A scoping review of how automation, self-tracking dose characteristics, and participant age are operationalized in digital physical activity interventions

Abstract

Background

Self-tracking physical activity can involve receiving behavioral feedback from a device, engaging in manual self-monitoring, or a combination of the two.

Objective

This scoping review examines the implementation of digital self-tracking interventions aimed at promoting physical activity among adults, emphasizing automation formats, dosage metrics, and age-specific adaptations.

Methods

A scoping review was conducted of randomized intervention studies published between 2007 and 2025. Eligible studies examined digital self-tracking interventions targeting physical activity in adult populations. Data were charted to summarize intervention characteristics, including self-tracking format, automation level, intervention duration, tracking frequency, and age-related patterns.

Results

A total of 197 studies involving 38,492 participants were included. Semi-automated approaches were used most frequently (52%), followed by automated (31%) and manual (7%) methods, and multiple-method approaches (10%). Wearable devices were the most common intervention format (37%), often combined with other digital applications (33%). Intervention duration averaged 21 weeks, and daily self-tracking was the most frequently reported tracking schedule (60%). Few age-related differences in self-tracking format and frequency were observed; however, interventions targeting younger adults tended to be shorter in duration, whereas those targeting middle-aged and older adults more often involved extended self-tracking periods.

Conclusions

Digital self-tracking interventions for physical activity in adults show substantial variability in format and dosage. The findings highlight the need for age-responsive and dosage-specific strategies to improve engagement and support physical activity behavior change.

Introduction

Digital health technologies such as wearable fitness trackers and mobile health applications have become increasingly common tools for tracking and promoting health behaviors.^1,2 Researchers have integrated these technologies into health behavior interventions in new ways. These tools help researchers reach wider populations and help participants track their health behaviors more easily.^3–5 Tracking is accomplished manually via recording behaviors in mobile applications (i.e., self-monitoring) and automatically via feedback from wearable devices (i.e., behavioral feedback). Despite the documented benefits of self-tracking to promote behavior change, the key challenge remains: how to implement tracking features and prescribe dosing to improve behavioral outcomes without overburdening participants. ⁶ Dosing or implementation variability may be a critical source of heterogeneity in treatment effects, but it is unclear which sources of variability warrant further investigation. This scoping review examines how digital physical activity interventions have implemented self-tracking and identifies potential sources of variability.

Self-tracking research uses diverse methods. These methods may influence participants’ willingness and ability to engage with tracking, as well as the effects of tracking on behavior change. Two techniques can be embedded in activity trackers to support behavior change: self-monitoring and behavioral feedback. Beyond the technique used, two design factors, the nature of the behavior change technique and the dosage of tracking, and one participant characteristic, age, are of particular interest for self-tracking.

The nature of the behavior change techniques involved in self-tracking may create treatment variability. Both techniques are grounded in control theory and serve to provide a basis for comparing recent behavior against a goal to regulate effort in goal pursuit. ⁷ However, they differ in the source of information and cognitive effort required to obtain that information. Self-monitoring involves participants actively monitoring and recording their own behaviors^8–10 and requires active engagement and reflection. In contrast, behavioral feedback typically involves automated tracking with passive data collection and summarized information delivered directly to users, reducing the cognitive effort required.

These behavior change techniques frequently differ in the amount of effort required to engage with the technique. Self-monitoring is an inherently manual process that requires effort. Manual recording, which requires active participation, inherently fosters self-reflection. Conversely, behavioral feedback is often automated with information being sensed, summarized for, and pushed to participants. Automated tracking offers ease but may not elicit the same degree of self-reflection. ¹¹ Consequently, these differing methods may lead to varied outcomes in intervention effectiveness. For example, Conroy et al. ⁶ evaluated the impacts of prompted behavioral feedback from automated tracking versus prompted self-monitoring that required manual recording of physical activity. They found an inverted-U association between the daily frequency of prompts for manual self-tracking and daily steps. About 2–3 prompts per day showed the strongest association with higher physical activity. In contrast, the daily frequency of automated behavioral feedback was not associated with daily steps.

Some interventions attempt to capitalize on the ease of effortless behavioral feedback and the widely-established efficacy of effortful self-monitoring by using semi-automated tracking methods. ¹¹ Semi-automated tracking combines automated data collection from wearable devices or apps with user-driven elements, such as manual logging of reflections, perceptions, or additional details about their physical activities. This approach leverages the accuracy and convenience of automation while still encouraging active participant engagement and self-reflection.

A second source of variability in implementing self-tracking in behavioral health interventions involves the dosage prescribed for the behavior change technique. The term ‘dosage’ refers to the specific quantity of a treatment or intervention administered over a defined period, encompassing dimensions such as frequency, duration, and mode.^12,13 In self-tracking interventions, frequency denotes how often the technique should be used (e.g., hourly, daily, or weekly monitoring), duration indicates the total time span of the intervention, and mode describes the method of delivery (e.g., wearable devices, emails, phone calls, or text messages). For self-monitoring, in particular, dosage can significantly affect the outcomes of the intervention because excessively high doses may lead to participant burden and disengagement, thereby reducing effectiveness. ⁶ Conversely, too low a dose prescribed might not provide enough engagement to promote behavior change.^6,14 However, overly low or high doses remain poorly defined and insufficiently guided in existing research. Therefore, understanding the scope of doses implemented is a necessary step toward understanding dose-response relations between the technique and subsequent behavior change.

