x,0)+A1{1vαA(Hun(x,t)+Mun(x,t)+Nun(x,t))},

    where Hu=ux, Mu=3ux3 and Nu=uux, and suppose that Hu, and Mu are also Lipschitzian with

    |HuHˉu|<1|uˉu|,|MuMˉu|<2|uˉu|,

    where u,ˉu are the function's distinct values. Now we proceed as follow

    DuDˉu=maxtJ|A1{1vαA(Hun+Mun+Nun)}A1{1vαA(H¯un+M¯un+N¯un)}|maxtJ|A1{1vαA(HunHˉu)}+A1{1vαA(MuMˉu)}+A1{1vαA(NuNˉu)}|maxtJ|1A1{1vαA(uˉu)}+2A1{1vαA(uˉu)}+3A1{1vαA(Nuˉu)}|maxtJ(1+2+3)|A1{1vαA(uˉu)}|=(1+2+3)t(α1)Γ(α)uˉu.

    The mapping is contraction under the condition 0<γ<1. As a result of the Banach fixed point theorem for contraction, Eq (3.1) has a unique solution.

    Next, we discuss convergence analysis of the problems.

    Theorem 2. (Convergence theorem) The solution of Eq (3.1) in general form will be convergence.

    Proof. The Banach space of all continuous functions on the interval J with the norm u(x,t) = maxtJu(x,t) is denoted as (C[J],.). Define the {Sn} sequence of partial sums that is Sn = nj=0. With nm, let Sn and Sm be arbitrary partial sums. In this Banach space, we will show that Sn is a Cauchy sequence. This can obtained by employing a new formulation of Adomian polynomials.

    P(Sn)=ˉAn+n1r1=0ˉAr1,Q(Sn)=ˉAn+n1r2=0ˉAr2.

    Now

    SnSm=|nj=0ujmk=0uk|=maxtJ|nj=m+1uj|maxtJ|A1{1vαA(nj=m+1H(uj1))}+A1{1vαA(nj=m+1M(uj1))}+A1{1vαA(nj=m+1(Aj1))}|=maxtJ|A1{1vαA(n1j=mH(uj))}+A1{1vαA(n1j=mM(uj))}+A1{1vαA(n1j=m(Aj))}|maxtJ|A1{1vαA(n1j=mH(Sn1)H(Sm1))}+A1{1vαA(n1j=mM(Sn1)M(Sm1))}+A1{1vαA(n1j=mN(Sn1)N(Sm1))}|
    1maxtJ|A1{1vαA((Sn1)(Sm1))}|+2maxtJ|A1{1vαA((Sn1)(Sm1))}|+3maxtJ|A1{1vαA((Sn1)(Sm1))}|=(1+2+3)tα1Γ(α)Sn1Sm1.

    Choosing n=m+1 then

    Sm+1SmγSmSm1γ2Sm1Sm2γmS1S0,

    with γ=(1+2+3)tα1Γ(α), by using the following triangular inequality

    SnSmSmSm+1Sm+1Sm+2γmS1S0(γm+γm+1++γn)S1S0γm(1+γ+γ2++γnm1)S1S0γm(1γnm1γ)u1.

    Now by definition 0<γ<1, we have 1γnm<1, thus we have

    SnSmγm1γmaxtJu1, (4.2)

    and also as |u|< (uis bounded), therefore, SnSm0, hence Sn is a Cauchy sequence in the Banach space B, hence nj=0uj is convergent.

    In this section, we consider two specific examples of time-fractional Korteweg-de Vries (TF-KdV) and time-fractional modified Korteweg-de Vries (TF-mKdV) equations in the form of Eq (1.1) with some initial conditions and apply ADM to find their approximate solutions.

    Next we consider two examples one for time-fractional order KdV and the other for time-fractional mKdV and use the propose method (ADM) to obtain an approximate solution.

    Example 1. For m=1 in Eq (3.1) we can take the following TF-KdV in Caputo's form [39]

    cDαtu+auux+b3ux3=0,  0<α1, (5.1)

    with initial condition of the form

    u0=u(x,0)=Asech2(kx). (5.2)

    The exact solution for α=1 of Eq (5.1) [39]

    u=Asech2(kx4k3bt), (5.3)

    where A=12k2ba is amplitude and 4bk3 is the speed of the wave function. Applying ADM to Eq (5.1) and decomposing the non-linear term uux by using the Adomian formula (3.7), we obtain the following Adomian polynomials

    A0=u0u0x,A1=u0u1x+u1u0x,A2=u0u2x+u1u1x+u2u0x,A3=u0u3x+u1u2x+u3u0x+u2u1x,

    putting the above values in Eq (3.8) with assumption h0(x)=u0(x,0) with h1(x)=h2(x)=h3(x)=0, we obtain the following single soliton solution for the TF-KdV Eq (5.1) in the form

    u0=h0(x),u1=tαΓ(α+1)f1(x),u2=t2αΓ(2α+1)f2(x),u3=t3αΓ(3α+1)f3(x),

    where

    f1(x)=ah0h0x+bh0xxx,f2(x)=a(h0f1x+2h0f1h0x)+bf1xxx,f3(x)=a[h0f2x+2h0f2h0x+Γ(2α+1)Γ(α+1)(2h0f1f1xh0x+f1h0x)]+bf2xxx.

    The final solution in the series form up to O(4) is given by

    U(x,t)=h0(x)+tαΓ(α+1)f1(x)t2αΓ(2α+1)f2(x)+t3αΓ(3α+1)f3(x). (5.4)

    For α=1, we obtain

    u(x,t)=12k2basech2(kx4k3bt).

    The ADM solution for different α are reported in Table 1, by considering k=0.1,a=2,b=0.2,t=1.

