Identifying the combination of TFs that dictate specific cell fate, their specific cistromes and examining their involvement in complex human traits remain a major challenge. Here, we focus on the retinal pigmented epithelium (RPE), an essential lineage for retinal development and function and the primary tissue affected in age-related macular degeneration (AMD), a leading cause of blindness. By combining mechanistic findings in stem-cell-derived human RPE, in vivo functional studies in mice and global transcriptomic and proteomic analyses, we revealed that the key developmental TFs LHX2 and OTX2 function together in transcriptional module containing LDB1 and SWI/SNF (BAF) to regulate the RPE transcriptome. Importantly, the intersection between the identified LHX2-OTX2 cistrome with published expression quantitative trait loci, ATAC-seq data from human RPE, and AMD genome-wide association study (GWAS) data, followed by functional validation using a reporter assay, revealed a causal genetic variant that affects AMD risk by altering TRPM1 expression in the RPE through modulation of LHX2 transcriptional activity on its promoter. Taken together, the reported cistrome of LHX2 and OTX2, the identified downstream genes and interacting co-factors reveal the RPE transcription module and uncover a causal regulatory risk single-nucleotide polymorphism (SNP) in the multifactorial common blinding disease AMD.Tissue This study reveals that the transcription factors LHX2 and OTX2 function together in a transcriptional module containing LDB1 and SWI/SNF to regulate the retinal pigmented epithelium differentiation program; a causal regulatory risk SNP contributes to age-related macular degeneration by altering the binding of LHX2-OTX2 to the TRPM1 promoter. Age-related macular degeneration (AMD) is the leading cause of irreversible visual impairment in older people, accounting for approximately 50% of legal blindness in western countries. Susceptibility to AMD depends on a combination of genetic components and environmental factors ,2. Severcis-regulatory elements (CREs) of the RPE and the TFs responsible for these regulatory elements’ selection is fundamental for understanding the mechanisms governing acquisition and maintenance of cell-specific transcriptional programs and for resolving the contribution of the CREs to complex diseases, such as AMD.The development of AMD involves interactions between several tissue types, with primary roles attributed to cells of the retinal pigmented epithelium (RPE), a monolayered barrier of polarized, pigmented epithelia located between the choriocapillaris and the photoreceptors, which is vital for the development, health, survival, and function of the retinal photoreceptors and the choroid ,4. In AMThe RPE originates from the bipotential neuroectoderm of the optic vesicle. Following specification and morphogenesis, the neuroectoderm cells turn into the bilayer optic cup, populated by an outer layer of pigmented epithelium (PE) progenitors and an inner layer of retinal progenitor cells. The RPE gradually acquires its cell-specific phenotype during embryogenesis and postnatal stages, which include the formation of a selective retina–blood barrier, protection from light-induced oxidative toxicity through the pigment granules, expression of the visual-cycle genes required for the recycling of retina cells, and renewal of the outer segment by circadian phagocytosis of the shed discs (reviewed in –7).Several TFs have been reported to play a role in RPE differentiation and maintenance based on phenotypic analyses of mutant mice. Among these, Otx2 the paired-type homeodomain (HD) TF, is essential for patterning of anterior neural plate and subsequent formation of forebrain, midbrain, and early eye primordia, and is also required later in development for RPE differentiation –12. Otx2cis-regulatory sites, the involvement of epigenetic remodelers, and the relevance to retinal disease mechanisms. Here, we studied Lhx2/LHX2 in developing mouse and human RPE generated from stem cells (hES-RPE). Through functional and genomic analyses, we reveal that LHX2 functions upstream and together with OTX2 on shared genomic regions (cistrome) in regulation of target genes. Through this feed-forward regulatory module LHX2-OTX2 control the RPE transcriptional program. Proteomic analyses further exposed co-factors of LHX2 and OTX2 that likely mediate the tissue-specific gene regulation, including LDB1 and the SWI/SNF chromatin remodeling complex. Finally, intersecting the map of the LHX2-OTX2 bound cistrome with published genomic data on AMD revealed a causal noncoding risk-SNP that acts by altering TRPM1 expression in the RPE through the modulation of LHX2 binding to its promoter. The study exemplifies how delineation of tissue-specific transcriptional regulators, their cistromes, and downstream gene-regulatory networks can provide insights into a complex disease’s pathology.Another important early eye determinant is the Lim HD TF Lhx2, the homolog of the fly Apterous (ap) gene. Lhx2 is required for optic vesicle formation , a functLhx2 in vivo in specified RPE progenitors, we conditionally deleted Lhx2 from the PE progenitors using the loxPLhx2 line , generating a union of 304 candidate causal SNPs. Then, we checked for overlaps  between these AMD SNPs and the peaks of LHX2 or OTX2 in hES-RPE, identified by our ChIP-seq analysis. Motif mapping was done using FIMO [p-values by Fisher’s method (metap R package).We first expanded the set of 15 tag SNPs  developing RPE the expression of neuronal markers Tubb3, Vsx2 , and NF-165  are not detected by indirect immunofluorescent analyses. DAPI was used for counterstaining of the nuclei. Scale bar is 50 μm, lower insets are 10 μm.In E16.5 control  and (TIF)Click here for additional data file.S2 FigExpression of tight junction protein ZO-1 (green) and the transcription regulator MITF (red) in (A) d5 and (B) d14 culture of hES-RPE. Scale bar is 10μm. (C) Gene set enrichment analysis comparing gene expression profiles in differentiated (d14) and de-differentiated (d5) hES-RPE cells shows a significant enrichment for the “RPE gene signature” among the genes up-regulated in d14. (D) The lentiviral transduction of control NS-shRNA or (E) LHX2-shRNA KD to hES-RPE (d14). GFP (green) marks the transduced cells. The indirect immunofluorescent analyses were conducted with antibodies against LHX2 , CDH1 , and OTX2 . Composite shown on right; adjacent are the separate channels. DAPI (blue) labels the nuclei. Scale bar is 10 μm.(TIF)Click here for additional data file.S3 Figp-values calculated by t test, two-tailed, paired Click here for additional data file.S4 Figp-values calculated by one-way ANOVA, matched samples, corrected with Dunnett’s for multiple comparisons (N = 6). In all 6 independent experiments, transcriptional activity of the CRE-TRPM1-C is higher than CRE-TRPM1-T . The data underlying this figure are detailed in Luciferase reporter activity induced by LHX2 overexpression on 309 bp that contain regulatory element bound by LHX2 in rs3809579. Both CRE-TRPM1-T and CRE-TRPM1-C alleles are significantly activated by LHX2 as compared to its activation of empty PGL4.23. The (TIF)Click here for additional data file.S1 Raw imageThe 293T cells overexpressing OTX2, LDB1, and LHX2 in the indicated combinations (Input) were subjected to immunoprecipitation with Flag antibodies followed by western blot analysis and immunolabeling with OTX2 antibody.(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableThe table includes fold change, the statistical analysis, and the LHX2 ChIP-seq peaks associated with the indicated genes. S2B - OTX2_28_target_set_(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 TableThe table includes the information on chromosomal coordinates (human genome (v19), peak intensity, the nearest gene, and its distance from the peak summit.(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 TableT tests analyses  was done between the proteins that were pulled down using specific antibodies  compared to pulled down using nonspecific IgG.The table lists the intensities (Log2) of peptides—identified in the IP-MS interactome analysis. Each row contains the name of proteins that could be reconstructed from a set of identified peptides. The MS data analysis was done by the MaxQuant software. The protein Intensities are the Summed up extracted ion current of all isotopic clusters associated with the identified amino acid sequence. (XLSX)Click here for additional data file.S10 TableThe table includes 2 tabs: CRE_OTX2_(XLSX)Click here for additional data file. 28 Apr 2022Dear Dr Ashery Padan, Thank you for submitting your manuscript entitled "The LHX2-OTX2 transcriptional-complex controls RPE differentiation and underlies a causal regulatory risk-SNP in age-related macular degeneration" for consideration as a Research Article by PLOS Biology.I apologize again for our delay in sending you an initial decision. I have now had a chance to discuss your study with an Academic Editor and with the PLOS Biology editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.https://www.editorialmanager.com/pbiology) within two working days, i.e. by May 02 2022 11:59PM.Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed the checks it will be sent out for review. To provide the metadata for your submission, please Login to Editorial Manager  if you have any questions or concerns, or would like to request an extension. We expect to receive your revised manuscript within 3 months. Please email us  and your responses to reviewer comments. If eligible, we will contact you to opt in or out. Please see here for more details:https://blogs.plos.org/plos/2019/05/plos-journals-now-open-for-published-peer-review/*PLOS Data Policy*http://journals.plos.org/plosbiology/s/data-availability) requires that you make available all data used to draw the conclusions arrived at in your manuscript. If you have not already done so, you must include any data used in your manuscript either in appropriate repositories, within the body of the manuscript, or as supporting information . For an example see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5Please note that as a condition of publication PLOS' data policy  such that it can be cited independently in the future. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at Thank you again for your submission to our journal. We hope that our editorial process has been constructive thus far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,LucasLucas Smith, Ph.D.Associate EditorPLOS Biologylsmith@plos.org------------------------------------REVIEWS:Reviewer #1: This is a very interesting paper dealing with transcriptional regulatory networks important for the retinal pigment epithelium of the eye. The authors combined mouse in vivo data with human RPE datasets for RPE regulatory regions and defined an interplany between LHX2 and OTX2.Matching this LHX2-OTX2 cistrome (ChIP-Seq) with ATAC-seq data for open chromatin regions and ultimately with GWAS SNP data for age-related macular degeneration, the authors make a clear point that the regulatory region of the AMD risk gene TRPM1 seems to be under risk SNP-dependent regulatory control.The group invested a lot of work and did a great job in applying sophisticated methods important for regulatorxy network analysis.I have only a few minor points of criticism that would balance the paper´s scope.-The RPE is in a tight connection with the underlying retina. Both LHX2 and other factors including Crx have been well studied in transcriptomic analyses e.g. using ChIP-Seq. Relevant papers, e.g. the first ChIP-Seq study on retinal cells  should be discussed.-The in vivo mouse model for LHX2  needs a better explanation why this promoter was used for cell specific KO and also a bit more background on LHX2 in general.-I could not find that the newly generated large scale sequencing datasets were deposited in public databases.Reviewer #2: The manuscript studied the gene regulatory networks centering the transcription factor LHX2 in retinal pigment epithelium (RPE) cells, and used the information to uncover causal non-coding regulatory risk SNPs for AMD. The authors first showed that LHX2 is required for the maintenance of RPE genes in mice RPE in vivo and in differentiated human RPE. They then found that LHX2 and OTX2 co-occupied on cis-regulatory elements and form a complex with chromatin remodeling factors to collectively regulate RPE gene expression. Lastly, they identified an AMD risk SNP that affects TRPM1 regulation by the LHX2-OTX2 complex by intersecting published ATAC-seq, GWAS and genomic datasets. The manuscript is of interest to readers in the field of genetics and genomics who study gene regulatory networks and their association with diseases. While the primary claims are well supported by the data, additional investigation and explanation are necessary to make the study more convincing. Major concerns:1. The manuscript mostly focused on the role of Lhx2 in maintaining RPE gene expression (in differentiated human RPE). But the first part of the results focused on Lhx2's function in specifying RPE. This seems to be a little disconnected. Have the authors tried to delete Lhx2 in mature RPE in mouse in vivo and assess its function in maintaining RPE genes?2. The authors claimed that LHX2 does not directly interact with OTX2, but forms a complex with OTX2 via LDB1. However, it is also possible that OTX2 and LHX2 form complexes with LDB1 independently. The OTX2/LDB1 and LHX2/LDB1 complexes bind to neighboring regions in the genome without real physical interactions. The authors need to provide additional information if they claim that OTX2 and LHX2 are in the same complex. 3. Combining LHX2/OTX2 ChIP-seq data with available GWAS and ATAC-seq datasets to identify non-coding risk variants is very interesting and could have wider impact in the field. However, the author didn't verify their findings experimentally. For example, would LHX2 activate a reporter construct containing the TRPM1 variant in differentiated human RPE?4. RNA-seq results were not verified by qPCR, RNA in situ or protein staining. Reviewer #3: The authors describe a set of experiments aimed at studying the role of transcription factors LHX2 and OTX2 in retinal pigment epithelium development and AMD disease development. They use transcriptome profiling in a conditional Lhx2 KO mouse model and Knockdown protocol in well-defined RPE model of differentiated human embryonic stem cells (hES-RPE). Chip-seq experiments in the hES-RPE cells are described to identify LHX2 and OTX2 genomic binding sites. From the observed regulation of OTX2 expression by LHX2 and the spatial association of the LHX2 and OTX2 putative genomic binding sites, the authors suggest that these TFs function jointly in the RPE. Since they are unable to demonstrate direct interaction between the two TFs they suggest that they both bind a third factor, LDB1. They also identify a single nucleotide polymorphic (SNP) that was described as an expression-quantitative trait locus (eQTL) that changes a putative LHX2 binding motif with a LHX2/OTX2 ChIP-seq defined binding site (peak). Based on analyses of existing RPE ATAC-seq data they conclude that the base change in the SNP change the LHX2 affinity to the locus which results in reduced expression of the target gene, TRPM1, and increased AMD risk.This manuscript describes a significant amount of very interesting work. However, it includes several deficits that should be corrected before publication. Specifically some of the experiments are poorly described, the figure legends are poorly written and some of the conclusions are not well supported by the data provided. Below are some comments that may help improve the manuscript.1. Page 13, last sentence of the first paragraph. Where is the data supporting higher affinity of LHX2 for the putative binding site with the C allele? Please describe the experiment that show that the C allele is associated with higher expression level of TRPM1 transcripts. Is this data from human RPE cells?2. The association of the increased AMD risk eQTL tag SNP rs3809579 with the ChIP-seq and ATAC-seq signals is interesting. However, very little data is provided to support a causal roll for this variant in the regulation of TRPM1 expression. The data includes 2 ATAC-seq samples with very few reads. To substantiate this observation the authors should introduce the C allele into the hES-RPE cells model, that will allow for direct assessment of the contribution of the C allele to LHX2 binding affinity, TRPM1 expression, and chromosome accessibility.3. The analysis of the ATAC-seq data is lacking. For example, have the authors removed duplicated fragments form the analysis?Figure3:1. Explain the schematic in figure 3A. What data was used to create this figure? What is the meaning of the different symbols? The description of this experiment in the result section is not clear.2. Explain the data presented in figure 3C. This should probably only be mentioned in the results section. What were the results for the LHX2 ChIP-seq peaks?3. Explain why the OTX2 motifs presented in figure 2J, figure 3C, and figure 3F are different. Similarly, the3 LHX2 motifs in figure 2J, figure 3F and figure 5C are different.4. Description of panel F figure 3 