Distribution of motor unit potential velocities in short static and prolonged dynamic contractions at low forces: use of the within-subject’s skewness and standard deviation variables

Behaviour of motor unit potential (MUP) velocities in relation to (low) force and duration was investigated in biceps brachii muscle using a surface electrode array. Short static tests of 3.8 s (41 subjects) and prolonged dynamic tests (prolonged tests) of 4 min (30 subjects) were performed as position tasks, applying forces up to 20% of maximal voluntary contraction (MVC). Four variables, derived from the inter-peak latency technique, were used to describe changes in the surface electromyography signal: the mean muscle fibre conduction velocity (CV), the proportion between slow and fast MUPs expressed as the within-subject skewness of MUP velocities, the within-subject standard deviation of MUP velocities [SD-peak velocity (PV)], and the amount of MUPs per second (peak frequency = PF). In short static tests and the initial phase of prolonged tests, larger forces induced an increase of the CV and PF, accompanied with the shift of MUP velocities towards higher values, whereas the SD-PV did not change. During the first 1.5–2 min of the prolonged lower force levels tests (unloaded, and loaded 5 and 10% MVC) the CV and SD-PV slightly decreased and the MUP velocities shifted towards lower values; then the three variables stabilized. The PF values did not change in these tests. However, during the prolonged higher force (20% MVC) test, the CV decreased and MUP velocities shifted towards lower values without stabilization, while the SD-PV broadened and the PF decreased progressively. It is argued that these combined results reflect changes in both neural regulatory strategies and muscle membrane state.
Introduction
Diverse laboratory conditions have been used in surface electromyography (sEMG) studies in order to gain insights into the neural regulatory strategies and muscle membrane alterations. The influence of force load on sEMG can be investigated by using force tasks or position tasks. The majority of studies have been performed as force tasks, which means that the subject controls the effort by maintaining a target force while the limb position is fixed. During the position tasks, in contrast, an inertial load is applied while the subject controls a target limb position. Both force and position tasks can be performed in static or dynamic conditions. Examples of the static force tasks are the well-known isometric experiments, with higher and lower force levels. In the recent past, force tasks in dynamic conditions have been performed sporadically, such as the cycling experiments by Pozzo et al. (2004) and Farina et al. (2004). Since Hunter et al. (2002) found that, with the same load torque, position tasks resulted in a shorter endurance time than force tasks, suggesting different regulatory mechanism for the both type of tasks, the position task studies gained field. Most position task experiments have been performed in static conditions, evaluating the underlying physiological phenomena during force versus position tasks (Hunter et al. 2002, 2003; Hunter and Enoka 2003; Rudroff et al. 2005, 2007). Potvin (1997) has described the changes in the sEMG during position tasks in dynamic conditions. Previous findings suggest that, as compared with force tasks, position tasks induce greater synaptic input into the motor neurons (Mottram et al. 2005a) and greater adaptation in the motor unit discharge (MacGillis et al. 2003).
Changes in muscle activity during (static and dynamic) position tasks have been assessed using two of the three traditional sEMG parameters, the power spectrum and the global sEMG amplitude. However, the third parameter, the mean muscle fibre conduction velocity (CV), has been lacking. Spectral estimates are generally accepted in fatigue experiments as equivalents of CV because they highly correlate with the CV’s changes (Bigland-Ritchie 1981; Eberstein and Beatie 1985; Arendt-Nielsen and Mills 1985). But this correlation holds true only for the constant forces and isometric conditions; thus, a replacement of CV by power spectrum assessments does not always seem feasible (Farina et al. 2002; Broman et al. 1985). The favour of CV above power spectrum is, furthermore, that it renders direct and absolute values of conduction velocity, and is less sensitive to the anatomical local relationships, such as depth of the motor unit (MU) in relation to the muscle and skin surface (Farina et al. 2002). Yet the limitation of these global CV measurements remains the lack of sensitivity to the changes at the level of an individual motor unit potential/ motor unit.
To accommodate with these limitations of CV, researchers have recently been trying to disentangle the propagation velocities of individual motor unit potentials (MUPs) from sEMG. One of the methods is the inter-peak latency method (IPL) proposed by Lange et al. (2002). The principle comprises calculating conduction velocities of the MUPs from the latencies between paired MUPs of two differential sEMG signals obtained parallel to the muscle fibres, and the distance between the recording electrodes. The negative peaks of two paired MUPs are then the elements determining the interpeak latency. As motor unit propagation velocity reflects the intrinsic physiological properties of a MU, such as fast-twitch or slow-twitch type (Buchthal et al. 1973; Andreassen and Arendt-Nielsen 1987), the IPL method renders many diverse MUP velocities. Lange at al. (2002) proposed using the standard deviation of MUP velocities as an additional measure that offers information about muscle fibre properties. Changes in these velocities during prolonged effort may indicate, for example, slowing/fatigue of the activated motor units and/or appearance of fast/newly recruited motor units. Such shifts in the activated MUs’ populations were shown by Houtman et al. (2003) by eliciting the MUP velocities with the IPL method and presenting their distribution in histograms. The IPL method has not been applied much. It yields insights into the diversity of MUP velocities and thereby the underlying changes in the MU activity. The method is simple and does not require expensive apparatus or software. When compared with techniques that assess the propagation patterns of MUPs by multi-channel/spatial resolution sEMG (Masuda and Sadoyama 1986; Rau et al. 1997), the IPL method is unable to distinguish and follow individual MUPs belonging to the specific MUs.
In the present study, the sEMG signal was described with four parameters derived from the IPL method: (1) the mean muscle CV which was the average of the obtained MUP velocities; the two statistical distribution variables, which were: (2) the within-subject MUP velocities’ skewness [Sk-peak velocity (PV)] and (3) the within-subject MUP velocities’ standard deviation (SD-PV), and (4) the peak frequency (PF), a variable expressing the amount of MUP activity (number of peaks = MUPs) per second.
The aim of the present study was to investigate with the four parameters the changes in MUPs’ velocities of the biceps brachii muscle (BB) during prolonged dynamic position tasks, in dependence of (low) force and duration. It is chosen for the dynamic position tasks as study design because their physiology promised the finding of a large variety of MUP velocities (great amount of activity due to the position tasks character, and diversity because of the recruitment/derecruitment changes within the dynamic cycle). Whole cycles of movement with their concentric and eccentric phases were analyzed together in order to evaluate a total of the sEMG activity with its evolution over time. To highlight the initial changes with the effect of force on it, the changes during the first 14.4 s of the dynamic tasks were evaluated separately. Additively, short static position tasks were performed in order to show the (early) changes on force, without any influence of movements on the signal.
Methods
Subjects
The study involved short static and prolonged dynamic experiments. Forty-one healthy and physically active males (24.8 ± 6.7 years, from 17 to 48) (mean ± SD) volunteered for the first experiment and 30 randomly chosen subjects from that group (25.4 ± 7.6 years, from 18 to 42) participated in both experiments. Exclusion criteria were drug abuse and the practice of bodybuilding. Three from a total of 44 subjects were excluded because of the impossibility to obtain a required correlation coefficient between the sEMG signals used to estimate the parameters’ values. The experimental protocol was conducted according to the Helsinki Declaration and approved by the local ethics committee. All participants gave written informed consent.
Experimental set up
Maximal voluntary contraction (MVC) of elbow flexors was measured at least 5 days before the experiment with a hand-held dynamometer (Lameris Instruments, Utrecht, The Netherlands). During the MVC measurements subjects were sitting upright. The shoulder was slightly abducted and flexed at 45°, the elbow was firmly sustained and flexed at 90°, and the forearm was supinated. The dynamometer was applied to the wrist by the break method (van der Ploeg and Oosterhuis 1991). The peak hold was switched off and the force was kept for at least 3 s. The mean of three maximal values was taken as a MVC. The MVC was assessed with elbow at 90°, although the tests were performed at the elbow angle of 135° (Philippou et al. 2004).
During the experiment subjects were sitting in a chair. The upper arm was slightly abducted and comfortably supported at 45° of the shoulder flexion, the forearm was free. When the elbow was stretched, the line of the upper arm-forearm was at 45° in relation to horizontal. When the elbow was flexed to the angle of 135°, the forearm was horizontal. The forearm was supinated during static and dynamic tests.
Static tests
Subjects were asked to hold the forearm horizontally (elbow angle was then 135°). A visual bar helped to maintain the correct (horizontal) position of the forearm. In the loaded tests a sack filled with lead and sand was placed in the palm. Three levels of force were applied in blocks that were 3 min apart: unloaded, loaded 10 and 20% MVC. A block consisted of three tests (three repetitions at the same level of force); every test lasted for 3.8 s and was within a block separated 30 s from one another.
Dynamic tests
All participants of the dynamic tests underwent previously static tests, separated by 5 min. Subjects were asked to swing the forearm from the stretched (elbow angle 180°) to horizontal position (elbow angle 135°), thus moving over an angle of 45°. They did it within a rate of 40 beats per minute (one up-and-down movement in one beat), given by a metronome sound. The visual bar indicated the horizontal position to which the lower arm returned after being stretched. Four force levels were applied: unloaded, and loaded 5, 10 and 20% MVC. The tests lasted for 4 min and were separated by 5 min.
EMG recording
Measurements were performed on the short head of the BB of a dominant arm. A surface electrode array consisted of three golden-coated electrodes (Harwin, P25-3526), diameter 1.5 mm, insulated in synthetic material plate, with a 10 mm distance between the electrodes (Sadoyama et al. 1985). The skin was cleaned with 95% ethanol. The electrode array was placed parallel to the muscle fibres (Sollie et al. 1985). The proximal electrode was positioned exactly on the distal one-quarter point of the upper arm, measured between the coracoid and the elbow crease. This place of the electrode was about halfway between the endplate zone and the tendon, securing a sufficient distance from the endplate (Sadoyama et al. 1985; Masuda and Sadoyama 1987). Bipolar derivation was made from the proximal to distal direction. The optimal electrode position was controlled by both the observation of the signal on the monitor and the estimation of a correlation coefficient (CC) of the two sEMG signals, which was accepted at r > 0.7 for unloaded, r > 0.85 for 5% MVC and r > 0.9 for higher loaded tests. (During the static and dynamic tests, the maximal CC for unloaded arm was usually lower than that for loaded arm, which was consistent with Hogrel et al. 1998, who have estimated for an unloaded arm r > 0.7). The ground electrode was placed on the lateral upper arm, slightly proximal from the derivation electrode. The temperature sensor was medial on the upper arm. Two obtained signals were differentially amplified (gain 2,000–10,000×) and band pass filtered at 2–250 Hz by EMG apparatus (Viking IV, US).
