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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Physiol.</journal-id>
<journal-title>Frontiers in Physiology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Physiol.</abbrev-journal-title>
<issn pub-type="epub">1664-042X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fphys.2020.574595</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Physiology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>The Effects of Displacing Sedentary Behavior With Two Distinct Patterns of Light Activity on Health Outcomes in Older Adults (Implications for COVID-19 Quarantine)</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Grant</surname>
<given-names>Dale</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<xref rid="c001" ref-type="corresp"><sup>&#x002A;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/778361/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Tomlinson</surname>
<given-names>David</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/567731/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Tsintzas</surname>
<given-names>Kostas</given-names>
</name>
<xref rid="aff2" ref-type="aff"><sup>2</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/857234/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Koli&#x0107;</surname>
<given-names>Petra</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Onambele-Pearson</surname>
<given-names>Gladys Leopoldine</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/473713/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Department of Sports and Exercise Sciences, Research Centre for Musculoskeletal Science and Sports Medicine, Manchester Metropolitan University</institution>, <addr-line>Manchester</addr-line>, <country>United Kingdom</country></aff>
<aff id="aff2"><sup>2</sup><institution>MRC Versus Arthritis Centre for Musculoskeletal Ageing Research, Faculty of Medicine &#x0026; Health Sciences, School of Life Sciences, The University of Nottingham Medical School, Queen&#x2019;s Medical Centre</institution>, <addr-line>Nottingham</addr-line>, <country>United Kingdom</country></aff>
<author-notes>
<fn id="fn1" fn-type="edited-by"><p>Edited by: Simon Rosenbaum, University of New South Wales, Australia</p></fn>
<fn id="fn2" fn-type="edited-by"><p>Reviewed by: Pantelis Theodoros Nikolaidis, University of West Attica, Greece; David Hupin, Centre Hospitalier Universitaire (CHU) de Saint-&#x00C9;tienne, France</p></fn>
<corresp id="c001">&#x002A;Correspondence: Dale Grant, <email>g.pearson@mmu.ac.uk</email></corresp>
<fn id="fn3" fn-type="other"><p>This article was submitted to Exercise Physiology, a section of the journal Frontiers in Physiology</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>11</day>
<month>12</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="collection">
<year>2020</year>
</pub-date>
<volume>11</volume>
<elocation-id>574595</elocation-id>
<history>
<date date-type="received">
<day>20</day>
<month>06</month>
<year>2020</year>
</date>
<date date-type="accepted">
<day>23</day>
<month>11</month>
<year>2020</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2020 Grant, Tomlinson, Tsintzas, Koli&#x0107; and Onambele-Pearson.</copyright-statement>
<copyright-year>2020</copyright-year>
<copyright-holder>Grant, Tomlinson, Tsintzas, Koli&#x0107; and Onambele-Pearson</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p><bold>Rationale:</bold> The COVID-19 pandemic is limiting outdoor and community-based activities, especially for older adults owing to the requirement for self-isolation, potentially increasing prolonged sedentary behavior (SB). Given a poor tolerance for intense exercise, SB displacement with light intensity physical activity (LIPA) is a promising health enhancing alternative. Therefore, the aims of this study were to investigate the effects of two different types of SB displacement on health outcomes in older adults and any differential impact of associated LIPA pattern.</p>
<p><bold>Method:</bold> 28 older women (age: 73 &#x00B1; 5 years, height: 1.60 &#x00B1; 0.07 m, weight: 67 &#x00B1; 10 kg, and BMI: 26.1 &#x00B1; 3.6 kg/m<sup>2</sup>) underwent overnight fasted dual energy x-ray absorptiometry (DEXA) imaging, blood sampling, and functional assessments before being randomly allocated to one of two groups: (1) single continuous bout of 45&#x2013;50 min LIPA daily (<italic>n</italic> = 14); or (2) SB fragmentation (SBF; ~48 min LIPA daily, 2 min LIPA for every 30 min of SB; <italic>n</italic> = 14). Compliance was systematically monitored using tri-axial accelerometery. All measures were taken at weeks 0 and 8.</p>
<p><bold>Results:</bold> Physical behavior significantly altered (decreased SB/increased LIPA; <italic>p</italic> &#x003C; 0.05) and to a similar extent in both groups. We observed a significant reduction in serum triglycerides [<italic>p</italic> = 0.045, effect size (<italic>&#x0273;<sub>p</sub></italic><sup>2</sup>) = 0.15; SBF: &#x2212;0.26 &#x00B1; 0.77 mmol/L, LIPA: &#x2212;0.26 &#x00B1; 0.51 mmol/L], improved 30 s sit-to-stand (STS) count (<italic>p</italic> = 0.002, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.32, 2 &#x00B1; 3 STS) and speed (<italic>p</italic> = 0.009, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.35, &#x2212;10 &#x00B1; 33%), as well as increased average handgrip strength (<italic>p</italic> = 0.001, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.45, 6 &#x00B1; 12%), and gait speed (<italic>p</italic> = 0.005, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.27, 0.09 &#x00B1; 0.16 m/s) in both groups. Interestingly, SBF caused a greater increase in peak handgrip strength (8 &#x00B1; 14%), compared to LIPA (2 &#x00B1; 10%; <italic>p</italic> = 0.04, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.38).</p>
<p><bold>Conclusion:</bold> SB displacement induced significant improvements in fasting triglycerides, gait speed, as-well as STS endurance/speed in older women. Frequent vs. continuous SB displacement also caused greater increases in handgrip strength. While both SB displacement protocols display promise as efficacious home-based interventions for self-isolating older adults, our results would suggest a physical functioning advantage of the SBF protocol for certain outcomes.</p>
</abstract>
<kwd-group>
<kwd>COVID-19</kwd>
<kwd>physical functioning</kwd>
<kwd>sedentary behaviour</kwd>
<kwd>sit-to-stand</kwd>
<kwd>triglyceride</kwd>
<kwd>light intensity physical activity</kwd>
</kwd-group>
<contract-sponsor id="cn1">Manchester Metropolitan University<named-content content-type="fundref-id">10.13039/100010014</named-content>
</contract-sponsor>
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<fig-count count="4"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="93"/>
<page-count count="11"/>
<word-count count="9441"/>
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</front>
<body>
<sec id="sec1" sec-type="intro">
<title>Introduction</title>