In addition to these design characteristics, age is a participant characteristic that may influence how self-tracking is implemented. Comfort with and capability of utilizing different technologies varies with age and directly influences engagement with digital health platforms. Older adults, for example, often face barriers to using digital health technologies such as lower digital literacy and unfamiliarity with complex interfaces, which can diminish their interaction with these technologies. ¹⁵ This engagement-related variability is compounded by cognitive factors such as decreased processing speed and reduced cognitive flexibility, which can affect their ability to adapt to new technological environments. ¹⁶ On the other hand, younger adults tend to embrace frequent and multifunctional digital interactions, aligning with their higher comfort levels and proficiency with advanced technology. ¹⁷ They may readily integrate digital health tools into their daily routines due to greater technological fluency and a more favorable attitude towards new technologies. ¹⁸ These differences may lead to age-specific customization of self-tracking interventions across the lifespan.

This scoping review seeks to characterize how self-tracking has been implemented in adult populations with digital tools to promote physical activity. Specifically, this review will map the range of self-tracking approaches. It will also describe how studies incorporate automation, dosage, and participant age into intervention design. Here, dosage refers to protocol-defined self-tracking characteristics, such as intended tracking frequency, intervention duration, and delivery mode, rather than participants’ received dose or actual engagement with self-tracking. Finally, it will summarize how studies operationalize and measure these factors. By synthesizing this information, we aim to identify gaps in the literature. We also aim to inform future hypothesis-driven research on the mechanisms linking engagement with digital self-tracking to behavioral outcomes. Results of this review will provide a foundation for future meta-analyses assessing factors that influence the engagement and efficacy of self-tracking interventions to promote physical activity behavior change.

Methods

This scoping review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) ¹⁹ (see Supplemental file 3). The review methods were established before the review began; however, no protocol was registered or published.

Eligibility criteria

This review focused on physical activity behavior change interventions that incorporated digital health technologies for self-monitoring. Eligible studies were randomized intervention studies that evaluated the effects of digital self-monitoring on physical activity outcomes. Studies were included if participants or groups were randomly allocated to intervention or comparison conditions, including individually randomized controlled trials, cluster-randomized trials, and factorial randomized trials. Studies without random allocation were excluded.

The selection criteria included full-text articles written in English and published between 2007 and 2025, a period chosen because smartphones and wearable devices became widely accessible after 2007 following the introduction of the first iPhone and Fitbit.^20,21 The research question was defined using the Participants, Interventions, Comparators, Outcomes, and Study Design (PICOS) framework as follows:

Category	Criteria
Participants	Adults aged ≥18 years, recruited from clinical or non-clinical settings.
Interventions	Digital technologies used for self-monitoring physical activity. Exclusion: Studies focused primarily on weight loss, as this area has been comprehensively reviewed (e.g., Patel et al. 10 ), allowing the present review to concentrate on digital PA interventions.
Comparators	No restrictions on the type of comparison arms (e.g., usual care, minimal intervention, active controls).
Outcomes	Primary outcomes: Device-based physical activity measures, including: • Step counts • Frequency of activity • Duration or intensity-based metrics. Self-monitoring dosage metrics: duration, frequency, amount, and mode of engagement with self-monitoring tools. Outcomes related to self-monitoring dosage were included even if not explicitly detailed in the original article.
Study Design	Randomized intervention studies, including individually randomized controlled trials, cluster-randomized trials, and factorial randomized trials. Studies without random allocation were excluded.

Data sources and search strategy

The search strategy for this review was developed in collaboration with a trained librarian and search specialist at The Pennsylvania State University to ensure both comprehensiveness and precision. We conducted searches across four major databases: PubMed, PsycINFO, Web of Science, and CINAHL. Our search strategy involved a combination of MeSH terms and keywords related to “physical activity”, “digital tools”, and “self-monitoring”. Initially, in PubMed, we identified suitable MeSH terms for each thematic area, enhancing the search’s sensitivity through strategically selected keywords under these headings. These terms were subsequently adapted and applied to the other databases, incorporating adjustments for the unique indexing terms specific to each database. The search terms for “self-monitoring” were informed by Patel et al. ¹⁰ The detailed search terms are available on the SearchRxiv website.^22–25 The completed search terms can also be viewed in supplemental file 1.

