# Physical Activity and Exercise Monitoring in Health and Medical Research: Technologies, Methods, and Applications — A Living Review

**Version:** 1.0, 4 October 2026  
**Manuscript type:** Narrative living review  
**Status:** Public web edition; not peer reviewed

## Abstract

In stroke rehabilitation, standardized clinical motor assessments and wearable records of everyday movement provide complementary information. [\[1\]](https://doi.org/10.3390/s22031050) Wearable sensor outputs depend on placement, processing, wear rules, and the population studied. [\[2\]](https://doi.org/10.1007/s40279-017-0716-0) [\[3\]](https://doi.org/10.1016/j.pmedr.2019.101001) Evidence for one device or outcome cannot establish another device's validity. [\[4\]](https://doi.org/10.1038/s41746-020-0260-4)

This narrative review examines ten clinical areas: lower-limb orthopedic recovery, stroke, frailty and falls, heart failure, cancer survivorship, chronic obstructive pulmonary disease (COPD), obesity, type 2 diabetes, Parkinson's disease, and diabetes-related foot disease. It distinguishes published findings from Actidot project records and from proposed research. Intervention studies suggest that step feedback can support some coached activity programs; findings vary by condition, comparator, duration, and outcome. Effects of multicomponent programs cannot be assigned to their counters alone. [\[5\]](https://doi.org/10.1161/STROKEAHA.123.044596) [\[6\]](https://doi.org/10.1038/s41746-023-00962-0) [\[7\]](https://doi.org/10.1001/jama.2016.12858) [\[8\]](https://doi.org/10.1016/j.numecd.2023.11.017)

Actidot is described here as a **dual-intensity step counter**. The owner's current rule classifies an already detected step as high-intensity when acceleration in any direction exceeds 0.5 g; other detected steps are low-intensity. The precise signal processing and underlying step-detection rule have not been verified. Historical product files use conflicting boundaries. No independent Actidot-specific validity or efficacy finding is inferred from the literature reviewed. [\[P1\]](#p1) [\[P2\]](#p2) [\[P3\]](#p3)

## Scope, sources, and evidence rules

This is a **targeted narrative review**, not a systematic review, meta-analysis, or clinical guideline. Sources were selected to test the main clinical and measurement claims; searches and source checks were performed through 4 October 2026 using publisher pages, PubMed records, accessible article text, and local project documents. The search was not exhaustive, and the manuscript makes no claim of duplicate screening or a formal risk-of-bias assessment. Reviews may share underlying studies; their study totals must not be added together.

Three evidence classes are kept separate:

1. **Published evidence:** a cited study or review supports only the population, intervention, comparator, endpoint, and period it actually examined.
2. **Documented Actidot material:** owner statements, manuals, a presentation, and project documents establish descriptions or plans, subject to their version and provenance. They do not establish device accuracy or clinical benefit. [\[P1\]](#p1) [\[P2\]](#p2) [\[P3\]](#p3) [\[P4\]](#p4) [\[P5\]](#p5)
3. **Research opportunity:** an explicitly proposed question, prompted by cited evidence, whose answer requires a new study.

The V3 framework separates technical verification, analytical validation of a sensor-derived measure against a suitable reference, and clinical validation for a particular use. For step recording, the useful first comparison is against independently observed steps under stated placement, speed, and patient conditions. The CADENCE-Adults validation study found substantially larger step-counting errors during slow than normal treadmill walking across tested devices; its results do not quantify Actidot performance or clinical free-living error. [\[4\]](https://doi.org/10.1038/s41746-020-0260-4) [\[9\]](https://doi.org/10.1186/s12966-022-01350-9)

**Terminology.** The agreed title retains “monitoring” for the wider field and for published papers. Actidot-specific prose uses **recording** and **step counting**. “High-intensity step” and “low-intensity step” are Actidot categories, not validated metabolic intensity, impact force, plantar pressure, or joint load. [\[P1\]](#p1) [\[3\]](https://doi.org/10.1016/j.pmedr.2019.101001)

## Chapter 1. Lower-Limb Orthopedic Injury and Postoperative Recovery

### Clinical evidence

A systematic review identified 136 wearable-technology papers in fracture management published from 2010 to 2019. Accelerometry and plantar-pressure technologies were common, especially during postoperative rehabilitation; the authors found no single preferred clinically validated wearable metric. The count is papers across fracture care, not 136 trials of ankle step counters. [\[10\]](https://doi.org/10.1007/s43465-022-00629-0)

