Effectiveness of Fitbit-Based Interventions in Improving 24-hour Movement Behaviors: A Systematic Review and Meta-Analysis

Abstract

Introduction

All activities performed by an individual over a 24-hour period can be classified into four categories—moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary behavior (SB), and sleep—collectively referred to as 24-hour movement behaviors. Fitbit devices can not only monitor all components of 24-hour movement behaviors but are also cost-effective and reliable. The aim of this meta-analysis was to assess the impact of Fitbit-based interventions on 24-hour movement behaviors outcomes.

Methods

To identify studies that employed Fitbit devices as intervention tools to improve 24-hour movement behaviors, the following five electronic databases were searched: PubMed, Embase, SCOPUS, Cochrane Library, and Web of Science. Study quality was evaluated using the Cochrane Risk-of-Bias tool. Meta-analysis was performed using a random-effects model to assess the pooled effects of Fitbit-based interventions on MVPA, LPA, SB, and sleep.

Results

Forty-five studies involving 5234 participants were included in this meta-analysis. Fitbit-based interventions can significantly increase MVPA (MD 4.44 min/day; 95% CI 2.77 to 6.10; P<0.05) and reduce SB (MD –10.36 min/day; 95% CI –18.40 to –2.32; P<0.05). However, these interventions fail to significantly increase LPA (MD –0.55 min/day; 95% CI –4.47 to 3.37; P=0.78) and sleep (MD 19.64 min/day; 95% CI –1.64 to 40.93; P=0.07). Interestingly, subgroup analyses showed that Fitbit-based interventions were more effective at improving MVPA, SB, and LPA in adults than in children and adolescents. Moreover, programs targeting multiple behavior changes simultaneously appeared to be more effective at reducing SB than those targeting changes in a single behavior.

Conclusions

Fitbit-based interventions can motivate a shift from SB and LPA to MVPA and sleep. Further, Fitbit devices may be a feasible and effective tool for improving 24-hour movement behaviors in adults. Nevertheless, additional studies are needed to explore the effectiveness of Fitbit-based interventions in improving 24-hour movement behaviors among children and adolescents.

Introduction

Moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary behavior (SB), and sleep are the four behaviors that impact both physical and mental health.^1-6 From a movement continuum perspective, these four behaviors encompass all activities performed by an individual over a 24-hour period and are thus jointly referred to as 24-hour movement behaviors. ⁷ Since the duration of a day is fixed, these four types of activities are interlinked and influence one another. ⁸ Specifically, increasing the time engaged in one activity inevitably reduces the time spent on the other activities. ⁸ Therefore, merely examining the isolated effects of a single behavior is insufficient to establish healthy activity patterns. Instead, the comprehensive impact of 24-hour movement behaviors on health must be considered from a “covariation” perspective. ⁹ Thus, the “24-hour movement behaviors” paradigm has gained widespread global recognition and has been incorporated into several physical activity guidelines. ¹⁰ In 2016, Canada released the world’s first 24-hour movement guidelines, providing comprehensive recommendations for optimal durations of physical activity, SB, and sleep.^11,12 Subsequently, Australia, New Zealand, and the World Health Organization (WHO) also released their own 24-hour movement guidelines, highlighting the importance of simultaneously meeting the recommended levels of MVPA, SB, and sleep to achieve optimal health benefits.^13-18 Despite these recommendations, adherence remains poor. A recent systematic review and meta-analysis involving 126129 children aged 5–13 years reported an overall compliance rate of only 10.4%. ¹⁹ Another survey study among 2739 adults revealed that only 0.6% fully adhered to the Canadian 24-Hour Movement Guidelines. ²⁰ These studies underscore the urgent need for effective intervention strategies that holistically address all components of 24-hour movement behaviors.

According to theories of behavior change, self-monitoring is an effective means of promoting behavioral changes.^21,22 Self-monitoring quantifies the gap between current activity levels and goals, thereby motivating individuals to change their behaviors.^22-24 In recent years, commercial health monitoring devices (e.g., smartwatches and wristbands) have become increasingly popular. ²⁵ These devices not only track physical activity, heart rate, sleep, energy expenditure, and other metrics, but also serve as motivational tools to promote behavioral change.^22,26-31 Fitbit is one of the most popular commercial health-monitoring devices today, with global sales exceeding 131 million units since its launch in 2009. ³² Furthermore, multiple studies have confirmed that Fitbit devices show acceptable validity in measuring SB,^33,34 LPA, ³⁵ MVPA,^36-39 and sleep outcomes.^38,40-42 Given the acceptable validity and popularity of Fitbit devices,^32-42 several studies have successfully incorporated these devices into lifestyle interventions to promote physical activity, reduce SB, treat obesity, and manage chronic diseases.^43-49 Importantly, Fitbit devices can monitor all types of 24-hour movement behaviors (including MVPA, LPA, SB, and sleep). Moreover, compared to traditional structured lifestyle interventions, Fitbit-based interventions are low-touch, cost-effective, and easy to implement at scale.^25,50-55 Therefore, Fitbit-based interventions may represent a promising approach for improving 24-hour movement behaviors.

