Prediction of Incident Depression in Middle-aged and Older Adults Using Digital Gait Biomarkers Extracted From Large-Scale Wrist Sensor Data

Sedentary behavior and physical activity are associated with risk of depression among adult and older populations: a systematic review and dose-response meta-analysis

Background

Depression symptoms are commonly experienced by adults and older people; however, there is uncertainty concerning the associations of lifestyle with the risk of depression. This study systematically reviewed and meta-analyzed observational data to assess the link between instrumented sedentary behavior (i-SB) and physical activity (i-PA) measures and depression risk among adult and older populations.

Methods

A systematic review across four databases was performed up to July 27, 2024, targeting studies linking i-SB, i-PA, and depression. The review included a dose-response meta-analysis, presenting results as odds ratios (OR) and 95% confidence intervals (95% CI).

Results

Fifty-one studies, encompassing 1,318,687 participants, fulfilled the inclusion criteria. The comparison between the most and least sedentary groups yielded a pooled OR of 1.09 (95% CI 1.05-1.13). The comparison between the least and most active participant groups yielded pooled ORs of 0.96 (95% CI 0.93-0.98) for light activity (LPA), 0.91 (95% CI 0.86-0.96) for moderate-to-vigorous activity (MVPA), 0.93 (95% CI 0.90-0.96) for total physical activity (TPA), and 0.87 (95% CI 0.81-0.94) for steps per day. After adjusting i-PA, a lower OR for i-SB did not indicate a significant link to increased depression risk. Meta-regression analyses confirmed a dose-response relationship between SB, MVPA, daily steps, and depression.

Conclusion

The association between i-SB and the risk of depression was not consistent with the results of previous self-reported studies. MVPA linked to the risk of depression was independent of i-SB, whereas the link between i-SB and the risk of depression was not independent of i-PA.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=546666, identifier CRD42024546666.

Daily Step Count and Depression in Adults: A Systematic Review and Meta-Analysis

Importance

Recent evidence syntheses have supported the protective role of daily steps in decreasing the risk of cardiovascular disease and all-cause mortality. However, step count-based recommendations should cover additional health outcomes.

Objective

To synthesize the associations between objectively measured daily step counts and depression in the general adult population.

Data Sources

In this systematic review and meta-analysis, a systematic search of the PubMed, PsycINFO, Scopus, SPORTDiscus, and Web of Science databases was conducted from inception until May 18, 2024, to identify observational studies using search terms related to physical activity, measures of daily steps, and depression, among others. Supplementary search methods were also applied.

Study Selection

All identified studies were uploaded to an online review system and were considered without restrictions on publication date or language. Included studies had objectively measured daily step counts and depression data.

Data Extraction and Synthesis

This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses and Meta-analysis of Observational Studies in Epidemiology reporting guidelines. Two independent reviewers extracted the published data.

Main Outcomes and Measures

Pooled effect sizes (correlation coefficient, standardized mean difference [SMD], and risk ratio [RR]) with 95% CIs were estimated using the Sidik-Jonkman random-effects method.

Results

Thirty-three studies (27 cross-sectional and 6 longitudinal [3 panel and 3 prospective cohort]) involving 96 173 adults aged 18 years or older (range of mean [SD] ages: 18.6 [0.6] to 91.2 [1.6] years) were included. Daily steps were inversely correlated with depressive symptoms in both cross-sectional and panel studies. Compared with fewer than 5000 steps/d, pooled SMDs from cross-sectional studies revealed that 10 000 or more steps/d (SMD, -0.26; 95% CI, -0.38 to -0.14), 7500 to 9999 steps/d (SMD, -0.27; 95% CI, -0.43 to -0.11), and 5000 to 7499 steps/d (SMD, -0.17; 95% CI, -0.30 to -0.04) were significantly associated with fewer depressive symptoms. Pooled estimates from prospective cohort studies indicated that participants with 7000 or more steps/d had reduced risk of depression compared with their counterparts with fewer than 7000 steps/d (RR, 0.69; 95% CI, 0.62-0.77). An increase of 1000 steps/d was associated with a lower risk of depression (RR, 0.91; 95% CI, 0.87-0.94).