    Table 1.  Evaluation between approximate (U(x,t)) versus an exact solution (u(x, t) for different values a = 0.2, b = 0.4, k = 0.1, 10x10 and 0t1 Exp.1.
    t U(α=0.3) U(α=0.5) U(α=0.7) U(α=0.9) U(α=1) Exact MAE
    x=-10
    0.00 0.0050869 0.0050869 0.0050869 0.0050869 0.0050869 0.0050869
    0.25 0.0050056 0.0050276 0.0050447 0.0050575 0.0050625 0.0050634
    0.50 0.0049834 0.0049994 0.0050155 0.0050302 0.0050367 0.0050400 4.0619E-05
    0.75 0.0049673 0.0049763 0.0049888 0.0050025 0.0050093 0.0050167
    1.00 0.0049542 0.0049559 0.0049633 0.0049742 0.0049803 0.0049935
    x=-5
    0.00 0.030238 0.030238 0.030238 0.030238 0.030238 0.030238
    0.25 0.029905 0.029984 0.030052 0.030105 0.030127 0.030128
    0.50 0.029825 0.029877 0.029933 0.029989 0.030015 0.030018 1.2525E-04
    0.75 0.02977 0.029793 0.029831 0.0050025 0.029902 0.029908
    1.00 0.029726 0.029722 0.029737 0.0049742 0.029787 0.029798
    x=0
    0.00 0.0720000 0.0720000 0.0720000 0.0720000 0.0720000 0.0720000
    0.25 0.071994 0.071997 0.071998 0.071999 0.0720000 0.0720000
    0.50 0.07199 0.071993 0.071996 0.071998 0.0719987 0.0719987 6.2213E-06
    0.75 0.071988 0.07199 0.071993 0.071995 0.071996 0.071996
    1.00 0.071985 0.071987 0.071989 0.071992 0.071993 0.071993
    x=5
    0.00 0.030238 0.030238 0.030238 0.030238 0.030238 0.030238
    0.25 0.030555 0.030483 0.03042 0.030369 0.030348 0.030349
    0.50 0.030625 0.030582 00.030532 0.030481 0.030457 0.03046 1.0040E-04
    0.75 0.030673 0.030657 0.030626 0.030587 0.030565 0.030571
    1.00 0.030711 0.030719 0.03071 0.030687 0.030671 0.030682
    x=10
    0.00 0.0050869 0.0050869 0.0050869 0.0050869 0.0050869 0.0050869
    0.25 0.005144 0.0051338 0.0050447 0.0051233 0.0051096 0.0051105
    0.50 0.0051538 0.0051496 0.0050155 0.0051431 0.0051308 0.0051341 1.4575E-05
    0.75 0.0051598 0.0051603 0.0049888 0.0051583 0.0051505 0.0051579
    1.00 0.0051641 0.0051684 0.0049633 0.0051706 0.0051687 0.0051819

     | Show Table
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    Figure 1(a, b) displays the zeroth and higher order solitary wave solutions (5.3) and (5.4) versus the space variable x respectively for time-fractional KdV Eq (5.1) for small time of t. It infers that at t>0 the higher order solitary wave solution is significantly modified. The wave three-dimensional profiles for solutions (5.3) and (5.4) in Figure 1(c, d) also reveal that variations in temporal variables affect the amplitude and spatial extensions of the higher order wave solutions. We have plotted the approximate solution (5.3) against t at x=0.5 and 1 respectively, with variation in the time-fractional index α [see Figure 2(a, b)]. A degree of enhancement in α reduces the spatial width of the solitary waves. Thus, the time-fractional index leads to a reduction in the wave dispersion that in turn localises the wave profile. The effect of the non-linearity coefficient "a" is shown in Figure 3(a). We see that the amplitude of the solitary wave decreases regularly when the values of the non-linearity coefficient are increasing. This definitely occurs for wave equations because the non-linearity coefficient has an inverse proportionality with the solitary wave amplitude. On the other hand, the wave amplitude is gradually increasing with the increasing values of the dispersion coefficient "b" in Figure 3(b). The dispersive coefficient is proportional to the amplitude of a solitary wave. We have depicted the higher order solution (5.4) for the TF-KdV Eq (5.1), verses x at time t=3 (solid curve), at 3.5 (dashed curve), and at 4 (dotted curve) [see Figure 4(a)]. Obviously, the enhancement in t gives rise to the pulse amplitude while increasing the spatial width. Moreover, the pulse shape solutions suffer from oscillations for t>0. The wave solution (5.4) presented in Figure 4(b) at different values of the temporal index (α) reveals strengthening of the wave with wider spatial extension at large α. One can see that the solitary wave solutions for the TF-KdV equation are observed. By employing an Aboodh decomposition (ADM) technique and with the addition of t the Caputo operator for the solitary excitations are derived in Eq (5.4). Importantly, the wave solution significantly changes in time. The large amplitude electrostatic excitations in the auroral zone [27], associated with the intensified electric field. These may be described by the solutions (5.3) and (5.4) for the TF-KdV equation of Exp.1. Our results obtained in this paper are so important because the waves produced by the solutions of the TF-KdV may have very high amplitudes. The amplitudes of these waves can be reduced by using the fractional order of the wave equations. In other words, by taking the fractional order of the time derivative term, a better analysis of the waves can be achieved.

    Figure 1.  In the top panel, (a) shows exact solution of Eq (5.3), while (b) shows comparison of approximate (ADM) and exact solution Eq (5.4) k=0.1,a=0.2,b=0.4, for small time t=0.2. The bottom panel (c, d) are corresponding surface 3D plots for both solutions against the temporal variables (x,t) with parameters (α=1 for exact, and 0.7, for the approximate solution, with fixed k=0.1,a=2,b=0.2,t=1).
    Figure 2.  The above plots show the dispersive behaviours of the solitary waves by varying time and the fractional order α with fixed special variable (a) x=0.5 and (b) x=1 of the exact (α=1 star black) solution Eq (5.3) and approximate (α<1 blue) solution Eq (5.4) respectively.
    Figure 3.  Solitary waves profiles for different values of (a) the non-linearity coefficient "a" with b = 0.4 and (b) the dispersive coefficient "b" with a = 0.2, when the wave number k = 0.1, the fractional index α=0.5 and t = 0.5 of Eq (5.4).
    Figure 4.  Plots of the approximate solution for (a) different values of t with α=0.9 (b) different values of α for larger time and the other parameters are a=0.9,k=0.5,b=0.9 of Eq (5.4).

    Example 2. For m=2,a=6,b=1, the modified time-fractional KdV [26] in Caputo's sense is given by

    CDαtv+6v2vx+3vx3=0,0<α1, (5.5)

    with

    v0=v(x,0)=csech2(k+cx). (5.6)

    When α=1, Eq (5.5) gives [26]

    v(x,t)=csech(k+cxct). (5.7)

    Applying the ADM to Eq (5.5) and decompose the non-linear term v2vx in Eq (5.5) by using the Adomian formula (3.7), we obtain

    A0=v20v0x,A1=v20v1x+v21v0x,A2=v20v2x+v21v1x+v22v0x,A3=v20v3x+v21v2x+v23v0x+v22v1x.