A previous meta-analysis have evaluated the effectiveness of Fitbit-based interventions in improving health-related outcomes. ⁴³ The results demonstrated that these interventions can significantly increase MVPA and reduce body weight. ⁴³ However, this meta-analysis included self-reported measures of MVPA and SB, which are susceptible to recall bias and may compromise the reliability of the meta-analysis results.^56-58 Moreover, only four studies that objectively measured SB were included in the meta-analysis, necessitating the cautious interpretation of its conclusions. Furthermore, the study focused only on MVPA and SB, ignoring the other components of 24-hour movement behaviors. Therefore, an updated meta-analysis that synthesizes evidence regarding the effectiveness of Fitbit-based interventions in improving the entire spectrum of 24-hour movement behaviors is required to guide researchers and the public in determining whether and how Fitbit devices can be used to improve these behaviors.

The objectives of the present meta-analysis were to (1) evaluate the impact of Fitbit-based interventions on 24-hour movement behaviors outcomes, including objectively measured MVPA, LPA, SB, and sleep, and (2) explore how population characteristics (such as age and health status) and intervention program features (such as intervention duration and intervention strategies) influence program effectiveness.

Methods

Study Registration

This meta-analysis was carried out in line with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines (checklist presented in Supplemental Material 1). ⁵⁹ The meta-analysis protocol was registered with PROSPERO (CRD420251274533).

Search Strategy

This meta-analysis search covered the following databases: PubMed, Embase, SCOPUS, Cochrane Library, and Web of Science. These databases were searched from inception to October 15, 2025. The search strategies for each database are detailed in Supplemental Material 2. Notably, additional studies were discovered from the reference lists of the included studies and previous systematic reviews.

Study Selection

Studies were included if (1) the program involved the use of Fitbit devices as intervention tools to improve 24-hour movement behavior outcomes for at least four weeks, (2) the study reported accelerometer-measured MVPA, LPA, SB, and sleep outcomes (excluding those evaluated using Fitbit), (3) the study was a randomized controlled trial (RCT), and (4) the control groups did not use Fitbit devices.

After removing duplicates, two authors independently screened the titles and abstracts of the screened papers. The final selection of eligible studies was based on full-text reviews, which were conducted independently by two authors. Any disagreements arising during the selection process were resolved by consulting a third author.

Data Extraction

A standardized data extraction form was created based on the Cochrane Handbook for Systematic Reviews of Interventions. ⁶⁰ The following data were extracted: (1) study characteristics (authors, title, country, year of publication, and sample size); (2) participant characteristics (age, sex, and health status); (3) intervention characteristics (intervention durations and intervention strategies); and (4) outcome measures (mean and standard deviation [SD] for each group pre- and post-intervention). If a study provided standard errors (SEs) or 95% confidence intervals (CIs), SD values were calculated prior to analysis. If a study included multiple Fitbit-based intervention groups, the data from each group were collectively regarded as an independent sample. If a study provided outcome data at multiple time points, immediate post-intervention data were used for analysis.

Risk of Bias Assessment

The risk of bias for included studies was assessed by two independent authors using the Cochrane risk of bias tool, which considered the following seven domains: (1) random sequence generation, (2) allocation concealment, (3) blinding of participants and personnel, (4) blinding of outcome assessment, (5) incomplete outcome data, (6) selective outcome reporting, and (7) other sources of bias. The risk of bias on each domain was graded as low, unclear, or high. Disagreements arising during the risk of bias assessment were resolved by consulting a third author.

Data Analysis

The meta-analysis was conducted using Review Manager 5.4 software. The following outcomes were analyzed: objectively measured MVPA (min/day), LPA (min/day), SB (min/day), and sleep (min/day). Since the outcome data were converted to the same unit of measurement, effect sizes were assessed by calculating the pooled mean difference (MD) with a 95% CI. Given the diversity of the included studies, random-effects models were used in this meta-analysis. The heterogeneity was evaluated using the Cochran Q-test and I² statistics, as follows: I² = 0%–40%, insignificant heterogeneity; I² = 30%–60%, moderate heterogeneity; I² = 50%–90%, substantial heterogeneity; and I² = 75%–100%, considerable heterogeneity. Subgroup analyses were carried out to explore the influence of population characteristics (healthy individuals vs. individuals with pre-existing clinical conditions, adults vs. children and adolescents) and intervention program features (duration of intervention <3 months vs. duration of intervention ≥3 months, targeting a single behavior change vs. targeting multiple behavior changes simultaneously, using ActiGraph accelerometers as outcome measurement tools vs. using other types of accelerometers as outcome measurement tools) on the effect size. The publication bias for each outcome was assessed through funnel plot analysis. Sensitivity analyses were conducted employing the leave-one-out method.