Following hip or knee arthroplasty, symptoms, functional tests, and everyday activity need separate interpretation. A seven-study meta-analysis found no statistically significant rise in objectively measured activity at six months, despite improvement in pain and function, but reported an increase at twelve months. [\[11\]](https://doi.org/10.1002/acr.23415) Another review found that changes in steps were clearer than changes in duration, intensity, or activity type. [\[12\]](https://doi.org/10.1016/j.physio.2018.04.003) A later 35-study synthesis reported improvements in some activity measures by six months and earlier recovery of functional performance. [\[13\]](https://doi.org/10.3390/jcm10245885) These findings differ in follow-up windows and endpoint definitions; none supports a universal postoperative step trajectory.

In total knee arthroplasty, a 2025 review included 31 publications: 18 focused on apps, eight on wearables, and five on both. Benefits of the programs cannot be assigned to the sensor alone. [\[14\]](https://doi.org/10.1016/j.arth.2025.01.034) Another review found step counts in 32 of 35 total-joint-arthroplasty studies but no settled mapping from wearable metrics to conventional outcomes. [\[15\]](https://doi.org/10.2196/84671) A narrower review of continuous home measurement after hip or knee arthroplasty included ten studies, eight reporting steps; its pooled time-course findings also depended on postoperative timing. [\[16\]](https://doi.org/10.1016/j.arth.2025.09.023) These reviews likely contain overlapping primary studies.

After anterior cruciate ligament (ACL) reconstruction, a 20-article scoping review found daily steps and moderate-to-vigorous activity among the common wearable outcomes, while few studies measured between-limb asymmetry. [\[17\]](https://doi.org/10.1177/23259671231191134) An 11-study review of limb differences mainly evaluated functional tasks, with varied sensors and procedures. Task-based asymmetry findings do not validate left-right daily step-count ratios or joint-loading estimates. [\[18\]](https://doi.org/10.1016/j.ptsp.2022.01.004)

### Documented Actidot material

The product owner calls Actidot a dual-intensity step counter. [\[P1\]](#p1) Historical product material describes ankle wearing and single-leg and bilateral configurations. [\[P3\]](#p3) The M2810 manual describes 10-minute bins and daily and 28-day reports, plus a WeChat binding and synchronization workflow. These are product-document claims, not independently validated measures. [\[P2\]](#p2)

The **current owner-stated classification** is: an already detected step with acceleration in any direction **greater than 0.5 g** is a **high-intensity step**; every other detected step is a **low-intensity step**. Equality at 0.5 g falls into the latter category. This statement does not define how a step is detected, whether axes are signed or absolute, whether gravity is removed, which sample or window is tested, or which hardware and firmware versions implement the rule. Those details require a versioned specification; stationary samples must not be interpreted as low-intensity steps. [\[P1\]](#p1)

The historical M2810 manual instead classifies changes in a combined acceleration measure using 0.5 g and 1.2 g boundaries; an English presentation shows a 1 g division. Neither historical boundary is silently adopted for the current owner-stated rule or retroactively assigned to existing datasets. [\[P2\]](#p2) [\[P3\]](#p3) [\[P1\]](#p1)

An ethics approval document from Shanghai Sixth People's Hospital, No. **2025-KY-038(K)**, has a printed review date of **18 February 2025** and concerns daily step counts and outcomes after surgery for lower-limb sports injury. The approval does not name Actidot or M2810. [\[P4\]](#p4) Separate workflow and consent files describe bilateral step recording, but exact model, firmware, and approved document version remain unverified. [\[P5\]](#p5) Recruitment, results, and current approval status have not been established from these records. The documents support a planned research question, not a completed device trial or demonstrated efficacy. [\[P4\]](#p4) [\[P5\]](#p5)

### Research opportunity

A prospective study could compare Actidot step trajectories with patient-reported outcomes and standardized functional tests after a defined surgery, while reporting wear time and missing data. [\[11\]](https://doi.org/10.1002/acr.23415) [\[2\]](https://doi.org/10.1007/s40279-017-0716-0) Candidate exploratory outputs include total and product-defined high/low step counts and, if present in the study model's export, 10-minute count bins; a bin with steps is not an active-minute measure, and zero counts require a non-wear rule. [\[P1\]](#p1) [\[P2\]](#p2) [\[2\]](https://doi.org/10.1007/s40279-017-0716-0)