Study selection

Electronic database searches were imported into EndNote software (Clarivate Analytics, London, UK) and duplicates were removed. The study selection process comprised two distinct phases, abstract, title, and full-text review, which were managed using Rayyan QCRI software. ²⁶

The review followed the same screening and coding protocol across three search rounds conducted in 2023, 2024, and 2025. Screening and coding were completed in pairs to ensure consistency. Each pair first co-reviewed 20 articles to calibrate assessment criteria before independently screening the remaining records. Inter-rater reliability was monitored throughout, with pairs required to achieve a Cohen’s kappa above 0.80 before proceeding independently. Approximately 25% of articles were jointly reviewed to establish baseline reliability. During full-text screening and data extraction, reviewers jointly coded an initial set of articles to refine coding criteria and then co-coded a subset to verify agreement before proceeding independently. Trial coding sessions and guided surveys were used to maintain consistency, and discrepancies were resolved through discussion or consultation with a third reviewer.

Data charting process and items

Data charting was conducted independently by two reviewers using a structured Qualtrics form (Qualtrics, Provo, UT) developed to capture key study characteristics and intervention features relevant to the review questions. The charting form was developed by the review team and included items related to participant characteristics (e.g., age, gender, ethnicity, study location, and health status) and digital self-tracking intervention characteristics, including intervention duration, delivery mode, tracking frequency, and self-monitoring format category (see Supplemental File 2).

Tracking frequency was defined as the prescribed number of self-tracking occasions over a given time period based on the intervention description provided in each study. For studies categorized as daily tracking, frequency was coded as the number of participant tracking occasions per day, such as logs, entries, or prompted check-ins.

The charting form included self-monitoring format items describing how physical activity was recorded and how feedback was delivered. These items were later used to derive the automation category for analysis.

Before full charting began, the reviewers pilot-tested the form on a subset of studies and refined items to improve clarity and consistency. After the review team confirmed the categories and definitions, the finalized form was used consistently across all included studies. The two reviewers then independently charted the studies. Discrepancies were resolved through discussion, with consultation from a third reviewer when needed. We did not contact study authors to obtain or confirm missing data. Separate inter-rater reliability estimates were not generated for individual charting variables, including automation format, because final extracted data were established through this independent charting and consensus-resolution process.

Data synthesis

Data management and analysis were conducted using R and RStudio.^27,28 For data management, the dplyr and tidyr packages were used to organize and classify study and self-monitoring characteristics.^29,30 Automation was a derived variable based on responses to the self-monitoring format items in the charting form (see Supplemental File 2). Based on these charted responses, interventions were grouped as manual, semi-automated, automated, or multiple methods. Manual referred to participant-reported recording without automated sensing; semi-automated referred to interventions combining automated tracking or feedback with participant input; automated referred to passive device-based tracking without manual entry; and multiple methods referred to interventions using two or more distinct self-tracking methods within the same study. Age group was also derived from charted study-level age information and classified for descriptive purposes as young adults (18–35 years), middle-aged adults (36–64 years), or older adults (≥65 years). These categories were guided by the APA Dictionary of Psychology’s developmental distinction between young adulthood, middle adulthood, and later adulthood, with the lower bound for young adulthood extended to 18 years to align with the adult eligibility criterion used in this review. ³¹ Descriptive summaries were conducted at the study level and were not weighted by sample size because the purpose of the review was to map intervention design and reporting characteristics across studies rather than estimate participant-level prevalence or pooled effects. The psych package was applied to summarize continuous variables, ³² while plots were created using ggplot2. ³³ World maps were generated using the sf, rnaturalearth, and RColorBrewer packages.^34–37

Results

Selection of sources of evidence

A total of 160 studies had been included in the previous version of the review. For the updated review, 5,757 records were identified through database searches, including PubMed (n = 727), PsycINFO (n = 497), CINAHL (n = 739), and Web of Science (n = 3,794). After removing 4,646 duplicate records, 1,111 records remained for title and abstract screening. Of these, 966 records were excluded, and 145 reports were retrieved for full-text review. Following full-text assessment, 108 reports were excluded for reasons including wrong outcome, outcome not measured with a device, no self-monitoring behavior, weight loss focus, wrong study design, wrong publication type, wrong population, duplicate records, or other reasons. A total of 37 new studies met the inclusion criteria. Combined with the 160 studies included in the previous version of the review, 197 studies were included in the final synthesis (Figure 1).^38–234 Detailed study selection processes for the earlier review stages are provided in Supplemental Figures S1 and S2 in Supplemental File 1.

Figure 1.

PRISMA 2020 flow diagram for the updated study selection process.

Note. This figure presents the study selection process for the 2025 updated review, including studies retained from the previous version of the review and new studies identified through updated database searches.

Characteristics of sources of evidence

The volume of published studies showed a marked increase after 2012 (see Figure 2). At the country level, studies were most frequently conducted in the United States (74 studies; 37.6%), followed by the United Kingdom (23 studies; 11.7%), Australia (21 studies; 10.7%), the Netherlands (13 studies; 6.6%), and South Korea (10 studies; 5.1%). Because multinational studies could contribute to more than one country, country percentages are not mutually exclusive and may sum to more than 100% (see Figure 3). There were 38,492 participants in total, with an average sample size of approximately 195 participants across the 197 studies (range: 8–1547 participants). The populations included young adults (18–35 years, 15%), middle-aged adults (36–64 years, 62%), and older adults (65 years and above, 21%), with a mean age of 51 (range: 20–83) (see Tables 1 and 2). Of the studies that reported sex (n=185), an average of 61% of participants were female (see Table 1).