<p>The rapid spread of Coronavirus disease 2019 (COVID-19) has prompted many nationwide lockdowns (<xref ref-type="bibr" rid="ref53">Lu et al., 2020</xref>; <xref ref-type="bibr" rid="ref81">Sohrabi et al., 2020</xref>). In most cases, it is understood that patients requiring intensive care, are more likely to be older (<xref ref-type="bibr" rid="ref88">Wang et al., 2020</xref>), prompting the call for all older adults (herein defined as &#x2265;65y), to shield themselves, by proceeding to immediately begin prolonged and strict self-isolation (<xref ref-type="bibr" rid="ref6">Armitage and Nellums, 2020</xref>). Habitually, older adults spend ~65&#x2013;80% of their waking hours performing sedentary behavior (SB; <xref ref-type="bibr" rid="ref93">Wullems et al., 2016</xref>; <xref ref-type="bibr" rid="ref52">Loyen et al., 2017</xref>). SB is associated with sarcopenic obesity (<xref ref-type="bibr" rid="ref41">Henson et al., 2018</xref>; <xref ref-type="bibr" rid="ref70">Reid et al., 2018</xref>), reduced bone mineral density (BMD; <xref ref-type="bibr" rid="ref65">Onambele-Pearson et al., 2019</xref>), heightened cardio-metabolic risk profile (<xref ref-type="bibr" rid="ref12">Biswas et al., 2015</xref>; <xref ref-type="bibr" rid="ref36">Hadgraft et al., 2020</xref>), frailty (<xref ref-type="bibr" rid="ref24">da Silva et al., 2019</xref>), and premature mortality (<xref ref-type="bibr" rid="ref32">Ekelund et al., 2019</xref>), in older adults. Furthermore, women tend to exhibit greater anabolic resistance and larger reductions in strength following disuse compared to men (<xref ref-type="bibr" rid="ref79">Smith et al., 2008</xref>, <xref ref-type="bibr" rid="ref80">2012</xref>). Self-isolation is likely to exacerbate SB, given that habitual SB is primarily accumulated at home, during social isolation (<xref ref-type="bibr" rid="ref48">Leask et al., 2015</xref>; <xref ref-type="bibr" rid="ref30">Dontje et al., 2018</xref>). Despite acknowledgement of their limited efficacy/palatability (<xref ref-type="bibr" rid="ref20">Chen et al., 2020</xref>; <xref ref-type="bibr" rid="ref44">Jim&#x00E9;nez-Pav&#x00F3;n et al., 2020</xref>; <xref ref-type="bibr" rid="ref51">Lippi et al., 2020</xref>), the default solution is simply recommending that older adults engage in moderate to vigorous physical activity [structured exercise (moderate to vigorous physical activity, MVPA)] with no clear directives <italic>vis-a-vis</italic> breaking up sitting time. However, many barriers inhibit long-term adherence to conventional MVPA recommendations (&#x2265;150 min/week, ~21 min/day; <xref ref-type="bibr" rid="ref91">World Health Organization, 2010</xref>) in older adults (<xref ref-type="bibr" rid="ref39">Hansen et al., 2019</xref>), including a lack confidence (<xref ref-type="bibr" rid="ref33">Forkan et al., 2006</xref>) and appropriate equipment (<xref ref-type="bibr" rid="ref71">Rhodes et al., 1999</xref>; <xref ref-type="bibr" rid="ref33">Forkan et al., 2006</xref>; <xref ref-type="bibr" rid="ref11">Bell et al., 2007</xref>). Given such barriers, older adults report a poor tolerance for intense physical activity, including greater perceived difficulty, and greater dropout rate (<xref ref-type="bibr" rid="ref64">Onamb&#x00E9;l&#x00E9;-Pearson et al., 2010</xref>; <xref ref-type="bibr" rid="ref15">Brawner et al., 2016</xref>). This can be problematic in the long term as only supramaximal MVPA engagement (&#x2265; 420 min/week, ~60 min/day), appears to offset the negative health effects of concurrent high SB time (<xref ref-type="bibr" rid="ref31">Ekelund et al., 2016</xref>; <xref ref-type="bibr" rid="ref55">Manas et al., 2019</xref>). Furthermore, given that sudden surges in exercise can compromise immune response (<xref ref-type="bibr" rid="ref78">Siedlik et al., 2016</xref>; <xref ref-type="bibr" rid="ref62">Nieman and Wentz, 2019</xref>), reduced protection from infections like COVID-19, is a further concern. Such limitations create scope for safer alternative home-based interventions to mitigate the potential for further compromised health during self-isolation.</p>
<p>Displacing or breaking up SB time is one such viable option. Promisingly, older adults perceive SB displacement as acceptable and easy to incorporate in their daily routine (<xref ref-type="bibr" rid="ref57">Matson et al., 2018</xref>). Light intensity physical activity (LIPA) during SB displacement, is a pre-requisite for long-term health benefits (<xref ref-type="bibr" rid="ref29">Dohrn et al., 2018</xref>; <xref ref-type="bibr" rid="ref19">Chastin et al., 2019</xref>; <xref ref-type="bibr" rid="ref82">Stamatakis et al., 2019</xref>), due to LIPA generating superior responses in both muscle activity (MA; <xref ref-type="bibr" rid="ref85">Tikkanen et al., 2013</xref>; <xref ref-type="bibr" rid="ref49">Lerma et al., 2016</xref>), and energy expenditure (<xref ref-type="bibr" rid="ref18">Carter et al., 2015</xref>; <xref ref-type="bibr" rid="ref49">Lerma et al., 2016</xref>; <xref ref-type="bibr" rid="ref74">Saeidifard et al., 2018</xref>), compared to stationary standing. Acute reductions in both postprandial glucose (<xref ref-type="bibr" rid="ref8">Bailey and Locke, 2015</xref>; <xref ref-type="bibr" rid="ref89">Welch et al., 2019</xref>) and triglycerides (TGs; <xref ref-type="bibr" rid="ref59">Miyashita et al., 2016</xref>; <xref ref-type="bibr" rid="ref45">Kashiwabara et al., 2017</xref>), as-well as chronic functional improvement (<xref ref-type="bibr" rid="ref9">Barone Gibbs et al., 2017</xref>; <xref ref-type="bibr" rid="ref40">Harvey et al., 2018</xref>), following SB displacement further highlights its potential to enhance cardio-metabolic health and physical function in older adults. However, despite a clearly established link with SB (<xref ref-type="bibr" rid="ref37">Hamer and Stamatakis, 2013</xref>; <xref ref-type="bibr" rid="ref3">Aggio et al., 2016</xref>), many functional markers like handgrip strength (HGS), have yet to be investigated. Furthermore, previous studies have merely displaced SB in arbitrary fashion without controlling for the prescribed pattern of LIPA. SB tends to be accumulated in prolonged uninterrupted bouts (<xref ref-type="bibr" rid="ref76">Schlaff et al., 2017</xref>), which are associated with worse health outcomes (<xref ref-type="bibr" rid="ref34">Gennuso et al., 2013</xref>, <xref ref-type="bibr" rid="ref35">2016</xref>; <xref ref-type="bibr" rid="ref28">Diaz et al., 2017</xref>), compared with a more fragmented pattern. Therefore, a longitudinal intervention trial is warranted to investigate the chronic effects of SB displacement on health in older adults, while elucidating what role the pattern (fragmentation vs. a single bout) of prescribed LIPA plays in benefiting anyone but especially self-isolated frail older adults such as during the COVID-19 pandemic.</p>
<p>Therefore, the aims of this study were to (1) compare the chronic effects of two distinct SB displacement interventions on commonly assessed markers of health in older adults and (2) examine the impact prescribed patterns of activity have on the aforementioned outcomes. Given the clearly established link between SB and poor health outcomes in older adults (<xref ref-type="bibr" rid="ref24">da Silva et al., 2019</xref>; <xref ref-type="bibr" rid="ref32">Ekelund et al., 2019</xref>; <xref ref-type="bibr" rid="ref36">Hadgraft et al., 2020</xref>), especially when SB is accumulated in a prolonged pattern (<xref ref-type="bibr" rid="ref34">Gennuso et al., 2013</xref>, <xref ref-type="bibr" rid="ref35">2016</xref>; <xref ref-type="bibr" rid="ref28">Diaz et al., 2017</xref>), it was hypothesized that (1) SB displacement would have small yet positive effects on markers of health and physical functioning in older adults and (2) SB fragmentation (SBF) throughout the day would induce greater health benefits compared to a single continuous bout of LIPA.</p>
</sec>
<sec id="sec2" sec-type="materials|methods">
<title>Materials and Methods</title>
<sec id="sec3">
<title>Participants and Experimental Design</title>