A bilateral study could test whether left-right counts add information after ACL reconstruction, but would need matched recording windows and independent observed-step and task-specific references; the other limb cannot automatically serve as a healthy control. [\[18\]](https://doi.org/10.1016/j.ptsp.2022.01.004) [\[4\]](https://doi.org/10.1038/s41746-020-0260-4) First, step detection and high/low classification require analytical validation in slow walking, assistive-device use, and rehabilitation movements. These are proposed studies, not established Actidot capabilities. [\[9\]](https://doi.org/10.1186/s12966-022-01350-9) [\[4\]](https://doi.org/10.1038/s41746-020-0260-4)

## Chapter 2. Stroke Rehabilitation and Real-World Activity Monitoring

A review of free-living wearable measurement after stroke included 32 articles and concluded that sensor outcomes provide information alongside clinic-based motor assessments. It covered upper- and lower-limb movement and varied sensor setups, so its study count is not a body of evidence for ankle counting alone. A separate 2026 review of rehabilitation randomized trials included 31 eligible studies and 26 distinct studies in meta-analyses; walking capacity and wearable-derived daily performance were separate outcomes. Apparent differences between pooled intervention categories are not direct head-to-head superiority tests. [\[1\]](https://doi.org/10.3390/s22031050) [\[19\]](https://doi.org/10.3390/s26144332)

In a 250-person randomized trial of chronic stroke, 12 weeks of step feedback and goal setting with skilled coaching increased steps in the step-activity arm and the combined training-plus-step arm; the high-intensity walking-training arm alone did not show a statistically significant within-arm step increase. This tests supported programs, not the independent therapeutic effect of a counter. [\[5\]](https://doi.org/10.1161/STROKEAHA.123.044596)

**Research opportunity.** In a specified stroke population, a bilateral ankle counter could be tested against observed steps at slow speeds, with assistive devices, and across affected-side impairment before its counts are used as an outcome. A later trial could separate feedback/coaching from passive recording and assess both daily steps and walking capacity. These are proposals motivated by the stroke reviews and trial. [\[1\]](https://doi.org/10.3390/s22031050) [\[19\]](https://doi.org/10.3390/s26144332) [\[5\]](https://doi.org/10.1161/STROKEAHA.123.044596) [\[9\]](https://doi.org/10.1186/s12966-022-01350-9)

## Chapter 3. Frailty, Falls, and Age-Related Functional Decline

A 29-study review found associations between wearable activity or gait measures and frailty status, but devices, body positions, and study settings varied. Those reported associations do not establish that a step count diagnoses frailty or predicts its onset. [\[20\]](https://doi.org/10.1186/s12984-021-00909-0)

Fall prediction is a different endpoint. A 2026 meta-analysis of 20 wearable prediction studies estimated sensitivity **0.55** (95% CI 0.42–0.67) and specificity **0.89** (0.84–0.93). Between-study heterogeneity was high (I² = 93% and 94%, respectively), limiting the portability of those pooled estimates. The pooled sensitivity leaves substantial missed-fall risk and does not validate Actidot or a simple step threshold as a fall-risk test. [\[21\]](https://doi.org/10.3389/fpubh.2026.1778750)

**Research opportunity.** Longitudinal step recording could be examined as one component of frailty assessment, alongside validated clinical measures. Any proposed fall-risk model would require prospective outcomes, prespecified thresholds, and external validation; step trends alone should not be treated as a diagnosis or alert. [\[20\]](https://doi.org/10.1186/s12984-021-00909-0) [\[21\]](https://doi.org/10.3389/fpubh.2026.1778750) [\[4\]](https://doi.org/10.1038/s41746-020-0260-4)

## Chapter 4. Heart Failure and Physical Activity Monitoring

A heart-failure accelerometry review identified 60 eligible studies using 27 brands and 46 models, with differing placements and valid-day rules. A later trial-methods review judged accelerometry technically feasible but emphasized the need to define a clinically interpretable endpoint and its collection and analysis rules. Neither review validates an ankle counter for heart-failure treatment decisions. [\[22\]](https://doi.org/10.1002/ehf2.12781) [\[23\]](https://doi.org/10.1016/j.cardfail.2024.01.016)