Figure 2.

Distribution of paper publications by year.

Note. The search was last updated in early 2025; therefore, the 2025 publication count represents a partial year and should not be interpreted as evidence of a decline in publication volume.

Figure 3.

Distribution of paper publications by country.

Table 1.

Summary statistics of sample-level demographic characteristics.

Variables	n	Mean	sd	Median	min	Max
Sample Size	197	195.39	253.02	116	8	1547
Average Age	193	50.81	14.93	50	19.62	83.00
Female Participants (%)	185	60.68	25.84	64.44	0	100
Hispanic Ethnicity
Hispanic (%)	26	26.11	36.28	9.90	1.51	100
Race
White (%)	70	78.76	16.30	83	22.80	100
African American (%)	37	17.68	19.69	10.00	2.00	72.5
Asian (%)	27	16.26	25.81	4.50	1.69	100
Native American (%)	5	1.94	1.97	1.00	0	5.0
Native Hawaiian (%)	4	2.26	2.12	2.02	0	5.0
More than one race (%)	9	5.28	5.15	3.45	1.0	17.3
Other (%)	57	11.04	12.25	8.0	1.0	80.2

Note. Race and ethnicity values are study-level percentages among reporting studies, not pooled participant-level proportions. Of 197 articles, 79 reported race or ethnicity information and 118 did not. The n for each category reflects the number of studies reporting that category; categories are not mutually exclusive and should not be summed.

Table 2.

Summary of study demographic categories by age group and health status.

Variables	Count	Percent (%)
Age Group
Young Adults	29	14.7
Middle-Aged Adults	123	62.4
Older Adults	41	20.8
Missing Age	4	2.0
Health Status
Healthy or unspecified health condition	102	51.8
Any Chronic Condition	95	48.2
Cardiovascular	20	10.2
Cancer	12	6.1
Metabolic and Endocrine	12	6.1
Respiratory	9	4.6
Joint and Bone	8	4.1
Neurological and Cognitive	4	2.0
Mental Health	2	1.0
Multiple Conditions	9	4.6
Other Chronic	19	9.6

Note. Percentages are based on the total number of articles included (N = 197). Chronic condition subcategories sum to 95 studies.

In total, 79 articles reported race or ethnicity of study participants. Among studies reporting each racial or ethnic category, the mean study-level proportions were 79% White, 26% Hispanic, 18% African American, and 16% Asian. Other racial groups (i.e., Native American, Native Hawaiian, and participants with multiple ethnicities) were less frequently represented (see Table 1). The health status of participants indicated that 95 studies (48.2%) included participants with chronic conditions. The most common chronic conditions included cardiovascular and circulatory diseases (10%), metabolic and endocrine disorders (6%), respiratory diseases (5%), and cancer (6%) (see Table 2).

Self-tracking logging modes, methods, and devices

Table 3 outlines the various logging modes, methods, and devices used for self-tracking. Semi-automated self-tracking was the most common logging mode, used by 52% of studies, followed by automation (31%), multiple methods (10%) and manual recording (7%). Wearable devices were the most popular devices for self-tracking (37%), either alone or with other applications (33%). Mobile apps alone were used by 23% of studies. Devices accounted for 62% of the self-tracking measurement methods.

Table 3.

Summary of self-tracking intervention characteristics: Logging modes, methods, and devices.

Intervention characteristic	Categories	Young adults(n=29)	Middle-aged adults (n=123)	Older adults (n=41)	Age-known subset(n=193)	Total (overall, n=197)
Tracking Method	Automated	12 (41%)	40 (33%)	10 (24%)	62 (32%)	62 (31%)
	Manual	2 (7%)	8 (7%)	4 (10%)	14 (7%)	14 (7%)
	Multiple methods	3 (10%)	13 (11%)	3 (7%)	19 (10%)	19 (10%)
	Semi-automated	12 (41%)	62 (50%)	24 (59%)	98 (51%)	102 (52%)
Digital Mode	Mobile App Only	11 (38%)	26 (21%)	8 (20%)	45 (23%)	45 (23%)
	Mobile App & Wearable	9 (31%)	20 (16%)	9 (22%)	38 (20%)	38 (19%)
	Wearable Only	5 (17%)	46 (37%)	19 (46%)	70 (36%)	73 (37%)
	Wearable with Other Applications	4 (14%)	19 (15%)	4 (10%)	27 (14%)	27 (14%)
	Web-Based Applications	0 (0%)	5 (4%)	1 (2%)	6 (3%)	7 (4%)
	Phone Calls & Others	0 (0%)	7 (6%)	0 (0%)	7 (4%)	7 (4%)
Measurement Method	Devices	18 (62%)	73 (59%)	30 (73%)	121 (63%)	123 (62%)
	Questionnaires	5 (17%)	13 (11%)	0 (0%)	18 (9%)	19 (10%)
	Questionnaires & Devices	6 (21%)	32 (26%)	6 (15%)	44 (23%)	45 (23%)
	Missing	0 (0%)	5 (4%)	5 (12%)	10 (5%)	10 (5%)

Note. This table displays the total number of observations along with the corresponding percentages formatted as ‘number (percentage%)’. n=193 represents the valid age population included in the analysis, while n=197 denotes the total number of papers reviewed in the study.