<p>Twenty-eight elderly women voluntarily participated in the study. Ethical approval was obtained [230118-ESS-DG-(2)], and written informed consent obtained prior to any procedures being performed, in line with the Declaration of Helsinki. Participants were recruited from the local community (Cheshire East) through advertising (posters, speaking engagements, etc.) and from a research volunteer database (local participants). Prior to the general data protection regulation deadline on May 25, 2018, recruitment packages (which included &#x201C;General Data Protection Regulation&#x201D; opt in/out permission slips, health questionnaires, participant information sheets, informed consent forms, and a pre-paid return envelope), were sent to all contacts aged 65&#x2013;85 years. Returned questionnaires were screened for potential eligibility. Exclusion criteria included recent history of lower limb disorders, or current chronic health conditions [e.g., cardiovascular disease (CVD), uncontrolled diabetes, active cancer, etc.], likely to affect their ability to safely and independently undertake a program of decreased SB. Estimation of required sample size to detect significant changes in the desired outcomes was based upon the fact that previous SB interventions in older adults that have observed improvements to physical function, utilized total sample sizes of ~25&#x2013;38 (<xref ref-type="bibr" rid="ref72">Rosenberg et al., 2015</xref>; <xref ref-type="bibr" rid="ref9">Barone Gibbs et al., 2017</xref>; <xref ref-type="bibr" rid="ref40">Harvey et al., 2018</xref>). The current achieved sample size of 28 older women, falls within this range. Participants underwent familiarization and, after 7 days, returned to the laboratories to undergo body composition analysis, blood sampling, and functional assessments. Participants were then randomly allocated in a 1:1 fashion to one of two groups: (1) SBF (<italic>n</italic> = 14) or (2) single bout LIPA (<italic>n</italic> = 14). All measures were taken at weeks 0 (baseline) and 8 (post intervention).</p>
</sec>
<sec id="sec4">
<title>Body Composition</title>
<p>A dual energy x-ray absorptiometry (DEXA) scanner (Hologic Discovery: Vertec Scientific Ltd., United Kingdom) was used (whole body procedure, EF 8.4 lSv; <xref ref-type="bibr" rid="ref86">Tomlinson et al., 2014</xref>), to ascertain BMD, lean body mass (LBM), and body fat percentage (BFP%) metrics.</p>
</sec>
<sec id="sec5">
<title>Blood Sampling</title>
<p>A 20 ml blood sample was drawn using a 0.5 Inch 23 g BD Needle (Mistry Medical Supplies, England). Whole blood analyses of fasting plasma glucose, total cholesterol, and TGs were performed using an Accutrend Plus (Roche Diagnostics Limited, United Kingdom), while glycated hemoglobin (HbA1C%) was analyzed using a 501 device (HemoCue, Sweden). Accordingly, both Accutrend and Hemocue have shown good reliability (<xref ref-type="bibr" rid="ref54">Luley et al., 2000</xref>; <xref ref-type="bibr" rid="ref60">Newman and Turner, 2005</xref>; <xref ref-type="bibr" rid="ref67">Phillips et al., 2014</xref>) and validity (<xref ref-type="bibr" rid="ref54">Luley et al., 2000</xref>; <xref ref-type="bibr" rid="ref23">Coqueiro et al., 2014</xref>; <xref ref-type="bibr" rid="ref42">Hirst et al., 2017</xref>), when compared to laboratory testing.</p>
</sec>
<sec id="sec6">
<title>Physical Function Assessment: Gait Speed, Sit-to-Stand Ability, and Handgrip Strength</title>
<p>A modified pressure sensor (Tekescan, United States) and height adjustable stool were used to reduce testing variability (<xref ref-type="bibr" rid="ref27">Demura and Yamada, 2007</xref>). Gait speed was assessed through the timed up and go test (TUG; <xref ref-type="bibr" rid="ref68">Podsiadlo and Richardson, 1991</xref>). Participants rose from the chair and walked at maximum speed to a marker 6 m away before returning to the seated position. Gait speed was defined as the quickest speed recorded over three trials [meters per second (m/s)]. Participants were then instructed to rise from the chair until the knee was fully extended and then return to a seated position. This was performed once as quickly as possible in the case of the one sit-to-stand (1STS, functional speed), and as many times as the participant could perform in an exact 30 s time frame for the 30STS (30 s STSs, functional endurance). A handgrip dynamometer (Takei, Japan), was used to assess HGS. Dynamometry is both a reliable and valid measure of strength in the elderly (<xref ref-type="bibr" rid="ref14">Bohannon and Schaubert, 2005</xref>; <xref ref-type="bibr" rid="ref1">Abizanda et al., 2012</xref>). Briefly, participants were instructed to maximally squeeze the handle and discontinue grasping at self-perceived maximum voluntary effort. Three trials were performed on each hand, with peak HGS defined as the maximum value achieved across both hands, and the average of three trials used to provide an average of both arms. Importantly, gait speed (<xref ref-type="bibr" rid="ref83">Studenski et al., 2011</xref>), STS ability (<xref ref-type="bibr" rid="ref22">Cooper et al., 2010</xref>), and grip strength (<xref ref-type="bibr" rid="ref75">Sasaki et al., 2007</xref>) are all significant predictors of mortality in older adults.</p>
</sec>
<sec id="sec7">
<title>Physical Behavior Interventions</title>
<p>The purpose of the two intervention groups was to manipulate the protocol for displacing SB time with added daily LIPA (45&#x2013;50 min in total). The interventions were confined to a 12-h period between 09:00 and 21:00. The prescribed amount of LIPA (45&#x2013;50 min) was based upon two key points. First, the WHO&#x2019;s MVPA recommendation (<xref ref-type="bibr" rid="ref91">World Health Organization, 2010</xref>) gives a theoretical starting point for what activity amount may be beneficial. Utilizing metabolic equivalent of task (MET) thresholds (SB: &#x003C;1.5 METs, LIPA: 1.5&#x2013;3.0 METs, MVPA: &#x003E;3.0 METs), 150 min/week translates into ~21 min/day moderate activity (~64 MET&#x00B7;min/day), meaning the same amount of MET&#x00B7;min/day, performed in LIPA (with a minimum intensity of 1.6 METs), would theoretically total ~40 min/day. Furthermore, the SBF group was instructed to fragment sitting time every 30 min over a 12-h period (09:00-21:00), based on recent epidemiological evidence linking a more prolonged sedentary accumulation pattern (&#x2265;30 min bouts) with greater all-cause mortality (<xref ref-type="bibr" rid="ref28">Diaz et al., 2017</xref>). Consequently, this totaled a maximum of 24 2-min LIPA bouts throughout the day (48 min). Envisaging a varied compliance response, the LIPA group was prescribed a range for their single continuous bout. Accordingly, the prescribed amount of LIPA (an additional 45&#x2013;50 min per day), was equally matched between the two groups, whereas the prescribed pattern (intermittent micro-bouts vs. single continuous bout) was different. Both intervention groups were provided with an illustrated booklet, which contained LIPA suggestions compiled from the compendium of physical activities (<xref ref-type="bibr" rid="ref4">Ainsworth et al., 2011</xref>). Importantly such activities were intentionally selected due to their simplicity, safety, and ease of implementation within the home environment.</p>