is missing.Figure 4:1. Please define "tars" in figure 4a.2. A basic explanation of the network examination using ingenuity pathway analysis is missing from the results section and from the figure legend.3. The western blot in figure 3 D is of low quality. The authors should provide a better-quality blot to support direct interaction between LBD1 and OTX1. The high background in the OTX2 and LBD1 IP lanes should be addressed. What is the rational of adding LHX2 to the experiment?Figure 5:1. Is the data presented in panel A from this study? If not, it should be referred to in the results or discussion sections with reference, not reproduced in a major figure.2. In B, please explain the various boxes. What exactly are ATAC-seq D1 and ATAC-seq D2?3. The schematics in C and D seem excessive.Reviewer #4: The manuscript of Cohen et al reports the genomic occupancy and functional role of the Lhx2 and Otx2 transcription factors in retinal pigmented epithelium. This data led to the hypothesis that a previously identified genetic variant associated with age-related macular degeneration is causal due to its decreased Lhx2 occupancy. Overall this work presents an exciting story with advances made in basic biology but additionally with a link from that data to a prevalent human disease. A strength is found in the diverse set of techniques used to generate the data to build the case for the hypothesis. However, there are further experiments that are required to fully support the conclusions that the manuscript leads to. The most important issue is to provide experimental evidence that the identified polymorphism does in fact affect Lhx2 binding and transcriptional activity.MajorThe link between the genomic occupancy of Lhx2 and the previously published eQTL data has several correlation links, but would be greatly strengthened by more empirical support to strengthen the disease-relevant conclusion. While the "C" in the first nucleotide position of the Lhx2 motif is certainly more represented in the Lhx2 consensus motif , it is not hugely overrepresented and a "T"  is found at this position. The authors use the correlation between the presence of the T allele and decreased expression of TRPM1 in Fig 5A to support the causal link between this allele and TRPM1 expression. But as this particular polymorphism is closely linked between other polymorphisms in the region it is not clear if this correlation differentiates it from the other nearby polymorphisms? That is, if a similar plot as shown in Fig5A was made for all of the polymorphisms in the region, would the rs3809579 polymorphism that they focus on stand out as better correlated with transcription than those other polymorphisms? The authors then use ATAC-Seq data from heterozygous individuals carrying the two alleles to show that the T allele is less represented than the C allele in the sequencing. This would be expected if this allele were bound less effectively by Lhx2. But it seems possible that it could also be because there are TRPM1 enhancers with variants that are more distally located and therefore the promoter region where the rs3809579 polymorphism might reflect the general transcription differences (with lower ATAC-Seq peaks at promoter regions perhaps correlating with lower transcription). Furthermore, though the authors illustrate in Fig 5D that ChIP-Seq could be used to look at Lhx2 binding between the two variants, the authors have not done this (this should be removed from the schematic). Performing this experiment would provide more direct evidence of a causal link to TRPM1 and would strengthen the conclusion. Secondly, it would be ideal to show using reporter assays that this region with this single polymorphism change has different transcriptional activity. This would be the prediction based on the manuscript's model and reporter assays allow for the effect of the polymorphism of interest to be directly tested. The ideal cell type would be an RPE cell population.In Fig. 4D, this experiment should have negative controls to support the specificity of the interactions. One control is included which is the IP with the Flag antibody on samples only transfected with Otx2 - however as shown in Fig 4D, the Otx2 lane has a lot of background and it is not clear what the result is for this sample. The second control would be to use a control antibody for the IP. Lastly, information about the repeatability of this result is not provided. Minor concernsFig 4 - Why is there no interaction detected between Lhx2 and Otx2 through Ldb1? This could use some discussion and also should be tested in their IP experiments (IP with Otx2 in the presence of Ldb1 and Lhx2 and probe for Lhx2 for example - and/or Lhx2 IP and Otx2 probe).Pg3 "…as for uncovering mechanism…" needs to be rephrasedPg 4 "Among these the Orthodenticle…" Incomplete sentence?Pg 4 "Otx2 is also maintained in the RPE in…where it required…" needs to be rephrasedPg 4 - "…Lim HD TF Lhx2, the orthologue…" Ortholog or paralog?Pg 5 - a preview of the results to be shown in the paper could be useful here.Dapi signal of E16.5 (J) - not very clear imagePg9 "…two enhancers associated with the OTX2 gene…" Is there evidence to suggest that they are indeed enhancers? That is have they been tested for reporter activity in one of the many reports of cis-regulatory elements in the Otx2 region?Pg 10 "… by which LHX2 activate gene expression…" needs to be rephrasedPg 11 "… - a known co-factor of LDB1 complexes…" no reference for this statementPg 12 "… and that their activity, specifically that of Otx2 - …" Not sure what this phrase (and the whole sentence together) means - it needs to be clarified and referenced if there is a reference.Pg 12 "…from numerous distal RPE enhancers that control the RPE specific transcriptional output" If this refers to the data that is only from this paper this seems to not be well supported. There is no evidence that these elements drive specific transcription or that they aren't also active in other cell types or that the BAF complex is critical for some or any activity. Pg 13 "This SNP is very common…" It seems most likely this refers to the T allele, but this should be stated more clearly.Pg 15 "…that if acts by altering TRPM1…" "it" instead of "if"Pg 15 "Thus, the regulation of the Trpm1 locos…" should be "locus"Pg. 17 - "…reveals an important role for LDB1 in mediating the interaction between LHX2 and OTX2" This statement is not formally supported as there is no data to support that they are in fact in a complex. Could it be that they bind in a mutually exclusive manner (to the same domain of LDB1 for example)?https://academic.oup.com/hmg/article/18/1/128/614283Pg 18 "There could be several complexes, and future studies…" At some point, in the discussion perhaps, it would be good to discuss the possibility of Crx as occupying some of those "Otx2 sites" Crx has been identified to be expressed in the RPE - this is one of the best characterizations : Fig 5A - this data needs to be described better, even though it seems to be reproduced from a previous publication - one cannot interpret it as it is currently presented.Pg 20 "Two vectors, which showed the strongest effect…" The metric that is being used and the assay to measure should be described. 16 Sep 2022Attachmentreviewers comments_response_14092022.docxSubmitted filename: Click here for additional data file. 17 Oct 2022Dear Dr Ashery Padan,Thank you for your patience while we considered your revised manuscript "The LHX2-OTX2 regulatory module controls RPE differentiation and underlies a causal regulatory risk-SNP in age-related macular degeneration" for consideration as a Research Article at PLOS Biology. Your revised study has now been evaluated by the PLOS Biology editors, the Academic Editor and by Reviewer 4.Both the Academic Editor and Reviewer 4 agree that the manuscript has been strengthened during the revision, but Reviewer 4 has raised a number of concerns with some of the new data added and the extent to which the conclusions are supported. We feel these concerns preclude publication of the manuscript in its current form, however, given our interest in the study and after discussion with the Academic Editor, we would like to give you the opportunity to address the comments from Reviewer 4, in another revised manuscript.We expect the revision to thoroughly clarify the new experiments, as Reviewer 4 suggests, and as some additional replicates/experimental work may be needed to better support the conclusions, we have opted to provide 3 months for the revision. We may send your revised manuscript back to Reviewer 4. plosbiology@plos.org) if you have any questions or concerns, or would like to