    Putting a=6 and b=1 in Eq (3.8) and assume that g0(x)=v(x,0) with g1(x)=g2(x)=g3(x), we obtain

    v0=g0(x),v1=tαΓ(α+1)q1(x),v2=t2αΓ(2α+1)q2(x),v3=t3αΓ(3α+1)q3(x),

    where

    q1(x)=6g20g0x+g0xxx,q2(x)=6(g20q1x+2g0q1g0x)+q1xxx,q3(x)=6[g20q2x+2g0q2g0x+Γ(2α+1)Γ(α+1)(2g0q1q1xg0x+q21g0x)]+q2xxx.

    The solution up to O(4) can be written as

    V(x,t)=g0(x)+tαΓ(α+1)q1(x)t2αΓ(2α+1)q2(x)+t3αΓ(3α+1)q3(x). (5.8)

    For α=1 we found the following exact solution

    v(x,t)=csech(k+cxct).

    We have displayed the zeroth (higher) order solitary wave solutions (5.7) and (5.8) in Figure 5(a, b) respectively, for the modified time-fractional KdV Eq (5.5) verses x for small t. Notice that the wave solution of the TF-mKdV equation suffers only a relatively modest steeping effect with a reduction in the wave dispersion. Moreover, at t>0 the higher-order solution illustrates noticeable modifications in the wave profiles. The three dimensional profiles Figure 5(c, d) for the time fractional (exact and approximate) solutions also confirm that both the solutions are quite similar. Figure 6(a, b) display the solution (5.8) at x=0.5 and 1 respectively with variation in α. Contrary to the TF-KdV solution, the wave solution for TF-mKdV admits reduced wave dispersion at t>0. We observed that, the dispersive approximate solution (α<1 the blue curves) approaching to the exact solution (α=1, black curve) when then α values are approaching to 1. In Figure 7(a), we depicted the approximate (ADM) solution against the special variable x by changing the wave speed "c = 0.1 (solid curve), 0.2 (dashed curve), and 0.3 (dotted curve)". We observed that when the speed "c" is increasing, the wave amplitude is increasing while its width is decreasing, which shows that the amplitude is directly proportional to the wave speed while the width of the wave is inversely related to the wave speed. Similarly, in Figure 7(b), we displayed the approximate solution against "x" by changing the wave number "k = 0.5 (solid curve), 1 (dashed curve), and 1.5 (dotted curve)". We see that, for different values of the wave number and equal amplitudes, wave profiles are obtained. The wave solution (5.5) for TF-mKdV in Figure 8(a) shows at different times that the solitary waves suffer from oscillation due to the external perturbations. It also shows that the solitary solution for the TF-mKdV is relatively less stable against the perturbations. Moreover, the increase in t and α modify the amplitude and width of the wave. Variation of the α value for the approximate solution is shown in Figure 8(b) against x. We observed from the Figure 8(a, b) when the temporal variable (t) and the fractional order α increasing the amplitudes of the wave profile getting larger values.

    Figure 5.  In the top panel, (a) shows exact solution for Eq (5.7) while (b) shows a comparison of exact and approximate solution (ADM) of Eq (5.8). The bottom panel (c, d) are the corresponding surface 3D plots against the temporal variables (x,t) with parameters (α=1 for exact, and 0.7, for the approximate solution, with fixed k=0,c=1,t=0.2).
    Figure 6.  Dispersive behaviours of the solitary waves by varying time and the fractional order α with fixed special variable (a) x=0.5 and (b) x=1 for exact (α=1 stars blue curve of Eq (5.7)) and approximate (α<1 blue curves Eq (5.8)) respectively.
    Figure 7.  Solitary waves profiles for different values of (a) the speed "c" with k = 0.5 and (b) the wave number "k", with c = 0.2 when the fractional index α=0.7 and t = 0.5 of Eq (5.8).
    Figure 8.  Plots of the approximate solution for (a) different values of t with α=0.9 (b) different values of α for larger time values and other parameters are c=0.6,k=0 of Eq (5.8).

    The mean absolute error (MAE) is a statistic that calculates the variance in errors amongst matching interpretations representing identical singularities. The relationships between an exact and an approximate solution, future time against beginning time, and one measuring method versus another quantity technique are all examples of y versus x. The MAE can be computed as

    MAE=1nni=1|yixi|, (5.9)

    where, xi is the expected value and yi is the real value of the system. The following figures are obtained from the error analysis in the Tables 14.

    Table 2.  Comparison between the approximate solution (V(x,t)), versus an exact solution (v(x,t)) for 10x10 and 0t1 with k = 0.5 and c = 0.82 of Exp.2.
    t V(α=0.3) V(α=0.5) V(α=0.7) V(α=0.9) V(α=1) Exact MAE
    x=-10
    0.00 0.010216 0.010216 0.010216 0.010216 0.010216 0.010216
    0.25 0.0095819 0.00972 0.0098439 0.0099468 0.0099898 0.0097175
    0.50 0.0094455 0.0095258 0.0096209 0.0097203 0.0097689 0.0092437 3.6330E-04
    0.75 0.0093537 0.0093809 0.0094359 0.0095107 0.0095529 0.008793
    1.00 0.0092825 0.0092616 0.0092729 0.0093128 0.0093417 0.0083642
    x=-5
    0.00 0.094515 0.094515 0.094515 0.094515 0.094515 0.094515
    0.25 0.088823 0.090001 0.091122 0.092066 0.09246 0.090002
    0.50 0.087702 0.088282 0.089091 0.08999 0.090436 0.085696 3.5E-03
    0.75 0.086993 0.087053 0.087436 0.08807 0.088455 0.081589
    1.00 0.086476 0.08609 0.086022 0.086289 0.08653 0.077671
    x=0
    0.00 0.447210 0.447210 0.447210 0.447210 0.447210 0.447210
    0.25 0.445160 0.446220 0.446770 0.447030 0.44710 0.446660
    0.50 0.444000 0.445150 0.446010 0.446560 0.446740 0.444990 1.46E-03
    0.75 0.443020 0.444030 0.445030 0.445830 0.446130 0.442230
    1.00 0.442140 0.442860 0.443860 0.444830 0.445250 0.438420
    x=5
    0.00 0.094515 0.094515 0.094515 0.094515 0.094515 0.094515
    0.25 0.099243 0.099243 0.099243 0.099243 0.099243 0.030349
    0.50 0.104190 0.104190 0.10419 0.030481 0.10419 0.104190 4.446E-03
    0.75 0.109380 0.109380 0.109380 0.0305870 0.10938 0.109380
    1.00 0.114800 0.114800 0.114800 0.0306870 0.1148 0.030682
    x=10
    0.00 0.010216 0.010216 0.010216 0.010216 0.010216 0.010216
    0.25 0.010928 0.010752 0.010606 0.010492 0.010447 0.010739
    0.50 0.011106 0.01117 0.011083 0.010978 0.010683 0.01129 4.3146E-04
    0.75 0.01133 0.011332 0.011289 0.011215 0.010924 0.011868
    1.00 0.114800 0.11480 0.114800 0.030687 0.011171 0.012477