Results

Study Selection

Figure 1 shows the overall literature screening and study selection process. The database search identified 27338 relevant records, with an additional 173 records obtained from other sources. After title and abstract screening, 485 studies were retained for full-text review. Of these, 128 studies were excluded because they did not use Fitbit devices as an intervention tool, 136 studies were excluded because their outcomes were not related to indicators of 24-hour movement behaviors, and 176 studies were excluded because they were not RCTs. Finally, 45 studies met all inclusion criteria and were included in the meta-analysis.

Figure 1.

Flow diagram of the study selection process

Study Characteristics

The characteristics of the included studies are summarized in Table 1. The studies were published between 2011 and 2025 and included a total of 5234 participants, with ages ranging from 12 to 74 years. Among the included studies, 39 involved adults and 6 involved children or adolescents.^52,61-65 Twenty-seven studies were conducted in clinical populations, including 14 in cancer survivors^62,63,66-78; 4 in individuals with rheumatoid arthritis^79-82; 2 in individuals with chronic low back pain^83,84; 2 in individuals with overweight or obesity^49,72; and 1 each in individuals with knee osteoarthritis, ⁸⁵ knee replacement, ⁸⁶ intellectual and developmental disabilities, ⁶⁴ a history of gestational diabetes, ⁸⁷ and prehypertension or stage 1 hypertension. ⁸⁸ The mean intervention duration was 3.84 months (range: 1–12 months). Regarding intervention strategies, 28 studies employed intervention strategies targeting a single behavior change (e.g., the program used Fitbit devices solely to promote physical activity),^{52,61-64,66-69,72-74,76-78,83,84,86-96} whereas 17 studies employed intervention strategies targeting multiple behavior changes simultaneously (e.g., the program used Fitbit devices to simultaneously improve physical activity and SB).^{49,65,70,71,75,79-82,85,97-103} With respect to outcome indicators, 41 studies assessed MVPA,^{49,52,61-67,69-86,89-102} 17 studies assessed LPA,^{49,61,64,65,67,69-71,76,78,83,84,90,93,94,96,102} 24 studies assessed SB,^{49,61,63-65,68,70,71,75,76,78-82,85,92-95,98,100,101,103} and 3 studies assessed sleep.^87,88,102

Table 1.