Data processing
The signals were simultaneously A/D converted (sampling 10 kHz, 12 bits acquisition). Data were stored on a personal computer. The signal was analyzed with LabVIEW 6.1 that also facilitated a partial on-line analysis. The peak selection and the correlation coefficient assessments for both static and dynamic tests were performed on 0.2 s epochs. In the static tests, measurements were taken every second during 0.8 s (comprising 4 epochs of 0.2 s). A static test was of 3.8 s duration and was repeated three times for each force level. The statistical analyses were performed on the data of these three repeated tests taken together. In the dynamic tests, the data were assembled every 30 s during 14.4 s (comprising 72 epochs of 0.2 s). The test duration was 4 min.
Peak selection
The basic principle was that of Lange et al. (2002). The software was custom designed and written in LabView. The orientation of the signals is up-negative. 1st step: finding zero line. 2nd step: finding the peak-to-peak amplitude of the largest MUP in an epoch of 0.2 s. 3d step: finding a peak-decline structure. The peak-decline is defined as a structure with a decline of ≥20% compared with the largest MUP amplitude of a 0.2 epoch, over ≤4 ms (≤40 sample points). 3d step: finding a peak. A peak is the highest (most negative) point previous to the decline, and must be ≥10 μV (the threshold of the noise level). 4th step: finding a pair peak. A pair peak is a peak in the second signal with the properties as previous, found in a time window between 1.49 and 4 ms after the peak of the first signal. This window is chosen assuming the physiological CV values of 2.5–6.67 m/s. 5th step: excluding double peak. If a peak from the first signal matches two different peaks from the second signal, then the first peak from the second signal is true. If during the fatiguing tests the CV severely diminishes, then the low limit of velocity is put at 1.3 m/s, making a window of 1.49–7.68 ms. As an objective pragmatic criterion to this change a lowering of the peak frequency with ≤30% was assumed, as compared with the first value of the 20% MVC test.
The parameters
The following calculations were performed: (1) basic calculation of PV, following the IPL method; (2) mean CV, expressed as an average value of the PVs; (3) within-subject skewness of the peak velocities (Sk-PV), expressed as a skewness of a PVs’ population of a subject; (4) within-subject standard deviation of the peak velocities (SD-PV), expressed as a standard deviation of a PVs’ population of a subject; and (5) peak frequency (PF), expressed as a number of peaks per second.
Statistics
One-way analysis of variance (ANOVA) with repeated measures on force was used to compare the dependent variables for static tests (three levels) and for the initial values of the dynamic tests (four levels). A two-way ANOVA with repeated measures on force ( four levels) and time (nine levels) was used to compare variables during the dynamic tests. In the case of interactions between force and time, to describe changes over time, when appropriate, four separated ANOVAs were performed with smaller time windows of 0–60, 60–120, 120–180 and 180–240 s. To be assured that the dependent variables met parametric assumptions, plots of residues were produced with SPSS program, model control as suggested by Kutner et al. (2005, p. 1157). No relevant deviations of model were detected. Pearson correlation coefficients were calculated to evaluate associations between variables. A level of P < 0.05 was used to identify statistical significance.
Results
Subjects’ physical characteristics are presented in the Table 1. The force of the biceps correlated positively with the upper arm circumference (r = 0.484, P < 0.01). No association was found between either force or upper arm circumference and the sEMG variables. The average skin temperature increased during the dynamic tests by 1.65°C (P < 0.001); it did not change during the static tests.
Table 1Characteristics of 41 participants to the static tests; 30 of them participated in the dynamic tests as wellNMinimumMaximumMeanSDAge (years)41164824.76.7Height (cm)41166.0197.0183.28.0Weight (kg)4160.095.074.39.2Force (N) right38148.5346.5246.141.9Force (N) left (for left-handed)3262.3267.3265.12.5Skin thickness (mm)411.44.22.4.7Circumference upper arm (cm)4124.031.027.52.0Length of biceps (cm)a4130.5039.5035.42.0Length lower arm/radius (cm)b3324.030.026.41.6Length lower arm/palm (cm)c3330.036.033.11.6Initial skin temp. (˚C)4130.833.832.3.8Temp. upper arm before dyn. tests (˚C)3030.834.232.3.9Temp. upper arm after dyn. tests (˚C)3032.235.733.91.1Room temp. (˚C)4120.024.022.31.0a From the coracoid to the elbow creaseb From the lateral epicondyle to the wrist creasec From the lateral epicondyle to the middle of the palm
Static tests
Mean muscle fibre conduction velocity (CV)
Histograms in Fig. 1 show an example of a PVs’ population in one subject during the static tests at three levels of force: unloaded, 10 and 20% MVC. Figure 2a presents the averages of the CVs over 41 subjects, calculated from the subject’s PVs. The CV of the unloaded test was the lowest (3.92 ± SD 0.27 m s−1) and it increased with augmenting force levels (effect of force P < 0.001). A positive correlation existed between the CVs of unloaded test with 10% MVC, and 10% with 20% MVC (all r > 0.505, P < 0.05).
Fig. 1Distribution of peak velocities of one subject during short static tests at three force levels: unloaded, and loaded 10 and 20% MVC. Note the shift of the velocities as a whole to the higher values with increasing level of forceFig. 2Behaviour of peak velocities (PVs) as effect of force, expressed with four parameters. a Mean conduction velocity (CV); b skewness of within-subject PVs; c SD of within-subject PVs (SD); and d number of peaks per second (peak frequency = PF). Averages and standard errors are given, obtained from 41 subjects in short static tests at three levels of force: unloaded, and loaded 10 and 20% of maximal voluntary contraction. With increasing forces, the CV and amount of activity (PF) increases, accompanied with augmenting proportion of fast peaks (the skewness value diminishes). However, the spread of peak velocities within an individual (SD) does not change
Skewness of peak velocities (Sk-PV)
Figure 2b presents the averages of the within-subject PVs’ skewnesses over 41 subjects (see also the histograms of PVs in Fig. 1). In all the tests a moderate positive skewness was found, which indicates a relative excess of lower velocities at the distribution scale. With increasing force the Sk-PV significantly diminished, indicating that the proportion of higher velocities increased (effect of force P < 0.05). The PVs’ population as a whole shifted towards higher values with increasing forces too, which can be seen on the histograms in Fig. 1.
Standard deviation of peak velocities (SD-PV)
The averages of the within-subject’s standard deviations of PVs are shown in Fig. 2c. The SD-PV did not change when the force levels increased (effect of force n.s.).
Peak frequency (PF)
Figure 2d shows the averages of PF in the static tests. The PF increased with increasing forces (effect of force P < 0.001).
Dynamic tests
Muscle fibre conduction velocity (CV)
Histograms in Fig. 3 show the PVs estimated from one subject during dynamic tests at four levels of force: unloaded, loaded with 5, 10 and 20 MVC. Figure 4a shows the averages of CV calculated from the subject’s PVs over 30 subjects. As in the static tests, the initial CV of the dynamic tests increased with level of force (effect of force P < 0.001). Further, a positive correlation was found between the CV of the static tests and the initial CV of the respective dynamic tests (all r > 0.409, P < 0.05). The lowest initial CV was that of the unloaded test with about 4 (3.6–4.5) m s−1 and the highest was at 20% MVC with approximately 4.6 (4.0–5.15) m s−1, increasing from the unloaded to 20% MVC test with 14 ± 9.6%. During all the dynamic tests the CV significantly declined (effects of time for the unloaded test P < 0.02; for other tests P < 0.001), whereby the decline was steeper with larger forces (interaction between force and time, P < 0.001). At the three lowest force levels (unloaded, and loaded 5 and 10% MVC), the CV had two phases: a decline phase lasting for about 120–150 s and a steady phase continuing to the end of a test. The steady state values approached the level of the unloaded test. During the 20% MVC test, however, the CV continued to decline, without a stable phase. Twelve of 30 subjects (40%) reported fatigue during the 20% MVC test and terminated the task prematurely between 90 and 210 s. The CV decreased for the fatigued subjects from 4.6 ± 0.3 (4.2–5.0) to 3.6 ± 0.3 (3.0–4.1) m s−1 and for the continuing subjects from 4.5 ± 0.3 (4.0–5.15) m s−1 to 3.8 ± 0.4 (3.0–4.7) m s−1. Neither the absolute initial CV nor the end CV differed significantly between the groups (P = 0.497 and P = 0.124, respectively). However, the relative decline of the CV tended to be larger for the fatigued subjects compared with the continuing subjects, for fatigued being about −20 (−7 to −35)% and for continuing −15 (+7 to −28)%; P = 0.074.