<p>Individual participant compliance was objectively monitored at weeks 0 and 8, using a thigh mounted GENEActiv original triaxial accelerometer (Activinsights Ltd., United Kingdom). Data were subsequently extracted using GENEA software, and a previously validated algorithm (<xref ref-type="bibr" rid="ref94">Wullems et al., 2017</xref>) used for baseline and post-intervention data analysis. Briefly, the aforementioned validation study calculated the incremental metabolic cost of 10 everyday tasks in 40 healthy older adults (~74 years; e.g., lying down, brisk treadmill walking, etc.), and used regression analysis to identify specific physical activity intensity ranges [utilizing MET thresholds (SB: &#x003C;1.5 METs, LIPA: 1.5&#x2013;3.0 METs, MVPA: &#x003E;3.0 METs)] mapped against the concurrently recorded GENEActiv gravitational pull and acceleration data. The robustly derived data on SB, standing, LIPA, and MVPA in older adults were used for further analyses. Participants were also further classified as physically active (&#x2265;150 min/week MVPA<sub>&#x2265;10 min bouts</sub>), or non-physically active (&#x003C;150 min/week MVPA<sub>&#x2265;10 min bouts</sub>), given that the World Health Organization (WHO) recommends a weekly MVPA engagement time of 150 min/week (<xref ref-type="bibr" rid="ref91">World Health Organization, 2010</xref>).</p>
<sec id="sec8">
<title>SBF Group</title>
<p>Participants were told that the purpose of their intervention was to reduce the amount of time spent performing SB (sitting, lying, or reclining) especially in prolonged uninterrupted bouts. Participants were instructed not to perform SB for more than 30 min at a time, and that for every 30 min of SB performed the participant should stand up and perform 2 min of upright LIPA (general ambulatory walking, side to side shuffling, washing dishes, etc.).</p>
</sec>
<sec id="sec9">
<title>LIPA Group</title>
<p>Participants were informed that the purpose of their intervention was to increase the amount of time spent performing LIPA while maintaining habitual routines. Participants were instructed to perform a continuous single bout of 45&#x2013;50 min LIPA (general ambulatory walking, side to side shuffling, washing dishes, etc.), every day for the duration of the 8-week intervention.</p>
</sec>
</sec>
<sec id="sec10">
<title>Palatability Assessment</title>
<p>During the post-test visit, participants were asked to complete a palatability questionnaire. Each question was designed to rate an aspect of the participants experience and gain insight on perceived quality of life (QoL).</p>
</sec>
<sec id="sec11">
<title>Statistical Analyses</title>
<p>Statistical analyses were carried out using SPSS (Version 26, SPSS Inc., Chicago, IL, United States). Normal distribution and equality of variances between groups were checked using the Shapiro-Wilk and Levene&#x2019;s tests, respectively. Baseline group differences were subsequently examined with an independent sample&#x2019;s T-test or Mann-Whitney U test (SBF vs. LIPA) as appropriate. The effects of the interventions were determined using 2 &#x00D7; 2 split plot ANOVA [two time phases (pre and post intervention) and two intervention groups]. In cases of non-normally distributed data, within group comparisons were made using the Wilcoxon-Sign Rank test, while, between group differences were analyzed through a Mann-Whitney U test on the relative changes from baseline. A Chi squared test was used to compare between group differences for ordinal/nominal data from the palatability questionnaire. Furthermore, Spearman bivariate correlations were utilized to investigate associations between the relative changes in LBM metrics and the relative changes in functional assessments. Data are reported as mean &#x00B1; SD [or median &#x00B1; interquartile range (IQR) for non-parametric data]. Statistical significance was accepted when <italic>p</italic> &#x2264; 0.05. Furthermore, a statistical trend was deemed to be present when <italic>p</italic> was in the range of between 0.05 and 0.10. Effect size (<italic>&#x0273;<sub>p</sub></italic><sup>2</sup>) was also reported, where <italic>p</italic> is significant.</p>
</sec>
</sec>
<sec id="sec12" sec-type="results">
<title>Results</title>
<sec id="sec13">
<title>Descriptive Characteristics of Participants at Baseline</title>
<p>The 28 older women (age: 73 &#x00B1; 5 years, height: 1.60 &#x00B1; 0.07 m, weight: 67 &#x00B1; 10 kg, and BMI: 26.1 &#x00B1; 3.6 kg.m<sup>2</sup>) were matched at baseline for all outcomes of interest (<italic>p</italic> &#x003E; 0.05), denoting a well-matched study sample (see <xref rid="tab1" ref-type="table">Table 1</xref>).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Participant characteristics at baseline.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Participants characteristics</th>
<th align="center" valign="top">SBF (<italic>n</italic> = 14)</th>
<th align="center" valign="top">LIPA (<italic>n</italic> = 14)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Age (y)</td>
<td align="left" valign="top">74 &#x00B1; 5</td>
<td align="left" valign="top">73 &#x00B1; 6</td>
</tr>
<tr>
<td align="left" valign="top">Weight (kg)</td>
<td align="left" valign="top">68.6 &#x00B1; 11.3</td>
<td align="left" valign="top">65.5 &#x00B1; 8.6</td>
</tr>
<tr>
<td align="left" valign="top">BMI (kg.m<sup>2</sup>)</td>
<td align="left" valign="top">26.9 &#x00B1; 3.6</td>
<td align="left" valign="top">25.3 &#x00B1; 3.6</td>
</tr>
<tr>
<td align="left" valign="top">Total lean body mass (LBM; kg)</td>
<td align="left" valign="top">39.3 &#x00B1; 5.7</td>
<td align="left" valign="top">37.2 &#x00B1; 3.9</td>
</tr>
<tr>
<td align="left" valign="top">Proportional T-score classification as osteoporotic/osteopenic (normal)</td>
<td align="left" valign="top">29% (71%)</td>
<td align="left" valign="top">43% (57%)</td>
</tr>
<tr>
<td align="left" valign="top">Proportion who live alone/(cohabitate)</td>
<td align="left" valign="top">36% (64%)</td>
<td align="left" valign="top">43% (57%)</td>
</tr>
<tr>
<td align="left" valign="top">Polypharmacy (<italic>n</italic>)</td>
<td align="left" valign="top">2 &#x00B1; 2</td>
<td align="left" valign="top">1 &#x00B1; 1</td>
</tr>
<tr>
<td align="left" valign="top">FRAT (number of positive responses)</td>
<td align="left" valign="top">1 &#x00B1; 1</td>
<td align="left" valign="top">1 &#x00B1; 1</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>FRAT, falls risk assessment tool; LIPA, light intensity physical activity; and SBF, sedentary behavior fragmentation.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec14">
<title>Intervention, Compliance, and Palatability</title>