Characteristics of the Included Studies

Study	Participants	Sample size (% female participants)	Duration	Interventions	Outcomes	Outcome measurement tools
Hartman et al. USA (2016)	Women aged 59.5 years with BMI ≥ 27.5 kg/m2	54 (100%)	6 months	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph GT3X+ accelerometer
Maxwell-Smith et al. Australia (2019)	Colorectal and endometrial cancer survivors with an average age of 64.1 years	67 (50.7%)	12 weeks	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	ActiGraph GT9X accelerometer
					SB
Kenfield et al. USA (2019)	Men aged 66 years with Prostate Cancer	60 (0%)	12 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	Actigraph GTX3+ accelerometer
Weiner et al. USA (2019)	Breast cancer survivors with an average age of 57 years	87 (100%)	12 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph GT3X+ accelerometer
					LPA
					SB
Cadmus-Bertram et al. USA (2019)	Breast and colorectal cancer survivors with an average age of 54.4 years	50 (96%)	12 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph GT3X+ accelerometer
					LPA
Van Blarigan et al. USA (2019)	Colorectal cancer survivors with an average age of 54 years	39 (56.4%)	12 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	Actigraph GTX3+ accelerometer
Li et al. Canada (2020)	Adults aged 64.9 years with knee osteoarthritis	51 (82.3%)	12 weeks	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	SenseWear Mini
					SB
Duncan et al. Australia (2020)	Adults aged 46.3 years with overweight or obese	116 (70.7%)	6 months	The intervention group used Fitbit devices to simultaneously improve physical activity and sleep.	MVPA	GENEActiv
					LPA
					SB
Christiansen et al. USA (2020)	Adults aged 67 years with knee replacement	43 (53.4%)	6 months	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	Actigraph GTX3+ accelerometer
Alzahrani et al. Australia (2021)	Adults aged 43.6 years with chronic low back pain	26 (42.3%)	8 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	Axivity AX3
					LPA
Erickson et al. USA (2021)	Adults aged 32.3 years with cancer	31 (80.6%)	12 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	Actigraph wGT3X-BT accelerometer
					LPA
Hardcastle et al. Australia (2021)	Cancer survivors with an average age of 64 years	64 (50%)	24 weeks	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	ActiGraph GT9X accelerometer
					LPA
					SB
Ptomey et al. USA (2022)	Adolescents aged 16 years with intellectual and developmental disabilities	68 (53%)	6 months	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	Actigraph wGT3X-BT accelerometer
					LPA
					SB
Alley et al. Australia (2022)	Adults aged 69.3 years	174 (78.7%)	12 weeks	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	ActiGraph GT9X accelerometer
					SB
Newton et al. USA (2022)	Adults aged 69.2 years	56 (73.2%)	12 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph WGT3X+ accelerometer
					LPA
					SB
Reutrakul et al. USA (2022)	Women aged 40.8 years with a history of gestational diabetes	14 (100%)	6 weeks	The intervention group used Fitbit devices solely to promote sleep.	Sleep	Actiwatch 2
Pischke et al. Germany (2022)	Adults aged 68.7 years	126 (66.7%)	3 months	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	Actigraph GTX3+ accelerometer
					SB
Baldwin et al. Australia (2024)	Adults aged 71.5 years	131 (71%)	12 months	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph accelerometer
Phillips et al. USA (2024)	Women aged 54.8 years with metastatic breast cancer	49 (100%)	12 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	Actigraph GT3X-BT accelerometer
					LPA
					SB
Barone et al. USA (2024)	Adults aged 45.2 years	271 (60.1%)	3 months	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	Actigraph GT3X accelerometer
					SB
Wipfli et al. USA (2024)	Adults aged 39.1 years	198 (74.9%)	6 months	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	SB	ActiGraph GT9X link accelerometer
Bronas et al. USA (2024)	Adults aged 61 years	38 (68.4%)	6 weeks	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	ActiGraph GT3X+ accelerometer
					SB
Oliveira et al. Australia (2024)	Adults aged 74 years	593 (70%)	12 months	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph GT3X+ accelerometer
					LPA
					SB
Hardcastle et al. Australia (2024)	Cancer survivors with an average age of 63.1 years	86 (85%)	12 weeks	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	ActiGraph GT9X accelerometer
					LPA
					SB
Alley et al. Australia (2025)	Adults aged 68.8 years	128 (78.1%)	12 weeks	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	ActiGraph GT9X accelerometer
Dennett et al. Australia (2025)	Cancer survivors with an average age of 64 years	22 (27.3%)	4 weeks	The intervention group used Fitbit devices solely to promote physical activity.	SB	activPAL™
Li et al. Canada (2025)	Adults aged 55.8 years with rheumatoid arthritis	118 (91.5%)	27 weeks	The intervention group used Fitbit devices to simultaneously improve physical activity and sleep.	MVPA	SenseWear Mini
					SB
Clemes et al. UK (2022)	Adults aged 48.4 years	209 (1.9%)	6 months	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	activPAL micro accelerometer
					LPA
					Sleep
Baron et al. USA (2019)	Adults aged 45.5 years with prehypertension or stage 1 hypertension	16 (50%)	6 weeks	The intervention group used Fitbit devices solely to promote sleep.	Sleep	Actiwatch Spectrum Plus
Hartman et al. USA (2018)	Breast cancer survivors with an average age of 57.2 years	87 (100%)	12 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph GT3X+ accelerometer
Devine et al. USA (2020)	Cancer survivors with an average age of 18.5 years	49 (48.9%)	12 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	Actigraph wGT3X-BT accelerometer
Li et al. Canada (2017)	Adults aged 55.5 years with rheumatoid arthritis	34 (82%)	1 month	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	SenseWear
					SB
Li et al. Canada (2020)	Adults aged 53.5 years with rheumatoid arthritis	118 (53.3%)	9 weeks	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	SenseWear Mini
					SB
Evans et al. USA (2017)	Children aged 12.3 years	42 (45.2%)	6 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	Sensewear Mini
Corepal et al. Northern Ireland (2019)	Adolescent aged 12-14 years	190 (53.1%)	22 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph GT3X/GT3X+ accelerometer
					LPA
					SB
Verswijveren et al. Australia (2022)	Adolescents aged 13.7 years	159 (41.5%)	12 weeks	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	ActiGraph GT3X+ accelerometer
					LPA
					SB
Mendoza et al. USA (2017)	Cancer survivors with an average age of 16.6 years	59 (59.3%)	10 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph GT3X+ accelerometer
					SB
Cadmus-Bertram et al. USA (2015)	Women aged 59.9 years	51 (100%)	16 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph GT3X+ accelerometer
					LPA
Muellmann et al. Germany (2019)	Adults aged 69.7 years	334 (56.5%)	12 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph GT3X+ accelerometer
					SB
Finkelstein et al. USA (2016)	Adults aged 35 years	800 (53.8)	6 months	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph GT3X+ accelerometer
Ashe et al. Canada (2015)	Women aged 64.1 years	19 (100%)	6 months	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	ActiGraph GTX3+ accelerometer
Simons et al. Belgium (2018)	Adults aged 25 years	130 (52.5%)	9 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph GT3X+ accelerometer
					LPA
Amorim et al. Australia (2019)	Adults aged 58.3 years with low back pain	68 (50%)	6 months	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph GT3X+ accelerometer
					LPA
Brown et al. USA (2018)	Cancer survivors with an average age of 52 years	48 (97%)	15 weeks	The intervention group used Fitbit devices solely to promote physical activity.	MVPA	ActiGraph GT3X+ accelerometer
Li et al. Canada (2018)	Adults aged 61.7 years with rheumatoid arthritis	61 (82%)	2 months	The intervention group used Fitbit devices to simultaneously improve physical activity and SB.	MVPA	SenseWear Mini
					SB