Self-tracking dosage applied

Tables 4 and 5 showcase the diversity in the duration and frequency of self-tracking practices across studies. The median intervention duration was approximately 12 weeks, with a broad range spanning from less than 1 week to nearly 4 years (see Table 4). The frequency of self-tracking prompts varied, with daily tracking being the most common (60%), followed by weekly and monthly monitoring (see Table 5). Most interventions lasted for a period of 1 to 6 months, with 29% who self-tracked for 1 to 3 months and 26% for 3 to 6 months (see Table 5). In terms of tracking numbers, daily self-tracking occurred at a mean frequency of 1.11 times per day, while weekly tracking averaged 1.26 times per week. Monthly tracking was rare, typically occurring 1–2 times per month (Table 4).

Table 4.

Distribution of intervention characteristics: Self-tracking duration and frequency.

Intervention characteristic	n	Mean	sd	Median	min	Max
Duration (Weeks)	195	21.25	23.78	12.00	0.43	208
Frequency
Daily Tracking Frequency	72	1.11	0.43	1.00	1.00	3.00
Weekly Tracking Frequency	27	1.26	0.66	1.00	1.00	3.00
Monthly Tracking Frequency	2	1.50	0.71	1.00	1.00	2.00

Note. This table summarizes intervention duration, planned total self-monitoring requirements, and self-tracking frequency based on available numerical data. The n for each characteristic represents the number of studies with sufficient non-missing information to calculate descriptive statistics. Missing, not reported, or insufficiently detailed values were excluded from the calculations. Frequency reflects the designed number of self-monitoring messages, logs, or tracking events per day, week, or month, as reported in the intervention protocol or methods.

Table 5.

Summary of self-tracking intervention doses: Duration and frequency.

Variables	Categories	Young adults (n=29)	Middle-aged adults (n=123)	Older adults (n=41)	Age-known subset(n=193)	Total (n=197)
Duration	<1 Week	0 (0%)	0 (0%)	1 (2%)	1 (1%)	1 (1%)
	1 Week–1 Month	6 (21%)	5 (4%)	2 (5%)	13 (7%)	13 (7%)
	1 Month–3 Months	12 (41%)	35 (28%)	11 (27%)	58 (30%)	58 (29%)
	3 Months–6 Months	6 (21%)	35 (28%)	9 (22%)	50 (26%)	52 (26%)
	6 Months–12 Months	3 (10%)	31 (25%)	8 (20%)	42 (22%)	42 (21%)
	12 Months and Above	1 (3%)	17 (14%)	10 (24%)	28 (15%)	29 (15%)
	Duration not specified	1 (3%)	0 (0%)	0 (0%)	1 (1%)	2 (1%)
Frequency	Per Day	18 (62%)	71 (58%)	27 (66%)	116 (60%)	118 (60%)
	Per Week	6 (21%)	21 (17%)	4 (10%)	31 (16%)	31 (16%)
	Per Month	0 (0%)	2 (2%)	0 (0%)	2 (1%)	2 (1%)
	Multiple frequency	3 (10%)	8 (7%)	3 (7%)	14 (7%)	14 (7%)
	Not specified/Missing	2 (7%)	21 (17%)	7 (17%)	30 (16%)	32 (16%)

Note. This table displays the total number of observations along with the corresponding percentages formatted as ‘number (percentage%)’. The n=193 represents the valid age population included in the analysis, while n=197 denotes the total number of papers reviewed in the study. Percentages may not sum to 100% due to rounding.

Self-tracked physical activity types and formats

Table 6 highlights the types and formats of self-tracked physical activity (PA) that were self-monitored. Aerobic activities were the most frequently tracked (90%). The remaining 10% studies tracked in strength or functional exercises, or combined aerobic and strength exercises, though these were less frequently tracked. Additionally, the table shows that steps (41%) and intensity-duration metrics (20%) were the primary formats used to track physical activity.

Table 6.

Overview of self-tracking physical activity: Types and metrics.

