{
    "abstract": "The effects of physical activity (PA) across different domains and intensities on depressive symptoms remain inconclusive. Incorporating the community-built environment (CBE) into longitudinal analyses of PA's impact on depressive symptoms is crucial.\nThis study aims to examine the effects of PA at different intensities-low-intensity PA (eg, walking activities) and moderate-to-vigorous-intensity PA (eg, activities requiring substantial effort and causing faster breathing or shortness of breath)-across leisure-time and occupational domains on depressive symptom trajectories among middle-aged and older adults. Additionally, it investigated how CBEs influence depressive symptoms and PA trajectories.\nThis longitudinal study included 6865 middle-aged and older adults from the China Health and Retirement Longitudinal Survey. A CBE variable system was developed using a community questionnaire to assess attributes of the physical built environment. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale. Latent growth curve modeling was applied to analyze 3 waves of the cohort data (2015, 2018, and 2020) to explore the differential effects of PA on depressive symptoms and the role of the CBE.\nIn the 2015 and 2018 waves, higher low-intensity leisure-time physical activity (LTPA) was associated with lower depressive symptoms (\u03b2=-.025, P=.01 and \u03b2=-.027, P=.005, respectively). Across all waves, moderate-to-vigorous-intensity LTPA showed no significant predictive effects (P=.21 in 2015, P=.57 in 2018, and P=.85 in 2020, respectively). However, higher occupational physical activity (OPA), particularly at moderate-to-vigorous intensities, was consistently associated with higher depressive symptoms. Parallel process latent growth curve modeling revealed that the initial level of total LTPA negatively predicted the initial level of depressive symptoms (\u03b2=-.076, P=.01). OPA exhibited dual effects, positively predicting the initial level of depressive symptoms (\u03b2=.108, P<.001) but negatively predicting their upward trajectory (\u03b2=-.136, P=.009). Among CBE variables, better infrastructure conditions (\u03b2=-.082, P<.001) and greater accessibility to public facilities (\u03b2=-.036, P=.045) negatively predicted the initial level of depressive symptoms. However, greater accessibility to public facilities positively predicted the upward trajectory of depressive symptoms (\u03b2=.083, P=.04). Better infrastructure conditions (\u03b2=.100, P=.002) and greater accessibility to public transport (\u03b2=.060, P=.01) positively predicted the initial level of total LTPA. Meanwhile, better infrastructure conditions (\u03b2=-.281, P<.001) and greater accessibility to public facilities (\u03b2=-.073, P<.001) negatively predicted the initial level of total OPA. Better infrastructure conditions positively predicted the declining trajectory of total OPA (\u03b2=.100, P=.004).\nThis study underscores the importance of considering the differential effects of PA across domains and intensities on depressive symptoms in public policies and guidelines. Given the influence of the environment on PA and depressive symptoms, targeted community measures should be implemented.",
    "authors": [
        {
            "affiliation": "Graduate School of Human-Environment Studies, Kyushu University, Fukuoka, Japan.",
            "firstname": "Kaili",
            "initials": "K",
            "lastname": "Zhang"
        },
        {
            "affiliation": "Zigong Academy of Urban Planning and Design, Zigong, Sichuan Province, China.",
            "firstname": "Bowen",
            "initials": "B",
            "lastname": "Huang"
        },
        {
            "affiliation": "Faculty of Human-Environment Studies, Kyushu University, Fukuoka, Japan.",
            "firstname": "Prasanna",
            "initials": "P",
            "lastname": "Divigalpitiya"
        }
    ],
    "conclusions": null,
    "copyrights": "\u00a9Kaili Zhang, Bowen Huang, Prasanna Divigalpitiya. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 13.01.2025.",