SB: sedentary behavior; LPA: light physical activity; MVPA: moderate-to-vigorous physical activity.

Risk of Bias Assessment

A summary of the risk of bias assessment is presented in Figure 2. In the random sequence generation domain, 1 studies were judged to have a high risk of bias. ⁵² In the allocation concealment domain, 7 studies were deemed to have an unclear risk of bias due to insufficient detail regarding the allocation concealment process.^{52,64,67,74,87,92,103} Given the nature of the technology-based interventions, all studies were judged to have a high risk of bias for blinding of participants and personnel. In contrast, because all outcomes were objectively measured, all studies were deemed to have a low risk of bias in the blinding of the outcome assessment domain. Meanwhile, 3 studies were judged to have a high risk of bias in the other sources of bias domain.^77,80,87 This bias was largely the consequence of significant differences in baseline data between the intervention and control groups. All studies were deemed to have a low risk of bias for selective outcome reporting. On the incomplete outcome data domain, 11 studies—all of which reported high attrition rates (>20%)—were assessed as having a high risk of bias.^{62,64,65,69,85,89,92,97-99,102}

Figure 2.

Risk of bias for each included study

Meta-Analysis Results

MVPA

Forty-one studies reported changes in MVPA following the intervention. These studies encompassed 2735 participants in the intervention group and 2290 in the control group.^{49,52,61-67,69-86,89-102} The meta-analysis, conducted using a random-effects model, showed that the Fitbit-based interventions significantly increased MVPA (MD 4.44 min/day; 95% CI 2.77 to 6.10; P<0.05). Heterogeneity was substantial and statistically significant (I² =75%; P<0.05) (Figure 3).

Figure 3.

Forest plot based on a random-effects model showing the effect of Fitbit-based interventions on MVPA (min/day)

LPA

Seventeen studies reported changes in LPA following the intervention. These studies included 1068 participants in the intervention group and 1006 in the control group.^{49,61,64,65,67,69-71,76,78,83,84,90,93,94,96,102} The meta-analysis, performed using a random-effects model, showed that the Fitbit-based interventions did not significantly increase LPA (MD –0.55 min/day; 95% CI –4.47 to 3.37; P=0.78). Heterogeneity was moderate and statistically significant (I² =57%; P<0.05) (Figure 4).

Figure 4.

Forest plot based on a random-effects model showing the effect of Fitbit-based interventions on LPA (min/day)

SB

Twenty-four studies reported changes in SB following the intervention. These studies included 1632 participants in the intervention group and 1507 in the control group.^{49,61,63-65,68,70,71,75,76,78-82,85,92-95,98,100,101,103} The meta-analysis, conducted using a random-effects model, showed that the Fitbit-based interventions significantly reduced SB (MD –10.36 min/day; 95% CI –18.40 to –2.32; P<0.05). Heterogeneity was considerable and statistically significant (I² =89%; P<0.05) (Figure 5).

Figure 5.

Forest plot based on a random-effects model showing the effect of Fitbit-based interventions on SB (min/day)

Sleep

Three studies reported changes in sleep duration following the intervention. These studies included 109 participants in the intervention group and 128 in the control group.^87,88,102 The meta-analysis, performed using a random-effects model, showed that the Fitbit-based interventions did not significantly increase sleep duration (MD 19.64 min/day; 95% CI –1.64 to 40.93; P=0.07). Heterogeneity was moderate and not statistically significant (I² =59%; P=0.09) (Figure 6).

Figure 6.