Fig. 3Changes in the distribution of peak velocities (PVs) over time for different levels of force. The PVs are obtained from one subject (the same as in Fig. 1 for static tests) during prolonged dynamic contractions at four levels of force: unloaded, and loaded 5, 10 and 20% of maximal voluntary contraction (MVC). Every histogram represents a number of PVs within a period of 14.4 s. Initially (at time zero), a global shift of peak velocities is visible towards higher values when forces augment. During the unloaded, and loaded 5 and 10% MVC tests, slower peaks are moderately increasing and faster peaks are diminishing. During the 20% MVC test, the peak velocities shift considerably as a whole towards lower regions, and the amount of peaks visibly diminishesFig. 4Effects of force and time on the behaviour of peak velocities (PVs), expressed with four parameters. a Mean conduction velocity (CV); b skewness of within-subject PVs; c SD of within-subject PVs (SD); and d number of peaks per second (peak frequency = PF). Averages and standard errors are given, obtained from 30 subjects during prolonged dynamic tests at four levels of load: unloaded, and loaded 5, 10 and 20% of maximal voluntary contraction (MVC). Note the difference in the decline pattern of the CV and the PF: the mean muscle conduction velocity declines over time for all levels of force, while the amount of activity remains stable for the three lower force level tests (unloaded, and loaded 5 and 10% MVC). In the higher force level test (20% MVC), the CV starts declining immediately, while the PF declines first gradually and later on steeply. Note the stable SD values from about 90 s for the three lower force tests, while the SD of the higher force test (20% MVC) clearly increases
Skewness of peak velocities (Sk-PV)
Histograms in Fig. 3 show the distribution of PVs of one subject and Fig. 4b presents the averages of the within-subject’s skewnesses of the PVs over 30 subjects. In the initial phase, consistent with the static tests, the PVs were most positively skewed (=skewed in favour of lower velocities) in the unloaded test, and the skewness diminished with increasing forces (effect of force P < 0.001). That means that lower PVs dominated in the unloaded test and the proportion of higher PVs increased when forces augmented. Thus, in the 20% MVC test, the initial PVs approached a normal distribution. In addition, with increasing forces the PVs as a whole group seem to shift towards higher values, as can be seen in the histograms Fig. 3a–d at time zero. During the tests, the skewness increased again, except for the unloaded test, indicating growing proportion of lower PVs over time and decreasing amount of higher PVs (histograms Fig. 3a–c). The larger the forces the steeper increase of skewness (for all tests together: effect of time P < 0.001; interaction between force and time P = 0.005; effect of time in unloaded test P = n.s.; for the tests 5, 10 and 20% MVC interaction between force and time P < 0.001). During the last 2 min of the 5 and 10% MVC tests the Sk-PV stabilized. In the 20% MVC test, however, the positivity still tended to increase up to the end of the test (over the time windows 120–180 and 180–240 s: effect of time for the 5, 10 and 20% MVC tests, n.s.; interaction between force and time, P = 0.106; for the 5 and 10% MVC tests the effect of time, n.s.; for 20% MVC test over 120–180 s n.s., over 180–240 s, P = 0.052). At the end of the 20% MVC test, the whole population of PVs appeared to shift towards the lower values too, as can be seen at the last two histograms in the Fig. 3d.
Taken together, in the initial phase of activity, the proportion of fast peaks increased with increasing force. In the prolonged tests loaded up to 10% MVC, the proportion of fast peaks declined again over the first 2 min and then stabilized at about the level of the unloaded test. During the 20% MVC test, however, the proportion of fast peaks still tended to decline up to the end of the test, accompanied with a growing amount of slow peaks.
Standard deviation of peak velocities (SD-PV)
The averages of SD-PVs of 30 subjects are presented in Fig. 4c. The initial SD-PV was for all force levels similar (P = 0.65), which resembled the static tests. The values in the dynamic tests were significantly higher compared with those of respective static tests (paired sample t test for the unloaded, 10 and 20% MVC tests, respectively P = 0.014; P = 0.027 and P = 0.001). During the tests the SD-PV changed significantly with time, depending on the force level (for all tests effect of time P = 0.011, interaction between force and time P < 0.001). The course of the SD-PV had two phases which were different for the three lower force levels (unloaded, 5 and 10% MVC), compared with 20% MVC. In the three lower force levels, the SD-PV first declined over about 90 s and then stabilized (interaction between force and time over 0–240 s P = n.s.; effect of time over the time windows 0–60 s P < 0.001, 60–120 s P = 0.075; 120–180 and 180–240 s for both P > 0.343). However, during the 20% MVC test, the decline, which lasted for approximately 60 s, was followed by an extreme increase (effect of time over 0–240 s P < 0.001; effect of time over 0–60 s P = 0.019; over 60–120 s, which was in opposite direction, P = 0.019, and 120–180 and 180–240 s P < 0.05). This pattern of results can also be seen in the histograms Fig. 3d.
Peak frequency (PF)
Figure 4d shows averages of PF over 30 subjects. The initial PF rose with increasing force levels (effect of force P < 0.001). Then, during the tests at three lowest force levels (unloaded, 5 and 10% MVC) the PF remained stable. But during the 20% MVC test the PF significantly diminished, at the beginning gradually and from about 120 s steeply (interaction between force and time for all the four tests, P < 0.001; for the three lowest force levels, P = 0.234; effect of time for the three lowest force levels P = 0.541; effect of time for 20% MVC P < 0.001). There was much variability among subjects in the size of decline in 20% MVC test. For those who were able to complete the test, the PF continued to decline up to the end, with exception of one subject in whom the PF increased instead. At 240th s the PF of the continuing subjects was reduced by −35 (−78 to +3)%.
Discussion
Changes in the distribution of MUP velocities as an effect of (low) force and duration were described with four parameters: (1) the global parameter of mean CV, (2) the within subject skewness of a population of MUP velocities; (3) the within-subject standard deviation of MUP velocities and (4) the amount of MUP activity, expressed as MUP frequency. First we will comment on the four parameters. Next, using these parameters, we will discuss the main findings.
The four parameters
The CV parameter renders a mean value of the motor unit potentials’ propagation velocities. The CV will increase or decrease, depending on the type of the activated (fast-twitch and slow-twitch) motor units. It will also change with the alterations in muscle membrane potential, which influences the depolarisation/repolarisation processes. For example, it decreases in muscular fatigue (Stalberg 1966; Milner-Brown and Miller 1986), and increases with a smaller interstimulus interval, such as that due to the rising rate coding (Gydikov and Christova 1984; Radicheva et al. 1986; Nishizono et al. 1989). Because of the lack of studies, it is not possible to compare the CV values with those of any other position tasks experiments. However, the estimates are consistent with those of the studies using static and dynamic force tasks, especially with those of Lange et al. (2002), obtained with the inter-peak latency method.
Skewness is used as an sEMG parameter in the present study for the first time. This statistical measure of deviation from a normal distribution, in this case expresses the proportion between slower and faster MUPs within an individual. It will increase with the growing proportion of slow/tonic/fatigue resistant MUs and will decrease with the augmenting proportion of fast/phasic/fatigable MUs. All the estimates were moderately positively skewed, which indicates a relative excess of lower MUP velocities.
The within-subject standard deviation of MUP velocities, a variable introduced by Lange et al. (2002) shows the spread of MUP velocities. For a fresh and healthy muscle, it will render information about diversity of the participating MUs. In a fatigued muscle, when membrane propagation is slowing, the SD-PV will broaden as a result of the temporal dispersion of velocities. Further, the SD-PV can be expected to narrow when the same velocities repeat, such as in a higher discharge rate of a certain group of MUs. In the brief static position tasks, the standard deviations were larger than those previously estimated by Lange and colleagues (2002) in the staticforce tasks, with respectively 0.55–0.62 and 0.3–0.52 m s−1. This difference can be due to the different type of tasks, as data are available suggesting that different excitatory/inhibitory inputs to the motor neurons play a part in the position tasks and the force tasks (Rudroff et al. 2005).
In the present study, the SD-PVs of the dynamic tests were larger than those of the static tests. This difference can have different explanations. With every contraction of a dynamic cycle, the muscle fibres’ diameter increases, leading to higher fibre propagation velocities (in a part of a cycle) (Arendt-Nielsen et al.1992). This problem was partially restrained by using a small movement angle of 45°. However, the most important role in the increase of SD-PVs’ during dynamic contractions might be played by the cyclic changes in the motor units’ discharge characteristics. Several studies deliver the supporting data. For example, during dynamic contractions the amount of activity differs between the concentric and eccentric phases, suggesting different regulatory strategies for the two phases (Potvin 1997). Previous studies have also shown that the rate of MU discharge is related to the movement’s velocity, and the (angle) velocity alters depending on the elbow angle (Gillis 1972; Milner-Brown et al. 1973a; Potvin 1997). Thus, the discharge rates will alter through a cycle. In addition, eccentric movements are shown to further the activation of high-threshold (fast propagating) motor units (Komi and Tesch 1979; Nardone et al. 1989).
Peak frequency (MUP frequency) expresses the amount of MUPs in a time. To our knowledge, it is used as sEMG parameter in the present study for the first time. The PF is comparable with the zero crossing parameter (Lynn 1979; Masuda et al. 1982; Hagg 1981). It is argued that the diminishing zero crossings’ number during prolonged exercises indicates decrease in MUs’ activity, as a sign of fatigue (Inbar et al 1986; Hagg and Suurküla 1991). Lange et al. (2002) mentioned a number of MUPs obtained during 1.5 s measurements in static force tasks, which renders the frequency of about 4–5 MUPs/s for 10% MVC test, and about 8 MUPs/s for 20% MVC test. These values are much lower than ours with 37, 47 and 50 MUPs/s (for respectively unloaded, loaded 10 and 20% MVC tests) in static position tasks. The findings are consistent with the interpretation that during position tasks more motor units are being recruited compared with force tasks, and the discharge rate of MUs is higher (Mottram et al. 2005a).
The initial changes on increasing force levels (in the static and dynamic tests)
The effects of force on the behaviour of MUPs in the short static tests and the initial phase of the prolonged dynamic tests were similar. With increasing forces the CV grew higher and MUP frequency increased. In the population of MUP velocities, not only the proportion of fast MUPs increased (see the skewness in Fig. 2b), but also the velocities as a whole shifted towards higher values (histograms in Figs. 1, 3a–d at time zero). Despite of the changes in the skewness, the standard deviation of MUP velocities remained unaltered.
The increases of the CV with increasing force are in accordance with the previous findings inforce tasks (Naeije and Zorn 1983; Broman et al. 1985; Sadoyama and Masuda 1987; Zwarts and Arendt-Nielsen 1988; Lange et al. 2002). It is generally accepted that these increases are caused by activating high threshold/fast/phasic motor units when demands of force are augmented (Henneman et al. 1965; Milner-Brown et al. 1973b; Gantchev et al. 1992; Gazzoni et al. 2001). This explanation was supported by the increasing proportion of fast MUPs found in the present study. However, the global shift of MUP velocities towards higher values may be induced by either replacing slow MUs by fast ones, or by increasing the propagation velocity of the muscle membrane due to the rising rate coding (Radicheva et al. 1986; Nishizono et al. 1989; Van der Hoeven and Lange 1994). Little is known about changes in the within-subject standard deviation of MUP velocities. Only Lange and colleagues (2002) mentioned (inforce tasks), contrary to our results, increases between 10 and 50% MVC (and no increases between 50 and 100%). The experiments of Lange et al. and the present short static experiments were both isometric, and the duration of the contraction did not differ much (our 3.8 vs. their 1.5 s). In fact, the two studies only differed in the type of task (position tasks applied by us vs. force tasks by Lange et al.). This task difference may play a part in the discrepancy of the standard deviation, as the excitatory and inhibitory inputs for the two tasks are supposed to be different (Rudroff et al. 2005). Thus, for the two tasks different types of motor units (with different velocities) may be activated.