The 621-person OUTSTEP-HF randomized trial measured both six-minute-walk distance and wrist-accelerometer daytime activity over 12 weeks while comparing two medicines. It found no statistically significant between-group difference in either endpoint. It illustrates that performance testing and daily activity can be specified together, rather than treating one as a substitute for the other; it is not an Actidot intervention study. [\[24\]](https://doi.org/10.1002/ejhf.2076)

**Research opportunity.** An Actidot feasibility and validation study in heart failure could define valid wear days, account for fatigue-limited slow walking, and compare step trajectories with symptom and functional measures. Clinical interpretation would need a separately justified endpoint and prospective analysis plan. [\[22\]](https://doi.org/10.1002/ehf2.12781) [\[23\]](https://doi.org/10.1016/j.cardfail.2024.01.016) [\[4\]](https://doi.org/10.1038/s41746-020-0260-4)

## Chapter 5. Cancer Survivorship and Rehabilitation

Evidence for exercise and for wearable-assisted activity programs must be kept distinct. In the CHALLENGE trial, 889 patients with resected colon cancer who had completed adjuvant chemotherapy were randomized to a three-year structured exercise program or health education. Disease-free survival favored the exercise program (hazard ratio **0.72**, 95% CI 0.55–0.94); musculoskeletal adverse events were more frequent in that arm. The trial does not estimate the effect of a step counter. [\[25\]](https://doi.org/10.1056/NEJMoa2502760)

A review of 35 randomized trials of wearable or pedometer interventions in cancer survivors reported improvements in several activity outcomes versus usual care. Fifteen trials concerned breast cancer and 25 used pedometers; intervention duration and co-interventions varied. This supports further study of supported recording programs, not extrapolation to all cancers or to Actidot efficacy. [\[26\]](https://doi.org/10.1016/j.jshs.2021.07.008)

**Research opportunity.** A cancer-specific study could assess whether validated step recording improves adherence measurement during a prescribed exercise program and whether feedback changes activity beyond the program alone. It should prespecify the cancer population, treatment phase, safety outcomes, and a comparator that isolates the added recording component. [\[26\]](https://doi.org/10.1016/j.jshs.2021.07.008) [\[25\]](https://doi.org/10.1056/NEJMoa2502760) [\[4\]](https://doi.org/10.1038/s41746-020-0260-4)

## Chapter 6. COPD and Pulmonary Rehabilitation

A 37-study systematic review of wearable interventions in COPD estimated **850 additional daily steps** (95% CI 494–1205) and a **5.81 m** difference in six-minute-walk distance (1.02–10.61 m), usually over short follow-up. The authors reported little consistent quality-of-life benefit and mixed exacerbation findings. Programs often combined a device with coaching or pulmonary rehabilitation, so the pooled results should not be assigned to a counter alone. [\[6\]](https://doi.org/10.1038/s41746-023-00962-0)

A later randomized trial compared full with light physical-activity coaching; **both groups received trackers**. It found no significant between-group step difference at 12 months. That result cannot establish no benefit relative to usual care, nor equivalence between the two coaching approaches. [\[27\]](https://doi.org/10.1164/rccm.202501-0170OC)

**Research opportunity.** A COPD study could validate step counts across slow walking and oxygen or aid use, then compare passive recording with a defined feedback or coaching program. Daily steps, walking capacity, symptoms, and longer-term maintenance should be separate endpoints. [\[9\]](https://doi.org/10.1186/s12966-022-01350-9) [\[6\]](https://doi.org/10.1038/s41746-023-00962-0) [\[27\]](https://doi.org/10.1164/rccm.202501-0170OC)

## Chapter 7. Obesity and Weight Management

Weight change is an intervention outcome, not a property of the wearable. A network meta-analysis included 31 trials of wearable-based programs in people with overweight/obesity and chronic comorbidities. Its comparisons mixed device-only and multicomponent programs; several credible intervals were wide or included no effect. Rankings therefore do not prove that every wearable strategy works. [\[28\]](https://doi.org/10.1136/bjsports-2020-103594)

In the IDEA randomized trial, adults in a wearable-enhanced lifestyle program lost an estimated **3.5 kg** at 24 months, versus **5.9 kg** in the standard behavioral program. The comparison tested two self-monitoring approaches embedded in lifestyle treatment; it refutes an assumption that adding a wearable necessarily improves weight loss, without proving all wearables are harmful. [\[7\]](https://doi.org/10.1001/jama.2016.12858)