Forest plot based on a random-effects model showing the effect of Fitbit-based interventions on sleep (min/day)

Subgroup Analyses

Subgroup analyses suggested that Fitbit-based interventions were more effective in increasing MVPA and reducing SB among adults than among children and adolescents. However, greater reductions in LPA were observed in children and adolescents than in adults following these interventions. The programs targeting multiple behavior changes simultaneously were more effective at reducing SB than those targeting changes in a single behavior. The programs using ActiGraph accelerometers as tools for outcome measurement were more effective at increasing MVPA and reducing SB than those using other types of accelerometers. No statistically significant differences were detected in the remaining subgroup comparisons (Table 2).

Table 2.

Results of Subgroup Analyses

​	MVPA				LPA				SB
	Studies, n	Effect size (95% CI)	P-value	I2 (%)	Studies, n	Effect size (95% CI)	P-value	I2 (%)	Studies, n	Effect size (95% CI)	P-value	I2 (%)
Adults	35	5.69 (4.10 to 7.28)	P<0.05	67%	14	1.67 (–2.21 to 5.54)	P=.40	53%	20	–12.10 (–21.13 to –3.06)	P<0.05	91%
Children and adolescents	6	–3.39 (–7.69 to 0.91)	P=.12	61%	3	–10.72 (–18.55 to –2.89)	P<0.05	0%	4	–2.23 (–15.58 to 11.12)	P=.74	0%
Healthy individuals	19	3.28 (1.47 to 5.10)	P<0.05	69%	9	–2.69 (–8.44 to 3.05)	P=.36	58%	10	–12.47 (–24.30 to –0.64)	P<0.05	92%
Clinical populations	22	6.15 (2.55 to 9.74)	P<0.05	80%	8	1.99 (–4.85 to 8.83)	P=.57	32%	14	–19.12 (–21.52 to –16.72)	P<0.05	0%
Intervention duration < 3 months	26	6.19 (3.90 to 8.48)	P<0.05	59%	9	0.43 (–9.24 to 10.10)	P=.93	72%	17	–14.07 (–28.73 to 0.59)	P=.06	84%
Intervention duration ≥ 3 months	15	2.22 (0.20 to 4.23)	P<0.05	65%	8	0.80 (–1.04 to 2.64)	P=.39	0%	7	–3.77 (–12.34 to 4.79)	P=.39	78%
Targeting a single behavior change	25	4.18 (2.12 to 6.25)	P<0.05	82%	12	–0.13 (–4.78 to 4.52)	P=.96	63%	10	–3.87 (–16.20 to 8.47)	P=.54	94%
Targeting multiple behavior changes simultaneously	16	4.81 (2.09 to 7.54)	P<0.05	38%	5	–2.49 (–11.72 to 6.74)	P=.60	38%	14	–18.38 (–35.66 to –1.11)	P<0.05	73%
Using ActiGraph accelerometers as outcome measurement tools	32	4.49 (2.76 to 6.23)	P<0.05	79%	14	–1.13 (–5.41 to 3.15)	P=.60	59%	17	–10.54 (–18.92 to –2.16)	P<0.05	92%
Using other types of accelerometers as outcome measurement tools	9	3.80 (–3.86 to 11.46)	P=.33	35%	3	5.86 (–21.42 to 33.14)	P=.67	63%	7	–7.76 (–37.51 to 21.99)	P=.61	0%

Publication Bias and Sensitivity Analyses

The funnel plots revealed no evidence of serious publication bias for MVPA, LPA, and SB (Supplemental Material 3,4, and 5). In addition, leave-one-out sensitivity analyses revealed consistent results for MVPA, LPA, and SB, demonstrating the robustness of the findings. However, as fewer than 10 studies included sleep outcomes, neither funnel plot analysis nor sensitivity analysis could be conducted for this parameter.

Discussion

Principal Findings

This is the first systematic review and meta-analysis to summarize the evidence on the effectiveness of Fitbit-based interventions in improving 24-hour movement behaviors. Our findings suggest that Fitbit-based interventions can significantly increase MVPA and reduce SB. However, these interventions may not be effective at improving LPA and sleep durations. Our subgroup analyses revealed that Fitbit-based interventions may be more effective at improving MVPA, SB, and LPA in adults than in children and adolescents. Moreover, programs targeting multiple behavior changes simultaneously appear to be more effective at reducing SB than those targeting changes in a single behavior.

MVPA accounts for the smallest proportion of 24-hour movement behaviors but has a considerable impact on health. Most previous studies have shown that the isochronous substitution of MVPA for the other 24-hour movement behaviors is beneficial in improving indicators of adiposity, cardiovascular health, and mental health.^4,104 Moreover, according to variance matrix analysis, MVPA is the most stable of the 24-hour movement behaviors and does not easily switch to other movement behaviors.^105,106 The 24-hour movement guidelines issued by Canada, Australia, and the WHO recommend at least 150 minutes of MVPA per week for adults and at least 60 minutes of MVPA per day for children and adolescents.^11,12,17,107 The findings of the present study suggest that Fitbit-based interventions can increase MVPA by 4.44 min/day, equivalent to one-fifth of the daily recommended MVPA duration for adults. Therefore, this increase achieved with Fitbit-based interventions is important and clinically meaningful. In line with these findings, Ringeval et al ⁴³ also found that Fitbit-based interventions have a significant impact on MVPA (MD 6.16; P<0.05). ⁴³ Therefore, Fitbit devices can be considered an effective tool for increasing MVPA in adults.