In short, increases of mean CV with increasing forces in the initial phase of muscle activity may be a result of both recruitment of fast/phasic motor units and faster membrane propagation.
Changes in the prolonged dynamic tests
Tests loaded below 20% MVC
The main feature of the prolonged tests loaded 5 and 10% MVC were changes in the CV, skewness and standard deviation over the first 90–120 s, followed by stabilizing. Thus, the CV first declined (with steeper decline for higher forces) and then stabilized at approximately the level of the unloaded test (Fig. 4a). The MUP velocities, which at the beginning of tests were shifted towards the higher values with increasing forces, re-shifted over the tests back to the lower values (skewness variable in Fig. 4b and histograms in Fig. 3a–c). Subsequently, the MUP velocities stabilized nearly at the level of the unloaded test too. The standard deviation narrowed first and later on stabilized at a new level (Fig. 4c). The MUP frequency held steady over these tests at the primary level determined by the used force (Fig. 4d). We suggest that this pattern of results may reflect an emerging equilibrium between phasic and tonic MU activity.
No comparison is possible between the parameters used here and those in any other prolonged position tasks study. The decline of CV during contractions at low forces was in contradiction with the studies in static force tasks, which reported increases during sustained isometric contractions at forces of 10–25% MVC (Zwarts and Arendt-Nielsen 1988; Arendt-Nielsen et al. 1989; Krogh-Lund and Jorgensen 1991; Krogh-Lund and Jorgensen 1992; Krogh-Lund 1993). This discrepancy may be caused by either/or both the different contraction types (static vs. dynamic) or different tasks (force vs. position tasks). Increases of the CV in prolonged isometric contractions at low force levels are supposed to be due to the recruitment of fast (anaerobic) MUs in response to the hindered blood flow (Crenshaw et al. 1997; Zwarts et al 1987). In the dynamic conditions, the blood supply is assumed to be undisturbed, so the aerobic MUs can be activated. The decline of the CV followed by stability, along with the changes in the skewness, suggest that, within the cyclically fluctuating activity, the global amount of initially recruited fast/fatigable/anaerobic MUs may successively diminish and the proportion of slow/fatigue resistant/aerobic MUs may augment. The maintaining activity of slow MUs is in accordance with the hypothesis that tonic/fatigue resistant (aerobic) MUs remain active through the whole muscular action (Grimby and Hannerz 1968; Hagg and Suurkula 1991).
On the other hand, the position character of the present tasks may have contributed to the discrepancy between the decline of CV in the present experiments and increases in previous studies, as firstly, the discharge characteristics of the same motor unit differ between the force and position tasks, and secondly, motor units show greater discharge adaptation during the position tasks (Mottram et al. 2005a, b; MacGilles et al. 2003). The evolution of the standard deviation parameter, with its narrowing followed by stabilizing, fits in with the idea that the rate coding may temporarily increase and consecutively adapt, resulting in a new balance.
The MUP frequency variable, expressing the amount of MU activity produced as a result of recruitment and rate coding did not change during these tests. This suggests that all the changes in recruitment and rate coding do not, in principle, affect the total amount of MU activity.
All subjects were able to complete the tests, and the sEMG parameters became stable in the course of time as well. Thus, one can assume that the three tests at lowest force levels were non-fatiguing. Taken together, the results of these apparently non-fatiguing dynamic position tasks suggest that following the initially increased activation of fast MUs, the proportion shifts after about 2 min in favour of slower MUs. The amount of activity seems to remain stable throughout the duration of the tests.
The test at 20% MVC
The changes encountered during the prolonged test at 20% MVC differed clearly from those at lower forces (Fig. 4). During the 20% MVC test, the CV dropped below the level of the unloaded test. At the same time the proportion of low MUP velocities increased (skewness increased), and finally the velocities’ population made a global move from higher towards lower values, while their standard deviation broadened clearly. The MUP frequency, in contrast with that of non-fatiguing tests, progressively diminished (histogram in Figs. 3d, 4d).
The CV’s decreases were in accordance with those shown by Farina et al. (2004) during fatiguing dynamic force tasks of vastus medialis muscle. The behaviour of the skewness and standard deviation suggests that a large majority of the MUPs became extremely slow at the end. This general slowness best matches the slowing of the muscle membrane propagation, which is generally accepted as sign of muscular fatigue (Milner-Brown et al. 1986; Miller et al. 1987). The diminishing MUP frequency can be due to the synchronisation of discharges (Datta and Stephens 1990; Semler and Nordstorm 1999) and diminishing motor unit activity (Hagg 1981; Hagg and Suurkulla 1991). The non-linear decline pattern of MUP frequency, first slow and later on steep, suggests successively exhausting available MUs’ reserves, resulting in lower firing frequencies and failing recruitment. Accordingly, many subjects reported fatigue and stopped exercising.
In short, during these apparently fatiguing dynamic position tasks, a global slowing of MUP velocities appears, suggesting a fatigued muscle membrane. The amount of MU activity seems to diminish progressively and finally the recruitment stops.
Some aspects of the method must be explained. The load was applied to the palm, whereas the MVC was assessed from the wrist. By applying load to the palm, we intended to mimic natural circumstances, such as holding something in the hand. Assessment of the MVC from the palm was not feasible, however, due to the relative weakness of the wrist’s flexors compared with the elbow flexors, which influenced the estimates. A reasonable alternative was to measure the MCV from the wrist. Distances measured over the forearm and the palm (Table 1) enabled calculation of the real exerted load torque, which was about 20–25% larger than the used one. The MVC was assessed at the elbow angle of 90° despite performing the tests at the angle of 135°. The reason was that, when applying the dynamometer at the wrist with high forces, the elbow angle being at 135°, subjects tended to overstretch the wrist and experienced pain. This was not the case at the 90° angle. The MVC values at 135 and 90° were similar, which was consistent with the findings of Philippou et al. (2004), so it was chosen for assessments at 90°.
In conclusion, we present a set of parameters derived from the interpeak latency method, which yields information about changes in MUP velocities’ distribution and amount of MUP activity. Skewness, standard deviation and peak frequency parameters appear to corroborate the results of a global muscle conduction velocity. Together they could contribute to quantifying the dynamics of motor unit activity and membrane fatigue. The interconnected results may be useful in ergonomics (for assessment of fatigue) and in sports (for eliciting specific capabilities, such as explosive or endurance capabilities).

motor unit potential
dynamic contraction
skewness
position task
surface electromyography
conduction velocity distribution


Multiple Determinants of Externalizing Behavior in 5-Year-Olds: A Longitudinal Model

In a community sample of 116 children, assessments of parent-child interaction, parent-child attachment, and various parental, child, and contextual characteristics at 15 and 28 months and at age 5 were used to predict externalizing behavior at age 5, as rated by parents and teachers. Hierarchical multiple regression analysis and path analysis yielded a significant longitudinal model for the prediction of age 5 externalizing behavior, with independent contributions from the following predictors: child sex, partner support reported by the caregiver, disorganized infant-parent attachment at 15 months, child anger proneness at 28 months, and one of the two parent-child interaction factors observed at 28 months, namely negative parent-child interactions. The other, i.e., a lack of effective guidance, predicted externalizing problems only in highly anger-prone children. Furthermore, mediated pathways of influence were found for the parent-child interaction at 15 months (via disorganized attachment) and parental ego-resiliency (via negative parent-child interaction at 28 months).

Externalizing behavior problems – including aggressive, destructive, and delinquent behavior – represent the most common type of mental health problems in children. Longitudinal studies have shown that the stability of externalizing problems is relatively high from preschool age into adolescence (Moffit, Caspi, Dickson, Silva, & Stanton, 1996; Smith, Calkins, Keane, Anastopoulos, & Shelton, 2004). Moreover, early externalizing problems are predictive of other forms of psychopathology and often interfere with the child’s personal, social and academic development (Campbell, 1995, 2002; Moffitt, 1993). Although developmental psychopathologists now generally assume such problems to have their roots in children’s early developmental histories, early identification of children at serious risk of developing such pathology is hampered by a lack of insight into the determinants leading to later maladaptive outcomes (Tremblay, 2006).
Although various theoretical models of the development of externalizing problems in the very first years of life (Campbell, Shaw, Gilliom, 2000; Moffitt, 1993, Greenberg, 1999; Sameroff & Chandler, 1975) differ in the emphasis they place on the role of certain etiological factors, they agree in the assumption that multiple factors from various domains additively and interactively contribute to the emergence and maintenance of externalizing problems. Taken together, four domains of factors have been proposed to contribute to their development: (a) parent-child interaction and parent-child attachment, (b) child characteristics (e.g., temperament and cognitive abilities), (c) parental characteristics (e.g., personality), and (d) contextual characteristics (e.g., socioeconomic status, partner support, and stressful life events).
To date, very few studies – all focusing on high-risk samples – have longitudinally examined the joint contribution of the various key factors to the onset of externalizing problems in the very first years of life (Aguilar, Sroufe, Egeland, & Carlson, 2000; Erickson, Sroufe, & Egeland, 1985; Lyons-Ruth, Alpern, & Repacholi, 1993; Shaw, Owens, Vondra, Keenan, & Winslow, 1996). Ours is the first study to assess predictors hypothesized to be the most important from all of the aforementioned domains both in infancy (i.e., at age 15 months) and in toddlerhood (i.e., at age 28 months) to predict the emergence of later externalizing behavior problems (i.e., at age 5 years) in a non high-risk community sample. All of the abovementioned earlier studies of the multiple determinants of externalizing behavior used samples characterized by one or more risk factors. The sample studied by Shaw et al. (1996) included only low-SES families. Aguilar et al. (2000) and Erickson et al. (1985) studied low-SES samples as well, but these were also characterized by high degrees of life stress and a large proportion of single-parent families. And the low-SES sample studied by Lyons-Ruth et al. (1993) was characterized by such additional risk factors as a relatively high incidence of a history of maternal psychiatric hospitalization, single parenthood, and child maltreatment. It remains to be seen whether similar results will also be found in studies with community samples. That is mainly because earlier research has shown that the predictive power of risk factors may increase in the presence of other risk factors (Farrington, 1995). Consequently, it is possible that factors that predict externalizing problems in samples with one or more risk factors fail to do so in samples with lower levels of risk, such as the present sample. In light of this, studying the early predictors of externalizing problems in a community sample constitutes an important extension of the existing research evidence collected in high-risk samples.