    "doi": "10.2196/64564\n10.2174/18749445-v15-e2202030\n10.1016/j.landurbplan.2022.104651\n10.1007/s11845-010-0633-9\n10.1146/annurev-publhealth-052620-091107?crawler=true&mimetype=application/pdf\n10.1146/annurev-publhealth-052620-091107\n10.1001/jama.286.19.2391\n10.1002/gps.2821\n10.1146/annurev-publhealth-040218-044106\n10.1016/j.jad.2023.12.039\n10.1016/j.jad.2013.01.035\n10.1016/j.eurpsy.2017.12.029\n10.1016/j.neubiorev.2021.10.041\n10.1186/s12877-022-03610-6\n10.1186/s12877-022-03610-6\n10.1016/j.psychres.2022.114406\n10.1093/aje/kwt294\n10.1093/eurheartj/suab115\n10.1192/j.eurpsy.2022.2350\n10.1186/s12889-023-16631-6\n10.1186/s12889-023-16631-6\n10.1016/j.puhe.2024.04.033\n10.1016/j.jpsychires.2021.05.053\n10.1016/j.jpsychires.2023.01.041\n10.1016/j.eclinm.2021.100927\n10.1016/j.healthplace.2022.102743\n10.1016/j.healthplace.2022.102889\n10.1038/nature10190\n10.3141/1780-10\n10.1016/j.envres.2023.117869\n10.1016/j.cities.2018.09.020\n10.1097/JGP.0b013e3181e9b9bf\n10.1016/j.healthplace.2016.06.002\n10.1186/s12889-020-09671-9\n10.1186/s12889-020-09671-9\n10.2196/44682\n10.1016/j.psychres.2021.114037\n10.1037//0022-006x.59.1.12\n10.1249/01.MSS.0000078924.61453.FB\n10.1016/S1361-9209(97)00009-6\n10.1016/j.envres.2023.117605\n10.1016/j.tbs.2023.100644\n10.1016/j.jad.2022.07.012\n10.1177/0962280212445834\n10.1080/10705519909540118\n10.1037/0021-9010.88.5.879\n10.1016/j.jad.2021.08.142\n10.1136/bjsports-2020-102955\n10.1016/j.amepre.2013.08.001\n10.1080/13607863.2013.801066\n10.1016/j.jad.2022.01.097\n10.1016/j.genhosppsych.2020.01.007\n10.1123/jcsp.2.2.95\n10.12691/jpar-2-1-1\n10.1136/bjsports-2019-101343\n10.1016/j.lanwpc.2022.100457\n10.1136/jech.2009.103077\n10.1093/geronb/gbab114\n10.1192/bjp.147.6.598\n10.1016/j.socscimed.2010.07.027\n10.1093/heapro/daaa036\n10.1002/gps.4217\n10.1007/s12529-018-9714-3\n10.1080/13607863.2015.1018867\n10.32891/jps.v7i2.1607\n10.1016/j.ufug.2023.127969\n10.1016/j.cities.2015.03.002",
    "journal": "JMIR public health and surveillance",
    "keywords": [
        "community-built environment",
        "depressive symptom trajectories",
        "latent growth curve modeling",
        "longitudinal study",
        "middle-aged and older adults",
        "physical activity"
    ],
    "methods": null,
    "publication_date": "2025-01-13",
    "pubmed_id": "39804686\n21076975\n34910580\n11712923\n22252938\n30633709\n38128736\n23489405\n29510298\n34742925\n36411413\n35074644\n24305575\n34650379\n36503700\n37684616\n38805868\n34118641\n36739848\n34189445\n35045352\n36027740\n21697947\n38070849\n20808136\n27322564\n33172439\n25376692\n36951932\n34098160\n8037935\n2002127\n12900694\n37956752\n35839942\n22523185\n14516251\n34706452\n33239350\n24139780\n23731057\n35090946\n32086172\n32265218\n35602414\n20515892\n34171092\n3830321\n20810197\n32277828\n25333218\n29498014\n25775108",
    "results": "In the 2015 and 2018 waves, higher low-intensity leisure-time physical activity (LTPA) was associated with lower depressive symptoms (\u03b2=-.025, P=.01 and \u03b2=-.027, P=.005, respectively). Across all waves, moderate-to-vigorous-intensity LTPA showed no significant predictive effects (P=.21 in 2015, P=.57 in 2018, and P=.85 in 2020, respectively). However, higher occupational physical activity (OPA), particularly at moderate-to-vigorous intensities, was consistently associated with higher depressive symptoms. Parallel process latent growth curve modeling revealed that the initial level of total LTPA negatively predicted the initial level of depressive symptoms (\u03b2=-.076, P=.01). OPA exhibited dual effects, positively predicting the initial level of depressive symptoms (\u03b2=.108, P<.001) but negatively predicting their upward trajectory (\u03b2=-.136, P=.009). Among CBE variables, better infrastructure conditions (\u03b2=-.082, P<.001) and greater accessibility to public facilities (\u03b2=-.036, P=.045) negatively predicted the initial level of depressive symptoms. However, greater accessibility to public facilities positively predicted the upward trajectory of depressive symptoms (\u03b2=.083, P=.04). Better infrastructure conditions (\u03b2=.100, P=.002) and greater accessibility to public transport (\u03b2=.060, P=.01) positively predicted the initial level of total LTPA. Meanwhile, better infrastructure conditions (\u03b2=-.281, P<.001) and greater accessibility to public facilities (\u03b2=-.073, P<.001) negatively predicted the initial level of total OPA. Better infrastructure conditions positively predicted the declining trajectory of total OPA (\u03b2=.100, P=.004).",
    "title": "Identifying Community-Built Environment's Effect on Physical Activity and Depressive Symptoms Trajectories Among Middle-aged and Older Adults: Chinese National Longitudinal Study.",
    "xml": "<Element 'PubmedArticle' at 0x788504c999e0>"
}