     | Show Table
    DownLoad: CSV
    Table 3.  Comparison of exact solution (u(x, t)) with approximate (U(x, t)) solution for α=0.5, a = 0.2, b = 0.4, and k = 1 of Exp.1.
    x t ξ=kx4bk3t Exact solution ADM Solution Error
    -50 0.00 -49.9434 -0.35995 -0.35995 2.5009×1008
    -40 0.25 -39.6454 -0.35965 -0.35965 1.9820×1007
    -30 0.50 -29.6453 -0.35745 -0.35746 2.1868×1006
    -20 0.75 -19.6423 -0.34165 -0.34170 5.1926×1005
    20 0.25 20.3634 0.11987 0.11988 -1.6363×1005
    30 0.50 30.3653 0.12000 0.12000 -3.3767×1007
    40 0.75 40.3634 0.12000 0.12000 -6.2851×1009
    50 1.00 50.3643 0.12000 0.12000 -1.1537×1010

     | Show Table
    DownLoad: CSV
    Table 4.  Comparison of exact (v(x, t)) solution with approximate (V(x, t) solution for k = 0.5, and c = 0.82 of Exp.2.
    x t ξ=k+cxct Exact solution ADM Solution Error
    -50 0.00 -45.5969 -0.35995 -0.35995 -2.8783×1007
    -40 0.25 -36.5415 -0.35966 -0.35966 -2.1145×1006
    -30 0.50 -27.4862 -0.35751 -0.35750 -1.4960×1005
    -20 0.75 -18.4308 -0.34207 -0.34199 -7.7286×1005
    20 0.25 17.7908 0.11986 0.11987 -1.3770×1005
    30 0.50 26.8462 0.12000 0.12000 -2.8188×1007
    40 0.75 35.9015 0.12000 0.12000 -5.2413×1009
    50 1.00 44.9569 0.12000 0.12000 -9.6194×1011

     | Show Table
    DownLoad: CSV

    The accuracy and effectiveness of the ADM are confirmed by using the error analysis given in Tables 1 and 2 followed by its MAE Figure 9(a, b) respectively. The other two Tables 3 and 4 confirm the errors that occur between the exact and approximate solution of the model. Their plots Figure 10(a, b) are shown, which are the graphical conformations for the error analysis between the two solutions (exact and approximate).

    Figure 9.  (a, b) 3D plots represent the mean absolute error (MAE) for different values of x and t for Tables 1 and 2 respectively.
    Figure 10.  (a, b) 3D plots represent the mean absolute error (MAE) for different values of x and t for Tables 3 and 4 respectively.

    We consider time-fractional Korteweg-de Vries (TF-KdV) and time-fractional modified Korteweg-de Vries (TF-mKdV) equations for solitary wave solutions with the help of an analytical method called the "Aboodh decomposition method (ADM)." The proposed method is a combination of the Aboodh transform and the decomposition method, which is a very authentic and valuable method for solving fractional-order non-linear problems. The approximate solution obtained with the help of ADM is compared with the exact solution of the models to verify the efficiency of the method. We observed from the graphical analysis in the manuscript the time-fractional order α can modify the wave profiles in such a way that when α values is increasing the amplitudes of the wave is reducing which is very much important to study the small amplitude's characteristics of such wave equations. The effect of the nonlinearity coefficient "a" and the dispersive coefficient "b" in Exp.1 are also discussed, form the graphical analysis we have shown that both of these coefficients have significant affect on the wave amplitudes as well as its widths. We have studied the higher-order series solutions (up to fourth order) for time-fractional KdV and modified KdV equations with the help of the ADM. The convergence analysis determined that the obtained results are similar to the exact solutions of the models. The uniqueness results confirmed that ADM is an effective and systematic scheme for solving non-linear dynamical problems. The analytical and numerical solutions are plotted by marginal changes in parameters x, t and α. It is observed that a significant variation in the index values causes a large variation in the amplitude of the wave profile. For small-time t, the series solutions coincide with the exact solutions of the models, and the solutions are stable. However, for large-time t the results show that the amplitudes of the wave profiles no longer remain constant at the given parameters. The results are tabulated for altered x, t and α. It is observed that mean absolute error (MAE) is decreasing for small-time and large values of α (fractional order). This means that for taking α values nearest to 1 (exact), the MAE values decrease, which means that the obtained results coincide with the solution of the considered models.

    We studied the analytical solution of the TF-KdV and TF-mKdV equations with the help of ADM. The effectiveness of the proposed method is studied with the help of graphical analysis and some numerical error tables. The method is also essential for K(m,n) equation obtained in [40]. Here, the author shows that K(m,n) equations are only Lax integrable for specific values of the parameters (α,m,n). For the integrable instances, nontrivial prolongation structures and Lax pairings are provided.

    Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R8). Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

    The authors declare that they have no conflicts of interest.



    Authors' contributions



    Conceptualization: Petros Galanis, Aglaia Katsiroumpa, Zoe Katsiroumpa; methodology: Petros Galanis, Aglaia Katsiroumpa, Zoe Katsiroumpa, Polyxeni Mangoulia, Evmorfia Koukia; software: Petros Galanis, Aglaia Katsiroumpa, Zoe Katsiroumpa, Parisis Gallos; validation: Zoe Katsiroumpa, Polyxeni Mangoulia, Parisis Gallos, Ioannis Moisoglou; formal analysis: Petros Galanis, Aglaia Katsiroumpa, Zoe Katsiroumpa, Parisis Gallos; resources: Petros Galanis, Zoe Katsiroumpa, Polyxeni Mangoulia, Parisis Gallos, Ioannis Moisoglou; data curation: Petros Galanis, Zoe Katsiroumpa, Polyxeni Mangoulia, Parisis Gallos, Ioannis Moisoglou; writing–original draft preparation: Petros Galanis, Aglaia Katsiroumpa, Zoe Katsiroumpa, Polyxeni Mangoulia, Parisis Gallos, Ioannis Moisoglou, Evmorfia Koukia; writing–review and editing: Petros Galanis, Aglaia Katsiroumpa, Zoe Katsiroumpa, Polyxeni Mangoulia, Parisis Gallos, Ioannis Moisoglou, Evmorfia Koukia; supervision: Petros Galanis.

    Conflict of interest



    Petros Galanis is an editorial board member for AIMS Public Health, and he is also a guest editor of AIMS Public Health Special Issue. Polyxeni Mangoulia and Ioannis Moisoglou are guest editors of AIMS Public Health Special Issues. They were not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.