request an extension.Please email us  and your responses to reviewer comments. If eligible, we will contact you to opt in or out. Please see here for more details:https://blogs.plos.org/plos/2019/05/plos-journals-now-open-for-published-peer-review/*Protocols deposition*https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocolsTo enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at Thank you again for your submission to our journal. We hope that our editorial process has been constructive thus far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,LukeLucas Smith, Ph.D.Associate EditorPLOS Biologylsmith@plos.org------------------------------------EDITORIAL REQUESTS: 1) ETHICS STATEMENT: Please update your ethics statement to include the full name of the IACUC/ethics committee that reviewed and approved the animal care and use protocol/permit/project license. Please also include an approval number. Please include the specific national or international regulations/guidelines to which your animal care and use protocol adhered. Please note that institutional or accreditation organization guidelines (such as AAALAC) do not meet this requirement.2) DATA AVAILABILITY: thank you for depositing your sequencing data on GEO. In addition to this, we ask that you provide all individual quantitative observations that underlie the data summarized in the figures and results of your paper be made available in one of the following forms:a) Supplementary files . Please ensure that all data files are uploaded as 'Supporting Information' and are invariably referred to  using the following format verbatim: S1 Data, S2 Data, etc. Multiple panels of a single or even several figures can be included as multiple sheets in one excel file that is saved using exactly the following convention: S1_Data.xlsx (using an underscore).b) Deposition in a publicly available repository. Please also provide the accession code or a reviewer link so that we may view your data before publication. Regardless of the method selected, please ensure that you provide the individual numerical values that underlie the summary data displayed in the following figure panels as they are essential for readers to assess your analysis and to reproduce it:Fig 3A,D; Fig 4D; Fig 6A,F-G; Fig S3; Fig S4;NOTE: the numerical data provided should include all replicates AND the way in which the plotted mean and errors were derived .>>Please also ensure that figure legends in your manuscript include information on where the underlying data can be found, and ensure your supplemental data file/s has a legend.>>Please ensure that your Data Statement in the submission system accurately describes where your data can be found.http://journals.plos.org/plosbiology/s/data-availability or this editorial: http://dx.doi.org/10.1371/journal.pbio.1001797For more information on the PLOS Data Policy, see ------------------------------------REVIEWS:Reviewer #4: The revised manuscript of Cohen-Gulkar et al. has addressed many of the concerns addressed by this reviewer. One of the key experiments proposed by multiple reviewers was to test the transcriptional activity of the two variants in RPE cells to determine if the SNP was causal in creating a transcriptional difference. The authors conducted a luciferase reporter experiment in a RPE cell line and show new results in Figures S4 and 6. The presented difference for this experiment is quite modest and some of the details are unclear and need clarification (see below). Given that this is a pivotal experiment for the model and the differences in the two alleles are not large, it is reasonable to ask if the evidence is strong enough to support the conclusion that there is a difference in SNP activity.1) The main text suggests that the results shown in Figure 6G are under conditions where Lhx2 is overexpressed. But the figure legend or the figure mark-up makes no mention of this. This should be clarified.2) Assuming that Figure 6G and SuppF4 both use Lhx2 misexpression, why are these data plotted differently - Supp4F shows a bar graph with the (empty) basal vector data in its own column. But Figure 6G does not have this data shown and instead has the empty vector factored into the data, as suggested by the y-axis notation. How was this done? Were samples randomly paired together? Was a separate set of controls done for each? Are the datapoints shown in Figure 6G the same as those shown in SuppF4? If a statistical test was done to directly compare the T and C variant activity shown in SuppF4, would these be statistically different? If not, does this suggest that the evidence for a functional difference in Fig 6G is not solid?3) The variability of the datapoints in SuppF4 suggests that reproducibility is an important issue. How many times has this experiment been repeated? This should be described. Can the authors provide a rationale for why they need to add Lhx2 to the mix to get this result - is there Lhx2 in these cells already, and if so, why isn't that sufficient to activate the reporter? Can reporters for the C and T alleles be introduced into the same cells so that sample variability is decreased and is instead controlled internally? Does the luciferase system allow one to use renilla to be driven by one of the variants and luciferase by the other (instead of renilla being driven by an unrelated element). 10 Nov 2022Attachmentrebuetell letter_3rd revision_9112022.docxSubmitted filename: Click here for additional data file. 16 Nov 2022Dear Dr Ashery Padan,Thank you for the submission of your revised Research Article "The LHX2-OTX2 regulatory module controls RPE differentiation and underlies a causal regulatory risk-SNP in age-related macular degeneration" for publication in PLOS Biology. Your manuscript has now been evaluated by the Academic Editor, Sui Wang, and the PLOS Biology editorial team, and I am pleased to say that we are satisfied by the revision and that we can, in principle, accept your manuscript for publication, provided you address any remaining formatting and reporting issues. These will be detailed in an email you should receive within 2-3 business days from our colleagues in the journal operations team; no action is required from you until then. Please note that we will not be able to formally accept your manuscript and schedule it for publication until you have completed any requested changes.**IMPORTANT: As you address these last formatting and reporting requests, to come, we also ask that you attend to the following editorial requests:1) Title: After some discussion within the team, we think that the title of your piece should be edited a bit, for clarity . We would suggest that you change it to something like: "The transcription factors LHX2 and OTX2 form a regulatory module that controls retinal pigmented epithelium differentiation and underlies a risk SNP in age-related macular degeneration"or "The LHX2-OTX2 transcriptional regulatory module controls retinal pigmented epithelium differentiation and underlies genetic risk for age-related macular degeneration"2) Data: Thank you for providing the data underlying your figures as a supplementary excel file. Can you please add a sentence to each relevant figure legend referencing this table? For example, to each figure legend you could add the sentence "the data underlying this figure can be found in Table ___.3) Ethics Statement: Please update your ethics statement to include the approval number for your animal care and use protocol, approved by the Tel Aviv University review board. 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Although the Eating Disorder Examination Questionnaire  is the most used instrument worldwide for the assessment of eating disorders symptoms, its factorial structure considerably varies, which limits its construct validity. Using exploratory factor analisys in data from a sample of overweight women, our group found a three-factors structure of the EDEQ Portuguese version , Although it was in accordance with other psychometric studies , it was different from the original matrix. Further investigation regarding its factor structure has been conducted, with studies supporting a modified seven-item-three-factors structure  with improved psychometric properties , including with Portuguese samples .To analyze if the EDEQ version composed of seven items and three factors is replicated in a Portuguese sample of overweight women.2) attending a weight loss treatment consultation in a public hospital.The EDEQ was administered to an outpatient sample of 276 women . The EDEQ7 Cronbach’s alphas for the total and its dimensions were α<.70.Confirmatory factor analysis (CFA) revealed an adequate fit of the EDEQ-7 second order model with three dimensions (χGiven its good psychometric properties, the overlap of the measurement model with those found with different samples and the reduced number of items, the EDEQ7 will be very useful both in research and clinical settings with/for overweight women.