The criteria on which we based our selection of the various predictors and our hypotheses regarding their joint contribution to the development of externalizing problems – led by various interrelated theoretical models – are described per domain in the following paragraphs.
Parent-child interaction and parent-child attachment
From a transactional perspective (Sameroff & Chandler, 1975), externalizing problems are assumed to emerge and to stabilize or change in children’s continuous interactions with their immediate environment, particularly in the interactions with their primary caregivers. Repeated observations of parental and child behaviors during parent-child interactions are thus indispensable to gain more insight into the early development of externalizing behavior problems. Multiple dimensions of parental behavior in parent-child interaction have been found to be associated with behavior problems in children at various ages. A lack of positive parenting behaviors, such as the expression of sensitivity, warmth, involvement, acceptance, and positive guidance, was reported to be related to externalizing problems in preschoolers (Brophy & Dunn, 2002; Côté, Vaillancourt, LeBlanc, Nagin, & Tremblay, 2006; Gardner, 1987; Pettit, Bates, & Dodge, 1997). In addition, high levels of negative parental control such as harsh discipline, intrusiveness, negativity, and hostility also proved associated with externalizing problems (Belsky, Woodworth, & Crnic, 1996; Rubin, Burgess, Dwyer, & Hastings, 2003; Campbell, Pierce, Moore, & Marakovitz, 1996). Based on the above evidence, we expected both a lack of positive parenting behaviors and negative parent-child interactions to predict externalizing behavior problems at age 5.
Another frequently mentioned risk factor and closely related to the quality of parent-child interaction, is the quality of attachment between infant and caregiver. By the end of the infant’s first year, four main patterns of infant-parent attachment – one secure and three insecure – can be distinguished that have been found to reflect the history of the caregiver’s responses to the child’s attachment behaviors (Ainsworth, Blehar, Waters, & Wall, 1978; Main & Solomon, 1986, 1990). Ainsworth and colleagues (1978) originally proposed three “organized” patterns of attachment. Presumably as a result of a history of caregiver sensitive responsiveness to their signals and needs, securely attached (B) infants use the caregiver as a secure base from which to explore the environment. Avoidant (A) infants are characterized by a tendency to minimize their attachment behaviors while under stress in the presence of the caregiver, probably resulting from earlier experiences with a caregiver who tended to reject or ignore the child’s expression of attachment behaviors. Infants with a resistant attachment (C), featuring both clinging and angry behaviors towards the caregiver when under stress, are presumed to maximize the expression of attachment behavior as an adaptation to their caregiver’s inconsistent responsiveness. Main and Solomon (1990) later added a fourth disorganized (D) pattern typified by seemingly contradictory attachment behaviors that is thought to reflect the breakdown or absence of a strategy for the infant to use the caregiver as a secure base in times of stress (Main & Solomon, 1990). Disorganized attachment is thought to ensue from extremely unpredictable or frightening behavior on the part of a maltreating and/or traumatized parent (cf. Lyons-Ruth & Jacobvitz, 1999).
With regard to the predictive value of attachment security versus insecurity for the later occurrence of externalizing problems, the empirical evidence is rather inconsistent. Although attachment insecurity was found to be associated with externalizing problems in high-risk samples as well as in community samples in studies that did not include the disorganized attachment classification (Erickson et al., 1985; Fagot & Kavanagh, 1990; Munson, McMahon, & Spieker, 2001; Shaw et al., 1996), other studies did not report such a relationship (Bates, Maslin, & Frankel, 1985; Goldberg, Corter, Lojkasek & Minde, 1990). However, in contrast to the other insecure attachment categories, disorganized attachment has consistently been found to be related to the later development of externalizing problems (for reviews, see Lyons-Ruth and Jacobvitz, 1999, and Van IJzendoorn, Schuengel, and Bakermans-Kranenburg, 1999; also see Lyons-Ruth et al., 1993; Vondra, Shaw, Swearingen, Cohen, & Owens, 2001).
In view of the above, we expected infant attachment insecurity and particularly attachment disorganization to be related to elevated levels of externalizing behavior at age 5. Considering that attachment patterns have been found to reflect the history of parent-child interactions, we expected the association between the early parent-child interaction and later externalizing problems to be at least partially mediated by the quality of the infant-parent attachment.
Child characteristics
Several child characteristics can prompt the development of externalizing behavior, both directly and indirectly by affecting the quality of parent-child interactions. A considerable body of research has shown associations between externalizing problems and child temperamental characteristics with a crucial role for negative emotionality denoting negative mood, irritability, and high-intensity reactions like anger (for a review, see Sanson, Hemphill, & Smart, 2004). In contrast, temperamental inhibition and fear/shyness may act as a protective factor as these characteristics have been demonstrated to be negatively associated with later externalizing problems (Lacourse, Nagin, Vitaro, Côté, Arsenault, & Tremblay, 2006; Sanson, Oberklaid, Prior, Amos, & Smart, 1996; Schwartz, Snidman, & Kagan, 1996). Nevertheless, the relationship between child temperament and externalizing behavior is not always clear or direct. Etiological models of externalizing behavior increasingly propose that children with temperamental difficulties are more vulnerable to negative rearing influences than children without such difficulties (Belsky, 1997; Moffitt, 1993). Several recent studies have substantiated this latter assumption (Bates, Pettit, Dodge, & Ridge, 1998; Belsky, Hsieh, & Crnic, 1998; Hemphill & Sanson, 2001; Lacourse et al., 2006; Leve, Kim, & Pears, 2005). In line with these findings, we anticipated high levels of child negativity and low levels of fearfulness in infancy and toddlerhood, particularly in interaction with parenting, to predict the development of externalizing problems.
Another contributory child characteristic is low cognitive or linguistic ability (Burt, Hay, Pawlby, Harold, & Sharp, 2004; Dekker, Koot, Van der Ende, & Verhulst, 2002; Moffitt, 1993). Therefore, we hypothesized that child cognitive ability at 15 months would be negatively related to the occurrence of externalizing problems at age 5 years.
Boys and girls have been found equally likely to exhibit externalizing behavior problems up until the age of about four years. By school entry, however, boys tend to exhibit up to 10 times the rate of externalizing problems for girls (Côté, et al, 2006; Mesman, Bongers, & Koot, 2001; Rubin et al., 2003). Accordingly, at age 5 we expected the boys in our sample to score higher on externalizing behavior than the girls.
Parental characteristics
Little research has focused on parental personality as a potential predictor of externalizing problems in children. The available studies only focused on aspects of parental psychopathology like antisocial personality (Shaw, Vondra, Hommerding, Keenan, & Dunn, 1994) or maternal depression (Cummings & Davies, 1994; Shaw, Gilliom, Ingoldsby, & Nagin, 2003). Less attention has been paid to the effects of more or less adaptive parental characteristics, which is why we included parental ego-resiliency as a potential predictor of externalizing problems. Block and Block (1980) defined ego-resiliency as a “resourceful adaptation to changing circumstances,” “flexible invocation of the available repertoire of problem-solving strategies,” and “the ability to maintain integrated performance while under stress” (p. 48). As such, ego-resilient individuals should be particularly well-equipped to cope with the often stressful task of parenting. And – as outlined before – higher quality parenting is hypothesized to predict lower levels of externalizing behavior in children. In the 15-month assessment (Van Bakel & Riksen-Walraven, 2002a), parental ego-resiliency was found to be related to the infants’ socioemotional development, partially via the quality of the parent-infant interaction. Based on the above and in line with the 15-month findings, we expected higher levels of parental ego-resiliency to predict lower levels of externalizing behavior in the children at age 5, and we expected this relation to be mediated by lower quality parent-child interactions.
Contextual characteristics
Besides parental and child attributes, specific characteristics of the social and economic context in which the parent-child interaction is embedded may also contribute to the development of externalizing problems (Belsky, 1984; Greenberg, Speltz, & DeKlyen, 1993). We included three characteristics of the child-rearing context that earlier studies have shown to be associated with externalizing problems: (1) high incidence of stressful life events (Aguilar et al., 2000), (2) a lack of partner support (Jouriles, Murphy, Farris, Smith, Richters, & Waters, 1991; Shaw et al., 1996), and (3) low socio-economic status (SES; Côté, et al, 2006; Shaw et al., 1996). It needs to be stressed, though, that in previous research the effects of sociodemographic factors like SES proved modest compared to the effects of parental or child risk factors (for a review, see Loeber and Dishion, 1983; also see Shaw et al., 1996). Nevertheless, based on earlier findings, we expected stressful life events, partner support, and SES to contribute to the development of externalizing behavior problems.
In sum, the aim of the present study was to predict the occurrence of externalizing behavior problems in a sample of 5-year-old children on the basis of various parental, child, dyadic, and contextual factors assessed at 15 and 28 months and at age 5. We expected the following specific factors to independently or interactively contribute to the development of externalizing problems: (a) a low quality of parent-child interaction at 15 and 28 months; (b) disorganized parent-infant attachment; (c) child temperament (i.e., high anger proneness and low fearfulness) at both 15 and 28 months and particularly in interaction with low quality parenting; (d) low levels of child cognitive ability at 15 months; (e) low parental ego-resiliency; (f) high incidence of stressful life events between 15 months and 5 years; (g) lack of partner support; and (h) low SES. Furthermore, the contribution of parental ego-resiliency to child externalizing behavior was expected to be mediated by the quality of parent-child interaction, and the contribution of the early parent-child interaction by the quality of infant-parent attachment.