    [1] StatistaSocial Media & User-generated content (2024). [cited 2025 February 01]. Available from: https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/
    [2] BacklinkoTikTok statistics you need to know (2024). [cited 2025 February 01]. Available from: https://backlinko.com/tiktok-users
    [3] Merchant RM, Lurie N (2020) Social media and emergency preparedness in response to novel coronavirus. JAMA 323: 2011-2012. https://doi.org/10.1001/jama.2020.4469
    [4] Helm PJ, Jimenez T, Galgali MS, et al. (2022) Divergent effects of social media use on meaning in life via loneliness and existential isolation during the coronavirus pandemic. J Soc Pers Relat 39: 1768-1793. https://doi.org/10.1177/02654075211066922
    [5] Khan MN, Ashraf MA, Seinen D, et al. (2021) Social media for knowledge acquisition and dissemination: The impact of the COVID-19 pandemic on collaborative learning driven social media adoption. Front Psychol 12: 648253. https://doi.org/10.3389/fpsyg.2021.648253
    [6] Rosen AO, Holmes AL, Balluerka N, et al. (2022) Is social media a new type of social support? Social media use in spain during the COVID-19 pandemic: A mixed methods study. Int J Environ Res Public Health 19: 3952. https://doi.org/10.3390/ijerph19073952
    [7] Yang JZ, Liu Z, Wong JC (2022) Information seeking and information sharing during the COVID-19 pandemic. Commun Q 70: 1-21. https://doi.org/10.1080/01463373.2021.1995772
    [8] Szeto S, Au AKY, Cheng SKL (2024) Support from social media during the COVID-19 Pandemic: A systematic review. Behav Sci (Basel) 14: 759. https://doi.org/10.3390/bs14090759
    [9] Hmidan A, Seguin D, Duerden EG (2023) Media screen time use and mental health in school aged children during the pandemic. BMC Psychol 11: 202. https://doi.org/10.1186/s40359-023-01240-0
    [10] Seguin D, Kuenzel E, Morton JB, et al. (2021) School's out: Parenting stress and screen time use in school-age children during the COVID-19 pandemic. J Affect Disord Rep 6: 100217. https://doi.org/10.1016/j.jadr.2021.100217
    [11] De Rosis S, Lopreite M, Puliga M, et al. (2021) The early weeks of the Italian Covid-19 outbreak: Sentiment insights from a Twitter analysis. Health Policy 125: 987-994. https://doi.org/10.1016/j.healthpol.2021.06.006
    [12] Lopreite M, Panzarasa P, Puliga M, et al. (2021) Early warnings of COVID-19 outbreaks across Europe from social media. Sci Rep 11: 2147. https://doi.org/10.1038/s41598-021-81333-1
    [13] Tsao SF, Chen H, Tisseverasinghe T, et al. (2021) What social media told us in the time of COVID-19: A scoping review. Lancet Digit Health 3: e175-e194. https://doi.org/10.1016/S2589-7500(20)30315-0
    [14] Haenlein M, Anadol E, Farnsworth T, et al. (2020) Navigating the new era of influencer marketing: How to be successful on Instagram, TikTok, & Co. Calif Manage Rev 63: 5-25. https://doi.org/10.1177/0008125620958166
    [15] Pedrouzo S, Krynski L (2023) Hyperconnected: Children and adolescents on social media. The TikTok phenomenon. Arch Argent Pediatr 121: e202202674. https://doi.org/10.5546/aap.2022-02674.eng
    [16] Grandinetti J (2023) Examining embedded apparatuses of AI in Facebook and TikTok. AI & Soc 38: 1273-1286. https://doi.org/10.1007/s00146-021-01270-5
    [17] Kang H, Lou C (2022) AI agency vs. human agency: understanding human–AI interactions on TikTok and their implications for user engagement. J Comput-Mediat Comm 27: zmac014. https://doi.org/10.1093/jcmc/zmac014
    [18] Montag C, Yang H, Elhai JD (2021) On the psychology of TikTok use: A first glimpse from empirical findings. Front Public Health 9: 641673. https://doi.org/10.3389/fpubh.2021.641673
    [19] TikTokMental and behavioral health (2024). [cited 2025 February 01]. Available from: https://www.tiktok.com/community-guidelines/en/mental-behavioral-health?lang=en
    [20] Lookingbill V (2022) Examining nonsuicidal self-injury content creation on TikTok through qualitative content analysis. Libr Inform Sci Res 44: 101199. https://doi.org/10.1016/j.lisr.2022.101199
    [21] Center for Countering Digital HateTikTok pushes harmful content promoting eating disorders and self-harm into users' feeds (2022). [cited 2025 February 01]. Available from: https://counterhate.com/research/deadly-by-design/
    [22] Karizat N, Delmonaco D, Eslami M, et al. (2021) Algorithmic folk theories and identity: How TikTok users co-produce knowledge of identity and engage in algorithmic resistance. Proc ACM Hum-Comput Interact 5: 1-44. https://doi.org/10.1145/3476046
    [23] Abbouyi S, Bouazza S, El Kinany S, et al. (2024) Depression and anxiety and its association with problematic social media use in the MENA region: A systematic review. Egypt J Neurol Psychiatry Neurosurg 60: 15. https://doi.org/10.1186/s41983-024-00793-0
    [24] Ahmed O, Walsh E, Dawel A, et al. (2024) Social media use, mental health and sleep: A systematic review with meta-analyses. J Affect Disord 367: 701-712. https://doi.org/10.1016/j.jad.2024.08.193
    [25] Alonzo R, Hussain J, Stranges S, et al. (2021) Interplay between social media use, sleep quality, and mental health in youth: A systematic review. Sleep Med Rev 56: 101414. https://doi.org/10.1016/j.smrv.2020.101414
    [26] Baker DA, Algorta GP (2016) The relationship between online social networking and depression: A systematic review of quantitative studies. Cyberpsychol Behav Soc Netw 19: 638-648. https://doi.org/10.1089/cyber.2016.0206
    [27] Casale S, Banchi V (2020) Narcissism and problematic social media use: A systematic literature review. Addict Behav Rep 11: 100252. https://doi.org/10.1016/j.abrep.2020.100252
    [28] Marino C, Gini G, Vieno A, et al. (2018) The associations between problematic Facebook use, psychological distress and well-being among adolescents and young adults: A systematic review and meta-analysis. J Affect Disord 226: 274-281. https://doi.org/10.1016/j.jad.2017.10.007