The increased use of wearable sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the older adult population in remote and rural areas, who may struggle with long commutes to clinics. However, the usability of such systems often discourages patients from adopting these services.This study aimed to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device.Older adults across 4 different regions  wore an activity tracker for 7 days under a free-living environment protocol. In total, 4 surveys were administered, and biometrics were measured by the researchers before the trial began. At the end of the trial period, the researchers administered 2 further surveys to gain insights into the perceived usability of the wearable device. These were the standardized System Usability Scale (SUS) and a custom usability questionnaire designed by the research team. Statistical analyses were performed to identify the key factors that affect participants’ intention to continue using the wearable device in the future. Machine learning classifiers were used to provide an early prediction of the intention to continue using the wearable device.The study was conducted with older adult volunteers  wearing a Xiaomi Mi Band 3 activity tracker for 7 days in a free-living environment. The results from the SUS survey showed no notable difference in perceived system usability regardless of region, sex, or age, eliminating the notion that usability perception differs based on geographical location, sex, or deviation in participants’ age. There was also no statistically significant difference in SUS score between participants who had previously owned a wearable device and those who wore 1 or 2 devices during the trial. The bespoke usability questionnaire determined that the 2 most important factors that influenced an intention to continue device use in an older adult cohort were device comfort (τ=0.34) and whether the device was fit for purpose (τ=0.34). A computational model providing an early identifier of intention to continue device use was developed using these 2 features. Random forest classifiers were shown to provide the highest predictive performance (80% accuracy). After including the top 8 ranked questions from the bespoke questionnaire as features of our model, the accuracy increased to 88%.This study concludes that comfort and accuracy are the 2 main influencing factors in sustaining wearable device use. This study suggests that the reported factors influencing usability are transferable to other wearable sensor systems. Future work will aim to test this hypothesis using the same methodology on a cohort using other wearable technologies. Advancements in health care have resulted in increases in life expectancy. As a consequence, a growing proportion of the population are older adults . This agThe Smart Sensor Devices for Rehabilitation and Connected Health project focuses on monitoring the physical capacity of older adults. The project evaluated wireless sensor systems and their capabilities for remote rehabilitation with a particular focus on end-user acceptance. The ultimate goals of technology-assisted personal health management are both continued long-term use of the device and improved well-being . This stUnderstanding the factors that influence continued device use is important as this will inform future wearable device design, ensuring that adoption and the impact of that adoption has the highest possible chance of success. This will, in turn, allow for the successful rollout of telehealth services in the future, such as remote rehabilitation, and overall increase the likelihood of improved well-being.Previous work has already shown that, for monitoring technology to be accepted by older adults, it must be easy to use and not impair mobility and independence . ResearcOlder adults are interested in smart wearable devices that offer functionality for daily living and are more likely to consider using one if compatible ; thus, dThe literature shows that usability challenges must be addressed to increase the likelihood of continued device use. Therefore, technology must be designed and implemented such that it is practical, unobtrusive, and well-received by older adults and, ultimately, promotes health benefits.Balance is one of the critical characteristics used to assess the functional capacity of older adults in the literature. However, despite the growing number of articles supporting the use of balance assessment technology, there are still substantial gaps in the full understanding of the technology. In particular, existing literature in the area does not consider factors that may affect the continued long-term use of wearable technology in real-world conditions. A previous study examined the real-world use of multiple wearable sensors, noting that participants found wrist-worn sensors to be the most favorable as they were adaptable and user-friendly . Core arThere are limited quantitative studies on long-term device usability to observe the associated influencing factors . User feWe conducted a study aimed to better understand the usability factors that most influence whether an older adult will decide to continue to use a wearable device. We hypothesized that initial perceptions related to human factors of a wearable sensor system can be used as a predictor of continued device use in the future. To test this hypothesis, a study was designed to analyze data related to older adults’ perceptions of wearable activity trackers after 7 days of use. Data were collected related to participant perceptions of wearable device human factors as well as measurements of participant functional status, health status, and wearable device activity tracker measurements from a cohort of 65 older adults aged ≥65 years. One of the objectives of the research was to assess what specific factors may affect participants’ intention to continue using the wearable device beyond the 7-day study period. There are three key methodologies that could be used for usability assessment: (1) inspection involving expert observation , (2) inquiry involving qualitative data collection , and (3) testing involving quantitative data collection in a real environment  . The metThis section will cover the protocol used during the data collection process and describe each of the questions asked and how they relate to measuring usability. Detailed information will also be provided for the participant cohort and the hardware used to capture their activity data. This section establishes the methods used to process and analyze the data and, finally, predict continued device use.This was a retrospective case series–based study. A series of older adults aged ≥65 years were given a wearable device and observed over a 1-week period. The usability data of the wearable device were then characterized among the participant series.free-living aspect indicates that data are collected from a participant’s normal everyday living environment, typically over a period of ≥24 hours. This approach aims to eliminate any social, behavioral, and environmental biases that would otherwise be present in other testing or simulated environments.The study was based on a free-living data collection protocol conducted over a 7-day period. A free-living data collection protocol is a common method of collecting data from participants, particularly in sensor-based studies. The Participants for this study were recruited from 4 different countries within the Northern Periphery and Arctic regions of Europe. These were namely Northern Ireland, Ireland, Finland, and Sweden. Inclusion criteria required participants to be aged ≥65 years, have the physical capacity to walk 20 m without the assistance of another person, and be cognitively able to answer questionnaires.Participants met the researchers performing the trial in person twice—once at the start of the trial and again 7 days later at the end of the trial. In the first meeting, the researchers took body measurements , trained the participants in device use using a standardized training manual, and asked them to complete 4 health-related questionnaires. Participants were then asked to complete two physical function tests: (1) the Five-Time Sit-to-Stand test (STS5) and (2) two 10-m walk tests. For the STS5 test, the total time to complete 5 repetitions of going from a seated position to a standing position was