Method
Participants
The original 15-month sample (M = 15.1; SD = 0.25) consisted of 129 healthy children (67 boys, 62 girls) and their primary caregivers. The children were recruited on the basis of the records from health-care centers in the Dutch city of Nijmegen. During nine consecutive months, all families (n = 639) with a 15-month-old baby from various socioeconomic backgrounds were sent a recruitment letter with a brief description of the research procedures and an invitation to participate in this study that aimed to “gain more insight into children’s development in the first years of life”. If they met the two specified eligibility criteria (i.e., sufficient fluency in Dutch and child without serious health problems) and were interested in participation, they were requested to return a response card. Of the 174 families who responded, 129 parent-child dyads (the maximum attainable given the time and resources available for the project) were randomly selected for the study. Of the infants, 73 were first-borns and 56 had one or more older siblings. The sample included 123 two-parent families and six single-parent families. In three families, the father was the primary caregiver and in the remainder of the families this was the mother. The percentages of single parents and fathers acting as primary caregivers are representative of families in the Netherlands with children in this age group. The primary caregivers were between the ages of 22 and 47 years at the time of recruitment. The level of education for the primary caregivers was indicated on a seven-point scale from low (elementary school) to high (college degree or more) with a mean of 4.95 (SD = 1.77); the category equivalent of 5 is high school degree. Of the 129 15-month-olds, 114 children (61 boys, 53 girls) participated in the second wave of measurements at age 28 months (M = 28.3; SD = 0.30) and 116 (62 boys, 54 girls) in the third assessment at age 5 years (M = 63.6 months; SD = 1.16). At this third assessment, there were 107 two-parent families, nine single-parent families, and again three families with the father as the primary caregiver.1
To determine whether there was selective drop-out or not, the 116 families participating in the 5-year assessment were compared to the 13 families that did not take part. Independent t-tests for all the major 15-month study variables revealed a significant difference for parental ego-resiliency only, t(126) = 2.08, p < .05, with a tendency for primary caregivers with lower levels of ego-resiliency to drop out.
Procedure
The 15-month data were available from an earlier study focusing on the determinants of parenting and infant development (Van Bakel & Riksen-Walraven, 2002a). The 15-month assessment involved data collection during a single home visit and a single visit of the primary caregiver and infant to the research center. During the home visit, the primary caregiver completed a Q-sort and a set of questionnaires assessing child temperament, parental ego-resiliency, partner support, and additional background information. Next, the parent-child interaction was videotaped during the performance of four instructional tasks lasting three to four minutes each. At the subsequent assessment at the research center, the child’s cognitive ability was assessed and the quality of the infant-parent attachment with an abbreviated version of the Strange Situation (Ainsworth et al., 1978). For a more detailed description of the 15-month data collection, see Van Bakel & Riksen-Walraven (2002a, 2002b)
The 28-month assessment was conducted during a single home visit. The primary caregiver was interviewed about stressful life events since the first assessment and asked to complete a questionnaire to assess child temperament. The parent-child interaction was videotaped during four instructional tasks comparable to those performed at 15 months.
At the age-5 assessment, the primary caregiver was interviewed at home about stressful life events since the previous assessment and asked to complete a questionnaire to assess child behavior problems. The child’s teacher completed a questionnaire to assess any behavior problems and asked to return this by mail, which was done by all but one teacher.
Instruments and measures
Quality of parent-child interaction (at 15 and 28 months)
The videotaped parent-child interaction episodes were rated using five 7-point scales (Erickson et al., 1985) to assess the quality of parental interactive behavior: (1) supportive presence or the provision of emotional support; (2) respect for the child’s autonomy or nonintrusiveness; (3) effective structure and limit setting; (4) quality of instructions; and (5) hostility. Subsequently, child interactive behavior was rated on four 7-point scales (Erickson, et al., 1985): (I) negativity or anger, dislike, and hostility towards the parent; (II) avoidance of interaction with the parent; (III) compliance to suggestions and directions given by the parent; and (IV) positive affect expressed towards the parent. Each interaction episode at 15 months was rated independently by two trained observers and each interaction episode at 28 months by four independent observers. Based on 25 cases (19%) for the 15-month assessment and 20 cases (18%) for the 28-month assessment, the interrater reliabilities expressed as adjusted kappas were all above 0.83. Evidence for the validity of the scales has been provided in various studies (see Van Bakel & Riksen-Walraven, 2000a, for a review).
To obtain robust dyadic measures for parent-child interaction, the ratings of parental and child interactive behavior were factor analyzed together, for the 15- and 28-month assessment separately, using varimax rotation. Scree plots of eigenvalues indicated the emergence of two clear and comparable factors at both ages. The two factors explained 68% of the variance in parent-child interaction at 15 months, and 71% of the variance at 28 months. The first factor, labeled effective guidance, was marked by high loadings on the following parent-child interaction variables (factor loadings for 15 and 28 months within parenthesis): effective structure and limit setting (.89; .95), high quality of instructions (.62; .76), and supportive presence (.79; .48) on the part of the parents; and compliance (.53; .84) and low avoidance (−.52; −.65) on the part of the child. The second factor, labeled negative interaction, was characterized by high loadings on the following variables (factor loadings for 15 and 28 months within parenthesis): parental hostility (.74; .85); low respect (−.63; −.78); child negativity (.85; .67); and an increasing lack of positive affect in both partners as evident from low parental supportive presence (−.48; −.77) and low child positive affect (−.35; −.54).
Quality of parent-child attachment (at 15 months)
An abbreviated version of the Strange Situation Procedure (Ainsworth et al., 1978), i.e., including one as opposed to two separation-reunion situations, was used to assess the quality of infant-parent attachment. A similarly abbreviated version of the Strange Situation has been found to be valid for the assessment of attachment quality both within normal (Lewis, Feiring, McGuffog, & Jaskir, 1984; Waters, Wippman, & Sroufe, 1979) and within clinical samples (Willemsen-Swinkels, Bakermans-Kranenburg, Buitelaar, Van IJzendoorn, & Van Engeland, 2000). Two trained coders (the second author and D.C van den Boom from the University of Amsterdam) rated the videotaped separation-reunion episodes and classified the infants as Secure (B), Avoidant (A), Resistant (C), or Disorganized/ disoriented (D) consistent with the directions provided by Ainsworth et al. (1978) and by Main & Solomon (1990). Intercoder reliability on 20 cases (16%) was adequate, with 95% agreement on the main classifications.
Child temperament (at 15 and 28 months)
Child temperament was evaluated using the Toddler Behavior Assessment Questionnaire (TBAQ; Goldsmith, 1994) The TBAQ comprises 111 items, organized in five 7-point scales. For the present study, two scales were used that have earlier been found associated with externalizing problems, i.e., Anger proneness (28 items, Cronbach’s alpha .89 and .88 for 15 and 28 months, respectively) and Social fear (19 items, Cronbach’s alpha .77 and .84 for 15 and 28 months, respectively).
Cognitive ability (at 15 months)
A Dutch version of the Bayley (1969) Mental Scale of Infant Development (Van der Meulen & Smrkovsky, 1983) was used to assess the child’s level of cognitive functioning at 15 months, expressed in the standardized Mental Developmental Index (MDI, M = 100; SD = 15).
Parental ego-resiliency (at 15 months)
Primary caregivers rated their own ego-resiliency using a Dutch translation of the California Adult Q-set (CAQ; Block, 1961, 1978) The CAQ consists of 100 descriptive statements that sample a broad domain of personal and interpersonal characteristics and functioning. The primary caregivers were asked to sort each statement into one of nine possible categories ranging from “least characteristic” to “most characteristic” in terms of salience for themselves. An ego-resiliency score was then attained by correlating the Q-sort description for each parent with the criterion profile provided by experts for a prototypically ego-resilient person (see Block, 1991). The ego-resiliency scores could range from +1.00 (very ego-resilient) to –1.00 (very ego-brittle).
Stressful life events (at 28 months and 5 years)
A semi-structured interview was used to obtain information regarding stressful life events occurring between the three assessments. The questions/items were derived from Saranson, Johnson, and Siegel’s (1978) Life Experiences Survey and Coddington’s (1972) Life Events Scale for Children. Both measures have sound psychometric properties and have been widely used in international research (Abela, 2001; Johnston, 1996). Stressful events that were likely to have a negative impact on the child’s development were selected for inclusion (e.g., loss of a loved one, serious physical or mental illness on the part of a parent or the child, divorce). All items required a ‘yes’ or ‘no’ response. The total number of stressful life events between 15 and 28 months and 28 months and age 5 were taken together to constitute the stressful life event scores between 15 months and age 5.
Partner support (at 15 months)
A subscale of a Dutch questionnaire to assess family problems (VGP; Koot, 1997) was used to assess the support the primary caregiver received from the partner. The subscale comprises five statements gauging partner support during child rearing, such as “My partner supports me in my role as a parent” and “My partner and I agree about child rearing.” Cronbach’s alpha was 0.82. Single parents were also asked to complete the questionnaire when they were still in contact with the other parent or were living with a new partner. Otherwise, they were assigned a minimum score. Evidence supporting the validity of the subscale has been reported in various studies (Van Bakel & Riksen-Walraven, 2002a; Van Zeijl et al., 2006).
Socioeconomic status (SES at 15 months)
SES scores were assigned on the basis of the level of education (along a 7-point scale) and level of occupation (along a 6-point scale) for both parents. SES scores based on education and occupation of both parents have frequently been used in other studies (e.g., Shaw et al., 1996). The levels of education and occupation for the two parents were first standardized and then summed to derive a single SES score. For single parents, the level of education and occupation for the primary caregiver were summed to compute the SES score (cf. Shaw et al., 1996).
Externalizing problems as rated by parents and teachers (5 years)
To assess externalizing problems at age 5, the parents completed the Dutch version of the Child Behavior Checklist for ages 4–18 (CBCL/4–18; Achenbach, 1991a; Verhulst, Van der Ende, & Koot, 1996). The Aggressive behavior subscale (20 items) and the Delinquent behavior subscale (13 items) from the CBCL/4–18 were summed (combined α = .86) to determine the externalizing score for the child at 5 years. Teachers completed the teacher version of the CBCL (TRF, Achenbach 1991b); the externalizing score was based on the scores for the subscales Aggressive behavior (25 items) and Delinquent behavior (9 items) which were summed (combined α = .94) to derive an externalizing score for each child.
Results
Results are presented in two sections. In the first section we report the results of preliminary analyses, i.e., the distributions of the various predictor and outcome variables as well as their intercorrelations, and the parental and teacher ratings of the children’s externalizing behaviors are compared and related to each other. The second section reports the main results of the study, namely the prediction of externalizing behavior at age 5 from the various predictors at earlier ages. First, hierarchical regression analysis is conducted to estimate the unique and interactive contributions of the multiple predictors to the variance in Externalizing scores at age 5. Next, the hypothesized mediated pathways are tested. Finally, path analysis using AMOS 5.0 (Arbuckle, 2003) is applied to test the complete longitudinal model comprising all the direct, moderated, and mediated pathways of influence between the predictors at the various ages and age 5 externalizing behavior.