SB typically accounts for the largest proportion of all 24-hour movement behaviors. However, previous studies have shown that the isochronous substitution of SB for the other activity behaviors tends to increase the risk of obesity, cardiovascular disease, and mental health problems.^4,104 Canada’s 24-hour activity guidelines recommend that children and adolescents limit screen time to 2 hours per day and reduce other sedentary activities, while adults should limit sedentary activities to no more than 8 hours per day.^11,12 Our findings indicate that Fitbit-based interventions can significantly reduce SB. In contrast to our results, Ringeval et al ⁴³ found Fitbit-based interventions to be ineffective at significantly reducing objectively measured SB (MD –10.62; P=0.4). ⁴³ This discrepancy may be attributed to the difference in the number of included studies, population characteristics, and intervention strategies between our study and the meta-analysis conducted by Ringeval et al. Specifically, their research only included 4 studies and 173 participants, whereas our meta-analysis included 24 studies and 3139 participants, increasing the reliability of the results. Therefore, we believe that Fitbit devices may, in fact, function as effective tools for reducing SB.

LPA is the most overlooked of the 24-hour movement behaviors, and its impact on health remains a matter of debate.^108-110 The current 24-hour movement guidelines do not provide any recommended LPA duration. In previous studies, the isochronous substitution of LPA for SB has appeared to improve health outcomes, while the isochronous substitution of LPA for MVPA has been found to have the opposite effect.^4,13,104,111 Our results indicate that Fitbit-based interventions have no significant effects on LPA. This could potentially be because the primary goal of the included studies was to increase MVPA or reduce SB, whereas no specific intervention strategies were implemented to increase LPA. Therefore, additional research is required to explore the mechanisms through which LPA affects health and understand how Fitbit devices can be leveraged to convert SB to LPA and achieve health benefits.

Sleep is an important component of 24-hour movement behaviors and also plays a key role in physical and mental health.^112,113 Adequate sleep has a positive impact on the growth and development of children and adolescents, and on the physical and mental health of adults.^112-117 Therefore, the 24-hour movement guidelines for Canada and Australia provide clear recommendations for sleep across each age group: 9 to 11 hours per night for children aged 5–13 years, 8 to 10 hours per night for adolescents aged 14–17 years, and 7–8 hours per night for adults.^11,12,107 Our results indicate that Fitbit-based interventions have no significant effects on sleep. This result is consistent with the findings reported by Lai et al, who found that wearable-based sleep interventions cannot significantly increase the total sleep duration (MD 10.17; P=0.14). ¹¹⁸ Indeed, it is challenging to modify long-established sleep habits through self-regulation alone. However, this result must be treated with caution, because only 3 studies that included sleep outcomes were included in our meta-analysis. Future studies should explore how additional support can be provided for sleep self-regulation, such as strengthening social support and increasing awareness regarding the benefits of sleep to promote more positive attitudes toward sleep.

Interestingly, our subgroup analyses suggested that Fitbit-based interventions are more effective at improving MVPA, SB, and LPA in adults than in children and adolescents. This may be because Fitbit devices monitor multiple behavioral outcomes, including steps, MVPA, SB, and sleep, among others. Children and adolescents may lack the cognitive abilities and executive functions required to understand and apply such complex data, potentially leading to poor self-regulation.^26,52 Two previous meta-analyses have evaluated the effectiveness of wearable activity tracker-based interventions in improving physical activity and SB among children and adolescents.^26,119 Notably, in both studies, these interventions significantly increased daily steps but failed to increase LPA or reduce SB.^26,119 However, the studies reported contrasting findings on the effectiveness of these interventions in increasing MVPA.^26,119 Collectively, these results suggest that neither Fitbit nor other wearable activity trackers are always effective at improving MVPA, LPA, and SB among children and adolescents. This may be because available commercial wearable activity trackers, which integrate a variety of activity monitoring features and data feedback, are primarily designed for adults. ¹²⁰ However, children and adolescents may not fully understand these features and data; instead, simple and direct step-count feedback may be more appealing to this demographic.^120,121 Therefore, future studies on the use of wearable activity trackers for improving MVPA, LPA, and SB among children and adolescents should involve increased parental support and health education. This could help children and adolescents better understand the concepts of MVPA, LPA, and SB, as well as the impact of these behaviors on health. As only 6 studies involving children and adolescents were included in this meta-analysis, however, the results should be treated with caution. A large number of RCTs will be needed in the future to explore the effectiveness of Fitbit-based interventions in improving 24-hour movement behaviors among children and adolescents.