    [29] Shannon H, Bush K, Villeneuve PJ, et al. (2022) Problematic social media use in adolescents and young adults: Systematic review and meta-analysis. JMIR Ment Health 9: e33450. https://doi.org/10.2196/33450
    [30] Wu W, Huang L, Yang F (2024) Social anxiety and problematic social media use: A systematic review and meta-analysis. Addict Behav 153: 107995. https://doi.org/10.1016/j.addbeh.2024.107995
    [31] Amin S, Iftikhar A, Meer A (2022) Intervening effects of academic performance between TikTok obsession and psychological wellbeing challenges in university students. Online Media Soc 3: 244-255. https://doi.org/10.71016/oms/qy5har60
    [32] Hendrikse C, Limniou M (2024) The use of Instagram and TikTok in relation to problematic use and well-Being. J Technol Behav Sci 9: 846-857. https://doi.org/10.1007/s41347-024-00399-6
    [33] Rogowska AM, Cincio A (2024) Procrastination mediates the relationship between problematic TikTok use and depression among young adults. J Clin Med 13: 1247. https://doi.org/10.3390/jcm13051247
    [34] Williams M, Lewin KM, Meshi D (2024) Problematic use of five different social networking sites is associated with depressive symptoms and loneliness. Curr Psychol 43: 20891-20898. https://doi.org/10.1007/s12144-024-05925-6
    [35] Conte G, Iorio GD, Esposito D, et al. (2024) Scrolling through adolescence: A systematic review of the impact of TikTok on adolescent mental health. Eur Child Adolesc Psychiatry 16. https://doi.org/10.1007/s00787-024-02581-w
    [36] Walker E, Hernandez AV, Kattan MW (2008) Meta-analysis: Its strengths and limitations. Cleve Clin J Med 75: 431-439. https://doi.org/10.3949/ccjm.75.6.431
    [37] Egger M, Smith GD, Phillips AN (1997) Meta-analysis: Principles and procedures. BMJ 315: 1533-1537. https://doi.org/10.1136/bmj.315.7121.1533
    [38] Finckh A, Tramèr MR (2008) Primer: strengths and weaknesses of meta-analysis. Nat Rev Rheumatol 4: 146-152. https://doi.org/10.1038/ncprheum0732
    [39] Ioannidis JPA, Lau J (1999) Pooling research results: Benefits and limitations of meta-Analysis. Jt Comm J Qual Improv 25: 462-469. https://doi.org/10.1016/S1070-3241(16)30460-6
    [40] Csikszentmihalyi M (2002) Flow: the classic work on how to achieve happiness. London, Rider: Random House.
    [41] Montag C, Lachmann B, Herrlich M, et al. (2019) Addictive features of social media/messenger platforms and freemium games against the background of psychological and economic theories. Int J Environ Res Public Health 16: 2612. https://doi.org/10.3390/ijerph16142612
    [42] Andreassen CS, Billieux J, Griffiths MD, et al. (2016) The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychol Addict Behav 30: 252-262. https://doi.org/10.1037/adb0000160
    [43] Andreassen CS, Torsheim T, Brunborg GS, et al. (2012) Development of a Facebook addiction scale. Psychol Rep 110: 501-517. https://doi.org/10.2466/02.09.18.PR0.110.2.501-517
    [44] Griffiths M (2005) A ‘components' model of addiction within a biopsychosocial framework. J Subst Abuse 10: 191-197. https://doi.org/10.1080/14659890500114359
    [45] Sun Y, Zhang Y (2021) A review of theories and models applied in studies of social media addiction and implications for future research. Addict Behav 114: 106699. https://doi.org/10.1016/j.addbeh.2020.106699
    [46] Varona MN, Muela A, Machimbarrena JM (2022) Problematic use or addiction? A scoping review on conceptual and operational definitions of negative social networking sites use in adolescents. Addict Behav 134: 107400. https://doi.org/10.1016/j.addbeh.2022.107400
    [47] Alonso J, Liu Z, Evans-Lacko S, et al. (2018) Treatment gap for anxiety disorders is global: Results of the world mental health surveys in 21 countries. Depress Anxiety 35: 195-208. https://doi.org/10.1002/da.22711
    [48] Kazdin AE (2000) Encyclopedia of psychology. Washington, DC: American Psychological Association 145-152.
    [49] Carstens E, Moberg GP (2000) Recognizing pain and distress in laboratory animals. ILAR J 41: 62-71. https://doi.org/10.1093/ilar.41.2.62
    [50] (2013) American Psychiatric AssociationDiagnostic and statistical manual of mental disorders: DSM-5. Washington: American Psychiatric Association 329-354. https://doi.org/10.1176/appi.books.9780890425596
    [51] Della Sala S (2021) Encyclopedia of behavioral neuroscience. San Diego: Elsevier Science & Technology 552-557.
    [52] Pavlova M, Latreille V (2019) Sleep disorders. Am J Med 132: 292-299. https://doi.org/10.1016/j.amjmed.2018.09.021
    [53] Peplau LA, Perlman D (1982) Loneliness: A sourcebook of current theory, research and therapy. New York: Wiley 1-18.
    [54] Ellis L, Hoskin A, Ratnasingam M (2018) Handbook of social status correlates. US: Elsevier 1-14.
    [55] Miller DN (2011) Life satisfaction. Encyclopedia of Child Behavior and Development . Boston, MA: Springer US 887-889. https://doi.org/10.1007/978-0-387-79061-9_1659
    [56] Huppert FA (2009) Psychological well-being: Evidence regarding its causes and consequences. Appl Psych Health Well 1: 137-164. https://doi.org/10.1111/j.1758-0854.2009.01008.x
    [57] Moher D, Shamseer L, Clarke M, et al. (2015) Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev 4: 1. https://doi.org/10.1186/2046-4053-4-1
    [58] Park CU, Kim HJ (2015) Measurement of inter-rater reliability in systematic review. Hanyang Med Rev 35: 44. https://doi.org/10.7599/hmr.2015.35.1.44
    [59] McHugh ML (2012) Interrater reliability: The kappa statistic. Biochem Med (Zagreb) 22: 276-282. https://doi.org/10.11613/BM.2012.031
    [60] Santos WM dos, Secoli SR, Püschel VA de A (2018) The Joanna Briggs institute approach for systematic reviews. Rev Lat Am Enfermagem 26: e3074. https://doi.org/10.1590/1518-8345.2885.3074
    [61] Nieminen P (2022) Application of standardized regression coefficient in meta-analysis. BioMedInformatics 2: 434-458. https://doi.org/10.3390/biomedinformatics2030028
    [62] Vittinghoff E (2005) Regression methods in biostatistics: Linear, logistic, survival, and repeated measures models. New York: Springer 65-82.