**Research opportunity.** If Actidot is studied in weight management, the causal question should be whether a defined step-recording and feedback component adds benefit to an otherwise matched program. Weight, activity, device use, and behavior support need distinct outcomes; high/low Actidot categories require validation before interpretation as exercise dose. [\[28\]](https://doi.org/10.1136/bjsports-2020-103594) [\[7\]](https://doi.org/10.1001/jama.2016.12858) [\[P1\]](#p1) [\[4\]](https://doi.org/10.1038/s41746-020-0260-4)

## Chapter 8. Type 2 Diabetes and Physical Activity

A review of **24 randomized trials** (1,969 participants) found that pedometer- or accelerometer-supported activity interventions reduced HbA1c by **0.22 percentage points** relative to controls (95% CI −0.40 to −0.05; I² = 77%). Its pedometer step-count analysis estimated **2,131 additional steps/day** (1,348–2,914; I² = 74%). The substantial heterogeneity and the use of trackers as motivating tools limit attribution to hardware alone. [\[8\]](https://doi.org/10.1016/j.numecd.2023.11.017)

In the SMARTER randomized trial, physician-delivered step prescriptions plus a pedometer increased daily steps versus ordinary activity advice over one year; its primary arterial-stiffness endpoint was inconclusive. The trial reinforces that the prescription and follow-up are part of the intervention. These studies do not establish glucose measurement by a step counter. [\[29\]](https://doi.org/10.1111/dom.12874)

**Research opportunity.** A diabetes trial could compare validated Actidot step recording plus a specified prescription with the same prescription without device feedback, using objectively measured activity and clinical outcomes. This chapter excludes active diabetes-related foot disease, for which weight bearing and offloading create different questions. [\[8\]](https://doi.org/10.1016/j.numecd.2023.11.017) [\[29\]](https://doi.org/10.1111/dom.12874) [\[30\]](https://doi.org/10.1002/dmrr.3552)

## Chapter 9. Parkinson's Disease and Free-Living Mobility Monitoring

A 26-study review of wearable walking measurement in Parkinson's disease reported wear periods ranging from hours to a year and multiple body locations, most often waist, ankle, or wrist. Such diversity limits direct comparison of step outcomes. [\[31\]](https://doi.org/10.3390/s22124551)

In 33 people with mild-to-moderate Parkinson's disease, two wrist trackers were compared with an ActivPAL reference during prescribed walking tasks. Step agreement varied with cadence, and tracker cadence did not strongly reflect oxygen uptake or perceived exertion. The reference was another device rather than manual ground truth; these findings do not validate Actidot or its high/low categories. [\[32\]](https://doi.org/10.1016/j.gaitpost.2018.04.034)

**Research opportunity.** An ankle-counter study could compare Actidot with observed steps during slow, variable-cadence, freezing-prone, and assisted walking, then investigate whether longer-term trajectories add information to clinical assessments. This requires explicit handling of medication state, wear time, and false steps from nonwalking movement. [\[31\]](https://doi.org/10.3390/s22124551) [\[32\]](https://doi.org/10.1016/j.gaitpost.2018.04.034) [\[4\]](https://doi.org/10.1038/s41746-020-0260-4)

## Chapter 10. Diabetes-Related Foot Disease: Weight Bearing, Offloading, and Adherence

A review found **27 publications from 21 studies** on objectively measured weight-bearing activity across stages of diabetes-related foot disease. People with active ulcers generally recorded fewer daily steps than at-risk groups, but these descriptive comparisons do not define a safe step target or show that fewer steps cause healing. Weight bearing also includes standing, which a step count alone omits. [\[30\]](https://doi.org/10.1002/dmrr.3552)

In a prospective study of **79 people** with plantar diabetic foot ulcers, removable offloading devices were used during an average **59%** of recorded activity (SD 22%). Greater adherence was associated with smaller ulcer size at six weeks after adjustment; an observational association does not prove causation. The investigators recorded activity and offloading-device use separately. A bilateral step counter without a measure of whether the offloading device is worn cannot establish offloading adherence, plantar pressure, or tissue stress. [\[33\]](https://doi.org/10.2337/dc15-2373)

**Research opportunity.** A foot-disease study could first validate steps and wear time, then synchronize step recording with an independent offloading-use measure and clinical ulcer assessment. Safety and treatment decisions require a foot-care protocol; Actidot high/low step counts cannot be interpreted as safe or unsafe foot loading without direct validation. [\[30\]](https://doi.org/10.1002/dmrr.3552) [\[33\]](https://doi.org/10.2337/dc15-2373) [\[P1\]](#p1) [\[4\]](https://doi.org/10.1038/s41746-020-0260-4)