recorded. For the 10-m walk test, the time to complete each of the two 10-m walks was recorded (WT10M1 and WT10M2) as well as the number of steps taken in each walk (WS10M1 and WS10M2).A date for the second meeting was agreed upon, and participants were given the wearable device to wear and bring home with them. At the second meeting, the participants met with the researcher and returned the wearable device. The participants were then asked to complete 2 posttrial questionnaires focusing on usability and human factors. The standard tests  and measures that were carried out as part of this study were used to assess the physical health of the participants. Although this study focused on usability and intention to continue using the device, data from the standard tests are part of a larger study, and further data analyses will be performed for potential future publication.Pretrial Questionnaires were as follows: (1) the 36-item Short Form Health Survey (SF-36) [The trial used 6 questionnaires to collect various data from the participants. A total of 4 questionnaires were administered before the trial began, and 2 were administered after trial completion. The four  (SF-36) , a set o (SF-36) , a set o (SF-36) , used to (SF-36) , used toResults section.The SF-36 questionnaire was used to understand the participants’ general health status. The MMSE and GDS questionnaires were used to understand the mental health status of the participants. A score of <25 on the MMSE indicates some degree of dementia, whereas a score of >4 on the GDS indicates some degree of depression. The data were used to characterize the overall health of the participant group and understand the relative health of the participants compared with the general population. The MDPQ was used to estimate their level of familiarity and experience using technology and whether this was linked to continued device use afterward. Questionnaire scores and statistics are discussed in the Posttrial Questionnaires were as follows: (1) the System Usability Scale (SUS) [The two le (SUS) , used asle (SUS) -28. Eachstrongly agree to strongly disagree. The questionnaire ended with 4 general questions that gathered data on length of time worn and use at night. The final and most important question regarding this study asked the participants if they would continue to use the device after the trial had finished. Responses to this final question were analyzed to gain further insights into what factors influence the intention to continue using the device.The SUS is a standardized and validated short 10-question survey to help validate the usability of a piece of hardware, software, or wearable device. However, to better understand the participants’ specific opinions of the wearable device usability, a bespoke usability questionnaire was designed, entitled “Accuracy, feasibility and acceptability of wireless monitoring in older people.” The questionnaire first collected dichotomous data on the participants’ familiarity with wearable devices and whether they liked the appearance. Then, a series of questions related to usability, accuracy, and acceptability were asked using an ordinal 5-category scale ranging from The bespoke usability questionnaire was designed as part of the European Union Interreg Northern Periphery and Arctic Smart Sensor Devices for Rehabilitation and Connected Health project. Experts on this project—who were from clinical, physiotherapy, and technological backgrounds in Sweden, Finland, Ireland, and Northern Ireland—worked together in a workshop meeting that was held in May 2019 in Derry/Londonderry to propose, agree, and finalize a set of questions appropriate to assess the different human factors associated with the wearable sensor system. The bespoke questionnaire was applied for the first time in this study.Each participant in the study was provided with a Xiaomi Mi Band 3 activity tracker, which was secured on the wrist of their nondominant hand. In addition, the participant was provided with a Huawei Y6 smartphone to facilitate interaction with the activity tracker software. Anonymous Google accounts were created to capture the activity data from each participant. Approximately half  of the participants were also requested to wear an Axivity AX3 wrist-worn accelerometer on their dominant hand. The initial plan was to have all participants wear 2 trackers as the raw data collection capabilities of the Axivity AX3 would have facilitated benchmarking between potentially new algorithms and the Xiaomi Mi Band 3. However, an initial feasibility study conducted in Finland on a small number of participants used the bespoke usability questionnaire to conclude that, generally, participants reported usability issues because of wearing 2 trackers. To help keep this from becoming an issue, it was decided to only allow half of the participants to wear 2 trackers. The distribution of those wearing 2 trackers versus 1 was approximately 50% across all the sites  except for the 100% (8/8) of participants at the site in Ireland who wore 2 trackers. Unfortunately, because of the unexpected impact of COVID-19 in March 2020, the trial in Ireland was interrupted midway, which resulted in 40% (8/20) of the participants receiving 2 trackers and the remaining 60% (12/20) of the participants being unable to take part.In total, 65 participants from the 4 locations took part in the study. The mean age of the participants was 70.52 (SD 5.65) years. The mean height of the population was 169.43 (SD 9.05) cm, and the mean weight was 73.45 (SD 13.09) kg. The cohort comprised 57% (37/65) women and 43% (28/65) men. A total of 91% (59/65) of the participants were right-handed, and 9% (6/65) were left-handed. The participants were recruited using leaflets and posters. Recruitment sought older adult volunteers wanting to experience the use of wearable technology such as activity trackers in their daily lives. They should be physically able to walk 20 m unaided and cognitively able to answer questionnaires. The participants were to have no underlying health conditions other than frailty. Targeted recruitment focused on recruiting participants in community centers focusing on older adults  and clinics .An analysis was performed on the collected data to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device. The analysis was divided into four main areas: (1) cohort characteristic analysis, (2) SUS analysis, (3) bespoke usability questionnaire analysis, and (4) predictive modeling. The following sections describe the methods used for each of these areas.Statistical analysis of participants’ demographics, health status, and selected usability results was performed to provide information on the characteristics of the cohort being analyzed in further sections. The cohort of 65 people comprised volunteers from Northern Ireland , the Republic of Ireland , Finland , and Sweden . For each participant, a set of 69 features were recorded. The features comprised body metrics, functional test measures, wearable device data, and questionnaire results.Analysis of the data was performed using SPSS  and the Spyder Python integrated development environment (version 5.1.5). Statistical analyses were performed using the Kendall τb, Pearson, or Spearman correlations where appropriate. The analysis also involved computing features such as the mean, variance, and SDs as well as exploring frequencies, histograms, distributions, and statistical tests.Participants were asked to answer the SUS questionnaire to evaluate the Xiaomi Mi Band 3 activity tracker after an average device use of 7.12 (SD 1.53) days. Only the usability of the wearable device was to be considered by the participants.SUS scores were analyzed to investigate whether geographical location, sex, number of wearables used, or age affected the usability rating. For our analysis, the participants who took part in the trial from Northern Ireland and the Republic of Ireland were grouped into 1 cohort comprising 22 participants because of the relatively small sample size  available for the Republic of Ireland and because of their geographical proximity, encompassing the island of Ireland. For the age analysis, we created 3 bins  and categorized the participants accordingly.The analysis of the SUS data aimed to understand whether geographic location, age, sex, or number of devices worn had an influence on the perceived usability of the device.t tests were carried out on SUS scores for the 2 groups. In addition, correlations between question 21 and all other questions from the bespoke questionnaire were carried out using the Kendall τb rank to identify specific factors that are linked to the intention to