Preliminary analyses
Table 1 presents the means and standard deviations for the study variables as well as their intercorrelations. The distribution of children across attachment categories (64% B, 14% A, 8% C, and 14% D) did not differ from the distribution reported by Van IJzendoorn et al. (1999) for “normal”, i.e., middle-class, non-clinical groups in North America. The scores for child cognitive ability were normally distributed, with a mean score close to the population mean of M = 100. The distribution of the scores for ego-resiliency was in line with the findings of earlier studies and the distribution of scores for partner support was mildly to moderately skewed to the left but showed sufficient variation. Table 1 also shows significant stability for the two parent-child interaction measures as well as for child social fear and anger proneness from 15 to 28 months. For the predictor variables, no differences were found between boys and girls.
Table 1Intercorrelations, means and standard deviations for the study variables1234567891011121314151617181915-month predictors1. Effective guidance–2. Negative interaction.00–3. B vs. non-B attachmenta.38**-.46**–4. A vs. non-A attachmenta−.27**.22*−.55**–5. C vs. non-C attachmenta−.05.11−.40**−.12–6. D vs. non-D attachmenta−.22*.33**−.53**−.16−.12–7. Child social fear.42**-.03.19*−.16−.06−.05–8. Child anger proneness.04.02.02.06−.00−.09.35**–9. Child cognitive ability.31**−.09.09−.07.05−.08.18.12–10. Child sex.10−.01.10.03−.15−.05.08.07.15–11. Parental ego-resiliency.23*−.07.17.07−.28**−.09−.00−.07.19*−.05–12. Partner support.16−.03.16.02−.21*−.07−.06−.01.12.05.17–13. SES.05−.17−.02.02.10−.07.11−.02.14−.02.17−.07–28-month predictors14. Effective guidance.32**−.06.21*−.23*.15−.18.25*.10.41**.04.09−.07.19–15. Negative interaction−.09.63**−.34**.11−.05.41**−.11−.02−.07−.08−.28**−.10−.29**.00–16. Child social fear.21*.14−.04−.15.10.13.47**.16.03.15−.15−.17.05.17.02–17. Child anger proneness−.01−.05−.02.15.01−.13.23*.56**.09.07−.02−.15.08−.05−.15.14–5-year predictor18. Life events 15 mo-5 yr.02.19*−.06−.15.10.16−.04.12−.12−.03−.25*−.35**−.10.10.19.16.22*–5-year outcome19. Externalizing behavior−.22*.31**−.33**−.02.05.45**−.18−.02−.09−.25**−.25**−.27**−.18−.17.51**−.11.11.23*–M0.00b0.00b0.640.140.080.143.683.38103.900.470.448.320.00c0.00b0.00b3.383.611.480.00cSD1.001.000.480.350.270.340.850.7216.930.500.192.052.811.001.000.800.721.311.73Note. N = 105–111.aAttachment was dummy coded as B (=1) vs. ACD (=0); A (=1) vs. BCD (=0); C (=1) vs. ABD (=0); and D (=1) vs. ABC (=0).bThe parent-child interaction variables are factor scores with a mean score of 0.00 and a standard deviation of 1.00.cSES and Externalizing behavior are composed variables which were first standardized and then summed.*p < .05. **p < .01Table 2Hierarchical regression results for predicting age 5 externalizing scores (Total Model)Hierarchical regression resultsBlockBSE BβΔR2(%)1D versus non-D attachmenta1.880.42.36**22.2Negative interaction 28 months0.650.15.35**10.7Sex (male)b0.610.26.18*4.3Anger proneness 28 months0.470.18.20**4.4cPartner support−0.140.06−.17*2.62Interaction termsEffective guidance × Anger proneness 28 months−0.330.14−.19**3.3R2 final model =47.6%F(6,94) final model =14.21**aD vs. non-D coded as D = 1 and non-D = 0.bSex was coded as 0 (girls) or 1 (boys).cAlthough anger proneness was not significantly correlated with externalizing behavior, it had a significant Beta weight. This effect seems due to a suppressor variable. As recommended by Tabachnick & Fidell (1989), predictors were systematically deleted from the regression equation to identify which variable is the suppressor, indicating that D versus non-D attachment acted as the suppressor variable.*p < .05. **p < .01.
The mean score on the Externalizing scale was 9.97 (SD = 6.35; N = 111) for the CBCL/4–18 with 22% (n = 24) of the children scoring in the clinical range (T-score of ≥63; Achenbach, 1991a). For the TRF, the mean score was 7.50 (SD = 9.10; N = 111) with 16% (n = 18) of the children scoring in the clinical range (T ≥ 63; Achenbach, 1991b). Both the CBCL and TRF Externalizing scores were significantly higher than those reported for 5-year-olds in the Dutch norm population for the CBCL/4–18 (M = 8.23, SD = 6.37; Verhulst et al., 1996) and the TRF (M = 4.99, SD = 6.58; Verhulst, Van der Ende, & Koot, 1997), t (185) = 2.26, p < .05 for the CBCL/4–18, and t (185) = 2.51, p < .05 for the TRF. A paired-samples t-test showed parents to report significantly more externalizing behaviors than school teachers, t (110) = 3.24, p < .01. The correlation between the CBCL and TRF scores was .51, p < .001. To obtain a robust measure of externalizing behavior, a composite Total Externalizing score was computed by first standardizing and then summing the children’s Externalizing scores on the CBCL and TRF. For sex differences on this measure, a t-test revealed a significantly higher score for boys on Total Externalizing (Mboys = 0.41, SD = 1.93; Mgirls = −0.44, SD = 1.35, t (109) = 2.70, p < .01).
Predicting age 5 externalizing scores
Hierarchical regression analysis
To examine the unique and interactive contributions of the predictors to the variance in Externalizing scores at age 5, hierarchical multiple regression analysis was conducted with Total Externalizing as the dependent variable and the predictors at the different ages as the independent variables. The predictors were entered in the first block. In the second block, the interaction terms hypothesized in the introduction to predict Externalizing scores were entered, i.e., child temperament (2 measures) × parent-child interaction (2 measures) at both 15 and 28 months, and child cognitive development × parent-child interaction (2 measures) at 15 months. The interaction terms were computed according to the procedure recommended by Aiken and West (1991). To limit the number of interaction terms in the regression analysis, only those interaction terms that significantly predicted externalizing behavior at age 5 when examined in isolation were included in the regression analysis (see also Leve et al., 2005), i.e., Effective guidance × Anger proneness at 28 months and Negative interaction × Anger proneness at 28 months. Table 2 presents the results for the final model.
The overall model proved significant and explained 47.6% of the variance in the Total Externalizing scores. In the first block, D versus non-D attachment at 15 months explained a significant 22% of the variance in the Total Externalizing scores at age 5. Negative interactions at 28 months explained an additional 11% of the variance, child sex accounted for another 4%, just like anger proneness at 28 months, and partner support at 15 months contributed another 3% to the regression equation. In the second block, Effective guidance × Anger proneness at 28 months explained another 3% of the variance in Total Externalizing scores at age 5.
Fig. 1Child externalizing behavior scores in relation to effective guidance for children high and low on anger-proneness
To examine the nature of the interaction effect of effective guidance and anger proneness at 28 months on externalizing behavior at age 5, the relationship between effective guidance and externalizing behavior was determined for children with high (1 SD above the mean) and low (1 SD below the mean) levels of anger proneness, following the procedures of Aiken and West (1991). The regression lines for high and low anger prone children are plotted in Fig. 1. The simple slope of effective guidance was significant at high levels of anger proneness (B = −.55, t(107) = −2.56, p < .05), but not at low levels of anger proneness (B = .19, t(107) = 1.01, ns.). Thus, a lack of effective guidance was associated with more externalizing behavior for highly anger prone children but not for low anger prone children.
Mediational analyses
Next, the hypothesized mediated pathways between the predictors and age 5 externalizing behavior were tested using regression analysis, according to the procedure outlined by Baron and Kenny (1986). According to this procedure, three conditions must hold in order to establish mediation: (a) the predictor is significantly associated with the outcome, (b) the predictor is significantly associated with the mediator, and (c) the mediator is significantly associated with the outcome. If these conditions hold in the predicted direction, mediation is proven when the effect of the predictor on the outcome is shown to decrease when the mediator is also entered as a predictor in the regression equation.
First, we tested whether D versus non-D attachment mediated the effect of the parent-child interaction (two measures) at 15 months on age 5 externalizing behavior. For both hypothesized pathways, the three abovementioned conditions for establishing mediation were met (see Table 1 for correlations). Furthermore, the effect of effective guidance at 15 months on externalizing behavior (β = −.22, p < .05) dropped to a nonsignificant level (β = −.13, ns) when D versus non-D attachment was controlled for (Sobel test: z = −2.11, p < .05), showing that D versus non-D attachment completely mediated the effect of effective guidance at 15 months on externalizing behavior. The effect of negative interactions at 15 months on externalizing behavior (β = −.31, p < .001) decreased but remained significant when D versus non-D attachment was controlled for (β = −.18, p < .05); Sobel’s test showed the decrease to be significant (z = 2.77, p < .01). These findings indicate that negative interactions at 15 months are associated with age 5 externalizing behavior both directly and via D versus non-D attachment.
