Research Article
Determining the Cut off Value of 10 Meter Walk Test for Fall Risk in Older Adults
Issue:
Volume 12, Issue 4, August 2026
Pages:
64-68
Received:
3 October 2025
Accepted:
13 October 2025
Published:
24 July 2026
DOI:
10.11648/j.ijdsa.20261204.11
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Abstract: Falls among older adults lead to severe outcomes, including injury, reduced activity, diminished quality of life, heightened fear of falling, and even mortality. Standard clinical tests such as the Berg Balance Scale (BBS) and Performance-Oriented Mobility Assessment (POMA) evaluate fall risk but are multi-component and time-intensive. The 10-Meter Walk Test (10MWT) offers a quick and feasible alternative, though a validated cut-off value for fall risk in older adults remains unestablished. Sixty-eight older adults were included based on predefined criteria. Informed consent, demographic information, and physical assessments (height, weight for Body Mass Index- BMI calculation) were collected. The 10MWT and BBS were administered to evaluate walking speed and fall risk. Statistical analysis was done using Statistical Package for Social Sciences (SPSS) v20.0 the demographic data were represented as their mean and standard deviation. The association between 10MWT and BBS was discovered using Pearson's correlation test. The optimal cut-off point for the 10MWT speed that predicts fall risk in older individuals was found using Receiver Operating Characteristic (ROC) Curve Analysis. The average age of the participants was found to be 73.43 years. A significant moderate correlation was observed between 10MWT and BBS scores, with Pearson’s coefficient r=0.418 (p = 0.000). The optimal walking speed cut-off for fall risk was identified at 1.24 m/s, with sensitivity and specificity values of 71.4% and 68.1%, respectively.
Abstract: Falls among older adults lead to severe outcomes, including injury, reduced activity, diminished quality of life, heightened fear of falling, and even mortality. Standard clinical tests such as the Berg Balance Scale (BBS) and Performance-Oriented Mobility Assessment (POMA) evaluate fall risk but are multi-component and time-intensive. The 10-Meter...
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Research Article
Performance Comparison of Auto-Regressive Moving Average (ARIMA) and Artificial Neural Networks (ANN) in Ginger Price and Output Forecasting in Nigeria (2021–2025)
Issue:
Volume 12, Issue 4, August 2026
Pages:
69-84
Received:
15 March 2026
Accepted:
25 March 2026
Published:
27 July 2026
DOI:
10.11648/j.ijdsa.20261204.12
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Abstract: This study analyzed and forecasted ginger output and price trends in Nigeria using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models. Secondary data on ginger production and price covering the period 1990–2020 were obtained from the Food and Agriculture Organization (FAO) and the National Agricultural Extension and Research Liaison Services (NAERLS). The data were analyzed to generate forecasts for the period 2021–2025. Stationarity of the time series was tested using the Augmented Dickey–Fuller (ADF) and Phillips–Perron tests, which indicated that both output and price series became stationary after first differencing. Model identification was conducted using the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF). The most suitable ARIMA models selected were ARIMA (6,1,5) for ginger output and ARIMA (9,1,5) for ginger price based on model diagnostics such as AIC, SBIC, R-squared, and volatility measures. For ANN forecasting, optimal network structures identified were 1–10–1 for ginger output and 1–16–1 for ginger price, which produced the lowest network errors. Forecast evaluation showed that both ARIMA and ANN models produced relatively low forecast errors, indicating good predictive performance. However, the ANN model demonstrated slightly higher forecasting accuracy than the ARIMA model for both ginger output and price. Forecast results indicate a steady increase in ginger production from about 608,080 metric tonnes in 2021 to 719,968 metric tonnes by 2025, while prices are projected to rise gradually over the same period. The findings suggest growing demand for ginger at local and international market.
Abstract: This study analyzed and forecasted ginger output and price trends in Nigeria using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models. Secondary data on ginger production and price covering the period 1990–2020 were obtained from the Food and Agriculture Organization (FAO) and the National Agricultural Exten...
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,
Aditi Tiwari