continue using the device.Analysis of data from the bespoke questionnaire focused on understanding responses to question 21: “Would you continue to use the device and app again after the trial is finished?” Various analyses were performed on this question to gain insights into what factors influence continued device use. Analysis of the statistical distributions of participants from the 2 groups (participants who indicated that they would continue using the device and participants who indicated that they would not continue using the device) was carried out. Independent 2-tailed A predictive model is frequently used in statistics and machine learning techniques to model the current data and predict future outcomes. For this part of the analysis, we evaluated models that may predict the intention to continue using a device after the monitoring period. These predictions were based on the usability questionnaire, where the answer to question 21 was predicted based on the answers to the other questions.An important criterion for wearable technologies is user acceptance. This increases the likelihood that individuals will continue to use the device long-term and beyond periods when they are being actively monitored. Factors potentially influencing a user’s acceptance of a wearable device include comfort, simplicity, and device intrusiveness. For example, if a device requires frequent interaction, then it could become too much of a burden.Approval for the research study was obtained from each of the participating institutions where required. The Ulster University Research Governance Ethics Committee granted approval under reference REC/19/0026; the University College Cork Clinical Research Ethics Committee of the Cork Teaching Hospitals granted approval under reference ECM 4(a) 16/10/19; and the Regional Research Ethical Review Board of Umeå University, Sweden, granted approval under reference 07-031M with extensions. At the Karelia University of Applied Sciences, Finland, no ethics approval from an institutional review board was required as the research adhered to the ethical principles of research with human participants as per the Finnish National Board on Research Integrity TENK guidelines . The resSummary statistics for each of the 4 health questionnaires  are presented in Results show that the cohort comprised participants who were, on average, in good health as defined by SF-36 results . Results showed that only 9% (6/65) of the participants scored <50 on the SF-36 general health component, implying that a small number of participants in the study perceived that they were struggling with health issues. The average MMSE value among all participants was 28.49 (SD 1.55). As previously stated, a score of <25 on the MMSE indicates some degree of dementia, whereas a score of >4 in the GDS indicates some degree of depression. Only 2% (1/65) of the participants scored <25 on the MMSE, with a score of 24. The average GDS score among all participants was 1.43 (SD 2.11), with only 9% (6/65) of the participants reporting scores of >4 in the GDS. The average MDPQ value was 3.53 (SD 1.26), which is between 3 (“not very easily”) and 4 (“somewhat easily”), indicating that our participants were between states when it comes to overall mobile phone device proficiency.The results of the SUS questionnaire showed that the average SUS score (N=65) was 67.15 (SD 18.27). ≤SUS<50), marginal (50≤SUS<70), and acceptable (70≤SUS≤100) [SUS scores can be represented using either grades or acceptability ranges. Acceptability ranges use 3 categories: not acceptable (0SUS≤100) . AnalysiAs shown in P value of .85; therefore, the variances can be assumed to be homogeneous, and equal variances are assumed. Observing the normal quantile-quantile plots for each region in The group means were compared using a 1-way ANOVA. A Levene test was performed, resulting in a On the basis of the results in t test was conducted to compare the SUS score between (1) the sexes and (2) the number of wearables used. The results suggest that there was no statistically significant difference in perceived system usability either given the participants’ sex (P=.66) or whether the participants wore 1 or both activity trackers (P=.40).An independent-sample H test was conducted, allowing for a comparison between the 3 age categories. The Levene test P value was <.001; therefore, the variances can be assumed to be not homogeneous. The results from the statistical test suggest that the means of the SUS scores for each age category were not statistically significant (P=.81).To analyze the SUS score by age, a Kruskal-Wallis Each of the SUS analyses showed that, regardless of comparing region, sex, wearables used, or age, there was no difference in perceived system usability.The final question in the bespoke usability questionnaire asked the participants if they intended to continue using the device after the trial had finished. In total, 65% (42/65) of the participants said that they would like to continue using the wearable device and phone app, whereas 35% (23/65) of the participants said that they would not like to continue using the wearable device and phone app. t test was performed on the SUS questionnaire scores. The results are presented in t test showed that the SUS scores of the 2 “continued use” groups were statistically significant.To evaluate whether there was a significant difference in SUS scores between participants who indicated that they would continue using the device and participants who indicated that they would not, an independent t test (P=.28).In addition to comparing usability with the intention to continue using the device, we evaluated what effect previous activity tracker experience (usability question 2) had on usability. In total, 20% (13/65) of the participants said that they had previously used a wrist-worn activity tracker, whereas 80% (52/65) of the participants said that they had never used a wrist-worn activity tracker before the trial. Statistical significance was evaluated for the SUS score of participants who had previous experience versus those who did not have previous experience. The results from an independent t test  showed tP value of .003, highlighting that both comfort at night and becoming more active are key early indicators of whether a user will continue using and wearing a device.Further analysis was performed on the intention to continue using the device (question 21) to evaluate how continued use was linked to other human factor and usability elements. Therefore, correlations between question 21 and the other bespoke questions were analyzed. The description of the correlation values and associated rank are presented in Using the activity tracker helped me be more active) being highly correlated with continued device use, the aim was to assess whether physical function, measured before the study period, influenced continued device use. The results of this analysis are presented in Further analysis was performed to evaluate the possible links between participants’ physical function and continued use in the future. Two 10-m walk test measurements and a sit-to-stand test  were compared with continued device use using the Kendall rank correlation coefficient. On the basis of question 17 . In addition, some participants reported that they believed that the wearable sensor had to be fastened extremely tightly to obtain an accurate reading. This factor may have also influenced their comfort perception.P value of ≤.005. A second subset was selected to include features with a P value of ≤.01 . Finally, a third subset was selected to include features with a P value of ≤.10 . For the remainder of the paper, the models developed using each of the 3 feature subsets will be known as the 2-feature model, 5-feature model, and 8-feature model.To train the predictive model, features were chosen from the results of the Kendall τb correlation from Initial experimentation was performed using multiple classifiers to obtain a performance baseline. This preliminary experimentation tested the following classifiers: decision tree, support vector machine, random forest, and k-nearest neighbor. From this experimentation, we found that random forest provided the highest predictive performance in classifying whether users would have an intention to continue using the device after the trial ended. For comparison with the random forest models, regression multinomial models were also performed. These multinomial models are helpful for the simplicity and interpretability of the predictive model.For the multinomial models, all data were included to observe and assess the statistical or discrimination power of the