An Intervention Pattern of Family Parent-Child Physical Activity Based on a Smartphone App for Preschool Children during COVID-19

Abstract

Objective. Children’s lifestyles, behaviors, and educational activities were affected by COVID-19. The preschool children struggled with the challenge of learning at home and avoiding a rapid decrease in physical activity (PA). This study tested the effectiveness of a family-based intervention that integrated the family and preschool based on a smartphone app to improve the moderate-to-vigorous PA (MVPA) and physical fitness of preschool children during COVID-19. Methods. This 8-week study was conducted using a quasiexperimental pre- and posttest design with a comparison group. A total of 66 pairs of preschool children (30 boys) and their parents and 44 preschool children (24 boys) and their parents composed the experimental group (EG) and the control group (CG), respectively. PA was measured using a GENEActiv waveform triaxial accelerometer. Children’s physical fitness was assessed using a battery test from the Chinese National Measurement Standards on People’s Physical Fitness for preschool children. Results. Preschool children and their parents in the EG participated in less sedentary ( p < 0.01 ) and more light PA ( p < 0.01 ) and MVPA ( p < 0.01 ) compared with those in the CG at the late PA assessment. The EG significantly improved the mean performance of tennis ball throw ( p < 0.05 ), balance beam walk ( p < 0.01 ), and continuous jumping on both feet ( p < 0.01 ) compared to the CG. Conclusions. The family parent-child PA intervention based on a smartphone app can effectively increase the MVPA of preschool children and their parents, reduce sedentary time, and improve preschool children’s physical fitness. Overall, the family parent-child PA intervention model based on a smartphone app for preschool children designed in this study is feasible and effective.

Effectiveness of mHealth App–Based Interventions for Increasing Physical Activity and Improving Physical Fitness in Children and Adolescents: Systematic Review and Meta-Analysis

“…As shown in Table 1 , the included studies have the following basic characteristics. The age of the participants varied from 3 to 18 years with 5 (18%) of the 28 studies involving preschool children [ 26 , 44 , 46 , 48 , 50 ], 8 (29%) studies involving children [ 28 - 31 , 33 , 36 , 37 , 51 ], and 15 (53%) studies involving adolescents [ 20 , 32 , 34 , 35 , 38 - 43 , 45 , 47 , 49 , 52 , 53 ]. The objectives of the studies differed, and the selection criteria for the target population varied inconsistently.…”

“…Most studies (16/28, 57%) focused on healthy children and adolescents; however, of the 28 studies, 9 (32%) included participants with overweight and those with obesity [ 29 - 31 , 35 , 36 , 39 , 43 , 45 , 52 ], 2 (7%) involved children with cancer [ 34 , 53 ], and 1 (4%) enrolled youth with congenital heart disease [ 40 ]. Of the 28 studies, 8 (29%) were conducted in Asia [ 31 , 36 , 37 , 39 - 41 , 44 , 45 ], while the remaining studies were performed in Oceania (7/28, 25%) [ 29 , 30 , 35 , 38 , 46 , 50 , 53 ], North America (5/28, 18%) [ 28 , 33 , 34 , 43 , 51 ], and Europe (8/28, 29%) [ 20 , 26 , 32 , 42 , 47 - 49 , 52 ].…”

“…The type of intervention used in 15 studies was stand-alone apps [ 20 , 26 , 28 , 32 , 33 , 40 , 41 , 43 - 47 , 49 - 51 ], and 13 studies used concerted intervention [ 29 - 31 , 34 - 39 , 42 , 48 , 52 , 53 ]. Participant engagement with the intervention was mentioned in 28 studies, 8 studies focused on parent-centered [ 26 , 31 , 36 , 37 , 44 , 46 , 48 , 50 ], the remaining studies were child centered. The mHealth apps were based on various theoretical paradigms, including self-regulation theory (SRT), SDT, and SCT.…”

“…The methods for random sequence generation were adequately reported in 28 studies, and 14 (50%) of the 28 studies described allocation concealment protocols [ 20 , 26 , 29 , 31 , 35 , 37 , 39 , 43 , 46 , 48 , 50 - 53 ]. Of the 28 studies, blinding of participants and personnel was unclear in 17 studies [ 20 , 28 - 33 , 38 - 41 , 43 - 45 , 47 , 49 , 51 ], high risk of bias was identified in 7 (25%) studies [ 26 , 34 , 35 , 37 , 50 , 52 , 53 ], and blinding of outcome assessment was unclear in 18 (64%) studies [ 18 , 28 - 34 , 38 , 40 , 41 , 44 , 45 , 47 - 51 , 53 ]. Four studies provided data regarding subjects lost to follow-up [ 30 - 32 , 34 ], while one study had unclear information on this aspect [ 51 ].…”

“…Heterogeneity tests revealed homogeneity between the studies ( I 2 =98%; P< .001), which required analysis using a random effect model. Meta-analysis found that mHealth app–based interventions significantly reduced SB (SMD –0.97, 95% CI –1.67 to –0.28; P =.006; Figure 4 [ 20 , 26 , 29 , 30 , 32 , 34 , 35 , 43 , 44 , 46 , 48 , 53 ]).…”