Next, we tested whether the effect of parental ego-resiliency on child externalizing behavior was mediated by the quality of the parent-child interaction (two measures) at 15 and 28 months. As evident from Table 1, the three conditions for establishing mediation only held for one of the four possible pathways, namely for the pathway mediated by negative interactions at 28 months. For this pathway, multiple regression showed the effect of parental ego-resiliency on externalizing behavior (β = −.25, p < .01) to decrease (Sobel test: z = −2.63, p < .01) to a nonsignificant level (β = −.12, ns) when negative interactions at 28 months was controlled for. These results indicate that the effect of parental ego-resiliency on children’s externalizing behavior was completely mediated by negative parent-child interactions at 28 months.Fig. 2Final path model summarizing the pattern of direct, moderated, and mediated relations between predictors at 15 and 28 months and age 5 externalizing behavior. Values given are standardized path coefficients. *p < .05. **p < .01
Path analysis
Finally, path analysis using AMOS 5.0 (Arbuckle, 2003) was applied to test the complete longitudinal model for predicting age 5 externalizing behavior including: (1) all the direct and moderated relations between the predictors and age 5 externalizing behavior found in the regression analysis; (2) the three significant mediated pathways resulting from the mediational analyses; and (3) the longitudinal relations between the 15- and 28-month assessments of the two parent-child interaction factors (i.e., effective guidance and negative parent-child interactions). A good model fit is indicated by a nonsignificant χ2 statistic, a Bentler comparative fit index (CFI) of .95 or above, and root mean square error of approximation (RMSEA) of .05 or below. The analysis of the initial model yielded a significant fit (χ2 = 21.86, df = 23, p = .53, CFI = 1.00, RMSEA = .00), indicating that the model fits the data well.2 The final model, including the standardized path coefficients, is depicted in Fig. 2.
Discussion
The present study is one of the first attempts, and the first with a community sample, to include the theoretically most important parental, child, dyadic, and contextual characteristics, longitudinally assessed in both infancy and toddlerhood, in the prediction of externalizing behavior problems at age 5. Data were gathered using multiple sources (parents and teachers) and multiple methods, including questionnaires, standardized tests and extensive observations of the parent-child interaction. Predictors from all four domains were found to be associated with children’s externalizing behavior at age 5, which is in accordance with the results of earlier studies using high-risk samples (Aguilar et al., 2000; Erickson et al., 1985; Lyons-Ruth et al., 1993; Shaw et al., 1996). The longitudinal model that emerged from our analyses provides more insight into the pattern of direct, moderated and mediated influences that work together to shape the development of externalizing behavior problems across the first five years of life.
As emphasized in the introduction, the present study was conducted on a non high-risk sample, in contrast to the above prior longitudinal studies that were all conducted on high-risk samples. After recruitment, when the children were 15 months of age, the sample appeared to be representative of the Dutch population of families with young children and therefore was not considered as a high-risk sample (see Van Bakel & Riksen-Walraven, 2002a). At the 5-year assessment, the selective drop-out of families with relatively low ego-resilient parents seemed to make the risk status of the remaining sample even lower. In this light, our finding that the children had higher CBCL and TRF externalizing scores than the relevant normative Dutch samples is puzzling. It may be that the level of externalizing behavior problems has increased over the last few years; unfortunately, no recent normative data are available. An alternative explanation may be that in middle-class communities, families with concerns about their children’s development are more likely to volunteer for research studies. Because relevant information is not available for the present sample, this remains an issue for further study.
The only hypothesized predictors that were not associated with later externalizing behavior were SES and child temperament and cognitive ability at 15 months. The lack of association between SES and externalizing behavior is in line with earlier studies that also reported parental and child characteristics to be more consistently associated with externalizing behavior than contextual characteristics (Shaw et al., 1996). Restriction of range in SES scores in our sample is not a likely explanation for the null findings given that the sample seems to be fairly representative of the Dutch population of families with children in the same age range, as indicated above. Yet, it may be that the variation of SES in the Netherlands is less than in other countries, such as the USA. Further research is recommended to examine how well the model that our analyses generated applies in community samples from other countries or for ethnically diverse samples of parents and children. The present nonsignificant relations between infant temperament and cognitive ability on the one hand and externalizing behavior on the other are in line with the results of two other recent longitudinal studies, both using high-risk samples (Aguilar et al., 2000; Brennan, Hall, Bor, Majman, & Williams, 2003), that also failed to find an association between early temperamental and neuropsychological risks on the one hand and persistent aggressive behavior on the other. Given that – as argued in the introduction – the predictive power of risk factors may be higher in high-risk samples than in lower-risk samples, it is not surprising that variables that failed to predict externalizing behavior in high-risk samples also failed to predict such problems in a lower-risk sample like ours. With regard to the predictive power of risk factors in relation to sample risk status, it should be noticed that comparing the predictive power of risk factors in studies differing in sample risk status is not the most elegant way to address this question. A more direct way to test whether risk factors interact with sample risk status is by testing for interactive effects in a sample with a greater range of risk (see, for example, Lacourse et al., 2006).
Although temperament assessed in infancy did not interact with the parent-child interaction in the prediction of externalizing behavior, temperament assessed in toddlerhood did. More specifically, a parent-child interaction characterized by a lack of effective guidance in toddlerhood predicted externalizing behavior only in highly anger-prone toddlers but not in less anger-prone toddlers. This finding seems to support Belsky’s (1997) differential susceptibility hypothesis that not all children are similarly affected by the same rearing experience. It should be kept in mind, however, that our effective guidance factor not only includes parental behavior, but child behavior as well. With the same reserve, our findings appear to also be in line with the results of studies that showed child negative emotionality to interact with parenting in predicting externalizing problems (Belsky et al., 1998; Leve et al., 2005). Rather than an association with negative parent-child interactions and hence in contrast to earlier reports, our findings showed temperament to interact with a different dimension of the parent-child interaction, namely a lack of effective guidance. This is not to say that negative parent-child interactions as such carried no weight in the prediction of externalizing problems in the present study. Quite the contrary, the factor proved to be one of the most powerful predictors of externalizing behavior problems in the present study and this was true for all the children in our sample and not just for a subsample of temperamentally difficult children.
Our results on the interaction between temperament and parenting in the development of externalizing problems extend those of earlier studies by showing that a specific temperament by parenting interaction may work particularly in a specific developmental phase. That the interaction between anger proneness and effective guidance proved to predict externalizing problems particularly for toddlers makes sense when interpreted from a developmental psychopathology perspective: establishing autonomy and learning to comply with social rules and expectations are major developmental tasks for toddlers that challenge the parents’ ability to provide effective guidance by imposing structure and setting limits while at the same time remaining emotionally supportive of the child. As a result of the child’s striving for autonomy, the second year is marked by parent-toddler conflicts. Toddlers that are prone to show angry behaviors in such discord situations are particularly in need of effective guidance in order to prevent their lack of emotional and behavioral self-control to further escalate and evolve into externalizing behavior problems.
Our finding that D attachment was among the strongest predictors of externalizing behavior at age 5 supports the results of previous research (Lyons-Ruth et al., 1993; Munson et al., 2001; Shaw et al., 1996; Vondra et al., 2001). Disorganized attachment has been found to reflect a history of disturbed parent-child interaction on the one hand and to predict later externalizing problems on the other (cf. Lyons-Ruth & Jacobvitz, 1999). The results of our path analysis have shed more light on the nature of the contributions of the parent-child interaction and parent-child attachment to the development of externalizing behavior problems. First of all, our path analysis showed D attachment to mediate the relations between the two aspects of parent-child interaction (i.e., effective guidance and negative interaction) at 15 months and externalizing behavior at age 5. In addition, the results showed negative parent-child interactions at 28 months to contribute significantly to later externalizing problems beyond D attachment at 15 months. These findings are in line with earlier findings by Erickson et al. (1985) and support Greenberg’s (1999) model that considers parent-child interaction and parent-child attachment as two different domains that contribute independently to a child’s development but that also reciprocally influence one another.
The results of this study may have clinical and policy implications. Many studies have shown that children born in disadvantaged environments are at risk for developing externalizing problems later in life. The present study showed that, also for children living in presumably lower-risk families, certain characteristics in infancy and toddlerhood indicate an increased risk of externalizing problems at preschool age. It should be kept in mind, however, that our sample may have been more at risk than originally thought, because a relatively large proportion of children turned out to have externalizing behavior scores above the subclinical cutoff. An important practical implication of our findings is that one of the most powerful predictors identified in the present study, i.e., negative parent-child interactions, is relatively easy to observe already in infancy and proves to be highly stable, which makes it an interesting candidate for inclusion in early community mental health screening procedures. And the specific interaction of the other parent-child interaction factor (i.e., effective guidance) with temperamental anger proneness in toddlerhood in predicting later externalizing problems also suggests that paying more attention to early parent-child interactions may be useful in early detection of children at risk and in preventive intervention programs for parents in community mental health care. But more research is needed, of course, to further explore these possible applications.
It is important to also point out limitations of the present study. First, although it is a strong point that data were collected using multiple methods and multiple sources, parental report was the sole source of information regarding four different predictors of externalizing problems, namely infant temperament, parental ego-resiliency, partner support, and stressful life events. Thus, response bias may have partially accounted for the results. What speaks against this, however, is that the four predictors were mostly uncorrelated with each other (see Table 1). Only stressful life events was significantly correlated with two other predictors (i.e., ego-resiliency and partner support), which may explain why negative life events did not independently contribute to the explanation of later externalizing problems beyond the other two predictors. Another limitation is that the present study focused on the role of children’s experiences with their primary caregivers in the development of externalizing problems, which leaves questions regarding the possible effects of the children’s experiences with other caregivers and with their peers, for example in child-care centers (cf. Gevers Deynoot-Schaub & Riksen-Walraven, 2006). A third limitation of the study is that we did not have additional information about earlier externalizing behavior and about characteristics of the children and families prior to our first 15-month assessment at our disposal. Future research that includes observations earlier in infancy may further improve our understanding of the mechanisms involved in the early development of externalizing problems. A fourth limitation lies in the relatively small sample size. The sample was small by epidemiological standards, particularly with regard to evaluating mediator and moderator effects. And as a fifth and final limitation of the present study it should be mentioned that we based our assessments of the quality of infant-parent attachment on an abbreviated version of the Strange Situation that includes one as opposed to two separations from the parent. Despite the evidence supporting the validity of the abbreviated procedure (see Method section), it is possible that we have underestimated the number of D children given that in the regular Strange Situation Procedure children may be classified as disorganized based on their reunion behavior after the second separation. Anyhow, the present study shows that D attachment, even when assessed with the abbreviated Strange Situation, was a powerful predictor of later externalizing behavior problems.
In sum, the longitudinal model our analyses generated provides more insight into the complex interplay among parental, child, dyadic and contextual characteristics that together shape the development of externalizing behavior from age 15 months onwards. Future research should reveal whether the predictive power of the model can be improved, for instance by including earlier measurements and by taking into account the children’s early experiences with the other parent and with their caregivers and peers in child-care facilities.

externalizing behavior
longitudinal model
parent-child interaction
disorganized attachment
early predictors