<p>No differences existed between groups regarding the number of intervention days (days from pre-lab visit to post) that the participants undertook (SBF: 56 &#x00B1; 2 days, LIPA:56 &#x00B1; 1 days; <italic>p</italic> = 0.37). Regarding 3D-accelerometer-based compliance data, both groups were matched for all variables at baseline. SB significantly decreased over time in both groups (<italic>p</italic> = 0.006, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.26), but did not exhibit a group &#x00D7; time interaction (<italic>p</italic> = 0.41; <xref rid="fig1" ref-type="fig">Figure 1</xref>). Similarly, mean SB bout time significantly decreased over time in both groups (<italic>p</italic> = 0.045, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.27), but did not exhibit a group &#x00D7; time interaction (<italic>p</italic> = 0.96; <xref rid="tab2" ref-type="table">Table 2</xref>). LIPA significantly increased over time in both groups (<italic>p</italic> = 0.04, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.15), but did not exhibit a group &#x00D7; time interaction (<italic>p</italic> = 0.11; <xref rid="fig1" ref-type="fig">Figure 1</xref>). Standing and MVPA time, however, did not significantly change (<xref rid="tab2" ref-type="table">Table 2</xref>). Concerning intervention palatability, promisingly, all participants agreed the instructions were easy to follow at home, with 89% reporting increased awareness of their daily sedentarism. Accordingly, 82% of participants reported feeling more positive about their health, and most importantly, 61% of participants stated they could definitely continue following this intervention long term. Furthermore, 54% of participants stated their intervention had motivated them to become more active. However, only 25% of participants stated they definitely felt more confident about performing household tasks following their respective interventions. There was no difference in self-reported satisfaction or continued adherence between groups (<italic>p</italic> &#x2265; 0.05).</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Group dependent changes relative to baseline for physical behaviour outcomes. Panel <bold>A</bold> represents changes in light intensity physical activity (LIPA), while panel <bold>B</bold> represents changes in sedentary behaviour.</p>
</caption>
<graphic xlink:href="fphys-11-574595-g001.tif"/>
</fig>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Pre and post values for health outcomes.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th colspan="2" rowspan="2"/>
<th align="center" valign="top" colspan="2">SBF (<italic>n</italic> = 14)</th>
<th align="center" valign="top" colspan="2">LIPA (<italic>n</italic> = 14)</th>
</tr>
<tr>
<th align="center" valign="top">Pre</th>
<th align="center" valign="top">Post</th>
<th align="center" valign="top">Pre</th>
<th align="center" valign="top">Post</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">LBM (dual energy x-ray absorptiometry, DEXA)</td>
<td align="left" valign="top">Arms (kg)</td>
<td align="left" valign="top">1.86 &#x00B1; 0.32</td>
<td align="left" valign="top">1.85 &#x00B1; 0.28</td>
<td align="left" valign="top">1.78 &#x00B1; 0.21</td>
<td align="left" valign="top">1.76 &#x00B1; 0.23</td>
</tr>
<tr>
<td/>
<td align="left" valign="top">Legs (kg)</td>
<td align="left" valign="top">6.18 &#x00B1; 1.19</td>
<td align="left" valign="top">6.04 &#x00B1; 0.95</td>
<td align="left" valign="top">5.89 &#x00B1; 0.75</td>
<td align="left" valign="top">5.84 &#x00B1; 0.68</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="3">Bone mineral density</td>
<td align="left" valign="top">Thoracic spine (g/cm<sup>3</sup>)</td>
<td align="left" valign="top">0.90 &#x00B1; 0.09</td>
<td align="left" valign="top">0.94 &#x00B1; 0.13</td>
<td align="left" valign="top">0.90 &#x00B1; 0.14</td>
<td align="left" valign="top">0.94 &#x00B1; 0.15</td>
</tr>
<tr>
<td align="left" valign="top">Lumbar spine (g/cm<sup>3</sup>)</td>
<td align="left" valign="top">0.98 &#x00B1; 0.16</td>
<td align="left" valign="top">0.96 &#x00B1; 0.15</td>
<td align="left" valign="top">0.97 &#x00B1; 0.16</td>
<td align="left" valign="top">0.98 &#x00B1; 0.16</td>
</tr>
<tr>
<td align="left" valign="top">Total (g/cm<sup>3</sup>)</td>
<td align="left" valign="top">1.10 &#x00B1; 0.11</td>
<td align="left" valign="top">1.09 &#x00B1; 0.11</td>
<td align="left" valign="top">1.11 &#x00B1; 0.15</td>
<td align="left" valign="top">1.11 &#x00B1; 0.14</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="6">Adiposity indices</td>
<td align="left" valign="top">Total (kg)</td>
<td align="left" valign="top">26.1 &#x00B1; 6.6</td>
<td align="left" valign="top">26.5 &#x00B1; 6.3</td>
<td align="left" valign="top">25.1 &#x00B1; 5.5</td>
<td align="left" valign="top">25.3 &#x00B1; 5.8</td>
</tr>
<tr>
<td align="left" valign="top">Android: Gynoid ratio</td>
<td align="left" valign="top">0.94 &#x00B1; 0.15</td>
<td align="left" valign="top">0.93 &#x00B1; 0.14</td>
<td align="left" valign="top">0.93 &#x00B1; 0.18</td>
<td align="left" valign="top">0.90 &#x00B1; 0.17</td>
</tr>
<tr>
<td align="left" valign="top">Waist (cm)</td>
<td align="left" valign="top">92 &#x00B1; 18</td>
<td align="left" valign="top">92 &#x00B1; 24</td>
<td align="left" valign="top">91 &#x00B1; 5</td>
<td align="left" valign="top">92 &#x00B1; 8</td>
</tr>
<tr>
<td align="left" valign="top">Hip (cm)</td>
<td align="left" valign="top">100 &#x00B1; 7</td>
<td align="left" valign="top">99 &#x00B1; 6<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
<td align="left" valign="top">100 &#x00B1; 8</td>
<td align="left" valign="top">99 &#x00B1; 8<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
</tr>
<tr>
<td align="left" valign="top">WHR</td>
<td align="left" valign="top">0.95 &#x00B1; 0.11</td>
<td align="left" valign="top">0.95 &#x00B1; 0.18</td>
<td align="left" valign="top">0.91 &#x00B1; 0.10</td>
<td align="left" valign="top">0.92 &#x00B1; 0.10</td>
</tr>
<tr>
<td align="left" valign="top">Body fat percentage (BFP, %)</td>
<td align="left" valign="top">39 &#x00B1; 7</td>
<td align="left" valign="top">38 &#x00B1; 5</td>
<td align="left" valign="top">38 &#x00B1; 7</td>
<td align="left" valign="top">38 &#x00B1; 7</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="4">Cardio-metabolic biomarkers</td>
<td align="left" valign="top">HBA1C (%)</td>
<td align="left" valign="top">5 &#x00B1; 1</td>
<td align="left" valign="top">6 &#x00B1; 0</td>
<td align="left" valign="top">6 &#x00B1; 1</td>
<td align="left" valign="top">6 &#x00B1; 1</td>
</tr>
<tr>
<td align="left" valign="top">Glucose (mmol/L)</td>
<td align="left" valign="top">5.34 &#x00B1; 0.98</td>
<td align="left" valign="top">5.01 &#x00B1; 1.73</td>
<td align="left" valign="top">4.94 &#x00B1; 0.86</td>
<td align="left" valign="top">4.73 &#x00B1; 0.88</td>
</tr>
<tr>
<td align="left" valign="top">Triglycerides (mmol/L)</td>
<td align="left" valign="top">2.19 &#x00B1; 0.82</td>
<td align="left" valign="top">1.94 &#x00B1; 0.50<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
<td align="left" valign="top">1.94 &#x00B1; 0.52</td>
<td align="left" valign="top">1.68 &#x00B1; 0.40<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
</tr>
<tr>
<td align="left" valign="top">Total cholesterol (mmol/L)</td>
<td align="left" valign="top">5.53 &#x00B1; 1.47</td>
<td align="left" valign="top">5.80 &#x00B1; 1.86</td>
<td align="left" valign="top">5.33 &#x00B1; 1.58</td>
<td align="left" valign="top">5.97 &#x00B1; 1.25</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="5">Physical function</td>
<td align="left" valign="top">Peak HGS (kg)</td>
<td align="left" valign="top">26.3 &#x00B1; 8.5</td>
<td align="left" valign="top">26.8 &#x00B1; 6.1<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref><xref rid="tfn2" ref-type="table-fn"><sup>&#x00D7;</sup></xref></td>
<td align="left" valign="top">26.5 &#x00B1; 4.4</td>
<td align="left" valign="top">26.5 &#x00B1; 7.3<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref><xref rid="tfn2" ref-type="table-fn"><sup>&#x00D7;</sup></xref></td>
</tr>
<tr>
<td align="left" valign="top">Average HGS (kg)</td>
<td align="left" valign="top">22.8 &#x00B1; 6.6</td>
<td align="left" valign="top">23.9 &#x00B1; 5.4<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
<td align="left" valign="top">22.9 &#x00B1; 5.7</td>
<td align="left" valign="top">23.8 &#x00B1; 7.2<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
</tr>
<tr>
<td align="left" valign="top">30STS</td>