Variables	Categories	Young adults (n=29)	Middle-aged adults (n=123)	Older adults(n=41)	Age-known subset (n=193)	Total (n=197)
Self-Tracked Activity Type	Aerobic	29 (100%)	109 (89%)	36 (88%)	174 (90%)	178 (90%)
	Aerobic + Strength	0 (0%)	6 (5%)	0 (0%)	6 (3%)	6 (3%)
	Aerobic + Functionality	0 (0%)	3 (2%)	3 (7%)	6 (3%)	6 (3%)
	Strength	0 (0%)	2 (2%)	2 (5%)	4 (2%)	4 (2%)
	Other	0 (0%)	3 (2%)	0 (0%)	3 (2%)	3 (2%)
Physical Activity Metric	Steps	16 (55%)	42 (34%)	23 (56%)	81 (42%)	81 (41%)
	Intensity-specific duration	4 (14%)	30 (24%)	6 (15%)	40 (21%)	40 (20%)
	Intensity-specific frequency	1 (3%)	6 (5%)	1 (2%)	8 (4%)	9 (5%)
	Intensity-based metric + steps	7 (24%)	33 (27%)	5 (12%)	45 (23%)	47 (24%)
	Steps and Meeting Guidelines	0 (0%)	2 (2%)	0 (0%)	2 (1%)	2 (1%)
	Step + Intensity-based metric + Meeting Guidelines	1 (3%)	2 (2%)	3 (7%)	6 (3%)	6 (3%)
	Intensity-based metric + Meeting Guidelines	0 (0%)	1 (1%)	0 (0%)	1 (1%)	1 (1%)
	Meeting Guidelines Only	0 (0%)	2 (2%)	1 (2%)	3 (2%)	3 (2%)
	Other	0 (0%)	5 (4%)	2 (5%)	7 (4%)	8 (4%)

Note. This table displays the total number of observations along with the corresponding percentages formatted as ‘number (percentage%)’. The n=193 represents the valid age population included in the analysis, while n=197 denotes the total number of papers reviewed in the study. Percentages may not sum to 100% due to rounding.

Dose differences in self-tracking targeted physical activity outcomes

Table 7 presents differences in self-tracking characteristics across studies targeting distinct physical activity outcomes. Intervention duration was most commonly between 1 and 6 months, particularly among studies assessing steps-only (62%), intensity-specific duration (55%), and intensity-specific frequency (67%). Longer durations of 6–12 months or 12 months and above were also relatively frequent (36%), especially in steps and intensity-based studies.

Table 7.

Summary of tracking duration, frequency of self-monitoring, and automation methods stratified by targeted physical activity (PA) outcome groups.

Variables	Categories	Steps (n=81)	Intensity-specific duration (n=40)	Intensity-specific frequency (n=9)	Intensity-based metric + steps(n=47)	Steps and meeting guidelines (n=2)	Step + intensity-based metric + meeting guidelines (n=6)	Intensity-based metric + meeting guidelines (n=1)	Meeting guidelines (n=3)	Other (n=8)	Age-known subset (n=193)	Total (n=197)
Duration Category	<1 Week	0 (0%)	1 (2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (1%)	1 (1%)
	1 Week–1 Month	8 (10%)	2 (5%)	0 (0%)	3 (6%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	13 (7%)	13 (7%)
	1 Month–3 Months	24 (30%)	12 (30%)	5 (56%)	11 (23%)	0 (0%)	1 (17%)	1 (100%)	0 (0%)	4 (50%)	58 (30%)	58 (29%)
	3 Months–6 Months	26 (32%)	10 (25%)	1 (11%)	9 (19%)	1 (50%)	2 (33%)	0 (0%)	1 (33%)	2 (25%)	50 (26%)	52 (26%)
	6 Months–12 Months	18 (22%)	7 (18%)	1 (11%)	13 (28%)	0 (0%)	1 (17%)	0 (0%)	0 (0%)	2 (25%)	42 (22%)	42 (21%)
	12 Months and Above	5 (6%)	8 (20%)	1 (11%)	10 (21%)	1 (50%)	2 (33%)	0 (0%)	2 (67%)	0 (0%)	28(15%)	29 (15%)
	Duration not specified	0 (0%)	0 (0%)	1 (11%)	1 (2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1(1%)	2 (1%)
Frequency Category	Per Day	57 (70%)	16 (40%)	7 (78%)	27 (57%)	1 (50%)	2 (33%)	0 (0%)	3 (100%)	5 (62%)	116 (60%)	118 (60%)
	Per Week	10 (12%)	13 (32%)	0 (0%)	5 (11%)	0 (0%)	1 (17%)	1 (100%)	0 (0%)	1 (12%)	31 (16%)	31 (16%)
	Per Month	1 (1%)	1 (2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (1%)	2 (1%)
	Multiple frequency	4 (5%)	0 (0%)	0 (0%)	5 (11%)	1 (50%)	3 (50%)	0 (0%)	0 (0%)	1 (12%)	14 (7%)	14 (7%)
	Not specified/Missing	9 (11%)	10 (25%)	2 (22%)	10 (21%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (12%)	30 (16%)	32 (16%)
Automation Category	Automated	32 (40%)	8 (20%)	1 (11%)	11 (23%)	1 (50%)	5 (83%)	0 (0%)	1 (33%)	3 (38%)	62 (32%)	62 (31%)
	Manual	3 (4%)	7 (18%)	0 (0%)	3 (6%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (12%)	14 (7%)	14 (7%)
	Multiple methods	10 (12%)	3 (8%)	0 (0%)	5 (11%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (12%)	19 (10%)	19 (10%)
	Semi-automated	36 (44%)	22 (55%)	8 (89%)	28 (60%)	1 (50%)	1 (17%)	1 (100%)	2 (67%)	3 (38%)	98 (51%)	102 (52%)

Note. This table displays the total number of observations along with the corresponding percentages formatted as ‘number (percentage%)’. The n=193 represents the valid age population included in the analysis, while n=197 denotes the total number of papers reviewed in the study.