Conclusions and Relevance

In this systematic review and meta-analysis of 33 observational studies involving 96 173 adults, higher daily step counts were associated with fewer depressive symptoms in cross-sectional and longitudinal studies in the general adult population. Further prospective cohort studies are needed to clarify the potential protective role of daily steps in mitigating the risk of depression during adulthood.

A Wrist-Worn Wearable Device Can Identify Frailty in Middle-Aged and Older Adults: The UK Biobank Study

OBJECTIVES

Digital gait biomarkers collected from body-worn devices can remotely and continuously collect movement types, quantity, and quality in real life. This study assessed whether digital gait biomarkers from a wrist-worn device could identify people with frailty in a large sample of middle-aged and older adults.

DESIGN

Cross-sectional study.

SETTING AND PARTICIPANTS

A total of 5822 middle-aged (43-64 years) and 4344 older adults (65-81 years) who participated in the UK Biobank study.

MEASURES

Frailty was assessed using a modified Fried's frailty assessment and was defined as having ≥3 of the 5 frailty criteria (weakness, low activity levels, slowness, exhaustion, and weight loss). Fourteen digital gait biomarkers were extracted from accelerometry data collected from wrist-worn sensors worn continuously by participants for up to 7 days.

RESULTS

A total of 238 (4.1%) of the middle-aged group and 196 (4.5%) of the older group were categorized as frail. Multivariable logistic regression analysis revealed that less daily walking (as assessed by step counts), slower maximum walking speed, and increased step time variability best-identified people with frailty in the middle-aged group [area under the curve (95% CI): 0.70 (0.66-0.73)]. Less daily walking, slower maximum walking speed, increased step time variability, and a lower proportion of walks undertaken with a manual task best-identified people with frailty in the older group [0.73 (0.69-0.76)].

CONCLUSIONS AND IMPLICATIONS

Our findings indicate that measures obtained from wrist-worn wearable devices worn in everyday life can identify individuals with frailty in both middle-aged and older people. These digital gait biomarkers may facilitate screening programs and the timely implementation of frailty-prevention interventions.

Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study

BACKGROUND

Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies.

OBJECTIVE

The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back-worn inertial sensors.

METHODS

Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back-worn inertial sensors.

RESULTS

The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids.

CONCLUSIONS

Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health.

TRIAL REGISTRATION

ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2021-050785.

Imagery rescripting and cognitive restructuring for inpatients with moderate and severe depression - a controlled pilot study

BACKGROUND

This controlled pilot study investigates the effect of the combined use of cognitive restructuring (CR) and imagery rescripting (IR) compared to treatment as usual among inpatients with moderate and severe depression. Alongside expert ratings and self-report tools, fitness wristbands were used as an assessment tool.

METHODS

In addition to the standard inpatient care (SIC) program, 33 inpatients with moderate and severe depression were randomly assigned to an intervention group (two sessions of IR and CR) or an active treatment-as-usual (TAU) control group (two sessions of problem-solving and build-up of positive activity). Depression severity was assessed by the Hamilton Depression Rating Scale-21 (HDRS-21), the Beck Depression Inventory-II (BDI-II), and as a diagnostic adjunct daily step count via the Fitbit Charge 3™. We applied for analyses of HDRS-21 and BDI-II, 2 × 2 repeated-measures analysis of variance (ANOVA), and an asymptotic Wilcoxon test for step count.

RESULTS

The main effect of time on both treatments was η2 = .402. Based on the data from the HDRS-21, patients in the intervention group achieved significantly greater improvements over time than the TAU group (η2 = .34). The BDI-II data did not demonstrate a significant interaction effect by group (η2 = .067). The daily hourly step count for participants of the intervention group was significantly higher (r = .67) than the step count for the control group.

CONCLUSIONS

The findings support the utilization of imagery-based interventions for treating depression. They also provide insights into using fitness trackers as psychopathological assessment tools for depressed patients.

TRIAL REGISTRATION

The trial is registered at the German Clinical Trials Register (Deutsches Register Klinischer Studien) under the registration number: DRKS00030809.