## Discussion: What a Step Record Can Support

Across these conditions, clinical capacity, daily performance, symptoms, and disease outcomes are related but distinct. The cited stroke, orthopedic, heart-failure, COPD, cancer, and diabetes studies used different designs and endpoints; none supplies a universal conversion from steps to recovery, metabolic intensity, fall risk, or clinical benefit. [\[19\]](https://doi.org/10.3390/s26144332) [\[11\]](https://doi.org/10.1002/acr.23415) [\[24\]](https://doi.org/10.1002/ejhf.2076) [\[6\]](https://doi.org/10.1038/s41746-023-00962-0) [\[25\]](https://doi.org/10.1056/NEJMoa2502760) [\[8\]](https://doi.org/10.1016/j.numecd.2023.11.017)

For a proposed Actidot study, the measurement plan should identify the model and firmware, anatomical placement, step-reference method, valid-day and non-wear rules, time synchronization, handling of zeros, and prespecified analysis. Bilateral counts need aligned wear periods and a rule for zero denominators; equal counts alone do not prove normal gait or equal limb loading. A bin with steps is not continuous active time. These are design requirements derived from device-methods reviews and the V3 framework, not results already demonstrated for Actidot. [\[2\]](https://doi.org/10.1007/s40279-017-0716-0) [\[22\]](https://doi.org/10.1002/ehf2.12781) [\[4\]](https://doi.org/10.1038/s41746-020-0260-4) [\[18\]](https://doi.org/10.1016/j.ptsp.2022.01.004)

The research sequence is: **(1) document the current algorithm and device version; (2) verify operation; (3) validate observed steps and high/low classification for the intended population; (4) test whether the resulting measure is clinically meaningful; and (5) if an intervention effect is claimed, evaluate the full intervention against an appropriate comparator.** This follows the V3 distinctions and avoids treating an ethics approval or product description as a clinical result. [\[4\]](https://doi.org/10.1038/s41746-020-0260-4) [\[P4\]](#p4) [\[P1\]](#p1)

## Conclusions

Wearable records can add daily-life activity information to clinical assessment, but interpretation depends on the exact population, device, endpoint, and study design. Published evidence supports specific uses of steps in research and some supported interventions; it does not transfer automatically to Actidot. [\[1\]](https://doi.org/10.3390/s22031050) [\[10\]](https://doi.org/10.1007/s43465-022-00629-0) [\[23\]](https://doi.org/10.1016/j.cardfail.2024.01.016) [\[4\]](https://doi.org/10.1038/s41746-020-0260-4)

Actidot is currently described as a **dual-intensity step counter** with owner-stated high- and low-intensity step categories. Its exact algorithm and version applicability, measurement accuracy, and clinical utility remain open to verification. The most defensible immediate research role is a clearly specified step-recording method within prospective studies, with claims limited to what those studies establish. [\[P1\]](#p1) [\[P2\]](#p2) [\[P3\]](#p3) [\[4\]](https://doi.org/10.1038/s41746-020-0260-4)

## Declarations and version control

**Commercial context:** This manuscript was prepared in the Actidot company project context. It is not represented as an independent appraisal of Actidot. Named authors, affiliations, funding sources, and final conflict-of-interest statements require author confirmation before submission.  
**AI assistance:** AI assisted drafting, source matching, and editorial checking. Human authors are responsible for the final interpretation and publication checks.  
**Update method:** Future versions should record the search date and queries, new or corrected sources, changes to conclusions, and Actidot model/firmware applicability. “Living review” describes intended revision, not an automated or formally systematic surveillance process.  
**Version history:** v1.0, 4 October 2026 — ten clinical chapters completed; first chapter and front matter rechecked; owner-stated >0.5 g step classification adopted; historical threshold conflict and validation gaps retained.

## References: Published Evidence

Numbered citations link directly to the source DOI. Product and project records use separate P numbers; these internal records are described below but are not publicly linked from this page.

**[1]** Bernaldo de Quirós M, et al. Quantification of Movement in Stroke Patients under Free Living Conditions Using Wearable Sensors: A Systematic Review. *Sensors*. 2022;22:1050. [doi:10.3390/s22031050](https://doi.org/10.3390/s22031050).