Daily self-tracking was the predominant frequency pattern across all outcome groups. Weekly tracking appeared most often in intensity-specific duration studies (32%).

In terms of automation, semi-automated methods were the most widely used across all outcome categories, followed by fully automated systems. Overall, these findings indicate that most physical activity outcome interventions adopted 1–6-month monitoring durations with daily, semi-automated self-tracking as the dominant approach.

Self-tracking differences by age

Descriptive age-related patterns were observed in the design and implementation of self-monitoring strategies across studies. As shown in Table 3, interventions targeting younger adults more frequently employed mobile applications or multi-modal systems that integrated apps with wearable devices, whereas studies involving middle-aged and older adults more commonly relied on wearable-only devices. Automation approaches also varied by age group. Although semi-automated tracking was the most prevalent strategy across all age groups, fully automated systems appeared more often in interventions designed for younger and middle-aged adults, while interventions targeting older adults more frequently used semi-automated or wearable-based systems with fewer interactive components. Collectively, these patterns reflect age-specific differences in investigator-selected self-monitoring designs and automation strategies across the literature.

Differences in tracking dose (Table 5) further highlight how intervention design was adapted based on participant age. Self-tracking interventions targeting young adults frequently lasted one to three months, whereas those targeting middle-aged and older adults often lasted six months or more. Daily self-tracking was dominant across all age groups. Weekly or multiple-frequency monitoring was less common in all age groups.

Finally, physical activity types and metrics (Table 6) also differed across age groups. Aerobic activity was nearly universal among younger adults but was combined with strength or functional training more frequently in middle-aged and older adults. Similarly, older adults were more often assessed using step-based metrics, while middle-aged groups incorporated more intensity-specific measures such as duration or frequency. Collectively, the results indicate an age-related progression in investigator-designed intervention and measurement strategies, ranging from brief, app-based aerobic tracking in younger adults to longer-duration, wearable-based monitoring emphasizing intensity or broader physical activity constructs in middle-aged and older adults.

Discussion

The aim of this scoping review was to characterize how digital self-tracking tools and techniques have been implemented in physical activity interventions for adults, with a focus on automation, dosage metrics, and age-specific adaptations. This review highlights the diverse designs of self-monitoring interventions, including the methods participants used to track their behavior, such as automated tools and devices or questionnaires, along with dosage metrics like frequency and duration. Additionally, it explored the types and formats of physical activity measured in these interventions. Many studies did not report the rationale behind their self-monitoring intervention designs, which presents challenges to understanding, replicating, and improving these programs. Therefore, this scoping review is an important step toward identifying reporting gaps and sources of heterogeneity to inform future hypothesis-driven research on digital self-tracking interventions.

Our findings showed that self-tracking approaches varied across studies, ranging from manual to semi-automated and fully automated methods. These patterns suggest that the automation format may be an important source of heterogeneity for future research. A key question that arises is how different automation formats affect participants’ awareness of behavioral discrepancies and their ability to achieve behavioral change. For example, does providing participants with an accelerometer alone sufficiently enhance awareness of discrepancies between their behavior and goals to drive behavioral change, or is it necessary to supplement automated methods with manual approaches to improve participants’ awareness and goal alignment? Prior work suggests that different tracking methods may be associated with different physical activity patterns. ⁶ Future studies could test whether and how different automation strategies are associated with self-awareness, engagement, and physical activity outcomes.

Interventions showed significant variation in prescribed dosing parameters, including frequency and duration. Although daily self-monitoring was frequently used, intervention duration varied across studies, with most interventions lasting 1–6 months and a median duration of 12 weeks. It remains unclear whether daily self-tracking is associated with greater change in physical activity than weekly self-tracking. Also, the dosage needed to sustain behavior may differ from that required to initiate change; more frequent tracking may be more relevant during early behavior change, whereas less frequent tracking may be sufficient during maintenance; however, these speculations require direct testing. Understanding these questions is critical for utilizing self-tracking tools and strategies that promote physical activity. Future research should investigate how varying tracking frequencies and durations influence both behavior change and maintenance, helping to inform future studies on how to balance engagement with participant burden.

Although age is a key demographic factor that may influence the format and dosage of self-tracking prescriptions, our findings suggest that age-related differences in the use of self-tracking tools were limited. The primary distinction was intervention duration, with longer interventions more often implemented in middle-aged and older adult samples. This pattern likely reflects differences in health conditions or study design priorities rather than intentional age-based tailoring to optimize behavior change. However, age is only one dimension of heterogeneity that may influence engagement with digital self-tracking. Factors such as income, technology familiarity, and cultural background may also shape access to, comfort with, and responsiveness to self-tracking tools. This consideration is particularly important given that the studies reporting race and ethnicity were composed largely of non-Hispanic White participants, which may limit the external validity of the current evidence base. Future research would benefit from recruiting more diverse populations and examining whether self-tracking formats and dosages differ not only by age, but also by socioeconomic, cultural, and technology-related characteristics.