<td align="left" valign="top">14 &#x00B1; 3</td>
<td align="left" valign="top">17 &#x00B1; 3<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
<td align="left" valign="top">17 &#x00B1; 3</td>
<td align="left" valign="top">18 &#x00B1; 4<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
</tr>
<tr>
<td align="left" valign="top">1STS (s)</td>
<td align="left" valign="top">2.49 &#x00B1; 1.02</td>
<td align="left" valign="top">2.15 &#x00B1; 0.70<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
<td align="left" valign="top">1.98 &#x00B1; 0.52</td>
<td align="left" valign="top">1.86 &#x00B1; 0.52<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
</tr>
<tr>
<td align="left" valign="top">Max gait speed (m/s)</td>
<td align="left" valign="top">1.17 &#x00B1; 0.22</td>
<td align="left" valign="top">1.26 &#x00B1; 0.19<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
<td align="left" valign="top">1.22 &#x00B1; 0.13</td>
<td align="left" valign="top">1.28 &#x00B1; 0.11<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
</tr>
<tr>
<td align="left" valign="top" rowspan="6">Daily physical behavior</td>
<td align="left" valign="top">SB time (hr/24 h)</td>
<td align="left" valign="top">9.6 &#x00B1; 1.2</td>
<td align="left" valign="top">9.2 &#x00B1; 1.6<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
<td align="left" valign="top">9.6 &#x00B1; 1.1</td>
<td align="left" valign="top">8.9 &#x00B1; 1.2<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
</tr>
<tr>
<td align="left" valign="top">Standing time (hrs/24 h)</td>
<td align="left" valign="top">1.0 &#x00B1; 0.6</td>
<td align="left" valign="top">1.0 &#x00B1; 0.6</td>
<td align="left" valign="top">1.4 &#x00B1; 1.1</td>
<td align="left" valign="top">1.5 &#x00B1; 0.7</td>
</tr>
<tr>
<td align="left" valign="top">LIPA time (hrs/24 h)</td>
<td align="left" valign="top">2.2 &#x00B1; 0.5</td>
<td align="left" valign="top">2.2 &#x00B1; 0.6<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
<td align="left" valign="top">2.1 &#x00B1; 0.4</td>
<td align="left" valign="top">2.3 &#x00B1; 0.5<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
</tr>
<tr>
<td align="left" valign="top">MVPA time (hrs/24 h)</td>
<td align="left" valign="top">3.0 &#x00B1; 1.0</td>
<td align="left" valign="top">2.8 &#x00B1; 1.0</td>
<td align="left" valign="top">2.5 &#x00B1; 0.8</td>
<td align="left" valign="top">2.8 &#x00B1; 0.7</td>
</tr>
<tr>
<td align="left" valign="top">Mean SB bout time (min)</td>
<td align="left" valign="top">31 &#x00B1; 8</td>
<td align="left" valign="top">27 &#x00B1; 9<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
<td align="left" valign="top">32 &#x00B1; 14</td>
<td align="left" valign="top">29 &#x00B1; 11<xref rid="tfn1" ref-type="table-fn"><sup>&#x002A;</sup></xref></td>
</tr>
<tr>
<td align="left" valign="top">Proportion meeting recommended MVPA time [&#x2265;150 min/week MVPA (&#x2265;10 min bouts; <xref ref-type="bibr" rid="ref91">World Health Organization, 2010</xref>)]/ below recommended MVPA</td>
<td align="left" valign="top">29%/71%</td>
<td align="left" valign="top">7%/93%</td>
<td align="left" valign="top">0%/100%</td>
<td align="left" valign="top">7%/93%</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="tfn1"><label>&#x002A;</label><p>Significant change from baseline.</p></fn>
<fn id="tfn2"><label>&#x00D7;</label><p>Significant group dependent effect.</p></fn>
<p>HGS, handgrip strength; LIPA, light intensity physical activity; MVPA, moderate to vigorous physical activity; SB, sedentary behavior; SBF, sedentary behavior fragmentation; STS, sit-to-stands; and WHR, waist to hip ratio.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec15">
<title>Bone Mineral Density</title>
<p>After accounting for previously identified co-variates [total body fat (TFAT), Android:Gynoid fat ratio (AGR), and BMI; <xref ref-type="bibr" rid="ref65">Onambele-Pearson et al., 2019</xref>], thoracic (<italic>p</italic> = 0.09, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.12), but not lumbar spine mineral density (<italic>p</italic> = 0.70), exhibited a trend to change over time. Importantly, thoracic spine did not exhibit a significant group &#x00D7; time interaction effect (<italic>p</italic> = 0.71) with changes similar in both groups (SBF: 5 &#x00B1; 14%, LIPA: 4 &#x00B1; 9%).</p>
</sec>
<sec id="sec16">
<title>Body Composition</title>
<p>Neither arm (<italic>p</italic> = 0.73), leg (<italic>p</italic> = 0.17), nor total (<italic>p</italic> = 0.20) LBM, significantly changed over time. Despite no change in BFP% (<italic>p</italic> = 0.12), we did observe trends for AGR (<italic>p</italic> = 0.08, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.11), and TFAT (<italic>p</italic> = 0.10, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.004), to change over time (<xref rid="tab2" ref-type="table">Table 2</xref>). We also observed a significant reduction in hip circumference over time (<italic>p</italic> = 0.02, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.19), as-well as a trend (<italic>p</italic> = 0.07, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.12), toward a group &#x00D7; time interaction effect (<xref rid="tab2" ref-type="table">Table 2</xref>) primarily driven through apparent greater reductions in SBF (&#x2212;1.79 &#x00B1; 2.34 cm), compared to LIPA (&#x2212;0.25 &#x00B1; 1.94 cm; <xref rid="fig2" ref-type="fig">Figure 2</xref>).</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Group dependent individual changes for hip circumference. Panel <bold>A</bold> represents individual changes following sedentary behaviour fragmentation (SBF); while panel <bold>B</bold> represents individual changes following continuous light intensity physical activity (LIPA). &#x002A;Represents a significant change over time.</p>
</caption>
<graphic xlink:href="fphys-11-574595-g002.tif"/>
</fig>
</sec>
<sec id="sec17">
<title>Cardio-Metabolic Biomarkers</title>
<p>We observed a significant main effect of time for fasting blood TG (<italic>p</italic> = 0.045, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.15), which was similar in the two groups, given no significant group &#x00D7; time interaction (<italic>p</italic> = 0.98; SBF: &#x2212;0.26 &#x00B1; 0.77 mmol/L, LIPA: &#x2212;0.26 &#x00B1; 0.51 mmol/L; <xref rid="fig3" ref-type="fig">Figure 3</xref>). No other cardio-metabolic serum biomarkers exhibited main effects for group, time, or group &#x00D7; time interactions.</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Group changes in fasting triglycerides from pre to post intervention. &#x002A;Represents a significant change over time. The dashed line (- - -) represents the fasting triglycerides threshold (2 mmol/L) below which significantly reduced CVD risk is observed (<xref ref-type="bibr" rid="ref43">Iso et al., 2014</xref>; <xref ref-type="bibr" rid="ref63">Nordestgaard and Varbo, 2014</xref>).</p>
</caption>
<graphic xlink:href="fphys-11-574595-g003.tif"/>
</fig>
</sec>
<sec id="sec18">
<title>Physical Function</title>