Additionally, our subgroup analysis also showed that programs targeting multiple behavior changes simultaneously achieved greater SB reductions than those targeting changes in a single behavior. However, this trend was not observed in the subgroup analysis for MVPA. According to variance matrix analysis, SB shows the highest probability of switching to other 24-hour movement behaviors.^105,106 Given that the four types of movement behaviors are interlinked, strategies aimed at increasing MVPA, LPA, and sleep may also lead to SB reductions.^8,122,123 However, MVPA is the most difficult to switch to other movement behaviors. ¹⁰⁵ Thus, strategies adopted to increase LPA and sleep or reduce SB rarely lead to increased MVPA. ¹²³ Thus, the evidence demonstrates that intervention strategies targeting multiple behavior changes simultaneously may be more appropriate for reducing SB, whereas those targeting a single behavior change may be more effective at increasing MVPA.

Overall, we found that Fitbit-based interventions can reduce SB by 10.36 min/day and LPA by 0.55 min/day, while increasing MVPA by 4.44 min/day and sleep by 19.64 min/day. These interventions can thus prompt the conversion of SB and LPA to MVPA and sleep. Previous research regarding the isochronous substitution of activity components has shown that such conversions tend to have a positive impact on health.^4,104,111 However, given that this meta-analysis only included a small number of studies involving children and adolescents, the current evidence is mostly relevant for adult populations. Notably, from the perspective of affordability and scalability, Fitbit-based interventions are low-touch, cost-effective, and easy to implement at scale. Therefore, we believe that Fitbit devices may serve as effective and feasible tools for improving 24-hour movement behaviors in adults.

Strengths and Limitations

This study has several strengths. Primarily, this study is the first to summarize the evidence on the effectiveness of Fitbit-based interventions on all components of 24-hour movement behaviors (including MVPA, LPA, SB, and sleep). Additionally, this meta-analysis focused only on studies using Fitbit devices—which are capable of monitoring all components of 24-hour movement behavior and are also cost-effective and reliable—as intervention tools. Finally, only RCTs examining objectively measured 24-hour movement behaviors were included, yielding a high level of evidence.

Nevertheless, some limitations must also be acknowledged. First, our meta-analyses revealed significant heterogeneity in MVPA, LPA, and SB outcomes among the included studies. This may be due to the diversity of study populations and outcome measurement approaches. Moreover, this study included children, adolescents, adults, healthy individuals, and clinical populations. Although this may have enhanced the generalizability of the findings, it may have also created a high degree of population heterogeneity. In addition, while this study included only outcomes measured with accelerometers, the outcomes were recorded using a variety of devices (e.g., the ActiGraph GT3X+ accelerometer, the ActiGraph GT9X accelerometer, the SenseWear Mini, the Axivity AX3, and the GENEActiv) and cut-points (e.g., Freedson cut-points, Troiano cut-points, Sasaki cut-points, and Kamada cut-points), which could create a high degree of heterogeneity in outcome measurements. Second, 11 studies were assessed as having a high risk of attrition bias, 7 as having a high risk of selection bias, and 3 as having a high risk of other types of bias. These methodological shortcomings may have affected the precision of the effect estimates. Finally, the majority of the included studies (39/45) did not report follow-up data. Thus, this study only assessed the immediate post-intervention effects and not long-term effects during the follow-up period. Therefore, whether the benefits of Fitbit-based interventions are sustained over time remains unclear. Additional studies are needed to explore the long-term sustainability of these positive effects.

Conclusions

Overall, the present study suggests that Fitbit-based interventions significantly increase MVPA and reduce SB. However, these interventions cannot significantly improve LPA and sleep. From the perspective of 24-hour movement behaviors, Fitbit-based interventions can motivate the conversion of SB and LPA to MVPA and sleep. Moreover, these interventions appear to be more effective at improving MVPA, SB, and LPA among adults than among children and adolescents. Given the results of our study and the reliability and cost-effectiveness of Fitbit devices, we believe that these devices may serve as feasible and effective tools for improving 24-hour movement behaviors in adults. Nevertheless, as only 6 studies involving children and adolescents were included in this meta-analysis, more RCTs are required to explore the effectiveness of Fitbit-based interventions in improving 24-hour movement behaviors in this population.

ORCID iD

Wentao Wang https://orcid.org/0000-0003-0801-1196

Ethical Considerations

Not applicable, because this study was a systematic review and did not involve new human participants.

Consent to Participate

Patient consent statements were not required as this study was a systematic review.