    [63] Higgins J (2003) Measuring inconsistency in meta-analyses. BMJ 327: 557-560. https://doi.org/10.1136/bmj.327.7414.557
    [64] Wallace BC, Schmid CH, Lau J, et al. (2009) Meta-analyst: Software for meta-analysis of binary, continuous and diagnostic data. BMC Med Res Methodol 9: 80. https://doi.org/10.1186/1471-2288-9-80
    [65] Al-Garni AM, Alamri HS, Asiri WMA, et al. (2024) Social media use and sleep quality among secondary school students in Aseer region: A cross-sectional study. Int J Gen Med 17: 3093-3106. https://doi.org/10.2147/IJGM.S464457
    [66] Asad K, Ali F, Awais M (2022) Personality traits, narcissism and TikTok addiction: A parallel mediation approach. Int J Media Inf Lit 7: 293-304. https://doi.org/10.13187/ijmil.2022.2.293
    [67] Sarman A, Tuncay S (2023) The relationship of Facebook, Instagram, Twitter, TikTok and WhatsApp/Telegram with loneliness and anger of adolescents living in Turkey: A structural equality model. J Pediatr Nurs 72: 16-25. https://doi.org/10.1016/j.pedn.2023.03.017
    [68] Sha P, Dong X (2021) Research on adolescents regarding the indirect effect of depression, anxiety, and stress between TikTok use disorder and memory loss. Int J Environ Res Public Health 18: 8820. https://doi.org/10.3390/ijerph18168820
    [69] Yang Y, Adnan H, Sarmiti N (2023) The relationship between anxiety and TikTok addiction among university students in China: Mediated by escapism and use intensity. Int J Media Inf Lit 8: 458-464. https://doi.org/10.13187/ijmil.2023.2.458
    [70] Yao N, Chen J, Huang S, et al. (2023) Depression and social anxiety in relation to problematic TikTok use severity: The mediating role of boredom proneness and distress intolerance. Comput Hum Behav 145: 107751. https://doi.org/10.1016/j.chb.2023.107751
    [71] López-Gil JF, Chen S, Jiménez-López E, et al. (2023) Are the use and addiction to social networks associated with disordered eating among adolescents? Findings from the EHDLA study. Int J Ment Health Addict 22: 3775-3789. https://doi.org/10.1007/s11469-023-01081-3
    [72] Masciantonio A, Bourguignon D, Bouchat P, et al. (2021) Don't put all social network sites in one basket: Facebook, Instagram, Twitter, TikTok, and their relations with well-being during the COVID-19 pandemic. PLoS One 16: e0248384. https://doi.org/10.1371/journal.pone.0248384
    [73] Landa-Blanco M, García YR, Landa-Blanco AL, et al. (2024) Social media addiction relationship with academic engagement in university students: The mediator role of self-esteem, depression, and anxiety. Heliyon 10: e24384. https://doi.org/10.1016/j.heliyon.2024.e24384
    [74] Sagrera CE, Magner J, Temple J, et al. (2022) Social media use and body image issues among adolescents in a vulnerable Louisiana community. Front Psychiatry 13: 1001336. https://doi.org/10.3389/fpsyt.2022.1001336
    [75] Blackburn MR, Hogg RC (2024) #ForYou? the impact of pro-ana TikTok content on body image dissatisfaction and internalisation of societal beauty standards. PLoS One 19: e0307597. https://doi.org/10.1371/journal.pone.0307597
    [76] Nasidi QY, Norde AB, Dahiru JM, et al. (2024) Tiktok usage, social comparison, and self-esteem among the youth: Moderating role of gender. Galactica Media 6: 121-137. https://doi.org/10.46539/gmd.v6i2.467
    [77] Hartanto A, Quek FYX, Tng GYQ, et al. (2021) Does social media use increase depressive symptoms? A reverse causation perspective. Front Psychiatry 12: 641934. https://doi.org/10.3389/fpsyt.2021.641934
    [78] Van Zalk N (2016) Social anxiety moderates the links between excessive chatting and compulsive internet use. Cyberpsychology 10: 3. https://doi.org/10.5817/CP2016-3-3
    [79] Adams SK, Kisler TS (2013) Sleep quality as a mediator between technology-related sleep quality, depression, and anxiety. Cyberpsychol Behav Soc Netw 16: 25-30. https://doi.org/10.1089/cyber.2012.0157
    [80] Liu S, Wing YK, Hao Y, et al. (2019) The associations of long-time mobile phone use with sleep disturbances and mental distress in technical college students: A prospective cohort study. Sleep 42. https://doi.org/10.1093/sleep/zsy213
    [81] Caplan SE (2002) Problematic internet use and psychosocial well-being: Development of a theory-based cognitive–behavioral measurement instrument. Comput Hum Behav 18: 553-575. https://doi.org/10.1016/S0747-5632(02)00004-3
    [82] Sanders CE, Field TM, Diego M, et al. (2000) The relationship of Internet use to depression and social isolation among adolescents. Adolescence 35: 237-242.
    [83] Davis RA (2001) A cognitive-behavioral model of pathological internet use. Comput Hum Behav 17: 187-195. https://doi.org/10.1016/S0747-5632(00)00041-8
    [84] Morahan-Martin J, Schumacher P (2003) Loneliness and social uses of the internet. Comput Hum Behav 19: 659-671. https://doi.org/10.1016/S0747-5632(03)00040-2
    [85] Kross E, Verduyn P, Demiralp E, et al. (2013) Facebook use predicts declines in subjective well-being in young adults. PLoS One 8: e69841. https://doi.org/10.1371/journal.pone.0069841
    [86] Chou HT, Edge N (2012) “They are happier and having better lives than I am”: The impact of using Facebook on perceptions of others' lives. Cyberpsychol Behav Soc Netw 15: 117-121. https://doi.org/10.1089/cyber.2011.0324
    [87] Sagioglou C, Greitemeyer T (2014) Facebook's emotional consequences: Why Facebook causes a decrease in mood and why people still use it. Comput Hum Behav 35: 359-363. https://doi.org/10.1016/j.chb.2014.03.003
    [88] Primack BA, Shensa A, Sidani JE, et al. (2017) Social media use and perceived social isolation among young adults in the U.S. Am J Prev Med 53: 1-8. https://doi.org/10.1016/j.amepre.2017.01.010
    [89] Tandoc EC, Ferrucci P, Duffy M (2015) Facebook use, envy, and depression among college students: Is facebooking depressing?. Comput Hum Behav 43: 139-146. https://doi.org/10.1016/j.chb.2014.10.053
    [90] Smith RH, Kim SH (2007) Comprehending envy. Psychol Bull 133: 46-64. https://doi.org/10.1037/0033-2909.133.1.46