model at once. For the random forest models, validation of the final classification was achieved using a train-test split validation of 70 to 30 to check model accuracy.The results from each of the 2-, 5-, and 8-feature models for both the multinomial and random forest models are shown in The findings from the multinomial 2-feature model display an overall accuracy of 80%, and the findings from the random forest 2-feature model correlate with an average accuracy of 80%, an average precision of 0.80, and an average recall value of 0.80. Both sets of results show that a reasonably accurate prediction can be made for usability question 21.After increasing the model’s features to 5, the multinomial model displays an overall percentage of 83.1%, and the random forest model correlates with an average accuracy of 80%, an average precision of 0.80, and an average recall value of 0.80. The multinomial results improved slightly compared with the findings from the 2-feature model, whereas the random forest results remained the same.The final model used 8 parameters. The findings from this multinomial model displayed an overall percentage of 84.6%, whereas the random forest model’s average accuracy increased to 85%, displaying an average precision of 0.88 and an average recall value of 0.85. Both sets of results improved from the 2-feature and 5-feature model findings. Nonetheless, the improvement from the 2-feature model to the 8-feature model was 5%.P=.005). On average, users who indicated that they would continue using the wearable device also indicated that the device had good usability, whereas users who indicated that they would not continue using the wearable device indicated that the device had poor usability. Therefore, participants who found the system easier to use were also more likely to want to continue using it. These results are in line with previous research findings that suggest that ease of use and device usability are important measures for technology acceptance yet are often overlooked in favor of device accuracy [Analysis of the usability of the wearable system by older adults indicated a significant correlation between usability and intention to continue using the system. Comparing SUS scores of participants who intended to continue using the device with SUS scores of those who did not resulted in a statistically significant difference  device comfort at night and (2) perception that the device helped increase activity. Inspecting the 5 questions that correlated the most with continued device use, 3 of the questions related to human factors, whereas 2 related to perceived accuracy.On the basis of feature subsets from 8 questions that correlated the most with continued device use, machine learning models were implemented to predict whether a participant would indicate that they would continue using a wearable device. These 8 questions related to opinions on comfort, data privacy concerns, and the participants’ perception (beliefs or attitudes) of device accuracy or monitoring their health. These models have the potential to act as an early indicator of participants not continuing to use the device. Factors such as discomfort at night can be identified early before users decide to stop using it. This may allow for interventions to be made to address user concerns early in research studies, for example. As an additional benefit, the accuracy of these models provides insights into what design features are important to encourage wearable technology uptake in older adult populations.The results indicated that the models could predict the likelihood of a participant having an intention to continue using the device with 80%-85% accuracy. Interestingly, the accuracy of the model only dropped by 5%-80% when we greatly simplified the questionnaire and only selected the top 2 correlated questions for prediction. As mentioned, these questions were related to evening comfort and whether the device helped increase activity levels. These results indicate that, by using a simple 2-question survey approach, it is possible to make accurate predictions about the likelihood of an older adult wanting to continue using a wearable device. This is useful as focus groups could leverage these questions to gain meaningful insights into their product development, or these questions could be included in a mobile app or web-based application to frequently report the usability of wearable products to ensure client satisfaction and better standards of quality assurance.Future design of devices should keep in mind that wearable sensors are likely to be used by older adult patients with health complaints who often have reduced fine motor skills . TherefoMost research on wearable sensor technologies currently places accuracy at the center of the design. This often comes at the expense of usability, which can ultimately have negative effects on continued device use . PreviouPrevious research has been conducted on usability evaluations by older adults using activity trackers . This stTo achieve the potential health benefits presented by wearable sensors and remote digital health technologies for older adult populations, it is vital that users continue to use the wearable sensors over long periods. However, there is limited work in the literature exploring the factors that influence long-term wearable device use among older adults.One of the key strengths of this study is that it assessed a broad spectrum of factors that could potentially influence continued device users among a diverse set of participants. This study provides clear evidence that usability, comfort, and motivation are key elements that must be considered for any wearable sensor-based application requiring long-term use.There are some limitations to this study that could be addressed in future research. First, the sample size was limited to 65 participants, and as such, may not be large enough to provide accurate insights into the behaviors of older adults. This limitation was imposed as data collection had to cease at the onset of the COVID-19 pandemic. Second, there may be bias in the results owing to the ethics approval granted for the study. Given that participants were to have no underlying health conditions other than frailty, the vast majority of the volunteers were considered healthy, reflected by the mean score of 72.54 achieved for general health in our cohort, calculated using the SF-36 questionnaire. To contextualize this, in a normative study on older adults  where no screening was imposed, the mean general health score was 53.06 . The facThis study used a combination of validated questionnaires to gather 65 participants’ opinions on the usability of an off-the-shelf wearable sensor system, the Xiaomi Mi Band 3. To gain further insights into the factors that may influence an older adult intending to continue using a wearable device, we also designed a bespoke usability questionnaire for this study. Various analyses were performed examining the statistics from the pretrial questionnaires; summary statistics of the SUS score with respect to region, sex, wearables used, and age; and findings that focused specifically on the final question from the bespoke usability questionnaire to determine what factors influence continued device use.The results from the SUS show that there was no notable difference in perceived system usability depending on region, sex, age, or previous experience, eliminating the notion that usability perception differs based on geographical location, sex, or deviation in participant age. Previous studies have suggested that usability and ease of use are as important as device accuracy when it comes to technology acceptance and device uptake. One of the main lessons learned from the results of this study was that the most important factor that influenced continued device use in an older adult cohort was device comfort. Feeling that the device was fit for purpose  was the second most important factor. In addition, it was observed that comfort matters the most when a wearable device is used while sleeping. These lessons could better inform the design of future wearable sensor systems for applications specifically targeting older adults.We presented a random forest model with 80% accuracy using these 2 features, which could be used as an early identifier of continued device use—for example, if the user is asked these 2 questions after the first day of the study, their response would be a clear sign of whether they are interested in using a wearable sensor system long-term. After including the top 8 ranked questions from the bespoke questionnaire as features of our model, the accuracy increased to 88%.