**[2]** Migueles JH, et al. Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations. *Sports Medicine*. 2017;47:1821–1845. [doi:10.1007/s40279-017-0716-0](https://doi.org/10.1007/s40279-017-0716-0).

**[3]** Bianchim MS, et al. Calibration and validation of accelerometry to measure physical activity in adult clinical groups: A systematic review. *Preventive Medicine Reports*. 2019;16:101001. [doi:10.1016/j.pmedr.2019.101001](https://doi.org/10.1016/j.pmedr.2019.101001).

**[4]** Goldsack JC, et al. Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies. *npj Digital Medicine*. 2020;3:55. [doi:10.1038/s41746-020-0260-4](https://doi.org/10.1038/s41746-020-0260-4).

**[5]** Thompson ED, et al. Increasing Activity After Stroke: A Randomized Controlled Trial of High-Intensity Walking and Step Activity Intervention. *Stroke*. 2024;55:5–13. [doi:10.1161/STROKEAHA.123.044596](https://doi.org/10.1161/STROKEAHA.123.044596).

**[6]** Shah AJ, et al. Wearable technology interventions in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. *npj Digital Medicine*. 2023;6:222. [doi:10.1038/s41746-023-00962-0](https://doi.org/10.1038/s41746-023-00962-0).

**[7]** Jakicic JM, et al. Effect of Wearable Technology Combined With a Lifestyle Intervention on Long-term Weight Loss: The IDEA Randomized Clinical Trial. *JAMA*. 2016;316:1161–1171. [doi:10.1001/jama.2016.12858](https://doi.org/10.1001/jama.2016.12858).

**[8]** de Oliveira VLP, de Paula TP, Viana LV. Pedometer- and accelerometer-based physical activity interventions in type 2 diabetes: A systematic review and meta-analysis. *Nutrition, Metabolism and Cardiovascular Diseases*. 2024;34:548–558. [doi:10.1016/j.numecd.2023.11.017](https://doi.org/10.1016/j.numecd.2023.11.017).

**[9]** Mora-Gonzalez J, et al. A catalog of validity indices for step counting wearable technologies during treadmill walking: the CADENCE-adults study. *International Journal of Behavioral Nutrition and Physical Activity*. 2022;19:117. [doi:10.1186/s12966-022-01350-9](https://doi.org/10.1186/s12966-022-01350-9).

**[10]** Marmor MT, et al. Use of Wearable Technology to Measure Activity in Orthopaedic Trauma Patients: A Systematic Review. *Indian Journal of Orthopaedics*. 2022;56:1112–1122. [doi:10.1007/s43465-022-00629-0](https://doi.org/10.1007/s43465-022-00629-0).

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## References: Product and Project Records

These internal records are primary sources for statements about the product or project, not peer-reviewed evidence of measurement validity, benefit, completed recruitment, or institutional endorsement. English descriptions are used for Chinese-language document titles; model numbers, version numbers, approval numbers, and page numbers are retained for traceability.

### P1

Product-owner clarification supplied for this review, 4 October 2026: Actidot is a dual-intensity step counter; a detected step is high-intensity if acceleration in any direction is greater than 0.5 g; other detected steps are low-intensity. No technical specification for gravity removal or absolute-axis handling was confirmed.

### P2

*M2810 Single-Device User Manual, Version 3.0* (English description), historical project PDF, pp. 2–4. Page 2 shows 0.5 g and 1.2 g boundaries for changes in a combined acceleration measure and report bins; pp. 3–4 describe binding, synchronization, and reports. Page 2 was visually inspected.

### P3

*Actidot in En.pdf*, historical project presentation, PDF pp. 3 and 7. It describes single-leg/bilateral configurations and a 1 g division. Page 7 was visually inspected; this division is not taken as the current rule.

### P4

*Ethics Approval for Postoperative Lower-Limb Rehabilitation* (English description), Shanghai Sixth People's Hospital project PDF, both pages visually inspected. Approval 2025-KY-038(K), printed review date 18 February 2025. The title concerns daily step counts and rehabilitation outcomes after surgery for lower-limb sports injuries. The letter does not name Actidot; approval renewal and study results have not been verified.

### P5

*Study No. 38 Clinical Trial Workflow* and *Clinical Trial Information and Informed Consent, Version 8* (English descriptions of local project DOCX files), inspected for bilateral step-recording procedures. Their relationship to the protocol and consent versions listed in the ethics approval, and the exact device used, have not been verified.