    [91] Block JJ (2008) Issues for DSM-V: Internet addiction. Am J Psychiatry 165: 306-307. https://doi.org/10.1176/appi.ajp.2007.07101556
    [92] Morrison CM, Gore H (2010) The relationship between excessive internet use and depression: A questionnaire-based study of 1,319 young people and adults. Psychopathology 43: 121-126. https://doi.org/10.1159/000277001
    [93] Meier EP, Gray J (2014) Facebook photo activity associated with body image disturbance in adolescent girls. Cyberpsychol Behav Soc Netw 17: 199-206. https://doi.org/10.1089/cyber.2013.0305
    [94] Wang R, Yang F, Haigh MM (2017) Let me take a selfie: Exploring the psychological effects of posting and viewing selfies and groupies on social media. Telemat Inform 34: 274-283. https://doi.org/10.1016/j.tele.2016.07.004
    [95] O'Keeffe GS, Clarke-Pearson K (2011) The impact of social media on children, adolescents, and families. Pediatrics 127: 800-804. https://doi.org/10.1542/peds.2011-0054
    [96] Qin Y, Musetti A, Omar B (2023) Flow experience is a key factor in the likelihood of adolescents' problematic TikTok use: The moderating role of active parental mediation. Int J Environ Res Public Health 20: 2089. https://doi.org/10.3390/ijerph20032089
    [97] Qin Y, Omar B, Musetti A (2022) The addiction behavior of short-form video app TikTok: The information quality and system quality perspective. Front Psychol 13: 932805. https://doi.org/10.3389/fpsyg.2022.932805
    [98] Elhai JD, Yang H, Fang J, et al. (2020) Depression and anxiety symptoms are related to problematic smartphone use severity in Chinese young adults: Fear of missing out as a mediator. Addict Behav 101: 105962. https://doi.org/10.1016/j.addbeh.2019.04.020
    [99] Pan W, Mu Z, Zhao Z, et al. (2023) Female users' TikTok use and body image: Active versus passive use and social comparison processes. Cyberpsychol Behav Soc Netw 26: 3-10. https://doi.org/10.1089/cyber.2022.0169
    [100] Geng Y, Gu J, Wang J, et al. (2021) Smartphone addiction and depression, anxiety: The role of bedtime procrastination and self-control. J Affect Disord 293: 415-421. https://doi.org/10.1016/j.jad.2021.06.062
    [101] Feng Y, Meng D, Guo J, et al. (2022) Bedtime procrastination in the relationship between self-control and depressive symptoms in medical students: From the perspective of sex differences. Sleep Med 95: 84-90. https://doi.org/10.1016/j.sleep.2022.04.022
    [102] Ha JH, Kim SY, Bae SC, et al. (2007) Depression and Internet addiction in adolescents. Psychopathology 40: 424-430. https://doi.org/10.1159/000107426
    [103] LeBourgeois MK, Hale L, Chang AM, et al. (2017) Digital media and sleep in childhood and adolescence. Pediatrics 140: S92-S96. https://doi.org/10.1542/peds.2016-1758J
    [104] Zubair U, Khan MK, Albashari M (2023) Link between excessive social media use and psychiatric disorders. Ann Med Surg (Lond) 85: 875-878. https://doi.org/10.1097/MS9.0000000000000112
    [105] El Asam A, Samara M, Terry P (2019) Problematic internet use and mental health among British children and adolescents. Addict Behav 90: 428-436. https://doi.org/10.1016/j.addbeh.2018.09.007
    [106] Kuss D, Griffiths M, Karila L, et al. (2014) Internet addiction: A systematic review of epidemiological research for the last decade. Curr Pharm Des 20: 4026-4052. https://doi.org/10.2174/13816128113199990617
    [107] Fan R (2023) The impact of TikTok short videos on anxiety level of juveniles in Shenzhen China. Proceedings of the 2022 International Conference on Science Education and Art Appreciation (SEAA 2022) . Paris: Atlantis Press SARL 535-542. https://doi.org/10.2991/978-2-494069-05-3_66
    [108] Mironica A, Popescu CA, George D, et al. (2024) Social media influence on body image and cosmetic surgery considerations: A systematic review. Cureus 16: e65626. https://doi.org/10.7759/cureus.65626
    [109] Vincente-Benito I, Ramírez-Durán MDV (2023) Influence of social media use on body image and well-being among adolescents and young adults: A systematic review. J Psychosoc Nurs Ment Health Serv 61: 11-18. https://doi.org/10.3928/02793695-20230524-02
    [110] Galanis P, Katsiroumpa A, Moisoglou I, et al. (2024) The TikTok addiction scale: Development and validation. AIMS Public Health 11: 1172-1197. https://doi.org/10.3934/publichealth.2024061
    [111] Kwon M, Kim DJ, Cho H, et al. (2013) The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS One 8: e83558. https://doi.org/10.1371/journal.pone.0083558
    [112] Chen IH, Strong C, Lin YC, et al. (2020) Time invariance of three ultra-brief internet-related instruments: Smartphone Application-Based Addiction Scale (SABAS), Bergen Social Media Addiction Scale (BSMAS), and the nine-item Internet Gaming Disorder Scale- Short Form (IGDS-SF9) (Study Part B). Addict Behav 101: 105960. https://doi.org/10.1016/j.addbeh.2019.04.018
    [113] Luo T, Qin L, Cheng L, et al. (2021) Determination the cut-off point for the Bergen social media addiction (BSMAS): Diagnostic contribution of the six criteria of the components model of addiction for social media disorder. J Behav Addict 10: 281-290. https://doi.org/10.1556/2006.2021.00025
    [114] Zarate D, Hobson BA, March E, et al. (2023) Psychometric properties of the Bergen social media addiction scale: An analysis using item response theory. Addict Behav Rep 17: 100473. https://doi.org/10.1016/j.abrep.2022.100473
    [115] Servidio R, Griffiths MD, Di Nuovo S, et al. (2023) Further exploration of the psychometric properties of the revised version of the Italian smartphone addiction scale–short version (SAS-SV). Curr Psychol 42: 27245-27258. https://doi.org/10.1007/s12144-022-03852-y
    [116] Galanis P, Katsiroumpa A, Moisoglou I, et al. Determining an optimal cut-off point for TikTok addiction using the TikTok addiction scale. [Preprint] (2025). https://doi.org/10.21203/rs.3.rs-4782800/v1
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