<p>A significant main effect for time was exhibited for gait speed (<italic>p</italic> = 0.005, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.27, 0.09 &#x00B1; 0.16 m/s), but not a group&#x00D7; time interaction effect (<italic>p</italic> = 0.44). There was also a significant main effect of time for 30STS (<italic>p</italic> = 0.002, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.32, 2 &#x00B1; 3 STS), 1STS (<italic>p</italic> = 0.009, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.35, &#x2212;10 &#x00B1; 33%; see <xref rid="fig4" ref-type="fig">Figure 4</xref>), and average HGS (<italic>p</italic> = 0.001, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.45, 6 &#x00B1; 12%). Furthermore, peak HGS was the only functional outcome to exhibit both a significant main effect of time (<italic>p</italic> = 0.044, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.27), and a group &#x00D7; time interaction (<italic>p</italic> = 0.04, <italic>&#x0273;<sub>p</sub></italic><sup>2</sup> = 0.38), with a greater increase in SBF than LIPA (SBF: 8 &#x00B1; 14% and LIPA: 2 &#x00B1; 10%; <xref rid="fig4" ref-type="fig">Figure 4</xref>). Interestingly, the relative change from baseline in arm LBM, was significantly associated with the relative change from baseline in peak HGS (<italic>R</italic><sup>2</sup> = 0.17 <italic>p</italic> = 0.03), accounting for ~17% of the explained variance when both groups were pooled. Furthermore, when sub-analyzed by group, such an association persisted in LIPA (<italic>R</italic><sup>2</sup> = 0.53, <italic>p</italic> = 0.004) but not SBF, accounting for 53% of the explained variance.</p>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Group dependent changes relative to baseline for physical function parameters. &#x002A;Represents a significant group &#x00D7; time interaction effect. HGS, handgrip strength and STS, sit-to-stands.</p>
</caption>
<graphic xlink:href="fphys-11-574595-g004.tif"/>
</fig>
</sec>
</sec>
<sec id="sec19" sec-type="discussions">
<title>Discussion</title>
<p>This is the first study to investigate the chronic effects of SB displacement on health outcomes in older women and provide recommendations to mitigate any negative health consequences. We hypothesized that SB displacement would have measurable and positive effects on markers of health and physical functioning in older adults. We observed significant improvements over time for circulating TG, hip circumference, gait speed, 30STS, 1STS time, average HGS, and peak HGS, thereby upholding our first hypothesis. We further hypothesized that SBF would induce greater benefits compared to continuous LIPA. Here, we observed a trend for hip circumference (<italic>p</italic> = 0.07) and a significant effect for peak HGS (<italic>p</italic> = 0.04) to exhibit the predicted SBF advantage. Consequently, the second hypothesis was partially upheld.</p>
<p>We observed significant functional improvements post-intervention. Firstly, increased muscular endurance (30STS) and enhanced gait speed are consistent findings across previous SB studies (<xref ref-type="bibr" rid="ref9">Barone Gibbs et al., 2017</xref>; <xref ref-type="bibr" rid="ref40">Harvey et al., 2018</xref>), potentially highlighting a specificity of training effect, improving one&#x2019;s ability to mobilize from a seated position. We also observed for the first time a decrease in the time taken to complete 1STS (an index of functional speed), further suggesting improved movement execution and enhanced muscular power. This positive effect is of notable impact given that inappropriate STS transitions are responsible for up to 41% of falls in care home residents (<xref ref-type="bibr" rid="ref69">Rapp et al., 2012</xref>). Importantly, peak HGS improved to a greater extent in SBF compared to LIPA. Holding onto the arm of a chair and pushing through one&#x2019;s arms are common cues given to older adults when performing STS (<xref ref-type="bibr" rid="ref46">Kindblom-Rising et al., 2010</xref>). Therefore, we propose that the increased STS frequency may have also increased the frequency with which the SBF participants utilized the arm stabilization tactic, subsequently, causing gradual functional adaptation in the upper body (including arm) musculature. Nevertheless, we advised all participants to implement many upright upper body tasks (sweeping up, etc.), and improvements in HGS have been reported following implementation of light upper body based movements (<xref ref-type="bibr" rid="ref61">Nicholson et al., 1997</xref>; <xref ref-type="bibr" rid="ref5">Anthony et al., 2013</xref>; <xref ref-type="bibr" rid="ref77">Sexton and Taylor, 2019</xref>). Interestingly, arm LBM did not significantly change from pre to post, yet the relative change in arm LBM significantly accounted for 17&#x2013;53% of the explained variance for the change in peak HGS. The greater association between muscle tissue content and peak HGS in the LIPA group may be linked to the fact that these participants were requested to perform various operational tasks in a continuous fashion, which appears to have caused a statistically insignificant yet clinically meaningful hypertrophic response leading to enhanced peak HGS. The observed improvements in lower body muscular endurance/power, in both intervention groups, as-well as HGS are compelling positive changes associated with an exercise intensity not customarily regarded as optimal.</p>
<p>We observed significant reductions in fasting circulating TG. Acutely interrupting sitting time with brief bouts of LIPA attenuates postprandial TG concentrations (<xref ref-type="bibr" rid="ref59">Miyashita et al., 2016</xref>; <xref ref-type="bibr" rid="ref45">Kashiwabara et al., 2017</xref>), and habitual LIPA is associated with reduced TG in older adults (<xref ref-type="bibr" rid="ref73">Ryan et al., 2015</xref>), which in turn is linked to reduced CVD risk (<xref ref-type="bibr" rid="ref7">Baigent et al., 2010</xref>). Our data demonstrate such acute effects, persist into accumulated long-term benefits. Importantly, fasting TG levels below 2 mmol/L, confer significantly reduced risk of CVD (<xref ref-type="bibr" rid="ref43">Iso et al., 2014</xref>; <xref ref-type="bibr" rid="ref63">Nordestgaard and Varbo, 2014</xref>), a level that was beneficially achieved by both groups, post intervention (SBF: 1.94 &#x00B1; 0.50 mmol/L and LIPA: 1.68 &#x00B1; 0.40 mmol/L). Increased lipoprotein lipase (LPL) is a probable underlying mechanism, given the significant role it plays in reduced CVD risk (<xref ref-type="bibr" rid="ref38">Hamilton et al., 2007</xref>). We thus propose persistent increases in the energy demand of contracting muscle facilitated enhanced substrate uptake. In contrast to previous evidence showing a more fragmented SB pattern is associated with decreased TG (<xref ref-type="bibr" rid="ref17">Carson et al., 2014</xref>; <xref ref-type="bibr" rid="ref16">Brocklebank et al., 2015</xref>), our data suggest the prescribed LIPA pattern is not of such relative importance, given that both groups decreased TG to a similar extent over time. Elucidating alterations in peripheral insulin sensitivity, requires a glucose tolerance test (<xref ref-type="bibr" rid="ref25">Davies et al., 2000</xref>; <xref ref-type="bibr" rid="ref66">Petersen and McGuire, 2005</xref>; <xref ref-type="bibr" rid="ref84">Tab&#x00E1;k et al., 2012</xref>), which we recommend future studies investigate. As they currently stand, our data simply suggest that chronic SB displacement in older women causes beneficial reductions in fasting circulating TG, irrespective of prescribed pattern.</p>