Older adults may track different types of physical activity based on their health-related goals. ²³⁵ For example, they might focus more on leisurely, low-intensity activities (e.g., aerobic exercises, flexibility routines, or step tracking) that promote health and well-being. In contrast, younger adults might engage in activities aimed at challenges or body composition changes, such as strength training or high-intensity exercises. ²³⁶ Additionally, older adults tend to demonstrate higher compliance with interventions than younger adults, which raises the possibility that formats requiring more manual input may be acceptable for some older adult populations. ²³⁷ Future research should explore these age-related factors to develop tailored self-tracking interventions that effectively support physical activity across different age groups.

These design questions are especially relevant as digital self-tracking systems become more automated. In this review, semi-automated self-tracking was the most common approach, and wearable devices were widely used across interventions. These findings suggest that automation is already a common feature of digital physical activity interventions. However, most studies did not clearly explain why specific levels of automation were selected or how automation was expected to support self-monitoring, engagement, or behavior change. Future research should more explicitly justify the use of manual, semi-automated, or automated self-tracking approaches and examine how these design choices influence participant engagement, awareness of behavioral discrepancies, and physical activity outcomes.

Limitations

This scoping review has several limitations that should be acknowledged. First, reliance on published literature may have introduced publication bias, as studies with significant or positive results are more likely to be published than those with null or negative findings. This could have skewed the overall description of digital self-monitoring tools represented in the published literature. Second, the review included only English-language publications, potentially excluding relevant studies in other languages. As a result, the countries represented in the included literature may be skewed, potentially limiting the generalizability of the findings.

Third, although this review characterized prescribed self-tracking dosage using frequency, duration, and mode, we did not synthesize participant-level dose received, such as completed logs, app interactions, feedback views, or adherence to self-tracking, because these metrics were inconsistently reported across studies. A further limitation is that our assessment of dosage was limited to three commonly examined dose dimensions, although other dosing parameters may also be relevant. Future reviews should examine a broader range of dosing dimensions and distinguish the prescribed from the received self-tracking dose.

Fourth, we highlighted the impact of age differences on the design and use of self-tracking interventions, but other demographic factors associated with physical activity, such as socioeconomic status, health status, and education, were not extensively explored and may influence outcomes. Although we describe age-related patterns in the design and use of self-tracking interventions, these findings should be interpreted as descriptive rather than inferential. Because age was generally reported only as a study-level summary and this review was designed to map the literature rather than test hypotheses, we did not conduct formal statistical tests of age differences. Future systematic reviews or meta-analyses with harmonized study-level data should formally examine whether intervention characteristics, self-tracking engagement, and physical activity outcomes differ by age.

Fifth, there was a limited amount of work specifically addressing self-monitoring for resistance training, despite its significant potential to support various aspects of healthy aging, such as mobility. ²³⁸ Further research on digital tools for resistance training would contribute valuable insights for promoting healthy aging. Additionally, because this study was designed as a scoping review to map the breadth and characteristics of the literature rather than synthesize effect estimates, we did not conduct a formal quality assessment of the included studies. Future systematic reviews should evaluate the effects of self-tracking interventions and incorporate formal quality assessment.

An additional limitation is that this review did not systematically extract safety-related intervention features, including adverse event monitoring, escalation procedures, clinical oversight, or procedures for responding to abnormal activity or health data. This is important because nearly half of the included studies targeted adults with chronic conditions, for whom self-tracking interventions may require additional safety considerations. Future research should more consistently report safety monitoring procedures and examine how digital self-tracking interventions can balance behavior change support with appropriate clinical safeguards for populations with chronic conditions. Lastly, the rapid evolution of digital health technologies means that the tools and methods evaluated in this review may quickly become outdated, necessitating ongoing research to keep pace with technological advancements.

Conclusion

This scoping review mapped how digital self-tracking interventions for physical activity have been designed and delivered across adult populations, with a focus on automation, dosage, and age-related patterns. Overall, the evidence shows substantial heterogeneity in self-tracking formats and dosage, with automated approaches commonly used but limited reporting on the rationale for intervention design. Although age is often considered important in digital intervention design, the studies reviewed showed only modest age-related differences, with longer intervention durations being more common in middle-aged and older adult samples. As a scoping review, these findings provide a preliminary overview of the evidence base rather than guidance for practice or policy. Future research should more directly test how self-tracking format, frequency, and duration influence engagement, physical activity change, and maintenance across diverse populations, and more focused systematic reviews may help clarify which strategies are most effective under specific conditions.

ORCID iDs

Jingchuan Wu https://orcid.org/0000-0001-7445-6033

David E. Conroy https://orcid.org/0000-0003-0204-4093