<p>Our data show reduced hip circumference following both interventions. Given that LIPA raises energy expenditure (<xref ref-type="bibr" rid="ref18">Carter et al., 2015</xref>; <xref ref-type="bibr" rid="ref49">Lerma et al., 2016</xref>), a chronically sustained LIPA increase likely created a negative energy balance (<xref ref-type="bibr" rid="ref50">Levine et al., 2005</xref>), beneficially reducing what we will assume were fat deposits around the hips. Furthermore, AGR also exhibited a trend to decrease over time. Together, these changes can be viewed as positive given that abdominal adiposity (Android) is more detrimental to health compared to lower body accumulation (<xref ref-type="bibr" rid="ref10">Bastien et al., 2014</xref>; <xref ref-type="bibr" rid="ref21">Chrysant and Chrysant, 2019</xref>). In support of our findings, a previous exercise intervention has noted a reduction in waist to hip ratio (<xref ref-type="bibr" rid="ref26">de Mendon&#x00E7;a et al., 2014</xref>). We also observed trends toward improved thoracic spine BMD. Accordingly, LIPA is associated with increased thoracic spine BMD in older adults (<xref ref-type="bibr" rid="ref65">Onambele-Pearson et al., 2019</xref>), where the authors speculated excessive kyphotic curvature likely increases (forward) shear forces between thoracic vertebrae while walking (<xref ref-type="bibr" rid="ref47">Kohrt et al., 1997</xref>), and thus places stress/strain on the bone structures, sufficient to cause adaptation. Our findings therefore support the notion of beneficial body composition changes (statistically significant and trends) following SB displacement in older women.</p>
<p>Together with the successful implementation of a randomized chronic SB intervention study in older adults, the novel manipulation of the prescribed LIPA pattern, makes the current study&#x2019;s design one of its primary strengths. Despite the lack of a control group limiting our design, the different patterns of LIPA prescription took priority. Furthermore, while the exclusive inclusion of older women somewhat limits the generalizability of our findings, we ultimately see this as a strength, given that muscle-tendon adaptation to resistance training appears to be gender dependent (<xref ref-type="bibr" rid="ref58">McMahon et al., 2018</xref>). Moreover, we collected data on a range of health and physical functioning markers, 3D-accelerometer-based compliance, and self-reported adherence following SB displacement. Given that we successfully altered objectively measured SB, LIPA, and SB bout length in our participants, this reinforces our conclusion, that SB displacement specifically with LIPA mediated the health improvements observed. Furthermore, both interventions, were similarly rated as easy to implement at home, increased awareness of habitual SB, self-perceived health, and marked likelihood to integrate into lifestyle in the long term. Our findings add to the knowledge-base in the topic of SB effects (<xref ref-type="bibr" rid="ref92">Wu et al., 2013</xref>; <xref ref-type="bibr" rid="ref57">Matson et al., 2018</xref>, <xref ref-type="bibr" rid="ref56">2019</xref>; <xref ref-type="bibr" rid="ref90">Wilson et al., 2019</xref>). Perhaps future studies could implement a similar intervention strategy, while elucidating the physiological mechanisms that underpin such positive changes, including muscle-tendon complex adaptation (e.g., neuromuscular adaptation), serum lipid transporters (e.g., LPL), and biological markers of inflammation (interleukin 6, tumor necrosis factor alpha, and C-reactive protein). Future studies should also investigate the effects of SB displacement on validated QoL assessments in older adults (e.g., SF-36 and EQ-5D; <xref ref-type="bibr" rid="ref13">Bohannon and DePasquale, 2010</xref>), as-well as comprehensive physical capacity assessments [e.g., 6 min walk test (6MWT; <xref ref-type="bibr" rid="ref2">Agarwala and Salzman, 2020</xref>)].</p>
<p>Given that older adults are being requested to &#x201C;shield&#x201D; and engage in prolonged and strict self-isolation (<xref ref-type="bibr" rid="ref6">Armitage and Nellums, 2020</xref>), this makes the results of the current study very applicable. Accordingly, we recruited community dwelling older women from the local community, the population in need of targeted activity interventions during COVID-19 related quarantine. Our results suggest displacing SB with LIPA enhances various markers of health status. Such an intervention can be carried out from the home environment, with minimal effort/support, and displays good likelihood of long-term compliance. However, it must be noted, participants received a fortnightly home visit from the principal investigator to facilitate compliance and troubleshoot issues, which under quarantine conditions is simply not permitted. Such a limitation could be somewhat mitigated through indirect means of contact (telephone calls, emails, video conferencing software, etc.). Nevertheless, our results suggest SB displacement with LIPA is an efficacious home-based intervention for self-isolating community dwelling older adults to mitigate the detrimental health consequences of prolonged sedentarism during quarantine.</p>
</sec>
<sec id="sec20" sec-type="conclusions">
<title>Conclusion</title>
<p>Due to the unrelenting global spread of pandemics such as COVID-19, further quarantine periods are looking increasingly likely for the general population but especially for frail older adults (<xref ref-type="bibr" rid="ref53">Lu et al., 2020</xref>; <xref ref-type="bibr" rid="ref81">Sohrabi et al., 2020</xref>). Following 8 weeks of SB displacement with LIPA, we observed significant improvements in blood biomarkers (fasting TGs), and markers of physical function (gait speed, STS endurance/speed, and hand grip strength) in older women. Frequent vs. continuous SB displacement also caused greater increases in peak HGS. Therefore, based on our results, we propose SB displacement is an efficacious home-based intervention for self-isolating older adults, where MVPA is especially challenging. Our data suggest that MVPA engagement is not always necessary for mitigating the detrimental health consequences of prolonged SB. We propose that the positive palatability and high adherence results from our LIPA interventions are testament to the potential for long-term and wide adoption of this type of exercise interventions by key end-users. Furthermore, certain outcomes may be enhanced favorably with fragmented physical activity throughout the day rather than a single bout of exercise, even though both do enhance markers of health and physical functioning.</p>
</sec>
<sec id="sec21">
<title>Data Availability Statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec id="sec22">
<title>Ethics Statement</title>
<p>The studies involving human participants were reviewed and approved by Ethical committee of the Manchester Metropolitan University. The patients/participants provided their written informed consent to participate in this study.</p>
</sec>
<sec id="sec23">
<title>Author Contributions</title>
<p>GO-P, DT, and KT designed the research. DG conducted the research. DG and GO-P analyzed data, wrote the paper, and had primary responsibility for final content. All authors contributed to the article and approved the submitted version.</p>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
</sec>
</body>
<back>
<ack>
<p>We would like to extend our gratitude to the Musculoskeletal Sciences and Sport Medicine Research Centre at Manchester Metropolitan University for their continued support.</p>
</ack>
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<fn-group><fn fn-type="financial-disclosure"><p><bold>Funding.</bold> Financial Support was provided by the Vice Chancellor Scholarship at Manchester Metropolitan University.</p></fn>
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